import logging

from sklearn.datasets import make_classification
from sklearn.datasets import make_regression
from sklearn.metrics import roc_auc_score

try:
    from sklearn.model_selection import train_test_split
except ImportError:
    from sklearn.cross_validation import train_test_split

from mla.ensemble.gbm import GradientBoostingClassifier, GradientBoostingRegressor
from mla.metrics.metrics import mean_squared_error

logging.basicConfig(level=logging.DEBUG)


def classification():
    # Generate a random binary classification problem.
    X, y = make_classification(
        n_samples=350,
        n_features=15,
        n_informative=10,
        random_state=1111,
        n_classes=2,
        class_sep=1.0,
        n_redundant=0,
    )
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.15, random_state=1111
    )

    model = GradientBoostingClassifier(
        n_estimators=50, max_depth=4, max_features=8, learning_rate=0.1
    )
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print(predictions)
    print(predictions.min())
    print(predictions.max())
    print("classification, roc auc score: %s" % roc_auc_score(y_test, predictions))


def regression():
    # Generate a random regression problem
    X, y = make_regression(
        n_samples=500,
        n_features=5,
        n_informative=5,
        n_targets=1,
        noise=0.05,
        random_state=1111,
        bias=0.5,
    )
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.1, random_state=1111
    )

    model = GradientBoostingRegressor(n_estimators=25, max_depth=5, max_features=3)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print(
        "regression, mse: %s"
        % mean_squared_error(y_test.flatten(), predictions.flatten())
    )


if __name__ == "__main__":
    classification()
    # regression()
