SimonBlanke / Hyperactive

    opt.search(search_config)

def test_long_term_memory_search_space_reduction():

    def model6(para, X_train, y_train):

        model = DecisionTreeClassifier(

            criterion=para["criterion"], max_depth=para["max_depth"]

        )

        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {

        model6: {"criterion": ["gini", "entropy"], "max_depth": range(1, 11)}

    }

    opt = Hyperactive(X, y, memory="long")

    opt.search(search_config)

    def model6(para, X_train, y_train):

        model = DecisionTreeClassifier(criterion=para["criterion"])

        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {model6: {"criterion": ["gini", "entropy"]}}

    opt = Hyperactive(X, y, memory="long")

    opt.search(search_config)

    assert best_para == opt2.results[model]

def test_long_term_memory_search_space_expansion():

    def model5(para, X_train, y_train):

        model = DecisionTreeClassifier(criterion=para["criterion"])

        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {model5: {"criterion": ["gini", "entropy"]}}

    opt = Hyperactive(X, y, memory="long")

    opt.search(search_config)

    def model5(para, X_train, y_train):

        model = DecisionTreeClassifier(

            criterion=para["criterion"], max_depth=para["max_depth"]

        )

        scores = cross_val_score(model, X_train, y_train, cv=2)

        return scores.mean()

    search_config = {

        model5: {"criterion": ["gini", "entropy"], "max_depth": range(1, 11)}

    }

    opt = Hyperactive(X, y, memory="long")

    opt.search(search_config)

def test_long_term_memory_search_space_reduction():