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Andras Schmelczer 2022-07-12 21:50:03 +02:00
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20
great_ai/tracing/add_ground_truth.py
View file

@ -1,7 +1,7 @@
from datetime import datetime
from math import ceil
from random import shuffle
from typing import Any, Iterable, List, TypeVar, cast
from typing import Any, Iterable, List, TypeVar, Union, cast
from uuid import uuid4
from ..constants import (
@ -20,14 +20,14 @@ def add_ground_truth(
inputs: Iterable[Any],
expected_outputs: Iterable[T],
*,
tags: List[str] = [],
tags: Union[List[str], str] = [],
train_split_ratio: float = 1,
test_split_ratio: float = 0,
validation_split_ratio: float = 0
) -> None:
"""Add training data (with optional train-test splitting).
Add and tag datapoints, wrap them into traces. The `inputs` are available via the
Add and tag data-points, wrap them into traces. The `inputs` are available via the
`.input` property, while `expected_outputs` under both the `.output` and `.feedback`
properties.
@ -46,6 +46,18 @@ def add_ground_truth(
... validation_split_ratio=0.5,
... )
>>> add_ground_truth(
... [1, 2],
... ['odd', 'even', 'odd'],
... tags='my_tag',
... train_split_ratio=1,
... test_split_ratio=1,
... validation_split_ratio=0.5,
... )
Traceback (most recent call last):
...
AssertionError: The length of the inputs and expected_outputs must be equal
Args:
inputs: The inputs. (X in scikit-learn)
expected_outputs: The ground-truth values corresponding to the inputs. (y in
@ -63,6 +75,8 @@ def add_ground_truth(
expected_outputs
), "The length of the inputs and expected_outputs must be equal"
tags = tags if isinstance(tags, list) else [tags]
sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio
assert sum_ratio > 0, "The sum of the split ratios must be a positive number"

2
great_ai/tracing/query_ground_truth.py
View file

@ -14,7 +14,7 @@ def query_ground_truth(
) -> List[Trace]:
"""Return training samples.
Combines, filters, and returns datapoints that have been either added by
Combines, filters, and returns data-points that have been either added by
`add_ground_truth` or were the result of a prediction after which their trace got
a feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint
(end-to-end feedback).