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regression_ensemble.py
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regression_ensemble.py
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import numpy as np
from sklearn.metrics import mean_squared_error,r2_score
from sklearn.neural_network import MLPRegressor
from sklearn.svm import SVR
# Order = [mlp,rbf,target]
def get_data():
train = np.load('data/regression/train_ens.npy')
X_train = train[:,:-1]
y_train = train[:,-1]
test = np.load('data/regression/test_ens.npy')
X_test = test[:,:-1]
y_test = test[:,-1]
return X_train,y_train,X_test,y_test
def get_stats(y_true,y_pred):
mse = mean_squared_error(y_true,y_pred)
r2 = r2_score(y_true,y_pred)
print 'MSE : ',mse
print 'R2_score : ',r2
def mean_ensemble(X):
output = np.mean(X,axis=1)
return output
def get_stacking_model():
model = MLPRegressor(hidden_layer_sizes=(20,20))
X_train,y_train,_,_ = get_data()
model.fit(X_train,y_train)
return model
def stacking_ensemble(X):
model = get_stacking_model()
ens_stack_output = model.predict(X)
return ens_stack_output
if __name__ == '__main__':
X_train,y_train,X_test,y_test = get_data()
print '------------- MLP------------- '
get_stats(y_test,X_test[:,0])
print '------------- RBF------------- '
get_stats(y_test,X_test[:,1])
print '------------- MEAN ENSEMBLE------------- '
ens_mean_output = mean_ensemble(X_test)
get_stats(y_test,ens_mean_output)
print '------------- STACKING ENSEMBLE------------- '
ens_stack_output = stacking_ensemble(X_test)
get_stats(y_test,ens_stack_output)