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CLASSIFICATION_METRICS = ['acc' , 'auc' , 'balacc' , 'logloss' ]
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REGRESSION_METRICS = ['mae' , 'r2' , 'rmse' ]
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METRICS = CLASSIFICATION_METRICS + REGRESSION_METRICS
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-
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def _get_mean_results_across_folds (df ) -> pd .DataFrame :
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""" Returns a dataframe with the task, id, metric and the mean values
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across folds
@@ -74,7 +73,7 @@ def generate_framework_def(
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# Using branch and https
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version = branch
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repo = f'https://github.com/{ username } /auto-sklearn.git'
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-
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+
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# Create the framework file
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lines = '\n ' .join ([
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f"---" ,
@@ -130,7 +129,6 @@ def create_comparison(
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Comparisons here is the difference between (targeted - baseline)
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Returns them in that specific order
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"""
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# Load in data and get the means across folds
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df_baseline_classification = pd .read_csv (baseline_csv_classification )
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df_baseline_regression = pd .read_csv (baseline_csv_regression )
@@ -140,6 +138,7 @@ def create_comparison(
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df_targeted_classification = pd .read_csv (targeted_csv_classification )
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df_targeted_regression = pd .read_csv (targeted_csv_regression )
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df_targeted = pd .concat ([df_targeted_classification , df_targeted_regression ])
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+
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df_targeted_means = _get_mean_results_across_folds (df_targeted )
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# Find the set intersection of tasks they have in common
@@ -454,7 +453,6 @@ def tolerance_colour(baseline_value, comparison_value, metric):
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if args .generate_framework_def :
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assert args .owner and args .branch and args .commit and args .user_dir
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generate_framework_def (args .user_dir , args .owner , args .branch , args .commit )
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elif args .compare_results :
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