116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
from datetime import datetime
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from math import ceil
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from random import shuffle
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from typing import Any, Iterable, List, TypeVar, Union, cast
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from uuid import uuid4
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from ..constants import (
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GROUND_TRUTH_TAG_NAME,
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TEST_SPLIT_TAG_NAME,
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TRAIN_SPLIT_TAG_NAME,
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VALIDATION_SPLIT_TAG_NAME,
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)
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from ..context import get_context
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from ..views import Trace
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T = TypeVar("T")
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def add_ground_truth(
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inputs: Iterable[Any],
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expected_outputs: Iterable[T],
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*,
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tags: Union[List[str], str] = [],
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train_split_ratio: float = 1,
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test_split_ratio: float = 0,
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validation_split_ratio: float = 0
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) -> None:
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"""Add training data (with optional train-test splitting).
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Add and tag data-points, wrap them into traces. The `inputs` are available via the
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`.input` property, while `expected_outputs` under both the `.output` and `.feedback`
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properties.
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All generated traces are tagged with `ground_truth` by default. Additional tags can
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be also provided. Using the `split_ratio` arguments, tags can be given randomly with
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a user-defined distribution. Only the ratio of the splits matter, they don't have to
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add up to 1.
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Examples:
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>>> add_ground_truth(
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... [1, 2, 3],
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... ['odd', 'even', 'odd'],
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... tags='my_tag',
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... train_split_ratio=1,
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... test_split_ratio=1,
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... validation_split_ratio=0.5,
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... )
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>>> add_ground_truth(
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... [1, 2],
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... ['odd', 'even', 'odd'],
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... tags='my_tag',
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... train_split_ratio=1,
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... test_split_ratio=1,
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... validation_split_ratio=0.5,
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... )
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Traceback (most recent call last):
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...
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AssertionError: The length of the inputs and expected_outputs must be equal
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Args:
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inputs: The inputs. (X in scikit-learn)
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expected_outputs: The ground-truth values corresponding to the inputs. (y in
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scikit-learn)
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tags: A single tag or a list of tags to append to each generated trace's tags.
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train_split_ratio: The probability-weight of giving each trace the `train` tag.
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test_split_ratio: The probability-weight of giving each trace the `test` tag.
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validation_split_ratio: The probability-weight of giving each trace the
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`validation` tag.
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"""
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inputs = list(inputs)
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expected_outputs = list(expected_outputs)
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assert len(inputs) == len(
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expected_outputs
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), "The length of the inputs and expected_outputs must be equal"
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tags = tags if isinstance(tags, list) else [tags]
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sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio
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assert sum_ratio > 0, "The sum of the split ratios must be a positive number"
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train_split_ratio /= sum_ratio
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test_split_ratio /= sum_ratio
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validation_split_ratio /= sum_ratio
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values = list(zip(inputs, expected_outputs))
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shuffle(values)
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split_tags = (
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[TRAIN_SPLIT_TAG_NAME] * ceil(train_split_ratio * len(inputs))
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+ [TEST_SPLIT_TAG_NAME] * ceil(test_split_ratio * len(inputs))
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+ [VALIDATION_SPLIT_TAG_NAME] * ceil(validation_split_ratio * len(inputs))
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)
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shuffle(split_tags)
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created = datetime.utcnow().isoformat()
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traces = [
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cast(
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Trace[T],
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Trace( # avoid ValueError: "Trace" object has no field "__orig_class__"
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trace_id=str(uuid4()),
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created=created,
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original_execution_time_ms=0,
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logged_values=X if isinstance(X, dict) else {"input": X},
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models=[],
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output=y,
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feedback=y,
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exception=None,
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tags=[GROUND_TRUTH_TAG_NAME, split_tag, *tags],
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),
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)
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for ((X, y), split_tag) in zip(values, split_tags)
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]
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get_context().tracing_database.save_batch(traces)
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