938175ddb4bd062d682c237f0ccc90d68cf410c3,examples/60_search/example_sequential.py,,,#,23

Before Change

// Build and fit the classifier
// ======================================

automl = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=120,
    per_run_time_limit=30,
    tmp_folder="/tmp/autosklearn_sequential_example_tmp",
    output_folder="/tmp/autosklearn_sequential_example_out",
    // Do not construct ensembles in parallel to avoid using more than one
    // core at a time. The ensemble will be constructed after auto-sklearn
    // finished fitting all machine learning models.
    ensemble_size=0,
    delete_tmp_folder_after_terminate=False,
)
automl.fit(X_train, y_train, dataset_name="breast_cancer")

// This call to fit_ensemble uses all models trained in the previous call
// to fit to build an ensemble which can be used with automl.predict()
automl.fit_ensemble(y_train, ensemble_size=50)

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Print the final ensemble constructed by auto-sklearn
// ====================================================

print(automl.show_models())

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Get the Score of the final ensemble
// ===================================

predictions = automl.predict(X_test)
print(automl.sprint_statistics())
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))

After Change

    // Build and fit the classifier
    // ======================================

    automl = autosklearn.classification.AutoSklearnClassifier(
        time_left_for_this_task=120,
        per_run_time_limit=30,
        tmp_folder="/tmp/autosklearn_sequential_example_tmp",
        output_folder="/tmp/autosklearn_sequential_example_out",
        // Do not construct ensembles in parallel to avoid using more than one
        // core at a time. The ensemble will be constructed after auto-sklearn
        // finished fitting all machine learning models.
        ensemble_size=0,
        delete_tmp_folder_after_terminate=False,
    )
    automl.fit(X_train, y_train, dataset_name="breast_cancer")

    // This call to fit_ensemble uses all models trained in the previous call
    // to fit to build an ensemble which can be used with automl.predict()
    automl.fit_ensemble(y_train, ensemble_size=50)

    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
    // Print the final ensemble constructed by auto-sklearn
    // ====================================================

    print(automl.show_models())

    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
    // Get the Score of the final ensemble
    // ===================================

    predictions = automl.predict(X_test)
    print(automl.sprint_statistics())
    print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))

Instances