98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
import json
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import os
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import random
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from functools import partial
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from operator import itemgetter
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import numpy as np
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import pytest
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from funcy import rcompose, lmap
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from image_prediction.encoder.encoders.hash_encoder import hash_image
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from image_prediction.exceptions import UnknownLabelFormat
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from image_prediction.label_mapper.mappers.probability import ProbabilityMapperKeys
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from image_prediction.locations import TEST_DATA_DIR
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from test.utils.label import map_labels
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@pytest.fixture
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def expected_predictions_mapped(
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label_format, batch_of_expected_string_labels, batch_of_expected_label_to_probability_mappings
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):
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if label_format == "index":
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return batch_of_expected_string_labels
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elif label_format == "probability":
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return batch_of_expected_label_to_probability_mappings
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else:
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raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
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@pytest.fixture
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def expected_predictions(label_format, batch_of_expected_numeric_labels, batch_of_expected_probability_arrays):
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if label_format == "index":
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return batch_of_expected_numeric_labels
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elif label_format == "probability":
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return batch_of_expected_probability_arrays
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else:
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raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
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@pytest.fixture
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def batch_of_expected_string_labels(batch_of_expected_numeric_labels, classes):
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return map_labels(batch_of_expected_numeric_labels, classes)
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@pytest.fixture
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def batch_of_expected_numeric_labels(batch_size, classes):
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return random.choices(range(len(classes)), k=batch_size)
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@pytest.fixture
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def batch_of_expected_label_to_probability_mappings(batch_of_expected_probability_arrays, classes):
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def map_probabilities(probabilities):
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lbl2prob = dict(sorted(zip(classes, map(rounder, probabilities)), key=itemgetter(1), reverse=True))
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most_likely = [*lbl2prob][0]
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return {ProbabilityMapperKeys.LABEL: most_likely, ProbabilityMapperKeys.PROBABILITIES: lbl2prob}
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rounder = rcompose(partial(np.round, decimals=4), float)
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return lmap(map_probabilities, batch_of_expected_probability_arrays)
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@pytest.fixture
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def batch_of_expected_probability_arrays(batch_size, classes):
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return [np.random.uniform(size=len(classes)) for _ in range(batch_size)]
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@pytest.fixture
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def output_batch_generator(expected_predictions):
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return iter(expected_predictions)
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@pytest.fixture
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def metadata_plus_mapped_prediction(expected_predictions_mapped, metadata):
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return [{"classification": epm, **mdt} for epm, mdt in zip(expected_predictions_mapped, metadata)]
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@pytest.fixture
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def metadata_formatted_plus_mapped_prediction_formatted(expected_predictions_mapped_and_formatted, metadata_formatted):
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return [
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{"classification": epm, **mdt}
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for epm, mdt in zip(expected_predictions_mapped_and_formatted, metadata_formatted)
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]
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@pytest.fixture
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def expected_predictions_mapped_and_formatted(expected_predictions_mapped):
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return [{k.value: v for k, v in epm.items()} for epm in expected_predictions_mapped]
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@pytest.fixture
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def real_expected_service_response():
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with open(os.path.join(TEST_DATA_DIR, "f2dc689ca794fccb8cd38b95f2bf6ba9_predictions.json"), "r") as f:
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yield json.load(f)
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@pytest.fixture
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def hashed_images(images):
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return lmap(hash_image, images)
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