| import joblib | |
| from sklearn.datasets import fetch_openml | |
| from sklearn.preprocessing import StandardScaler, OneHotEncoder | |
| from sklearn.compose import make_column_transformer | |
| from sklearn.pipeline import make_pipeline | |
| from sklearn.model_selection import train_test_split, RandomizedSearchCV | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import accuracy_score, classification_report | |
| dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto") | |
| data_df = dataset.data | |
| target = 'Machine failure' | |
| numeric_features = [ | |
| 'Air temperature [K]', | |
| 'Process temperature [K]', | |
| 'Rotational speed [rpm]', | |
| 'Torque [Nm]', | |
| 'Tool wear [min]' | |
| ] | |
| categorical_features = ['Type'] | |
| print("Creating data subsets") | |
| X = data_df[numeric_features + categorical_features] | |
| y = data_df[target] | |
| Xtrain, Xtest, ytrain, ytest = train_test_split( | |
| X, y, | |
| test_size=0.2, | |
| random_state=42 | |
| ) | |
| preprocessor = make_column_transformer( | |
| (StandardScaler(), numeric_features), | |
| (OneHotEncoder(handle_unknown='ignore'), categorical_features) | |
| ) | |
| model_logistic_regression = LogisticRegression(n_jobs=-1) | |
| print("Estimating Best Model Pipeline") | |
| model_pipeline = make_pipeline( | |
| preprocessor, | |
| model_logistic_regression | |
| ) | |
| param_distribution = { | |
| "logisticregression__C": [0.001, 0.01, 0.1, 0.5, 1, 5, 10] | |
| } | |
| rand_search_cv = RandomizedSearchCV( | |
| model_pipeline, | |
| param_distribution, | |
| n_iter=3, | |
| cv=3, | |
| random_state=42 | |
| ) | |
| rand_search_cv.fit(Xtrain, ytrain) | |
| print("Logging Metrics") | |
| print(f"Accuracy: {rand_search_cv.best_score_}") | |
| print("Serializing Model") | |
| saved_model_path = "model.joblib" | |
| joblib.dump(rand_search_cv.best_estimator_, saved_model_path) | |