From b88ddb3066bd874683db6cacf41e49a1ad774a6c Mon Sep 17 00:00:00 2001 From: Michal Gulczynski Date: Tue, 11 Jun 2024 19:56:09 +0200 Subject: [PATCH] ium_07 sacred --- sacred/sacred_model_creator.py | 6 ++++++ sacred/sacred_use_model.py | 4 ++++ 2 files changed, 10 insertions(+) diff --git a/sacred/sacred_model_creator.py b/sacred/sacred_model_creator.py index 9f3127f..6c5a605 100644 --- a/sacred/sacred_model_creator.py +++ b/sacred/sacred_model_creator.py @@ -63,6 +63,12 @@ def run_experiment(test_size, random_state, model_filename): X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=random_state) Y_train = np.ravel(Y_train) Y_test = np.ravel(Y_test) + + ex.add_resource(X_train) + ex.add_resource(X_test) + ex.add_resource(Y_train) + ex.add_resource(Y_test) + scaler = StandardScaler() numeric_columns = X_train.select_dtypes(include=['int', 'float']).columns X_train_scaled = scaler.fit_transform(X_train[numeric_columns]) diff --git a/sacred/sacred_use_model.py b/sacred/sacred_use_model.py index 8bdf517..1823f8b 100644 --- a/sacred/sacred_use_model.py +++ b/sacred/sacred_use_model.py @@ -26,6 +26,10 @@ def run_evaluation(model_filename, test_dataset_filename): X_test = test_df.drop(columns='playlist_genre') Y_test = np.ravel(Y_test) scaler = StandardScaler() + + ex.add_resource(X_test) + ex.add_resource(Y_test) + numeric_columns = X_test.select_dtypes(include=['int', 'float']).columns X_test_scaled = scaler.fit_transform(X_test[numeric_columns]) Y_pred = model.predict(X_test_scaled)