67 lines
1.9 KiB
Python
67 lines
1.9 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
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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: List[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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get_context() # this resets the seed
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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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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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Trace[T](
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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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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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