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from valor_implementation import _calculate_pr_curves
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from optimized_implementation import _calculate_pr_curves_optimized
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- n = 1000
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+ n = 10000000
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n_class = 3
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predictions_per_datum = 3
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groundtruth_df = generate_groundtruth (n , n_class )
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prediction_df = generate_predictions (n , n_class , 3 )
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print (f"Groundtruth Dataframe Memory Usage: { groundtruth_df .memory_usage (index = True ).sum ()} " )
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- print (f"Prediction Dataframe Memory Usage: { groundtruth_df .memory_usage (index = True ).sum ()} " )
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+ print (f"Prediction Dataframe Memory Usage: { prediction_df .memory_usage (index = True ).sum ()} " )
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## Randomly drop some samples.
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## groundtruth_df = groundtruth_df.sample(frac=0.9)
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end = time .time ()
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print (f"Valor completed { n } in { end - start } " )
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-
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start = time .time ()
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fast_df = _calculate_pr_curves_optimized (
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groundtruth_df = groundtruth_df ,
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