Recommender_keras/recommend.py at master · CVxTz/Recommender_keras · GitHub

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from utils import *
from sklearn.metrics import mean_absolute_error
import pickle


train, test, max_user, max_work, mapping_work = get_data()

#pickle.dump(mapping_work, open('mapping_work.pkl', 'wb'))


#######################################################################
model = get_model_1(max_work, max_user)

history = model.fit([get_array(train["movieId"]), get_array(train["userId"])], get_array(train["rating"]), nb_epoch=10,
                    validation_split=0.2, verbose=0)

#model.save_weights("model_1.h5")

predictions = model.predict([get_array(test["movieId"]), get_array(test["userId"])])

test_performance = mean_absolute_error(test["rating"], predictions)

print(" Test Mae model 1 : %s " % test_performance)

#######################################################################
model = get_model_2(max_work, max_user)

history = model.fit([get_array(train["movieId"]), get_array(train["userId"])], get_array(train["rating"]), nb_epoch=10,
                    validation_split=0.2, verbose=0)

predictions = model.predict([get_array(test["movieId"]), get_array(test["userId"])])

test_performance = mean_absolute_error(test["rating"], predictions)

print(" Test Mae model 2 : %s " % test_performance)

#######################################################################
model = get_model_3(max_work, max_user)

history = model.fit([get_array(train["movieId"]), get_array(train["userId"])], get_array(train["rating"]), nb_epoch=10,
                    validation_split=0.2, verbose=0)

predictions = model.predict([get_array(test["movieId"]), get_array(test["userId"])])

test_performance = mean_absolute_error(test["rating"], predictions)

print(" Test Mae model 3 : %s " % test_performance)