From b54bcd78f21d1117585ca41130ed2f627f3cd9c9 Mon Sep 17 00:00:00 2001 From: <> Date: Sun, 10 Jul 2022 17:49:51 +0000 Subject: [PATCH] Deployed c046d64 with MkDocs version: 1.3.0 --- objects.inv | Bin 494 -> 476 bytes reference/index.html | 783 ++++++++++++++++--------------------- reference/views/index.html | 2 +- search/search_index.json | 2 +- sitemap.xml.gz | Bin 209 -> 209 bytes 5 files changed, 333 insertions(+), 454 deletions(-) diff --git a/objects.inv b/objects.inv index b6ae2293884f945fac08b1cf288a33a10f5626e2..4d743e337f16dc0f95132676e87959f1ca20afd9 100644 GIT binary patch delta 347 zcmV-h0i^!!1Kb0UihoN_gfI}k`zuKFD#oifW1{g_OeXD68iy(Nu?hd)!tNSfMM}U+ zI~~5q(5LZ)5zy8zWAOM|^}~Z=457NOJT>G=6y06lRJT{=4As=L8)|bwt<~7msL&)) z(K`7x@IcTxO}xU`hw%ly3#_C~sEG za$Uxan9sW5`c;+>fwS_5=TL?|)@UtKR?s delta 365 zcmV-z0h0dQ1MUNmihs*a#2^&i`xQ-eE5@xmW1{gZZf@X~lDUP-W0L;8wDZW!IGv)c z8#vsa2c-qHCyanbd<@|Av+DX6$rwWQTzRU=la=&z|4=>N=>t_!$F3LpLMr6Y)8J4i zE2DAthKY2xHahuIe!wk3>lE=I$RIJ@ppVROqPj3BNKD}mfqzHQxx65*Lys**@`ZeM z8ZzG-S-RrL44zsS2@b`gSGy(z484^_a#SkIT(SzHPq8TyeI@e!?`3(pX`JORbtxJ) z(NGYQ3GJ?(T-PIe(nV8*KsFBai_rpCMynHQ5}hYyWQ57W8X+bCS~t45!Fzhpk+H3hX{I>T=xe$Vjwjk$)i05H}>fPbBT{>pLDwsu;RAJ4cS Lu+;V&fao1YBw4V1 diff --git a/reference/index.html b/reference/index.html index b2d79a8..0e9359e 100644 --- a/reference/index.html +++ b/reference/index.html @@ -472,42 +472,16 @@
  • - save_model - - - -
  • - use_model - - - -
  • @@ -725,42 +699,16 @@
  • - save_model - - - -
  • - use_model - - - -
  • @@ -1127,7 +1075,10 @@ 279 280 281 -282
    class GreatAI(Generic[T, V]):
+282
+283
+284
+285
    class GreatAI(Generic[T, V]):
     __name__: str
     __doc__: str
 
@@ -1144,228 +1095,231 @@
         self._wrapped_func = wraps(func)(freeze_arguments(self._cached_func))
 
         wraps(func)(self)
-        self.__doc__ = f"GreatAI wrapper for interacting with the `{self.__name__}` function.\n\n{dedent(self.__doc__ or '')}"
-
-        self.version = str(get_context().version)
-        flat_model_versions = ".".join(f"{k}-v{v}" for k, v in model_versions)
-        if flat_model_versions:
-            self.version += f"+{flat_model_versions}"
-
-        self.app = FastAPI(
-            title=snake_case_to_text(self.__name__),
-            version=self.version,
-            description=self.__doc__
-            + f"\n\nFind out more in the [dashboard]({DASHBOARD_PATH}).",
-            docs_url=None,
-            redoc_url=None,
-        )
-
-        self._bootstrap_rest_api()
-
-    @overload
-    @staticmethod
-    def create(  # type: ignore
-        # "Overloaded function signatures 1 and 2 overlap with incompatible return types"
-        # https://github.com/python/mypy/issues/12759
-        func: Callable[..., Awaitable[V]],
-    ) -> "GreatAI[Awaitable[Trace[V]], V]":
-        ...
-
-    @overload
-    @staticmethod
-    def create(
-        func: Callable[..., V],
-    ) -> "GreatAI[Trace[V], V]":
-        ...
-
-    @staticmethod
-    def create(
-        func: Union[Callable[..., Awaitable[V]], Callable[..., V]],
-    ) -> Union["GreatAI[Awaitable[Trace[V]], V]", "GreatAI[Trace[V], V]"]:
-        """Decorate a function by wrapping it in a GreatAI instance.
-
-        The function can be typed, synchronous or async. If it has
-        unwrapped parameters (parameters not affected by a @parameter
-        or @use_model decorator), those will be automatically wrapped.
-
-        The return value is replaced by a Trace (or Awaitable[Trace]),
-        while the original return value is available under the `.output`
-        property.
-
-        For configuration options, see great_ai.configure.
-
-        Examples:
-            >>> @GreatAI.create
-            ... def my_function(a):
-            ...     return a + 2
-            >>> my_function(3).output
-            5
-
-            >>> @GreatAI.create
-            ... def my_function(a: int) -> int:
-            ...     return a + 2
-            >>> my_function(3)
-            Trace[int]...
-
-            >>> my_function('3').output
-            Traceback (most recent call last):
-                ...
-            TypeError: type of a must be int; got str instead
-
-        Args:
-            func: The prediction function that needs to be decorated.
+        self.__doc__ = (
+            f"GreatAI wrapper for interacting with the `{self.__name__}` "
+            + f"function.\n\n{dedent(self.__doc__ or '')}"
+        )
+
+        self.version = str(get_context().version)
+        flat_model_versions = ".".join(f"{k}-v{v}" for k, v in model_versions)
+        if flat_model_versions:
+            self.version += f"+{flat_model_versions}"
+
+        self.app = FastAPI(
+            title=snake_case_to_text(self.__name__),
+            version=self.version,
+            description=self.__doc__
+            + f"\n\nFind out more in the [dashboard]({DASHBOARD_PATH}).",
+            docs_url=None,
+            redoc_url=None,
+        )
+
+        self._bootstrap_rest_api()
+
+    @overload
+    @staticmethod
+    def create(  # type: ignore
+        # Overloaded function signatures 1 and 2 overlap with incompatible return types
+        # https://github.com/python/mypy/issues/12759
+        func: Callable[..., Awaitable[V]],
+    ) -> "GreatAI[Awaitable[Trace[V]], V]":
+        ...
+
+    @overload
+    @staticmethod
+    def create(
+        func: Callable[..., V],
+    ) -> "GreatAI[Trace[V], V]":
+        ...
+
+    @staticmethod
+    def create(
+        func: Union[Callable[..., Awaitable[V]], Callable[..., V]],
+    ) -> Union["GreatAI[Awaitable[Trace[V]], V]", "GreatAI[Trace[V], V]"]:
+        """Decorate a function by wrapping it in a GreatAI instance.
+
+        The function can be typed, synchronous or async. If it has
+        unwrapped parameters (parameters not affected by a @parameter
+        or @use_model decorator), those will be automatically wrapped.
+
+        The return value is replaced by a Trace (or Awaitable[Trace]),
+        while the original return value is available under the `.output`
+        property.
+
+        For configuration options, see great_ai.configure.
+
+        Examples:
+            >>> @GreatAI.create
+            ... def my_function(a):
+            ...     return a + 2
+            >>> my_function(3).output
+            5
+
+            >>> @GreatAI.create
+            ... def my_function(a: int) -> int:
+            ...     return a + 2
+            >>> my_function(3)
+            Trace[int]...
+
+            >>> my_function('3').output
+            Traceback (most recent call last):
+                ...
+            TypeError: type of a must be int; got str instead
 
-        Returns:
-            A GreatAI instance wrapping `func`.
+        Args:
+            func: The prediction function that needs to be decorated.
 
-        """
-        return GreatAI[Trace[V], V](
-            func,
-        )
-
-    def __call__(self, *args: Any, **kwargs: Any) -> T:
-        return self._wrapped_func(*args, **kwargs)
+        Returns:
+            A GreatAI instance wrapping `func`.
+        """
+
+        return GreatAI[Trace[V], V](
+            func,
+        )
 
-    @overload
-    def process_batch(
-        self,
-        batch: Sequence[Tuple],
-        *,
-        concurrency: Optional[int] = None,
-        unpack_arguments: Literal[True],
-        do_not_persist_traces: bool = ...,
-    ) -> List[Trace[V]]:
-        ...
-
-    @overload
-    def process_batch(
-        self,
-        batch: Sequence,
-        *,
-        concurrency: Optional[int] = None,
-        unpack_arguments: Literal[False] = ...,
-        do_not_persist_traces: bool = ...,
-    ) -> List[Trace[V]]:
-        ...
-
-    def process_batch(
-        self,
-        batch: Sequence,
-        *,
-        concurrency: Optional[int] = None,
-        unpack_arguments: bool = False,
-        do_not_persist_traces: bool = False,
-    ) -> List[Trace[V]]:
-        wrapped_function = self._wrapped_func
-
-        def inner(value: Any) -> T:
-            return (
-                wrapped_function(*value, do_not_persist_traces=do_not_persist_traces)
-                if unpack_arguments
-                else wrapped_function(
-                    value, do_not_persist_traces=do_not_persist_traces
-                )
-            )
-
-        async def inner_async(value: Any) -> T:
-            return await cast(
-                Awaitable,
-                (
-                    wrapped_function(
-                        *value, do_not_persist_traces=do_not_persist_traces
-                    )
-                    if unpack_arguments
-                    else wrapped_function(
-                        value, do_not_persist_traces=do_not_persist_traces
-                    )
-                ),
-            )
-
-        return list(
-            tqdm(
-                parallel_map(
-                    inner_async
-                    if get_function_metadata_store(self).is_asynchronous
-                    else inner,
-                    batch,
-                    concurrency=concurrency,
-                ),
-                total=len(batch),
-            )
-        )
-
-    @staticmethod
-    def _get_cached_traced_function(
-        func: Callable[..., Union[V, Awaitable[V]]]
-    ) -> Callable[..., T]:
-        @lru_cache(maxsize=get_context().prediction_cache_size)
-        def func_in_tracing_context_sync(
-            *args: Any,
-            do_not_persist_traces: bool = False,
-            **kwargs: Any,
-        ) -> T:
-            with TracingContext[V](
-                func.__name__, do_not_persist_traces=do_not_persist_traces
-            ) as t:
-                result = func(*args, **kwargs)
-                return cast(T, t.finalise(output=result))
-
-        @alru_cache(maxsize=get_context().prediction_cache_size)
-        async def func_in_tracing_context_async(
-            *args: Any,
-            do_not_persist_traces: bool = False,
-            **kwargs: Any,
-        ) -> T:
-            with TracingContext[V](
-                func.__name__, do_not_persist_traces=do_not_persist_traces
-            ) as t:
-                result = await cast(Callable[..., Awaitable], func)(*args, **kwargs)
-                return cast(T, t.finalise(output=result))
-
-        return cast(
-            Callable[..., T],
-            (
-                func_in_tracing_context_async
-                if get_function_metadata_store(func).is_asynchronous
-                else func_in_tracing_context_sync
-            ),
-        )
-
-    def _bootstrap_rest_api(
-        self,
-    ) -> None:
-        route_config = get_context().route_config
-
-        if route_config.prediction_endpoint_enabled:
-            bootstrap_prediction_endpoint(self.app, self._wrapped_func)
+    def __call__(self, *args: Any, **kwargs: Any) -> T:
+        return self._wrapped_func(*args, **kwargs)
+
+    @overload
+    def process_batch(
+        self,
+        batch: Sequence[Tuple],
+        *,
+        concurrency: Optional[int] = None,
+        unpack_arguments: Literal[True],
+        do_not_persist_traces: bool = ...,
+    ) -> List[Trace[V]]:
+        ...
+
+    @overload
+    def process_batch(
+        self,
+        batch: Sequence,
+        *,
+        concurrency: Optional[int] = None,
+        unpack_arguments: Literal[False] = ...,
+        do_not_persist_traces: bool = ...,
+    ) -> List[Trace[V]]:
+        ...
+
+    def process_batch(
+        self,
+        batch: Sequence,
+        *,
+        concurrency: Optional[int] = None,
+        unpack_arguments: bool = False,
+        do_not_persist_traces: bool = False,
+    ) -> List[Trace[V]]:
+        wrapped_function = self._wrapped_func
+
+        def inner(value: Any) -> T:
+            return (
+                wrapped_function(*value, do_not_persist_traces=do_not_persist_traces)
+                if unpack_arguments
+                else wrapped_function(
+                    value, do_not_persist_traces=do_not_persist_traces
+                )
+            )
+
+        async def inner_async(value: Any) -> T:
+            return await cast(
+                Awaitable,
+                (
+                    wrapped_function(
+                        *value, do_not_persist_traces=do_not_persist_traces
+                    )
+                    if unpack_arguments
+                    else wrapped_function(
+                        value, do_not_persist_traces=do_not_persist_traces
+                    )
+                ),
+            )
+
+        return list(
+            tqdm(
+                parallel_map(
+                    inner_async
+                    if get_function_metadata_store(self).is_asynchronous
+                    else inner,
+                    batch,
+                    concurrency=concurrency,
+                ),
+                total=len(batch),
+            )
+        )
+
+    @staticmethod
+    def _get_cached_traced_function(
+        func: Callable[..., Union[V, Awaitable[V]]]
+    ) -> Callable[..., T]:
+        @lru_cache(maxsize=get_context().prediction_cache_size)
+        def func_in_tracing_context_sync(
+            *args: Any,
+            do_not_persist_traces: bool = False,
+            **kwargs: Any,
+        ) -> T:
+            with TracingContext[V](
+                func.__name__, do_not_persist_traces=do_not_persist_traces
+            ) as t:
+                result = func(*args, **kwargs)
+                return cast(T, t.finalise(output=result))
+
+        @alru_cache(maxsize=get_context().prediction_cache_size)
+        async def func_in_tracing_context_async(
+            *args: Any,
+            do_not_persist_traces: bool = False,
+            **kwargs: Any,
+        ) -> T:
+            with TracingContext[V](
+                func.__name__, do_not_persist_traces=do_not_persist_traces
+            ) as t:
+                result = await cast(Callable[..., Awaitable], func)(*args, **kwargs)
+                return cast(T, t.finalise(output=result))
+
+        return cast(
+            Callable[..., T],
+            (
+                func_in_tracing_context_async
+                if get_function_metadata_store(func).is_asynchronous
+                else func_in_tracing_context_sync
+            ),
+        )
+
+    def _bootstrap_rest_api(
+        self,
+    ) -> None:
+        route_config = get_context().route_config
 
-        if route_config.docs_endpoints_enabled:
-            bootstrap_docs_endpoints(self.app)
+        if route_config.prediction_endpoint_enabled:
+            bootstrap_prediction_endpoint(self.app, self._wrapped_func)
 
-        if route_config.dashboard_enabled:
-            bootstrap_dashboard(
-                self.app,
-                function_name=self.__name__,
-                documentation=self.__doc__,
-            )
-
-        if route_config.trace_endpoints_enabled:
-            bootstrap_trace_endpoints(self.app)
+        if route_config.docs_endpoints_enabled:
+            bootstrap_docs_endpoints(self.app)
+
+        if route_config.dashboard_enabled:
+            bootstrap_dashboard(
+                self.app,
+                function_name=self.__name__,
+                documentation=self.__doc__,
+            )
 
-        if route_config.feedback_endpoints_enabled:
-            bootstrap_feedback_endpoints(self.app)
+        if route_config.trace_endpoints_enabled:
+            bootstrap_trace_endpoints(self.app)
 
-        if route_config.meta_endpoints_enabled:
-            bootstrap_meta_endpoints(
-                self.app,
-                self._cached_func,
-                ApiMetadata(
-                    name=self.__name__,
-                    version=self.version,
-                    documentation=self.__doc__,
-                    configuration=get_context().to_flat_dict(),
-                ),
-            )
+        if route_config.feedback_endpoints_enabled:
+            bootstrap_feedback_endpoints(self.app)
+
+        if route_config.meta_endpoints_enabled:
+            bootstrap_meta_endpoints(
+                self.app,
+                self._cached_func,
+                ApiMetadata(
+                    name=self.__name__,
+                    version=self.version,
+                    documentation=self.__doc__,
+                    configuration=get_context().to_flat_dict(),
+                ),
+            )
    @@ -1469,10 +1423,7 @@ property.

    Source code in great_ai/deploy/great_ai.py - 
     95
- 96
- 97
- 98
+        
     98
  99
 100
 101
@@ -1512,50 +1463,53 @@ property.
    
 135
 136
 137
-138
    @staticmethod
-def create(
-    func: Union[Callable[..., Awaitable[V]], Callable[..., V]],
-) -> Union["GreatAI[Awaitable[Trace[V]], V]", "GreatAI[Trace[V], V]"]:
-    """Decorate a function by wrapping it in a GreatAI instance.
-
-    The function can be typed, synchronous or async. If it has
-    unwrapped parameters (parameters not affected by a @parameter
-    or @use_model decorator), those will be automatically wrapped.
-
-    The return value is replaced by a Trace (or Awaitable[Trace]),
-    while the original return value is available under the `.output`
-    property.
-
-    For configuration options, see great_ai.configure.
-
-    Examples:
-        >>> @GreatAI.create
-        ... def my_function(a):
-        ...     return a + 2
-        >>> my_function(3).output
-        5
-
-        >>> @GreatAI.create
-        ... def my_function(a: int) -> int:
-        ...     return a + 2
-        >>> my_function(3)
-        Trace[int]...
-
-        >>> my_function('3').output
-        Traceback (most recent call last):
-            ...
-        TypeError: type of a must be int; got str instead
-
-    Args:
-        func: The prediction function that needs to be decorated.
+138
+139
+140
+141
    @staticmethod
+def create(
+    func: Union[Callable[..., Awaitable[V]], Callable[..., V]],
+) -> Union["GreatAI[Awaitable[Trace[V]], V]", "GreatAI[Trace[V], V]"]:
+    """Decorate a function by wrapping it in a GreatAI instance.
+
+    The function can be typed, synchronous or async. If it has
+    unwrapped parameters (parameters not affected by a @parameter
+    or @use_model decorator), those will be automatically wrapped.
+
+    The return value is replaced by a Trace (or Awaitable[Trace]),
+    while the original return value is available under the `.output`
+    property.
+
+    For configuration options, see great_ai.configure.
+
+    Examples:
+        >>> @GreatAI.create
+        ... def my_function(a):
+        ...     return a + 2
+        >>> my_function(3).output
+        5
+
+        >>> @GreatAI.create
+        ... def my_function(a: int) -> int:
+        ...     return a + 2
+        >>> my_function(3)
+        Trace[int]...
+
+        >>> my_function('3').output
+        Traceback (most recent call last):
+            ...
+        TypeError: type of a must be int; got str instead
 
-    Returns:
-        A GreatAI instance wrapping `func`.
+    Args:
+        func: The prediction function that needs to be decorated.
 
-    """
-    return GreatAI[Trace[V], V](
-        func,
-    )
+    Returns:
+        A GreatAI instance wrapping `func`.
+    """
+
+    return GreatAI[Trace[V], V](
+        func,
+    )
    @@ -1715,41 +1669,17 @@ property.

    -
    - - - -

    - save_model - - -#

    - -
    - - - -
    - - - - - - - - -
    -

    +

    save_model(model, key, *, keep_last_n=None) -#

    +# -
    +

    Save (and optionally serialise) a model in order to use by use_model.

    The model can be a Path or string representing a path in which case the @@ -1917,60 +1847,25 @@ In case model is an object, it is serialised using dill - - -

    - -
    - -
    - -
    - - - -

    - use_model - - -#

    - -
    - - - -
    - - - - - - - - -
    -

    +

    use_model(key, *, version='latest', model_kwarg_name='model') -#

    +# -
    +

    Inject a model into a function.

    -

    Load a model specified by key and version using the -currently active LargeFile implementation. If it's a -single object, it is deserialised using dill. If it's -a directory of files, a pathlib.Path instance is given.

    -

    By default, the function's model parameter is replaced -by the loaded model. This can be customised by changing -model_kwarg_name.

    -

    Multiple models can be loaded by decorating the same -function with use_model multiple times.

    +

    Load a model specified by key and version using the currently active LargeFile +implementation. If it's a single object, it is deserialised using dill. If it's a +directory of files, a pathlib.Path instance is given.

    +

    By default, the function's model parameter is replaced by the loaded model. This +can be customised by changing model_kwarg_name. Multiple models can be loaded by +decorating the same function with use_model multiple times.

    Examples:

    >>> from great_ai import save_model
@@ -2050,11 +1945,7 @@ Returns:
 82
 83
 84
-85
-86
-87
-88
-89
    def use_model(
+85
    def use_model(
     key: str,
     *,
     version: Union[int, Literal["latest"]] = "latest",
@@ -2062,75 +1953,63 @@ Returns:
 ) -> Callable[[F], F]:
     """Inject a model into a function.
 
-    Load a model specified by `key` and `version` using the
-    currently active `LargeFile` implementation. If it's a
-    single object, it is deserialised using `dill`. If it's
-    a directory of files, a `pathlib.Path` instance is given.
-
-    By default, the function's `model` parameter is replaced
-    by the loaded model. This can be customised by changing
-    `model_kwarg_name`.
-
-    Multiple models can be loaded by decorating the same
-    function with `use_model` multiple times.
-
-    Examples:
-            >>> from great_ai import save_model
-            >>> save_model(3, 'my_number')
-            'my_number:...'
-            >>> @use_model('my_number')
-            ... def my_function(a, model):
-            ...     return a + model
-            >>> my_function(4)
-            7
-
-        Args:
-            key: The model's name as stored by the LargeFile implementation.
-            version: The model's version as stored by the LargeFile implementation.
-            model_kwarg_name: the parameter to use for injecting the loaded model
-        Returns:
-            A decorator for model injection.
-    """
+    Load a model specified by `key` and `version` using the currently active `LargeFile`
+    implementation. If it's a single object, it is deserialised using `dill`. If it's a
+    directory of files, a `pathlib.Path` instance is given.
+
+    By default, the function's `model` parameter is replaced by the loaded model. This
+    can be customised by changing `model_kwarg_name`. Multiple models can be loaded by
+    decorating the same function with `use_model` multiple times.
+
+    Examples:
+            >>> from great_ai import save_model
+            >>> save_model(3, 'my_number')
+            'my_number:...'
+            >>> @use_model('my_number')
+            ... def my_function(a, model):
+            ...     return a + model
+            >>> my_function(4)
+            7
+
+        Args:
+            key: The model's name as stored by the LargeFile implementation.
+            version: The model's version as stored by the LargeFile implementation.
+            model_kwarg_name: the parameter to use for injecting the loaded model
+        Returns:
+            A decorator for model injection.
+    """
+
+    assert (
+        isinstance(version, int) or version == "latest"
+    ), "Only integers or the string literal `latest` is allowed as a version"
 
-    assert (
-        isinstance(version, int) or version == "latest"
-    ), "Only integers or the string literal `latest` is allowed as a version"
-
-    model, actual_version = _load_model(
-        key=key,
-        version=None if version == "latest" else version,
-    )
-
-    def decorator(func: F) -> F:
-        assert_function_is_not_finalised(func)
-
-        store = get_function_metadata_store(func)
-        store.model_parameter_names.append(model_kwarg_name)
-
-        @wraps(func)
-        def wrapper(*args: List[Any], **kwargs: Dict[str, Any]) -> Any:
-            tracing_context = TracingContext.get_current_tracing_context()
-            if tracing_context:
-                tracing_context.log_model(Model(key=key, version=actual_version))
-            return func(*args, **kwargs, **{model_kwarg_name: model})
-
-        return cast(F, wrapper)
-
-    return decorator
+    model, actual_version = _load_model(
+        key=key,
+        version=None if version == "latest" else version,
+    )
+
+    def decorator(func: F) -> F:
+        assert_function_is_not_finalised(func)
+
+        store = get_function_metadata_store(func)
+        store.model_parameter_names.append(model_kwarg_name)
+
+        @wraps(func)
+        def wrapper(*args: List[Any], **kwargs: Dict[str, Any]) -> Any:
+            tracing_context = TracingContext.get_current_tracing_context()
+            if tracing_context:
+                tracing_context.log_model(Model(key=key, version=actual_version))
+            return func(*args, **kwargs, **{model_kwarg_name: model})
+
+        return cast(F, wrapper)
+
+    return decorator
    - - - - - - - -
    diff --git a/reference/views/index.html b/reference/views/index.html index e9ba624..c041c90 100644 --- a/reference/views/index.html +++ b/reference/views/index.html @@ -531,7 +531,7 @@

    - Bases: Generic[T], HashableBaseModel

    + Bases: Generic[T], HashableBaseModel

    diff --git a/search/search_index.json b/search/search_index.json index 4541c61..392123f 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database quasi-shadow deployment Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and async inference methods Easy integration with other remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces Support for direct file input Hello world # pip install great-ai hello-world.py from great_ai import GreatAI @GreatAI . create #(1) def hello_world ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your hello_world function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, hello_world could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process. terminal great-ai hello-world.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phase of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, a number of best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Production use # GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Overview"},{"location":"#features","text":"Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database quasi-shadow deployment Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and async inference methods Easy integration with other remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces Support for direct file input","title":"Features"},{"location":"#hello-world","text":"pip install great-ai hello-world.py from great_ai import GreatAI @GreatAI . create #(1) def hello_world ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your hello_world function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, hello_world could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process. terminal great-ai hello-world.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.","title":"Hello world"},{"location":"#why-is-this-great","text":"GreatAI fits between the prototype and deployment phase of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, a number of best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust","title":"Why is this GREAT?"},{"location":"#why-greatai","text":"There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub","title":"Why GreatAI?"},{"location":"#production-use","text":"GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Production use"},{"location":"explanation/","text":"Explanation # A lot more details and discussion about the problem context and approaches of GreatAI can be found in my thesis. : Download","title":"Explanation"},{"location":"explanation/#explanation","text":"A lot more details and discussion about the problem context and approaches of GreatAI can be found in my thesis. : Download","title":"Explanation"},{"location":"open_s3/","text":"open(S3) # Storing, versioning, and downloading files from S3 made as easy as using open() in Python. Caching included. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support the deletion of large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. Solution # pip install open-large Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # By default the latest version is returned # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : print ( f . readlines ()[ 0 ]) Automatically creates a file, writes to it, uploads it to S3, and then queries the most recent version of it. In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reading and writing is supported along with the different keywords open() accepts. The local cache can be configured with these properties: LargeFile . cache_path = Path ( '.cache' ) LargeFile . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the \"remote\" file, this pattern can be applied: path_to_model = LargeFile ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFile ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way, both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFile(\"my-file\").delete() . It will still reside in your local cache afterwards, its deletion will happen next time your local cache has to be pruned. Command-line example # The package can be used as a module from the command-line to give you more flexibility. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files endpoint_url = this is optional, for backblaze, use this: https://s3.us-west-002.backblazeb2.com Just like in example secrets . Print the expected options # python3 -m large_file --help Upload some files # python3 -m large_file --backend s3 --secrets secrets.ini --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. python3 -m large_file --backend s3 -secrets ~/.aws/credentials --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # python3 -m large_file --backend s3 --secrets ~/.aws/credentials --delete my_first_file.json","title":"[open(S3)](https://pypi.org/project/open-large/)"},{"location":"open_s3/#opens3","text":"Storing, versioning, and downloading files from S3 made as easy as using open() in Python. Caching included.","title":"open(S3)"},{"location":"open_s3/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support the deletion of large files, making it easy for them to eat-up the LFS quota and explode the size of your repos.","title":"Motivation"},{"location":"open_s3/#solution","text":"pip install open-large","title":"Solution"},{"location":"open_s3/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # By default the latest version is returned # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : print ( f . readlines ()[ 0 ]) Automatically creates a file, writes to it, uploads it to S3, and then queries the most recent version of it. In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"open_s3/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reading and writing is supported along with the different keywords open() accepts. The local cache can be configured with these properties: LargeFile . cache_path = Path ( '.cache' ) LargeFile . max_cache_size = \"30 GB\"","title":"More details"},{"location":"open_s3/#i-only-need-a-path","text":"In case you only need a path to the \"remote\" file, this pattern can be applied: path_to_model = LargeFile ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFile ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way, both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFile(\"my-file\").delete() . It will still reside in your local cache afterwards, its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"open_s3/#command-line-example","text":"The package can be used as a module from the command-line to give you more flexibility.","title":"Command-line example"},{"location":"open_s3/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files endpoint_url = this is optional, for backblaze, use this: https://s3.us-west-002.backblazeb2.com Just like in example secrets .","title":"Setup"},{"location":"open_s3/#print-the-expected-options","text":"python3 -m large_file --help","title":"Print the expected options"},{"location":"open_s3/#upload-some-files","text":"python3 -m large_file --backend s3 --secrets secrets.ini --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored.","title":"Upload some files"},{"location":"open_s3/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. python3 -m large_file --backend s3 -secrets ~/.aws/credentials --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"open_s3/#delete-remote-files","text":"python3 -m large_file --backend s3 --secrets ~/.aws/credentials --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/call-remote/","text":"","title":"Call remote"},{"location":"how-to-guides/create-service/","text":"How to instantiate a GreatAI service # The core value of the great-ai library lies in its great_ai.deploy.GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function with the GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of the GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-heavy operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create always have to come last. There are two built-in decorators that you can use to customise your function. Using use_model # If you have previously saved a model with save_model , you can inject it into your function by calling use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before GreatAI.create . Feel free to use @use_model in other places of the code base, it works equally well outside of GreatAI services. Using parameter # If you wish to turn of logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model decorator, are automatically decorated with @parameter when GreatAI.create is called. ```python \"greeter_with_validation.py\" from great_ai import GreatAI, use_model @GreatAI.create @use_model('name_of_my_model', version='latest') def type_safe_greeter(your_name: str, model) -> str: return f'Hi {your_name}!' assert type_safe_greeter('Andras').output == 'Hi Andras' !!! important You must call `@parameter` before `GreatAI.create`. Feel free to use `@parameter` in other places of the code base, it works equally well outside of GreatAI services. ## Complex example Refer to the following example summarising the options you have when instantiating a GreatAI service. ```python title=\"complex.py\" from great_ai import save_model, GreatAI, parameter, use_model save_model(4, 'secret-number') #(2) @GreatAI.create @parameter('positive_number', validator=lambda n: n > 0) @use_model('secret-number', version='latest', model_kwarg_name='secret') def add_number(positive_number: int, secret: int) -> int: return positive_number + secret assert add_number(1).output == 5 Refer to storing models for specifying where to store your models.","title":"How to instantiate a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-instantiate-a-greatai-service","text":"The core value of the great-ai library lies in its great_ai.deploy.GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function with the GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of the GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to instantiate a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-heavy operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create always have to come last. There are two built-in decorators that you can use to customise your function.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before GreatAI.create . Feel free to use @use_model in other places of the code base, it works equally well outside of GreatAI services.","title":"Using use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn of logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model decorator, are automatically decorated with @parameter when GreatAI.create is called. ```python \"greeter_with_validation.py\" from great_ai import GreatAI, use_model @GreatAI.create @use_model('name_of_my_model', version='latest') def type_safe_greeter(your_name: str, model) -> str: return f'Hi {your_name}!' assert type_safe_greeter('Andras').output == 'Hi Andras' !!! important You must call `@parameter` before `GreatAI.create`. Feel free to use `@parameter` in other places of the code base, it works equally well outside of GreatAI services. ## Complex example Refer to the following example summarising the options you have when instantiating a GreatAI service. ```python title=\"complex.py\" from great_ai import save_model, GreatAI, parameter, use_model save_model(4, 'secret-number') #(2) @GreatAI.create @parameter('positive_number', validator=lambda n: n > 0) @use_model('secret-number', version='latest', model_kwarg_name='secret') def add_number(positive_number: int, secret: int) -> int: return positive_number + secret assert add_number(1).output == 5 Refer to storing models for specifying where to store your models.","title":"Using parameter"},{"location":"how-to-guides/scraping/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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AsyncHTTPTransport ( retries = 2 ) limits = httpx . Limits ( max_connections = None ) timeout = httpx . Timeout ( connect = 60.0 , read = 300 , write = 60 , pool = None ) async with httpx . AsyncClient ( transport = transport , limits = limits , timeout = timeout ) as client : pass import httpx transport = httpx.AsyncHTTPTransport(retries=2) limits = httpx.Limits(max_connections=None) timeout = httpx.Timeout(connect=60.0, read=300, write=60, pool=None) async with httpx.AsyncClient( transport=transport, limits=limits, timeout=timeout ) as client: pass","title":"Scraping"},{"location":"how-to-guides/store-models/","text":"","title":"Store models"},{"location":"how-to-guides/using-service/","text":"How to use a GreatAI service # After creating a GreatAI service by wrapping your prediction function, it's time to do some prediction. Let's use the following example: ```python \"type_safe_greeter.py\" from great_ai import GreatAI @GreatAI.create def type_safe_greeter(your_name: str) -> str: return f'Hi {your_name}' ```","title":"How to use a GreatAI service"},{"location":"how-to-guides/using-service/#how-to-use-a-greatai-service","text":"After creating a GreatAI service by wrapping your prediction function, it's time to do some prediction. Let's use the following example: ```python \"type_safe_greeter.py\" from great_ai import GreatAI @GreatAI.create def type_safe_greeter(your_name: str) -> str: return f'Hi {your_name}' ```","title":"How to use a GreatAI service"},{"location":"reference/","text":"Reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 class GreatAI ( Generic [ T , V ]): __name__ : str __doc__ : str def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` function. \\n\\n { dedent ( self . __doc__ or '' ) } \" self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # \"Overloaded function signatures 1 and 2 overlap with incompatible return types\" # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 20 , route_config = RouteConfig ()) # Source code in great_ai/context.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 20 , route_config : RouteConfig = RouteConfig (), ) -> None : global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model # save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model # use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validator = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validator = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validator Callable [[ Any ], bool ] Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validator=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validator: Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validator ( argument ): raise ArgumentValidationError ( f \"Argument { parameter_name } in { func . __name__ } did not pass validation\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value ) # Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai # call_remote_great_ai_async # Ground-truth # add_ground_truth # query_ground_truth # delete_ground_truth #","title":"Reference"},{"location":"reference/#reference","text":"from great_ai import *","title":"Reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 class GreatAI ( Generic [ T , V ]): __name__ : str __doc__ : str def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` function. \\n\\n { dedent ( self . __doc__ or '' ) } \" self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # \"Overloaded function signatures 1 and 2 overlap with incompatible return types\" # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.configure","text":"Source code in great_ai/context.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 20 , route_config : RouteConfig = RouteConfig (), ) -> None : global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"","title":"save_model"},{"location":"reference/#great_ai.models.save_model.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"","title":"use_model"},{"location":"reference/#great_ai.models.use_model.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validator = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validator Callable [[ Any ], bool ] Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validator=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validator: Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validator ( argument ): raise ArgumentValidationError ( f \"Argument { parameter_name } in { func . __name__ } did not pass validation\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"","title":"call_remote_great_ai"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"","title":"call_remote_great_ai_async"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"","title":"add_ground_truth"},{"location":"reference/#great_ai.query_ground_truth","text":"","title":"query_ground_truth"},{"location":"reference/#great_ai.delete_ground_truth","text":"","title":"delete_ground_truth"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence return list ( tqdm ( parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ), total = len ( input_values ), dynamic_ncols = True , ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. For more info, check-out the scraping guide . chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase.\"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.simple_parallel_map.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence return list ( tqdm ( parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ), total = len ( input_values ), dynamic_ncols = True , ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.threaded_parallel_map.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. For more info, check-out the scraping guide .","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.logger.get_logger.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase.\"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) } )\"","title":"View models"},{"location":"reference/views/#view-models","text":"Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) } )\"","title":"View models"},{"location":"simple/data/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a domain classifier on the semantic scholar dataset \u00b6 Part 1: obtain and clean data The blue boxes show the steps implemented in this notebook. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this case, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 1 MAX_CHUNK_COUNT = 1 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"Processing {len(chunks)} out of the {len(manifest.split())} available chunks\" Out[2]: 'Processing 1 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai import parallel_map , clean , is_english , predict_language def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ])) ] preprocessed_chunks = parallel_map ( preprocess_chunk , chunks ) from typing import List, Tuple import json import gzip from great_ai import parallel_map, clean, is_english, predict_language def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} {c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"])) ] preprocessed_chunks = parallel_map(preprocess_chunk, chunks) Spacy model en_core_web_sm not found locally, downloading... Collecting en-core-web-sm==3.3.0 Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0-py3-none-any.whl (12.8 MB) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 12.8/12.8 MB 3.6 MB/s eta 0:00:00 Requirement already satisfied: spacy<3.4.0,>=3.3.0.dev0 in ./.env/lib/python3.10/site-packages (from en-core-web-sm==3.3.0) (3.3.1) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (4.64.0) Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (0.9.1) Requirement already satisfied: srsly<3.0.0,>=2.4.3 in ./.env/lib/python3.10/site-packages (from 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already satisfied: click<9.0.0,>=7.1.1 in ./.env/lib/python3.10/site-packages (from typer<0.5.0,>=0.3.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (8.1.3) Requirement already satisfied: MarkupSafe>=2.0 in ./.env/lib/python3.10/site-packages (from jinja2->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (2.1.1) Installing collected packages: en-core-web-sm Successfully installed en-core-web-sm-3.3.0 2022-06-25 14:21:57,983 | WARNING | Limiting concurrency to 1 because there are only 1 chunks 2022-06-25 14:21:57,984 | INFO | Starting parallel map (concurrency: 1, chunk size: 1) 2022-06-25 14:21:57,984 | WARNING | Running in series, there is no reason for parallelism 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [03:26<00:00, 206.86s/it] In [4]: Copied! from itertools import chain preprocessed_data = list ( chain ( * preprocessed_chunks )) X , y = zip ( * preprocessed_data ) # X is the input, y is the expected (ground truth) output from itertools import chain preprocessed_data = list(chain(*preprocessed_chunks)) X, y = zip( *preprocessed_data ) # X is the input, y is the expected (ground truth) output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each datapoint with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) 2022-06-25 14:25:24,989 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:25:24,990 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:25:24,991 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:25:24,992 | INFO | Settings: configured \u2705 2022-06-25 14:25:24,993 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:25:24,995 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:25:24,996 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:25:24,997 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:25:24,998 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:25:24,998 | WARNING | > Find out more at https://se-ml.github.io/practices/ Next: Part 2 \u00b6","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/data/#train-a-domain-classifier-on-the-semantic-scholar-dataset","text":"Part 1: obtain and clean data The blue boxes show the steps implemented in this notebook.","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this case, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 1 MAX_CHUNK_COUNT = 1 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"Processing {len(chunks)} out of the {len(manifest.split())} available chunks\" Out[2]: 'Processing 1 out of the 6002 available chunks'","title":"Extract"},{"location":"simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai import parallel_map , clean , is_english , predict_language def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ])) ] preprocessed_chunks = parallel_map ( preprocess_chunk , chunks ) from typing import List, Tuple import json import gzip from great_ai import parallel_map, clean, is_english, predict_language def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} {c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"])) ] preprocessed_chunks = parallel_map(preprocess_chunk, chunks) Spacy model en_core_web_sm not found locally, downloading... Collecting en-core-web-sm==3.3.0 Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0-py3-none-any.whl (12.8 MB) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 12.8/12.8 MB 3.6 MB/s eta 0:00:00 Requirement already satisfied: spacy<3.4.0,>=3.3.0.dev0 in ./.env/lib/python3.10/site-packages (from en-core-web-sm==3.3.0) (3.3.1) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (4.64.0) Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (0.9.1) Requirement already satisfied: srsly<3.0.0,>=2.4.3 in ./.env/lib/python3.10/site-packages (from 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already satisfied: click<9.0.0,>=7.1.1 in ./.env/lib/python3.10/site-packages (from typer<0.5.0,>=0.3.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (8.1.3) Requirement already satisfied: MarkupSafe>=2.0 in ./.env/lib/python3.10/site-packages (from jinja2->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (2.1.1) Installing collected packages: en-core-web-sm Successfully installed en-core-web-sm-3.3.0 2022-06-25 14:21:57,983 | WARNING | Limiting concurrency to 1 because there are only 1 chunks 2022-06-25 14:21:57,984 | INFO | Starting parallel map (concurrency: 1, chunk size: 1) 2022-06-25 14:21:57,984 | WARNING | Running in series, there is no reason for parallelism 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [03:26<00:00, 206.86s/it] In [4]: Copied! from itertools import chain preprocessed_data = list ( chain ( * preprocessed_chunks )) X , y = zip ( * preprocessed_data ) # X is the input, y is the expected (ground truth) output from itertools import chain preprocessed_data = list(chain(*preprocessed_chunks)) X, y = zip( *preprocessed_data ) # X is the input, y is the expected (ground truth) output","title":"Transform"},{"location":"simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each datapoint with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) 2022-06-25 14:25:24,989 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:25:24,990 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:25:24,991 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:25:24,992 | INFO | Settings: configured \u2705 2022-06-25 14:25:24,993 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:25:24,995 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:25:24,996 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:25:24,997 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:25:24,998 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:25:24,998 | WARNING | > Find out more at https://se-ml.github.io/practices/","title":"Production-ready backend"},{"location":"simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a domain classifier on the semantic scholar dataset \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment from it using the GreatAI Python package. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validator = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validator=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) 2022-06-25 14:57:20,629 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:57:20,629 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:57:20,630 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:57:20,631 | INFO | Settings: configured \u2705 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:57:20,632 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:57:20,634 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:57:20,634 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:57:20,635 | WARNING | > Find out more at https://se-ml.github.io/practices/ 2022-06-25 14:57:20,642 | INFO | Latest version of small-domain-prediction is 0 (from versions: 0) 2022-06-25 14:57:20,643 | INFO | File small-domain-prediction-0 found in cache Check accuracy on the test split \u00b6 In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) 2022-06-25 14:57:21,004 | INFO | Starting parallel map (concurrency: 12, chunk size: 26) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3054/3054 [00:31<00:00, 96.38it/s] precision recall f1-score support Art 0.35 0.27 0.31 33 Biology 0.77 0.84 0.80 299 Business 0.44 0.61 0.51 82 Chemistry 0.79 0.67 0.72 289 Computer Science 0.74 0.79 0.77 341 Economics 0.65 0.56 0.60 61 Engineering 0.53 0.52 0.53 202 Environmental Science 0.51 0.49 0.50 61 Geography 0.54 0.26 0.35 58 Geology 0.69 0.56 0.62 48 History 0.36 0.17 0.23 29 Materials Science 0.68 0.83 0.74 248 Mathematics 0.77 0.57 0.66 122 Medicine 0.97 0.79 0.87 715 Philosophy 0.50 0.05 0.10 19 Physics 0.72 0.75 0.74 162 Political Science 0.39 0.49 0.43 57 Psychology 0.49 0.81 0.61 141 Sociology 0.32 0.53 0.40 87 accuracy 0.70 3054 macro avg 0.59 0.56 0.55 3054 weighted avg 0.72 0.70 0.70 3054","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/deploy/#train-a-domain-classifier-on-the-semantic-scholar-dataset","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment from it using the GreatAI Python package. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validator = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validator=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) 2022-06-25 14:57:20,629 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:57:20,629 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:57:20,630 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:57:20,631 | INFO | Settings: configured \u2705 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:57:20,632 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:57:20,634 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:57:20,634 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:57:20,635 | WARNING | > Find out more at https://se-ml.github.io/practices/ 2022-06-25 14:57:20,642 | INFO | Latest version of small-domain-prediction is 0 (from versions: 0) 2022-06-25 14:57:20,643 | INFO | File small-domain-prediction-0 found in cache","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/deploy/#check-accuracy-on-the-test-split","text":"In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) 2022-06-25 14:57:21,004 | INFO | Starting parallel map (concurrency: 12, chunk size: 26) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3054/3054 [00:31<00:00, 96.38it/s] precision recall f1-score support Art 0.35 0.27 0.31 33 Biology 0.77 0.84 0.80 299 Business 0.44 0.61 0.51 82 Chemistry 0.79 0.67 0.72 289 Computer Science 0.74 0.79 0.77 341 Economics 0.65 0.56 0.60 61 Engineering 0.53 0.52 0.53 202 Environmental Science 0.51 0.49 0.50 61 Geography 0.54 0.26 0.35 58 Geology 0.69 0.56 0.62 48 History 0.36 0.17 0.23 29 Materials Science 0.68 0.83 0.74 248 Mathematics 0.77 0.57 0.66 122 Medicine 0.97 0.79 0.87 715 Philosophy 0.50 0.05 0.10 19 Physics 0.72 0.75 0.74 162 Political Science 0.39 0.49 0.43 57 Psychology 0.49 0.81 0.61 141 Sociology 0.32 0.53 0.40 87 accuracy 0.70 3054 macro avg 0.59 0.56 0.55 3054 weighted avg 0.72 0.70 0.70 3054","title":"Check accuracy on the test split"},{"location":"simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a domain classifier on the semantic scholar dataset \u00b6 Part 2: train a model The blue boxes show the steps implemented in this notebook. In Part 1 , we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3. In this part, we hyperparameter-optimise and train a simple, Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] 2022-06-25 14:50:29,879 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:50:29,880 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:50:29,881 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:50:29,881 | INFO | Settings: configured \u2705 2022-06-25 14:50:29,882 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:50:29,885 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:50:29,885 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:50:29,885 | WARNING | > Find out more at https://se-ml.github.io/practices/ In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 1.962260 0.147449 0.935357 0.063659 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.485030 0.463849 0.481840 0.476906 0.009324 1 10 1.942605 0.111027 0.952361 0.066812 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482890 0.459556 0.479362 0.473936 0.010270 2 19 2.145152 0.068978 1.002291 0.047358 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.467330 0.442994 0.464302 0.458208 0.010829 3 22 1.971888 0.126950 0.739795 0.071551 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.454830 0.422902 0.450677 0.442803 0.014174 4 6 1.861275 0.013389 1.058907 0.111122 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.456127 0.422456 0.443827 0.440803 0.013912 5 11 1.825397 0.105754 0.892227 0.057003 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.438232 0.440464 0.422667 0.433788 0.007916 6 23 1.693333 0.009667 0.501491 0.006545 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.433915 0.439470 0.416031 0.429805 0.010001 7 8 2.008045 0.145330 0.944559 0.155925 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.436178 0.425724 0.418396 0.426766 0.007297 8 20 1.749200 0.022959 0.889532 0.047517 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428215 0.425398 0.411051 0.421555 0.007516 9 9 1.960889 0.098004 0.985957 0.080925 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.430638 0.406619 0.420213 0.419157 0.009834 10 18 1.807799 0.064891 0.881872 0.030810 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.402402 0.372353 0.386189 0.386981 0.012280 11 1 2.009232 0.043125 0.899676 0.036977 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.389797 0.372619 0.388358 0.383591 0.007781 12 4 1.868087 0.094739 1.005353 0.101466 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379323 0.364667 0.379060 0.374350 0.006848 13 21 1.958430 0.039639 0.890963 0.012546 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.366936 0.343361 0.363883 0.358060 0.010468 14 5 1.940692 0.018320 0.898865 0.030651 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.354850 0.349237 0.342737 0.348941 0.004950 15 2 1.855691 0.029506 0.866492 0.038048 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.349005 0.341832 0.328617 0.339818 0.008444 16 17 1.798559 0.103497 0.888273 0.069050 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.312332 0.297655 0.307471 0.305819 0.006104 17 14 2.016041 0.232138 0.967630 0.146144 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.304942 0.296921 0.297121 0.299661 0.003735 18 13 1.829513 0.112645 0.885848 0.027726 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.301539 0.285396 0.297272 0.294736 0.006830 19 0 1.905362 0.018052 0.885552 0.023985 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.295635 0.276759 0.296270 0.289555 0.009052 20 16 1.793688 0.049995 0.921301 0.060980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.286746 0.272260 0.277084 0.278696 0.006023 21 3 2.078568 0.045549 0.963691 0.048281 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.276452 0.265509 0.268949 0.270303 0.004569 22 12 1.839506 0.048910 0.921812 0.010374 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.183196 0.186144 0.180323 0.183221 0.002376 23 15 1.909279 0.110639 1.056087 0.129738 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.165277 0.165840 0.167088 0.166068 0.000757 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) 2022-06-25 14:50:53,592 | INFO | Copying file for small-domain-prediction-0 2022-06-25 14:50:53,613 | INFO | Compressing small-domain-prediction-0 2022-06-25 14:50:53,917 | INFO | Uploading /tmp/tmpvxez8op8/small-domain-prediction-0.tar.gz to Mongo (GridFS) 2022-06-25 14:50:53,972 | INFO | Uploading small-domain-prediction-0.tar.gz 0.26/1.85 MB (14.1%) 2022-06-25 14:50:53,974 | INFO | Uploading small-domain-prediction-0.tar.gz 0.52/1.85 MB (28.2%) 2022-06-25 14:50:53,975 | INFO | Uploading small-domain-prediction-0.tar.gz 0.78/1.85 MB (42.3%) 2022-06-25 14:50:53,977 | INFO | Uploading small-domain-prediction-0.tar.gz 1.04/1.85 MB (56.4%) 2022-06-25 14:50:53,979 | INFO | Uploading small-domain-prediction-0.tar.gz 1.31/1.85 MB (70.5%) 2022-06-25 14:50:53,980 | INFO | Uploading small-domain-prediction-0.tar.gz 1.57/1.85 MB (84.7%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.83/1.85 MB (98.8%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.85/1.85 MB (100.0%) 2022-06-25 14:50:53,985 | INFO | Model small-domain-prediction uploaded with version 0 Out[6]: 'small-domain-prediction:0' Next: Part 3 \u00b6","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/train/#train-a-domain-classifier-on-the-semantic-scholar-dataset","text":"Part 2: train a model The blue boxes show the steps implemented in this notebook. In Part 1 , we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3. In this part, we hyperparameter-optimise and train a simple, Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] 2022-06-25 14:50:29,879 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:50:29,880 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:50:29,881 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:50:29,881 | INFO | Settings: configured \u2705 2022-06-25 14:50:29,882 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:50:29,885 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:50:29,885 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:50:29,885 | WARNING | > Find out more at https://se-ml.github.io/practices/ In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show()","title":"Load data that has been extracted in part 1"},{"location":"simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 1.962260 0.147449 0.935357 0.063659 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.485030 0.463849 0.481840 0.476906 0.009324 1 10 1.942605 0.111027 0.952361 0.066812 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482890 0.459556 0.479362 0.473936 0.010270 2 19 2.145152 0.068978 1.002291 0.047358 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.467330 0.442994 0.464302 0.458208 0.010829 3 22 1.971888 0.126950 0.739795 0.071551 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.454830 0.422902 0.450677 0.442803 0.014174 4 6 1.861275 0.013389 1.058907 0.111122 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.456127 0.422456 0.443827 0.440803 0.013912 5 11 1.825397 0.105754 0.892227 0.057003 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.438232 0.440464 0.422667 0.433788 0.007916 6 23 1.693333 0.009667 0.501491 0.006545 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.433915 0.439470 0.416031 0.429805 0.010001 7 8 2.008045 0.145330 0.944559 0.155925 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.436178 0.425724 0.418396 0.426766 0.007297 8 20 1.749200 0.022959 0.889532 0.047517 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428215 0.425398 0.411051 0.421555 0.007516 9 9 1.960889 0.098004 0.985957 0.080925 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.430638 0.406619 0.420213 0.419157 0.009834 10 18 1.807799 0.064891 0.881872 0.030810 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.402402 0.372353 0.386189 0.386981 0.012280 11 1 2.009232 0.043125 0.899676 0.036977 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.389797 0.372619 0.388358 0.383591 0.007781 12 4 1.868087 0.094739 1.005353 0.101466 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379323 0.364667 0.379060 0.374350 0.006848 13 21 1.958430 0.039639 0.890963 0.012546 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.366936 0.343361 0.363883 0.358060 0.010468 14 5 1.940692 0.018320 0.898865 0.030651 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.354850 0.349237 0.342737 0.348941 0.004950 15 2 1.855691 0.029506 0.866492 0.038048 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.349005 0.341832 0.328617 0.339818 0.008444 16 17 1.798559 0.103497 0.888273 0.069050 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.312332 0.297655 0.307471 0.305819 0.006104 17 14 2.016041 0.232138 0.967630 0.146144 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.304942 0.296921 0.297121 0.299661 0.003735 18 13 1.829513 0.112645 0.885848 0.027726 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.301539 0.285396 0.297272 0.294736 0.006830 19 0 1.905362 0.018052 0.885552 0.023985 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.295635 0.276759 0.296270 0.289555 0.009052 20 16 1.793688 0.049995 0.921301 0.060980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.286746 0.272260 0.277084 0.278696 0.006023 21 3 2.078568 0.045549 0.963691 0.048281 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.276452 0.265509 0.268949 0.270303 0.004569 22 12 1.839506 0.048910 0.921812 0.010374 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.183196 0.186144 0.180323 0.183221 0.002376 23 15 1.909279 0.110639 1.056087 0.129738 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.165277 0.165840 0.167088 0.166068 0.000757 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"simple/train/#export-the-model-using-greatai","text":"In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) 2022-06-25 14:50:53,592 | INFO | Copying file for small-domain-prediction-0 2022-06-25 14:50:53,613 | INFO | Compressing small-domain-prediction-0 2022-06-25 14:50:53,917 | INFO | Uploading /tmp/tmpvxez8op8/small-domain-prediction-0.tar.gz to Mongo (GridFS) 2022-06-25 14:50:53,972 | INFO | Uploading small-domain-prediction-0.tar.gz 0.26/1.85 MB (14.1%) 2022-06-25 14:50:53,974 | INFO | Uploading small-domain-prediction-0.tar.gz 0.52/1.85 MB (28.2%) 2022-06-25 14:50:53,975 | INFO | Uploading small-domain-prediction-0.tar.gz 0.78/1.85 MB (42.3%) 2022-06-25 14:50:53,977 | INFO | Uploading small-domain-prediction-0.tar.gz 1.04/1.85 MB (56.4%) 2022-06-25 14:50:53,979 | INFO | Uploading small-domain-prediction-0.tar.gz 1.31/1.85 MB (70.5%) 2022-06-25 14:50:53,980 | INFO | Uploading small-domain-prediction-0.tar.gz 1.57/1.85 MB (84.7%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.83/1.85 MB (98.8%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.85/1.85 MB (100.0%) 2022-06-25 14:50:53,985 | INFO | Model small-domain-prediction uploaded with version 0 Out[6]: 'small-domain-prediction:0'","title":"Export the model using GreatAI"},{"location":"simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 1.380641 0.054306 0.739966 0.053385 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 1.284987 0.113903 0.696876 0.003757 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 1.291148 0.101837 0.686561 0.083989 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 1.413231 0.153288 0.765960 0.099535 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 1.433934 0.155240 0.636608 0.024064 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 1.270732 0.013414 0.620984 0.025393 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) None precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 1.380641 0.054306 0.739966 0.053385 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 1.284987 0.113903 0.696876 0.003757 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 1.291148 0.101837 0.686561 0.083989 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 1.413231 0.153288 0.765960 0.099535 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 1.433934 0.155240 0.636608 0.024064 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 1.270732 0.013414 0.620984 0.025393 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) None precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see GreatAI in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # The training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) The deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that it is always given to the function. Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: great-ai deploy.ipynb or build a Docker image.","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see GreatAI in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#the-training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' )","title":"The training notebook"},{"location":"tutorial/#the-deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that it is always given to the function. Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. 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this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggles, or worse, simply ignored. With GreatAI , it has become a matter of 2 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) 2022-07-09 22:38:56 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:38:56 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:38:56 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:38:56 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:38:56 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:38:56 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:38:56 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:38:56 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:38:56 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:38:56 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:38:56 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:38:56 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:38:56 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:38:56 | INFO | File my-domain-predictor-5 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) # the original return value is under the 'output' key predict_domain(\"Mountains are just big rocks.\") # the original return value is under the 'output' key Out[2]: Trace[str]({ 'created': '2022-07-09T20:38:56.394746', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 5}], 'original_execution_time_ms': 4.999, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'aad1f83d-a81f-4b8b-898e-d02f8076616f'}) In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-09 22:38:58 | INFO | Converting notebook to Python script 2022-07-09 22:38:58 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-09 22:38:58 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-09 22:39:00 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:39:00 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:39:00 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:39:00 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:39:00 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:39:00 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:39:00 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:39:00 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:39:00 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:39:00 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:39:00 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:39:00 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:39:00 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:39:00 | INFO | File my-domain-predictor-5 found in cache 2022-07-09 22:39:00 | INFO | Started server process [882179] 2022-07-09 22:39:00 | INFO | Waiting for application startup. 2022-07-09 22:39:00 | INFO | Application startup complete. ^C 2022-07-09 22:39:04 | INFO | Shutting down 2022-07-09 22:39:04 | INFO | Waiting for application shutdown. 2022-07-09 22:39:04 | INFO | Application shutdown complete. 2022-07-09 22:39:04 | INFO | Finished server process [882179] Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already has an established routine for deploying services. There are three main approaches to deploy a GreatAI service. Manual deployment \u00b6 Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach, however, it might be fitting for some contexts. Containerised deployment \u00b6 Run the notebook directly in a container or create a service for it using your favourite orchestrator. docker run \\ -p 6060 :6060 \\ --volume ` pwd ` :/app \\ --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service \u00b6 Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as pytorch or tensorflow. FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to # cache them in the image so that you don't # have to download it each time a container starts # RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor COPY . . CMD [ \"deploy.ipynb\" ] Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggles, or worse, simply ignored. With GreatAI , it has become a matter of 2 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) 2022-07-09 22:38:56 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:38:56 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:38:56 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:38:56 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:38:56 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:38:56 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:38:56 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:38:56 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:38:56 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:38:56 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:38:56 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:38:56 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:38:56 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:38:56 | INFO | File my-domain-predictor-5 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) # the original return value is under the 'output' key predict_domain(\"Mountains are just big rocks.\") # the original return value is under the 'output' key Out[2]: Trace[str]({ 'created': '2022-07-09T20:38:56.394746', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 5}], 'original_execution_time_ms': 4.999, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'aad1f83d-a81f-4b8b-898e-d02f8076616f'}) In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-09 22:38:58 | INFO | Converting notebook to Python script 2022-07-09 22:38:58 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-09 22:38:58 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-09 22:39:00 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:39:00 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:39:00 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:39:00 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:39:00 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:39:00 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:39:00 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:39:00 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:39:00 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:39:00 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:39:00 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:39:00 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:39:00 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:39:00 | INFO | File my-domain-predictor-5 found in cache 2022-07-09 22:39:00 | INFO | Started server process [882179] 2022-07-09 22:39:00 | INFO | Waiting for application startup. 2022-07-09 22:39:00 | INFO | Application startup complete. ^C 2022-07-09 22:39:04 | INFO | Shutting down 2022-07-09 22:39:04 | INFO | Waiting for application shutdown. 2022-07-09 22:39:04 | INFO | Application shutdown complete. 2022-07-09 22:39:04 | INFO | Finished server process [882179] Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already has an established routine for deploying services. There are three main approaches to deploy a GreatAI service.","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#manual-deployment","text":"Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach, however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"tutorial/deploy/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite orchestrator. docker run \\ -p 6060 :6060 \\ --volume ` pwd ` :/app \\ --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"tutorial/deploy/#use-a-platform-as-a-service","text":"Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as pytorch or tensorflow. FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to # cache them in the image so that you don't # have to download it each time a container starts # RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor COPY . . CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [ ]: Copied! % pip install great - ai %pip install great-ai First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a seperate JSON). Let's write a function which takes a single line, and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [1]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. In [2]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8705.41it/s] Let's do the same for the test data. In [3]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:02<00:00, 7963.06it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [4]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[4]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [5]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [6]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\". This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey-zone for software engineering. In order to use this model in production, we have to make it accessible on some possibly shared infrastructure. In [7]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") 2022-07-09 22:33:18 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:33:18 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:33:18 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:33:18 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:33:18 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:33:18 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:33:18 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:33:18 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:33:18 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:33:18 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:33:18 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:33:18 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:33:19 | INFO | Copying file for my-domain-predictor-5 2022-07-09 22:33:19 | INFO | Compressing my-domain-predictor-5 2022-07-09 22:33:20 | INFO | Model my-domain-predictor uploaded with version 5 Out[7]: 'my-domain-predictor:5' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [ ]: Copied! % pip install great - ai %pip install great-ai First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a seperate JSON). Let's write a function which takes a single line, and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [1]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. In [2]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8705.41it/s] Let's do the same for the test data. In [3]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:02<00:00, 7963.06it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [4]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[4]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [5]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [6]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\". This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey-zone for software engineering. In order to use this model in production, we have to make it accessible on some possibly shared infrastructure. In [7]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") 2022-07-09 22:33:18 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:33:18 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:33:18 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:33:18 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:33:18 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:33:18 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:33:18 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:33:18 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:33:18 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:33:18 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:33:18 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:33:18 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:33:19 | INFO | Copying file for my-domain-predictor-5 2022-07-09 22:33:19 | INFO | Compressing my-domain-predictor-5 2022-07-09 22:33:20 | INFO | Model my-domain-predictor uploaded with version 5 Out[7]: 'my-domain-predictor:5'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]} \ No newline at end of file +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database quasi-shadow deployment Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and async inference methods Easy integration with other remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces Support for direct file input Hello world # pip install great-ai hello-world.py from great_ai import GreatAI @GreatAI . create #(1) def hello_world ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your hello_world function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, hello_world could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process. terminal great-ai hello-world.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phase of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, a number of best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Production use # GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Overview"},{"location":"#features","text":"Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database quasi-shadow deployment Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and async inference methods Easy integration with other remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces Support for direct file input","title":"Features"},{"location":"#hello-world","text":"pip install great-ai hello-world.py from great_ai import GreatAI @GreatAI . create #(1) def hello_world ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your hello_world function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, hello_world could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either ways, it is imperative to have continuos oversight of the services you provide and data you process. terminal great-ai hello-world.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.","title":"Hello world"},{"location":"#why-is-this-great","text":"GreatAI fits between the prototype and deployment phase of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, a number of best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust","title":"Why is this GREAT?"},{"location":"#why-greatai","text":"There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity use those, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub","title":"Why GreatAI?"},{"location":"#production-use","text":"GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Production use"},{"location":"explanation/","text":"Explanation # A lot more details and discussion about the problem context and approaches of GreatAI can be found in my thesis. : Download","title":"Explanation"},{"location":"explanation/#explanation","text":"A lot more details and discussion about the problem context and approaches of GreatAI can be found in my thesis. : Download","title":"Explanation"},{"location":"open_s3/","text":"open(S3) # Storing, versioning, and downloading files from S3 made as easy as using open() in Python. Caching included. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support the deletion of large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. Solution # pip install open-large Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # By default the latest version is returned # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : print ( f . readlines ()[ 0 ]) Automatically creates a file, writes to it, uploads it to S3, and then queries the most recent version of it. In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reading and writing is supported along with the different keywords open() accepts. The local cache can be configured with these properties: LargeFile . cache_path = Path ( '.cache' ) LargeFile . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the \"remote\" file, this pattern can be applied: path_to_model = LargeFile ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFile ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way, both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFile(\"my-file\").delete() . It will still reside in your local cache afterwards, its deletion will happen next time your local cache has to be pruned. Command-line example # The package can be used as a module from the command-line to give you more flexibility. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files endpoint_url = this is optional, for backblaze, use this: https://s3.us-west-002.backblazeb2.com Just like in example secrets . Print the expected options # python3 -m large_file --help Upload some files # python3 -m large_file --backend s3 --secrets secrets.ini --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. python3 -m large_file --backend s3 -secrets ~/.aws/credentials --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # python3 -m large_file --backend s3 --secrets ~/.aws/credentials --delete my_first_file.json","title":"[open(S3)](https://pypi.org/project/open-large/)"},{"location":"open_s3/#opens3","text":"Storing, versioning, and downloading files from S3 made as easy as using open() in Python. Caching included.","title":"open(S3)"},{"location":"open_s3/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support the deletion of large files, making it easy for them to eat-up the LFS quota and explode the size of your repos.","title":"Motivation"},{"location":"open_s3/#solution","text":"pip install open-large","title":"Solution"},{"location":"open_s3/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # By default the latest version is returned # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : print ( f . readlines ()[ 0 ]) Automatically creates a file, writes to it, uploads it to S3, and then queries the most recent version of it. In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"open_s3/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reading and writing is supported along with the different keywords open() accepts. The local cache can be configured with these properties: LargeFile . cache_path = Path ( '.cache' ) LargeFile . max_cache_size = \"30 GB\"","title":"More details"},{"location":"open_s3/#i-only-need-a-path","text":"In case you only need a path to the \"remote\" file, this pattern can be applied: path_to_model = LargeFile ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFile ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way, both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFile(\"my-file\").delete() . It will still reside in your local cache afterwards, its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"open_s3/#command-line-example","text":"The package can be used as a module from the command-line to give you more flexibility.","title":"Command-line example"},{"location":"open_s3/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files endpoint_url = this is optional, for backblaze, use this: https://s3.us-west-002.backblazeb2.com Just like in example secrets .","title":"Setup"},{"location":"open_s3/#print-the-expected-options","text":"python3 -m large_file --help","title":"Print the expected options"},{"location":"open_s3/#upload-some-files","text":"python3 -m large_file --backend s3 --secrets secrets.ini --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored.","title":"Upload some files"},{"location":"open_s3/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. python3 -m large_file --backend s3 -secrets ~/.aws/credentials --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"open_s3/#delete-remote-files","text":"python3 -m large_file --backend s3 --secrets ~/.aws/credentials --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/call-remote/","text":"","title":"Call remote"},{"location":"how-to-guides/create-service/","text":"How to instantiate a GreatAI service # The core value of the great-ai library lies in its great_ai.deploy.GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function with the GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of the GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-heavy operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create always have to come last. There are two built-in decorators that you can use to customise your function. Using use_model # If you have previously saved a model with save_model , you can inject it into your function by calling use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before GreatAI.create . Feel free to use @use_model in other places of the code base, it works equally well outside of GreatAI services. Using parameter # If you wish to turn of logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model decorator, are automatically decorated with @parameter when GreatAI.create is called. ```python \"greeter_with_validation.py\" from great_ai import GreatAI, use_model @GreatAI.create @use_model('name_of_my_model', version='latest') def type_safe_greeter(your_name: str, model) -> str: return f'Hi {your_name}!' assert type_safe_greeter('Andras').output == 'Hi Andras' !!! important You must call `@parameter` before `GreatAI.create`. Feel free to use `@parameter` in other places of the code base, it works equally well outside of GreatAI services. ## Complex example Refer to the following example summarising the options you have when instantiating a GreatAI service. ```python title=\"complex.py\" from great_ai import save_model, GreatAI, parameter, use_model save_model(4, 'secret-number') #(2) @GreatAI.create @parameter('positive_number', validator=lambda n: n > 0) @use_model('secret-number', version='latest', model_kwarg_name='secret') def add_number(positive_number: int, secret: int) -> int: return positive_number + secret assert add_number(1).output == 5 Refer to storing models for specifying where to store your models.","title":"How to instantiate a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-instantiate-a-greatai-service","text":"The core value of the great-ai library lies in its great_ai.deploy.GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function with the GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of the GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to instantiate a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-heavy operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create always have to come last. There are two built-in decorators that you can use to customise your function.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before GreatAI.create . Feel free to use @use_model in other places of the code base, it works equally well outside of GreatAI services.","title":"Using use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn of logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model decorator, are automatically decorated with @parameter when GreatAI.create is called. ```python \"greeter_with_validation.py\" from great_ai import GreatAI, use_model @GreatAI.create @use_model('name_of_my_model', version='latest') def type_safe_greeter(your_name: str, model) -> str: return f'Hi {your_name}!' assert type_safe_greeter('Andras').output == 'Hi Andras' !!! important You must call `@parameter` before `GreatAI.create`. Feel free to use `@parameter` in other places of the code base, it works equally well outside of GreatAI services. ## Complex example Refer to the following example summarising the options you have when instantiating a GreatAI service. ```python title=\"complex.py\" from great_ai import save_model, GreatAI, parameter, use_model save_model(4, 'secret-number') #(2) @GreatAI.create @parameter('positive_number', validator=lambda n: n > 0) @use_model('secret-number', version='latest', model_kwarg_name='secret') def add_number(positive_number: int, secret: int) -> int: return positive_number + secret assert add_number(1).output == 5 Refer to storing models for specifying where to store your models.","title":"Using parameter"},{"location":"how-to-guides/scraping/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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} } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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AsyncHTTPTransport ( retries = 2 ) limits = httpx . Limits ( max_connections = None ) timeout = httpx . Timeout ( connect = 60.0 , read = 300 , write = 60 , pool = None ) async with httpx . AsyncClient ( transport = transport , limits = limits , timeout = timeout ) as client : pass import httpx transport = httpx.AsyncHTTPTransport(retries=2) limits = httpx.Limits(max_connections=None) timeout = httpx.Timeout(connect=60.0, read=300, write=60, pool=None) async with httpx.AsyncClient( transport=transport, limits=limits, timeout=timeout ) as client: pass","title":"Scraping"},{"location":"how-to-guides/store-models/","text":"","title":"Store models"},{"location":"how-to-guides/using-service/","text":"How to use a GreatAI service # After creating a GreatAI service by wrapping your prediction function, it's time to do some prediction. Let's use the following example: ```python \"type_safe_greeter.py\" from great_ai import GreatAI @GreatAI.create def type_safe_greeter(your_name: str) -> str: return f'Hi {your_name}' ```","title":"How to use a GreatAI service"},{"location":"how-to-guides/using-service/#how-to-use-a-greatai-service","text":"After creating a GreatAI service by wrapping your prediction function, it's time to do some prediction. Let's use the following example: ```python \"type_safe_greeter.py\" from great_ai import GreatAI @GreatAI.create def type_safe_greeter(your_name: str) -> str: return f'Hi {your_name}' ```","title":"How to use a GreatAI service"},{"location":"reference/","text":"Reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 class GreatAI ( Generic [ T , V ]): __name__ : str __doc__ : str def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 20 , route_config = RouteConfig ()) # Source code in great_ai/context.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 20 , route_config : RouteConfig = RouteConfig (), ) -> None : global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validator = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validator = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validator Callable [[ Any ], bool ] Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validator=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validator: Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validator ( argument ): raise ArgumentValidationError ( f \"Argument { parameter_name } in { func . __name__ } did not pass validation\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value ) # Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai # call_remote_great_ai_async # Ground-truth # add_ground_truth # query_ground_truth # delete_ground_truth #","title":"Reference"},{"location":"reference/#reference","text":"from great_ai import *","title":"Reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 class GreatAI ( Generic [ T , V ]): __name__ : str __doc__ : str def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see great_ai.configure. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.configure","text":"Source code in great_ai/context.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 20 , route_config : RouteConfig = RouteConfig (), ) -> None : global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validator = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validator Callable [[ Any ], bool ] Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 def parameter ( parameter_name : str , * , validator : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validator=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validator: Optional validator to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validator ( argument ): raise ArgumentValidationError ( f \"Argument { parameter_name } in { func . __name__ } did not pass validation\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"","title":"call_remote_great_ai"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"","title":"call_remote_great_ai_async"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"","title":"add_ground_truth"},{"location":"reference/#great_ai.query_ground_truth","text":"","title":"query_ground_truth"},{"location":"reference/#great_ai.delete_ground_truth","text":"","title":"delete_ground_truth"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence return list ( tqdm ( parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ), total = len ( input_values ), dynamic_ncols = True , ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. For more info, check-out the scraping guide . chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase.\"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.simple_parallel_map.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence return list ( tqdm ( parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ), total = len ( input_values ), dynamic_ncols = True , ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.threaded_parallel_map.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. For more info, check-out the scraping guide .","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.logger.get_logger.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase.\"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) } )\"","title":"View models"},{"location":"reference/views/#view-models","text":"Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) } )\"","title":"View models"},{"location":"simple/data/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a domain classifier on the semantic scholar dataset \u00b6 Part 1: obtain and clean data The blue boxes show the steps implemented in this notebook. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this case, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 1 MAX_CHUNK_COUNT = 1 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"Processing {len(chunks)} out of the {len(manifest.split())} available chunks\" Out[2]: 'Processing 1 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai import parallel_map , clean , is_english , predict_language def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ])) ] preprocessed_chunks = parallel_map ( preprocess_chunk , chunks ) from typing import List, Tuple import json import gzip from great_ai import parallel_map, clean, is_english, predict_language def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} {c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"])) ] preprocessed_chunks = parallel_map(preprocess_chunk, chunks) Spacy model en_core_web_sm not found locally, downloading... Collecting en-core-web-sm==3.3.0 Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0-py3-none-any.whl (12.8 MB) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 12.8/12.8 MB 3.6 MB/s eta 0:00:00 Requirement already satisfied: spacy<3.4.0,>=3.3.0.dev0 in ./.env/lib/python3.10/site-packages (from en-core-web-sm==3.3.0) (3.3.1) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (4.64.0) Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (0.9.1) Requirement already satisfied: srsly<3.0.0,>=2.4.3 in ./.env/lib/python3.10/site-packages (from 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already satisfied: click<9.0.0,>=7.1.1 in ./.env/lib/python3.10/site-packages (from typer<0.5.0,>=0.3.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (8.1.3) Requirement already satisfied: MarkupSafe>=2.0 in ./.env/lib/python3.10/site-packages (from jinja2->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (2.1.1) Installing collected packages: en-core-web-sm Successfully installed en-core-web-sm-3.3.0 2022-06-25 14:21:57,983 | WARNING | Limiting concurrency to 1 because there are only 1 chunks 2022-06-25 14:21:57,984 | INFO | Starting parallel map (concurrency: 1, chunk size: 1) 2022-06-25 14:21:57,984 | WARNING | Running in series, there is no reason for parallelism 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [03:26<00:00, 206.86s/it] In [4]: Copied! from itertools import chain preprocessed_data = list ( chain ( * preprocessed_chunks )) X , y = zip ( * preprocessed_data ) # X is the input, y is the expected (ground truth) output from itertools import chain preprocessed_data = list(chain(*preprocessed_chunks)) X, y = zip( *preprocessed_data ) # X is the input, y is the expected (ground truth) output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each datapoint with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) 2022-06-25 14:25:24,989 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:25:24,990 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:25:24,991 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:25:24,992 | INFO | Settings: configured \u2705 2022-06-25 14:25:24,993 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:25:24,995 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:25:24,996 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:25:24,997 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:25:24,998 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:25:24,998 | WARNING | > Find out more at https://se-ml.github.io/practices/ Next: Part 2 \u00b6","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/data/#train-a-domain-classifier-on-the-semantic-scholar-dataset","text":"Part 1: obtain and clean data The blue boxes show the steps implemented in this notebook.","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this case, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 1 MAX_CHUNK_COUNT = 1 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"Processing {len(chunks)} out of the {len(manifest.split())} available chunks\" Out[2]: 'Processing 1 out of the 6002 available chunks'","title":"Extract"},{"location":"simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai import parallel_map , clean , is_english , predict_language def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: # Extract response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ])) ] preprocessed_chunks = parallel_map ( preprocess_chunk , chunks ) from typing import List, Tuple import json import gzip from great_ai import parallel_map, clean, is_english, predict_language def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: # Extract response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} {c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"])) ] preprocessed_chunks = parallel_map(preprocess_chunk, chunks) Spacy model en_core_web_sm not found locally, downloading... Collecting en-core-web-sm==3.3.0 Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0-py3-none-any.whl (12.8 MB) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 12.8/12.8 MB 3.6 MB/s eta 0:00:00 Requirement already satisfied: spacy<3.4.0,>=3.3.0.dev0 in ./.env/lib/python3.10/site-packages (from en-core-web-sm==3.3.0) (3.3.1) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (4.64.0) Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in ./.env/lib/python3.10/site-packages (from spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (0.9.1) Requirement already satisfied: srsly<3.0.0,>=2.4.3 in ./.env/lib/python3.10/site-packages (from 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already satisfied: click<9.0.0,>=7.1.1 in ./.env/lib/python3.10/site-packages (from typer<0.5.0,>=0.3.0->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (8.1.3) Requirement already satisfied: MarkupSafe>=2.0 in ./.env/lib/python3.10/site-packages (from jinja2->spacy<3.4.0,>=3.3.0.dev0->en-core-web-sm==3.3.0) (2.1.1) Installing collected packages: en-core-web-sm Successfully installed en-core-web-sm-3.3.0 2022-06-25 14:21:57,983 | WARNING | Limiting concurrency to 1 because there are only 1 chunks 2022-06-25 14:21:57,984 | INFO | Starting parallel map (concurrency: 1, chunk size: 1) 2022-06-25 14:21:57,984 | WARNING | Running in series, there is no reason for parallelism 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [03:26<00:00, 206.86s/it] In [4]: Copied! from itertools import chain preprocessed_data = list ( chain ( * preprocessed_chunks )) X , y = zip ( * preprocessed_data ) # X is the input, y is the expected (ground truth) output from itertools import chain preprocessed_data = list(chain(*preprocessed_chunks)) X, y = zip( *preprocessed_data ) # X is the input, y is the expected (ground truth) output","title":"Transform"},{"location":"simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each datapoint with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) 2022-06-25 14:25:24,989 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:25:24,990 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:25:24,991 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:25:24,992 | INFO | Settings: configured \u2705 2022-06-25 14:25:24,993 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:25:24,994 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:25:24,995 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:25:24,996 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:25:24,997 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:25:24,998 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:25:24,998 | WARNING | > Find out more at https://se-ml.github.io/practices/","title":"Production-ready backend"},{"location":"simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a domain classifier on the semantic scholar dataset \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment from it using the GreatAI Python package. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validator = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validator=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) 2022-06-25 14:57:20,629 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:57:20,629 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:57:20,630 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:57:20,631 | INFO | Settings: configured \u2705 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:57:20,632 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:57:20,634 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:57:20,634 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:57:20,635 | WARNING | > Find out more at https://se-ml.github.io/practices/ 2022-06-25 14:57:20,642 | INFO | Latest version of small-domain-prediction is 0 (from versions: 0) 2022-06-25 14:57:20,643 | INFO | File small-domain-prediction-0 found in cache Check accuracy on the test split \u00b6 In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) 2022-06-25 14:57:21,004 | INFO | Starting parallel map (concurrency: 12, chunk size: 26) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3054/3054 [00:31<00:00, 96.38it/s] precision recall f1-score support Art 0.35 0.27 0.31 33 Biology 0.77 0.84 0.80 299 Business 0.44 0.61 0.51 82 Chemistry 0.79 0.67 0.72 289 Computer Science 0.74 0.79 0.77 341 Economics 0.65 0.56 0.60 61 Engineering 0.53 0.52 0.53 202 Environmental Science 0.51 0.49 0.50 61 Geography 0.54 0.26 0.35 58 Geology 0.69 0.56 0.62 48 History 0.36 0.17 0.23 29 Materials Science 0.68 0.83 0.74 248 Mathematics 0.77 0.57 0.66 122 Medicine 0.97 0.79 0.87 715 Philosophy 0.50 0.05 0.10 19 Physics 0.72 0.75 0.74 162 Political Science 0.39 0.49 0.43 57 Psychology 0.49 0.81 0.61 141 Sociology 0.32 0.53 0.40 87 accuracy 0.70 3054 macro avg 0.59 0.56 0.55 3054 weighted avg 0.72 0.70 0.70 3054","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/deploy/#train-a-domain-classifier-on-the-semantic-scholar-dataset","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment from it using the GreatAI Python package. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validator = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validator=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) 2022-06-25 14:57:20,629 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:57:20,629 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:57:20,630 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:57:20,631 | INFO | Settings: configured \u2705 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:57:20,631 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:57:20,632 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:57:20,633 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:57:20,634 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:57:20,634 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:57:20,635 | WARNING | > Find out more at https://se-ml.github.io/practices/ 2022-06-25 14:57:20,642 | INFO | Latest version of small-domain-prediction is 0 (from versions: 0) 2022-06-25 14:57:20,643 | INFO | File small-domain-prediction-0 found in cache","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/deploy/#check-accuracy-on-the-test-split","text":"In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) 2022-06-25 14:57:21,004 | INFO | Starting parallel map (concurrency: 12, chunk size: 26) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3054/3054 [00:31<00:00, 96.38it/s] precision recall f1-score support Art 0.35 0.27 0.31 33 Biology 0.77 0.84 0.80 299 Business 0.44 0.61 0.51 82 Chemistry 0.79 0.67 0.72 289 Computer Science 0.74 0.79 0.77 341 Economics 0.65 0.56 0.60 61 Engineering 0.53 0.52 0.53 202 Environmental Science 0.51 0.49 0.50 61 Geography 0.54 0.26 0.35 58 Geology 0.69 0.56 0.62 48 History 0.36 0.17 0.23 29 Materials Science 0.68 0.83 0.74 248 Mathematics 0.77 0.57 0.66 122 Medicine 0.97 0.79 0.87 715 Philosophy 0.50 0.05 0.10 19 Physics 0.72 0.75 0.74 162 Political Science 0.39 0.49 0.43 57 Psychology 0.49 0.81 0.61 141 Sociology 0.32 0.53 0.40 87 accuracy 0.70 3054 macro avg 0.59 0.56 0.55 3054 weighted avg 0.72 0.70 0.70 3054","title":"Check accuracy on the test split"},{"location":"simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a domain classifier on the semantic scholar dataset \u00b6 Part 2: train a model The blue boxes show the steps implemented in this notebook. In Part 1 , we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3. In this part, we hyperparameter-optimise and train a simple, Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] 2022-06-25 14:50:29,879 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:50:29,880 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:50:29,881 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:50:29,881 | INFO | Settings: configured \u2705 2022-06-25 14:50:29,882 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:50:29,885 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:50:29,885 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:50:29,885 | WARNING | > Find out more at https://se-ml.github.io/practices/ In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 1.962260 0.147449 0.935357 0.063659 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.485030 0.463849 0.481840 0.476906 0.009324 1 10 1.942605 0.111027 0.952361 0.066812 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482890 0.459556 0.479362 0.473936 0.010270 2 19 2.145152 0.068978 1.002291 0.047358 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.467330 0.442994 0.464302 0.458208 0.010829 3 22 1.971888 0.126950 0.739795 0.071551 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.454830 0.422902 0.450677 0.442803 0.014174 4 6 1.861275 0.013389 1.058907 0.111122 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.456127 0.422456 0.443827 0.440803 0.013912 5 11 1.825397 0.105754 0.892227 0.057003 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.438232 0.440464 0.422667 0.433788 0.007916 6 23 1.693333 0.009667 0.501491 0.006545 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.433915 0.439470 0.416031 0.429805 0.010001 7 8 2.008045 0.145330 0.944559 0.155925 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.436178 0.425724 0.418396 0.426766 0.007297 8 20 1.749200 0.022959 0.889532 0.047517 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428215 0.425398 0.411051 0.421555 0.007516 9 9 1.960889 0.098004 0.985957 0.080925 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.430638 0.406619 0.420213 0.419157 0.009834 10 18 1.807799 0.064891 0.881872 0.030810 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.402402 0.372353 0.386189 0.386981 0.012280 11 1 2.009232 0.043125 0.899676 0.036977 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.389797 0.372619 0.388358 0.383591 0.007781 12 4 1.868087 0.094739 1.005353 0.101466 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379323 0.364667 0.379060 0.374350 0.006848 13 21 1.958430 0.039639 0.890963 0.012546 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.366936 0.343361 0.363883 0.358060 0.010468 14 5 1.940692 0.018320 0.898865 0.030651 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.354850 0.349237 0.342737 0.348941 0.004950 15 2 1.855691 0.029506 0.866492 0.038048 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.349005 0.341832 0.328617 0.339818 0.008444 16 17 1.798559 0.103497 0.888273 0.069050 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.312332 0.297655 0.307471 0.305819 0.006104 17 14 2.016041 0.232138 0.967630 0.146144 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.304942 0.296921 0.297121 0.299661 0.003735 18 13 1.829513 0.112645 0.885848 0.027726 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.301539 0.285396 0.297272 0.294736 0.006830 19 0 1.905362 0.018052 0.885552 0.023985 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.295635 0.276759 0.296270 0.289555 0.009052 20 16 1.793688 0.049995 0.921301 0.060980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.286746 0.272260 0.277084 0.278696 0.006023 21 3 2.078568 0.045549 0.963691 0.048281 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.276452 0.265509 0.268949 0.270303 0.004569 22 12 1.839506 0.048910 0.921812 0.010374 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.183196 0.186144 0.180323 0.183221 0.002376 23 15 1.909279 0.110639 1.056087 0.129738 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.165277 0.165840 0.167088 0.166068 0.000757 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) 2022-06-25 14:50:53,592 | INFO | Copying file for small-domain-prediction-0 2022-06-25 14:50:53,613 | INFO | Compressing small-domain-prediction-0 2022-06-25 14:50:53,917 | INFO | Uploading /tmp/tmpvxez8op8/small-domain-prediction-0.tar.gz to Mongo (GridFS) 2022-06-25 14:50:53,972 | INFO | Uploading small-domain-prediction-0.tar.gz 0.26/1.85 MB (14.1%) 2022-06-25 14:50:53,974 | INFO | Uploading small-domain-prediction-0.tar.gz 0.52/1.85 MB (28.2%) 2022-06-25 14:50:53,975 | INFO | Uploading small-domain-prediction-0.tar.gz 0.78/1.85 MB (42.3%) 2022-06-25 14:50:53,977 | INFO | Uploading small-domain-prediction-0.tar.gz 1.04/1.85 MB (56.4%) 2022-06-25 14:50:53,979 | INFO | Uploading small-domain-prediction-0.tar.gz 1.31/1.85 MB (70.5%) 2022-06-25 14:50:53,980 | INFO | Uploading small-domain-prediction-0.tar.gz 1.57/1.85 MB (84.7%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.83/1.85 MB (98.8%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.85/1.85 MB (100.0%) 2022-06-25 14:50:53,985 | INFO | Model small-domain-prediction uploaded with version 0 Out[6]: 'small-domain-prediction:0' Next: Part 3 \u00b6","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/train/#train-a-domain-classifier-on-the-semantic-scholar-dataset","text":"Part 2: train a model The blue boxes show the steps implemented in this notebook. In Part 1 , we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3. In this part, we hyperparameter-optimise and train a simple, Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Train a domain classifier on the semantic scholar dataset"},{"location":"simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] 2022-06-25 14:50:29,879 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-06-25 14:50:29,880 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver 2022-06-25 14:50:29,881 | INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo 2022-06-25 14:50:29,881 | INFO | Settings: configured \u2705 2022-06-25 14:50:29,882 | INFO | \ud83d\udd29 tracing_database: MongodbDriver 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 large_file_implementation: LargeFileMongo 2022-06-25 14:50:29,883 | INFO | \ud83d\udd29 is_production: False 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-06-25 14:50:29,884 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-06-25 14:50:29,885 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-06-25 14:50:29,885 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-06-25 14:50:29,885 | WARNING | > Find out more at https://se-ml.github.io/practices/ In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show()","title":"Load data that has been extracted in part 1"},{"location":"simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 1.962260 0.147449 0.935357 0.063659 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.485030 0.463849 0.481840 0.476906 0.009324 1 10 1.942605 0.111027 0.952361 0.066812 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482890 0.459556 0.479362 0.473936 0.010270 2 19 2.145152 0.068978 1.002291 0.047358 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.467330 0.442994 0.464302 0.458208 0.010829 3 22 1.971888 0.126950 0.739795 0.071551 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.454830 0.422902 0.450677 0.442803 0.014174 4 6 1.861275 0.013389 1.058907 0.111122 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.456127 0.422456 0.443827 0.440803 0.013912 5 11 1.825397 0.105754 0.892227 0.057003 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.438232 0.440464 0.422667 0.433788 0.007916 6 23 1.693333 0.009667 0.501491 0.006545 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.433915 0.439470 0.416031 0.429805 0.010001 7 8 2.008045 0.145330 0.944559 0.155925 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.436178 0.425724 0.418396 0.426766 0.007297 8 20 1.749200 0.022959 0.889532 0.047517 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428215 0.425398 0.411051 0.421555 0.007516 9 9 1.960889 0.098004 0.985957 0.080925 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.430638 0.406619 0.420213 0.419157 0.009834 10 18 1.807799 0.064891 0.881872 0.030810 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.402402 0.372353 0.386189 0.386981 0.012280 11 1 2.009232 0.043125 0.899676 0.036977 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.389797 0.372619 0.388358 0.383591 0.007781 12 4 1.868087 0.094739 1.005353 0.101466 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.379323 0.364667 0.379060 0.374350 0.006848 13 21 1.958430 0.039639 0.890963 0.012546 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.366936 0.343361 0.363883 0.358060 0.010468 14 5 1.940692 0.018320 0.898865 0.030651 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.354850 0.349237 0.342737 0.348941 0.004950 15 2 1.855691 0.029506 0.866492 0.038048 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.349005 0.341832 0.328617 0.339818 0.008444 16 17 1.798559 0.103497 0.888273 0.069050 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.312332 0.297655 0.307471 0.305819 0.006104 17 14 2.016041 0.232138 0.967630 0.146144 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.304942 0.296921 0.297121 0.299661 0.003735 18 13 1.829513 0.112645 0.885848 0.027726 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.301539 0.285396 0.297272 0.294736 0.006830 19 0 1.905362 0.018052 0.885552 0.023985 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.295635 0.276759 0.296270 0.289555 0.009052 20 16 1.793688 0.049995 0.921301 0.060980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.286746 0.272260 0.277084 0.278696 0.006023 21 3 2.078568 0.045549 0.963691 0.048281 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.276452 0.265509 0.268949 0.270303 0.004569 22 12 1.839506 0.048910 0.921812 0.010374 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.183196 0.186144 0.180323 0.183221 0.002376 23 15 1.909279 0.110639 1.056087 0.129738 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.165277 0.165840 0.167088 0.166068 0.000757 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"simple/train/#export-the-model-using-greatai","text":"In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) 2022-06-25 14:50:53,592 | INFO | Copying file for small-domain-prediction-0 2022-06-25 14:50:53,613 | INFO | Compressing small-domain-prediction-0 2022-06-25 14:50:53,917 | INFO | Uploading /tmp/tmpvxez8op8/small-domain-prediction-0.tar.gz to Mongo (GridFS) 2022-06-25 14:50:53,972 | INFO | Uploading small-domain-prediction-0.tar.gz 0.26/1.85 MB (14.1%) 2022-06-25 14:50:53,974 | INFO | Uploading small-domain-prediction-0.tar.gz 0.52/1.85 MB (28.2%) 2022-06-25 14:50:53,975 | INFO | Uploading small-domain-prediction-0.tar.gz 0.78/1.85 MB (42.3%) 2022-06-25 14:50:53,977 | INFO | Uploading small-domain-prediction-0.tar.gz 1.04/1.85 MB (56.4%) 2022-06-25 14:50:53,979 | INFO | Uploading small-domain-prediction-0.tar.gz 1.31/1.85 MB (70.5%) 2022-06-25 14:50:53,980 | INFO | Uploading small-domain-prediction-0.tar.gz 1.57/1.85 MB (84.7%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.83/1.85 MB (98.8%) 2022-06-25 14:50:53,982 | INFO | Uploading small-domain-prediction-0.tar.gz 1.85/1.85 MB (100.0%) 2022-06-25 14:50:53,985 | INFO | Model small-domain-prediction uploaded with version 0 Out[6]: 'small-domain-prediction:0'","title":"Export the model using GreatAI"},{"location":"simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 1.380641 0.054306 0.739966 0.053385 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 1.284987 0.113903 0.696876 0.003757 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 1.291148 0.101837 0.686561 0.083989 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 1.413231 0.153288 0.765960 0.099535 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 1.433934 0.155240 0.636608 0.024064 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 1.270732 0.013414 0.620984 0.025393 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) None precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"mag/train.txt\" , encoding = \"utf-8\" ) as f : training_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"mag/test.txt\" , encoding = \"utf-8\" ) as f : test_data = list ( tqdm ( parallel_map ( preprocess , f . readlines ()))) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"mag/train.txt\", encoding=\"utf-8\") as f: training_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"mag/test.txt\", encoding=\"utf-8\") as f: test_data = list(tqdm(parallel_map(preprocess, f.readlines()))) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 84000it [00:09, 8647.60it/s] 22399it [00:03, 6368.63it/s] In [2]: Copied! import pandas as pd from collections import Counter import plotly.express as px df = pd . DataFrame ( Counter ( y_train ) . most_common (), columns = [ \"domain\" , \"count\" ]) px . bar ( df , \"domain\" , \"count\" , width = 1200 , height = 400 ) . show () import pandas as pd from collections import Counter import plotly.express as px df = pd.DataFrame(Counter(y_train).most_common(), columns=[\"domain\", \"count\"]) px.bar(df, \"domain\", \"count\", width=1200, height=400).show() In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 1.227354 0.011710 0.652844 0.040511 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.279783 0.037969 0.634761 0.068606 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.332854 0.171405 0.743611 0.109792 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.253222 0.072134 0.612344 0.016771 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.472035 0.080849 0.654935 0.044302 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 1.380641 0.054306 0.739966 0.053385 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 1.284987 0.113903 0.696876 0.003757 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 1.291148 0.101837 0.686561 0.083989 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 1.268873 0.042412 0.645649 0.022738 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.147120 0.009335 0.599006 0.045637 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.186988 0.057330 0.642233 0.078248 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 1.413231 0.153288 0.765960 0.099535 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 1.193013 0.079000 0.691768 0.027448 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.043618 0.098733 0.450375 0.061660 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.301459 0.062143 0.660748 0.056054 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 1.433934 0.155240 0.636608 0.024064 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 1.325535 0.073539 0.672542 0.085835 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.237677 0.070063 0.651091 0.102538 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.394873 0.105286 0.637073 0.041708 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 1.270732 0.013414 0.620984 0.025393 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 1.177219 0.039633 0.586308 0.044247 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.165583 0.046999 0.603675 0.031774 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 0.907388 0.121543 0.354655 0.023775 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.210845 0.041339 0.653606 0.028117 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) None precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see GreatAI in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # The training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) The deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that it is always given to the function. Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: great-ai deploy.ipynb or build a Docker image.","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see GreatAI in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#the-training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' )","title":"The training notebook"},{"location":"tutorial/#the-deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that it is always given to the function. Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. 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this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggles, or worse, simply ignored. With GreatAI , it has become a matter of 2 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) 2022-07-09 22:38:56 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:38:56 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:38:56 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:38:56 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:38:56 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:38:56 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:38:56 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:38:56 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:38:56 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:38:56 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:38:56 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:38:56 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:38:56 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:38:56 | INFO | File my-domain-predictor-5 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) # the original return value is under the 'output' key predict_domain(\"Mountains are just big rocks.\") # the original return value is under the 'output' key Out[2]: Trace[str]({ 'created': '2022-07-09T20:38:56.394746', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 5}], 'original_execution_time_ms': 4.999, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'aad1f83d-a81f-4b8b-898e-d02f8076616f'}) In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-09 22:38:58 | INFO | Converting notebook to Python script 2022-07-09 22:38:58 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-09 22:38:58 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-09 22:39:00 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:39:00 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:39:00 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:39:00 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:39:00 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:39:00 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:39:00 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:39:00 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:39:00 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:39:00 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:39:00 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:39:00 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:39:00 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:39:00 | INFO | File my-domain-predictor-5 found in cache 2022-07-09 22:39:00 | INFO | Started server process [882179] 2022-07-09 22:39:00 | INFO | Waiting for application startup. 2022-07-09 22:39:00 | INFO | Application startup complete. ^C 2022-07-09 22:39:04 | INFO | Shutting down 2022-07-09 22:39:04 | INFO | Waiting for application shutdown. 2022-07-09 22:39:04 | INFO | Application shutdown complete. 2022-07-09 22:39:04 | INFO | Finished server process [882179] Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already has an established routine for deploying services. There are three main approaches to deploy a GreatAI service. Manual deployment \u00b6 Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach, however, it might be fitting for some contexts. Containerised deployment \u00b6 Run the notebook directly in a container or create a service for it using your favourite orchestrator. docker run \\ -p 6060 :6060 \\ --volume ` pwd ` :/app \\ --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service \u00b6 Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as pytorch or tensorflow. FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to # cache them in the image so that you don't # have to download it each time a container starts # RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor COPY . . CMD [ \"deploy.ipynb\" ] Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggles, or worse, simply ignored. With GreatAI , it has become a matter of 2 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) 2022-07-09 22:38:56 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:38:56 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:38:56 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:38:56 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:38:56 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:38:56 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:38:56 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:38:56 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:38:56 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:38:56 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:38:56 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:38:56 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:38:56 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:38:56 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:38:56 | INFO | File my-domain-predictor-5 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) # the original return value is under the 'output' key predict_domain(\"Mountains are just big rocks.\") # the original return value is under the 'output' key Out[2]: Trace[str]({ 'created': '2022-07-09T20:38:56.394746', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 5}], 'original_execution_time_ms': 4.999, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'aad1f83d-a81f-4b8b-898e-d02f8076616f'}) In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-09 22:38:58 | INFO | Converting notebook to Python script 2022-07-09 22:38:58 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-09 22:38:58 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-09 22:39:00 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:39:00 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:39:00 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:39:00 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:39:00 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:39:00 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:39:00 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:39:00 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:39:00 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:39:00 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:39:00 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:39:00 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:39:00 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:39:00 | INFO | Latest version of my-domain-predictor is 5 (from versions: 0, 1, 2, 3, 4, 5) 2022-07-09 22:39:00 | INFO | File my-domain-predictor-5 found in cache 2022-07-09 22:39:00 | INFO | Started server process [882179] 2022-07-09 22:39:00 | INFO | Waiting for application startup. 2022-07-09 22:39:00 | INFO | Application startup complete. ^C 2022-07-09 22:39:04 | INFO | Shutting down 2022-07-09 22:39:04 | INFO | Waiting for application shutdown. 2022-07-09 22:39:04 | INFO | Application shutdown complete. 2022-07-09 22:39:04 | INFO | Finished server process [882179] Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already has an established routine for deploying services. There are three main approaches to deploy a GreatAI service.","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#manual-deployment","text":"Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach, however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"tutorial/deploy/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite orchestrator. docker run \\ -p 6060 :6060 \\ --volume ` pwd ` :/app \\ --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"tutorial/deploy/#use-a-platform-as-a-service","text":"Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as pytorch or tensorflow. FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to # cache them in the image so that you don't # have to download it each time a container starts # RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor COPY . . CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [ ]: Copied! % pip install great - ai %pip install great-ai First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a seperate JSON). Let's write a function which takes a single line, and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [1]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. In [2]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8705.41it/s] Let's do the same for the test data. In [3]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:02<00:00, 7963.06it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [4]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[4]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [5]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [6]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\". This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey-zone for software engineering. In order to use this model in production, we have to make it accessible on some possibly shared infrastructure. In [7]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") 2022-07-09 22:33:18 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:33:18 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:33:18 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:33:18 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:33:18 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:33:18 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:33:18 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:33:18 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:33:18 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:33:18 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:33:18 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:33:18 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:33:19 | INFO | Copying file for my-domain-predictor-5 2022-07-09 22:33:19 | INFO | Compressing my-domain-predictor-5 2022-07-09 22:33:20 | INFO | Model my-domain-predictor uploaded with version 5 Out[7]: 'my-domain-predictor:5' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [ ]: Copied! % pip install great - ai %pip install great-ai First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a seperate JSON). Let's write a function which takes a single line, and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [1]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. In [2]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8705.41it/s] Let's do the same for the test data. In [3]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:02<00:00, 7963.06it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [4]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[4]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [5]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [6]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\". This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey-zone for software engineering. In order to use this model in production, we have to make it accessible on some possibly shared infrastructure. In [7]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") 2022-07-09 22:33:18 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-09 22:33:18 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-09 22:33:18 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-09 22:33:18 | INFO | GreatAI (v0.1.0): configured \u2705 2022-07-09 22:33:18 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-09 22:33:18 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-09 22:33:18 | INFO | \ud83d\udd29 is_production: False 2022-07-09 22:33:18 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-09 22:33:18 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-09 22:33:18 | INFO | \ud83d\udd29 dashboard_table_size: 20 2022-07-09 22:33:18 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-09 22:33:18 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-09 22:33:18 | INFO | Fetching cached versions of my-domain-predictor 2022-07-09 22:33:19 | INFO | Copying file for my-domain-predictor-5 2022-07-09 22:33:19 | INFO | Compressing my-domain-predictor-5 2022-07-09 22:33:20 | INFO | Model my-domain-predictor uploaded with version 5 Out[7]: 'my-domain-predictor:5'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]} \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz index b109dd9761cd8251b41f92233e6df37f2e57ed9b..f45a8079e8f808866a3a7af4ff182eccccaaa2ed 100644 GIT binary patch delta 14 Vcmcb}c#)A!zMF%?PH-aIX#gL01Qq}Q delta 14 Vcmcb}c#)A!zMF%?MPMS^X#gLQ1RDSV