# Path: mla/ensemble/gbm.py
# class GradientBoostingClassifier(GradientBoosting):
#     def fit(self, X, y=None):
#         # Convert labels from {0, 1} to {-1, 1}
#         y = (y * 2) - 1
#         self.loss = LogisticLoss()
#         super(GradientBoostingClassifier, self).fit(X, y)
# 
# class GradientBoostingRegressor(GradientBoosting):
#     def fit(self, X, y=None):
#         self.loss = LeastSquaresLoss()
#         super(GradientBoostingRegressor, self).fit(X, y)
# 
# Path: mla/metrics/metrics.py
# def mean_squared_error(actual, predicted):
#     return np.mean(squared_error(actual, predicted))


import logging
from sklearn.datasets import make_classification
from sklearn.datasets import make_regression
from sklearn.metrics import roc_auc_score
    from sklearn.model_selection import train_test_split
    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)

# Path: mla/ensemble/gbm.py
# class GradientBoostingClassifier(GradientBoosting):
#     def fit(self, X, y=None):
#         # Convert labels from {0, 1} to {-1, 1}
#         y = (y * 2) - 1
#         self.loss = LogisticLoss()
#         super(GradientBoostingClassifier, self).fit(X, y)
# 
# class GradientBoostingRegressor(GradientBoosting):
#     def fit(self, X, y=None):
#         self.loss = LeastSquaresLoss()
#         super(GradientBoostingRegressor, self).fit(X, y)
# 
# Path: mla/metrics/metrics.py
# def mean_squared_error(actual, predicted):
#     return np.mean(squared_error(actual, predicted))


# Path: examples/gbm.py
import logging
from sklearn.datasets import make_classification
from sklearn.datasets import make_regression
from sklearn.metrics import roc_auc_score
    from sklearn.model_selection import train_test_split
    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()))