predictions_bounded = np.maximum(y_pred,
np.ones(num_example) *
min(y_true)) # bound the lower values
predictions_bounded = np.minimum(
predictions_bounded,
np.ones(num_example) * max(y_true)) # bound the higher values
RMSE = math.sqrt(mean_squared_error(y_true, predictions_bounded))
return RMSE
if __name__ == '__main__':
# Data loading
LD = LoadData(0.8) #80% train
batch_size = 32
train_batch = LD.get_batches(LD.train_user,
LD.train_item,
LD.train_rating,
batch_size=batch_size)
test_batch = LD.get_batches(LD.test_user,
LD.test_item,
LD.test_rating,
batch_size=batch_size)
Train_data = [
LD.train_user, LD.train_item, LD.train_friends, LD.train_sim,
LD.train_rating
]
Train_data = [np.reshape(x, [-1, 1]) for x in Train_data]
Test_data = [
LD.test_user, LD.test_item, LD.test_friends, LD.test_sim,
LD.test_rating
