Ejemplo n.º 1
0
def test_custom_cascade_layer_workflow_partial_mode():

    model = CascadeForestClassifier(partial_mode=True)

    n_estimators = 4
    estimators = [DecisionTreeClassifier() for _ in range(n_estimators)]
    model.set_estimator(estimators)  # set custom base estimators

    predictor = DecisionTreeClassifier()
    model.set_predictor(predictor)

    model.fit(X_train, y_train)
    y_pred_before = model.predict(X_test)

    # Save and Reload
    model.save(save_dir)

    model = CascadeForestClassifier()
    model.load(save_dir)

    # Predictions after loading
    y_pred_after = model.predict(X_test)

    # Make sure the same predictions before and after model serialization
    assert_array_equal(y_pred_before, y_pred_after)

    model.clean()  # clear the buffer
    shutil.rmtree(save_dir)
Ejemplo n.º 2
0
def test_classifier_custom_cascade_layer_workflow_in_memory():

    model = CascadeForestClassifier()

    n_estimators = 4
    estimators = [DecisionTreeClassifier() for _ in range(n_estimators)]
    model.set_estimator(estimators)  # set custom base estimators

    predictor = DecisionTreeClassifier()
    model.set_predictor(predictor)

    model.fit(X_train_clf, y_train_clf)
    y_pred_before = model.predict(X_test_clf)

    # Save and Reload
    model.save(save_dir)

    model = CascadeForestClassifier()
    model.load(save_dir)

    # Predictions after loading
    y_pred_after = model.predict(X_test_clf)

    # Make sure the same predictions before and after model serialization
    assert_array_equal(y_pred_before, y_pred_after)

    assert (model.get_estimator(0, 0, "custom") is
            model._get_layer(0).estimators_["0-0-custom"].estimator_)

    model.clean()  # clear the buffer
    shutil.rmtree(save_dir)
Ejemplo n.º 3
0
def test_custom_estimator_missing_fit():
    class tmp_estimator:
        def __init__(self):
            pass

    model = CascadeForestClassifier()
    with pytest.raises(AttributeError) as excinfo:
        model.set_estimator([tmp_estimator()])
    assert "The `fit` method of estimator" in str(excinfo.value)

    with pytest.raises(AttributeError) as excinfo:
        model.set_predictor(tmp_estimator())
    assert "The `fit` method of the predictor" in str(excinfo.value)