ium_07 sacred
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@ -63,6 +63,12 @@ def run_experiment(test_size, random_state, model_filename):
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=random_state)
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=random_state)
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Y_train = np.ravel(Y_train)
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Y_train = np.ravel(Y_train)
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Y_test = np.ravel(Y_test)
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Y_test = np.ravel(Y_test)
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ex.add_resource(X_train)
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ex.add_resource(X_test)
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ex.add_resource(Y_train)
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ex.add_resource(Y_test)
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scaler = StandardScaler()
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scaler = StandardScaler()
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numeric_columns = X_train.select_dtypes(include=['int', 'float']).columns
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numeric_columns = X_train.select_dtypes(include=['int', 'float']).columns
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X_train_scaled = scaler.fit_transform(X_train[numeric_columns])
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X_train_scaled = scaler.fit_transform(X_train[numeric_columns])
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@ -26,6 +26,10 @@ def run_evaluation(model_filename, test_dataset_filename):
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X_test = test_df.drop(columns='playlist_genre')
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X_test = test_df.drop(columns='playlist_genre')
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Y_test = np.ravel(Y_test)
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Y_test = np.ravel(Y_test)
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scaler = StandardScaler()
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scaler = StandardScaler()
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ex.add_resource(X_test)
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ex.add_resource(Y_test)
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numeric_columns = X_test.select_dtypes(include=['int', 'float']).columns
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numeric_columns = X_test.select_dtypes(include=['int', 'float']).columns
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X_test_scaled = scaler.fit_transform(X_test[numeric_columns])
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X_test_scaled = scaler.fit_transform(X_test[numeric_columns])
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Y_pred = model.predict(X_test_scaled)
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Y_pred = model.predict(X_test_scaled)
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