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import random |
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import numpy as np |
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from llm_studio.app_utils.sections.histogram_card import compute_quantile_df |
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def test_quantiles_are_computed_correctly(): |
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for _ in range(5): |
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data = np.random.random_integers(0, 1000, 100_000).tolist() |
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a = round(random.uniform(0, 1), 2) |
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b = round(random.uniform(a, 1), 2) |
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a, b = min(a, b), max(a, b) |
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df_quantile = compute_quantile_df(data, a, b) |
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first = df_quantile[ |
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df_quantile["data_type"] == f"first {int(a * 100)}% quantile" |
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] |
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last = df_quantile[ |
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df_quantile["data_type"] == f"last {100 - int(b * 100)}% quantile" |
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] |
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sorted_data = sorted(data) |
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expected_first_quantile_range = sorted_data[ |
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int(len(sorted_data) * a) - 1 : int(len(sorted_data) * a) + 1 |
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] |
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expected_last_quantile_range = sorted_data[ |
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-int(len(sorted_data) * (1 - b)) - 1 : -int(len(sorted_data) * (1 - b)) + 1 |
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] |
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assert first.iloc[-1][0] in expected_first_quantile_range |
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assert last.iloc[0][0] in expected_last_quantile_range |
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