import numpy as np

import load_ml100k

import regression

import corrneighbours

from sklearn import linear_model, metrics

import norm

def predict(train):

tr_train,tr_test = load_ml100k.get_train_test(train, random_state=34)

tr_predicted0 = regression.predict(tr_train)

tr_predicted1 = regression.predict(tr_train.T).T

tr_predicted2 = corrneighbours.predict(tr_train)

tr_predicted3 = corrneighbours.predict(tr_train.T).T

tr_predicted4 = norm.predict(tr_train)

tr_predicted5 = norm.predict(tr_train.T).T

stack_tr = np.array([

tr_predicted0[tr_test > 0],

tr_predicted1[tr_test > 0],

tr_predicted2[tr_test > 0],

tr_predicted3[tr_test > 0],

tr_predicted4[tr_test > 0],

tr_predicted5[tr_test > 0],

]).T

lr = linear_model.LinearRegression()

lr.fit(stack_tr, tr_test[tr_test > 0])

stack_te = np.array([

tr_predicted0.ravel(),

tr_predicted1.ravel(),

tr_predicted2.ravel(),

tr_predicted3.ravel(),

tr_predicted4.ravel(),

tr_predicted5.ravel(),

]).T

return lr.predict(stack_te).reshape(train.shape)

def main():

train,test = load_ml100k.get_train_test(random_state=12)

predicted = predict(train)

r2 = metrics.r2_score(test[test > 0], predicted[test > 0])

print('R2 stacked: {:.2%}'.format(r2))

if __name__ == '__main__':

main()