from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
from time import time

tuned_params = {'n_estimators': [5000, 10000], 
                'max_depth': [2, 4], 
                'min_samples_split': [1, 2],
                'learning_rate': [0.001, 0.01]}

print("# Tuning hyper-parameters for mean_squared_error")

gscv = GridSearchCV(GradientBoostingRegressor(loss = 'ls', random_state=0), 
                    tuned_params, cv=5, scoring='mean_squared_error')

gscv.fit(X, y)