def load(self, ratings, train, validation):
self.train = train
self.validation = validation
self.num_users = movielens_extractor.get_num_users(ratings)
self.num_items = movielens_extractor.get_num_items(ratings)
self.train = train
self.validation = validation
self.mean_rating = np.mean(self.train[:, 2])
self.ratings_test = np.float64(validation[:, 2])
self.item_features = 0.1 * NormalRandom.generate_matrix(
self.num_items, self.num_features)
self.user_features = 0.1 * NormalRandom.generate_matrix(
self.num_users, self.num_features)
self.df_post_item = self.df_item + self.num_items
self.df_post_user = self.df_user + self.num_users
# num_user, num_item, ratings = build_ml_1m()
self.matrix = build_rating_matrix(self.num_users, self.num_items,
train)
self.matrix = self.matrix.T
self.counter_prob = 1
self.probe_rat_all = self.pred(self.item_features, self.user_features,
self.validation, self.mean_rating)
self.estimate()
def load(self, train, validation):
# self.train =\
# movielens_extractor.reviews_to_numpy_matrix(train_reviews)
self.train = train
self.validation = validation
self.num_users = movielens_extractor.get_num_users(self.train)
self.num_items = movielens_extractor.get_num_items(self.train)
self.mean_rating = movielens_extractor.get_mean_rating(self.train)
self.batch_size = int(np.ceil(float(len(self.train) / self.num_batches)))
self.alpha = self.epsilon / self.batch_size
self.logger.debug('epsilon: %f', self.epsilon)
self.logger.debug('alpha: %f', self.alpha)
# latent variables
self.user_features = 0.1 * np.random.rand(self.num_users, self.num_features)
self.item_features = 0.1 * np.random.rand(self.num_items, self.num_features)
user_features_inc = np.zeros((self.num_users, self.num_features))
item_features_inc = np.zeros((self.num_items, self.num_features))
converge = 1e-4
last_rmse = None
for epoch in xrange(self.num_epochs):
# In each cycle the training vector is shuffled
np.random.shuffle(self.train)
for batch in xrange(self.num_batches):
data = self.train[batch * self.batch_size: (batch + 1) * self.batch_size]
users = data[:, 0]
items = data[:, 1]
ratings = data[:, 2]
# Default prediction is the mean rating
ratings = ratings - self.mean_rating
# compute predictions
predicted_ratings = np.sum(self.user_features[users, :] * self.item_features[items, :], 1)
# compute gradients
error_list = predicted_ratings - ratings
error_matrix = np.tile(error_list, (self.num_features, 1)).T
item_gradients =\
error_matrix * self.user_features[users, :] + self.var_lambda * self.item_features[items, :]
user_gradients =\
error_matrix * self.item_features[items, :] + self.var_lambda * self.user_features[users, :]
user_feature_gradients =\
np.zeros((self.num_users, self.num_features))
item_feature_gradients =\
np.zeros((self.num_items, self.num_features))
# In the above line the gradient is calculated for every rating,
# but it has to be grouped (by summing it) for each user and
# item features
for i in xrange(self.batch_size):
user_feature_gradients[users[i], :] += user_gradients[i, :]
item_feature_gradients[items[i], :] += item_gradients[i, :]
# Update item and user features
# The update is done using the momentum technique of gradient descent
user_features_inc = self.momentum * user_features_inc +\
self.alpha * user_feature_gradients
# self.user_features = self.user_features - \
# self.alpha * user_feature_gradients
self.user_features = self.user_features - user_features_inc
item_features_inc = self.momentum * item_features_inc +\
self.alpha * item_feature_gradients
# self.item_features = self.item_features - \
# self.alpha * item_feature_gradients
self.item_features = self.item_features - item_features_inc
# compute RMSE
# train errors
train_preds = self.predict(self.train)
train_rmse = RMSE(train_preds, np.float16(self.train[:, 2]))
# validation errors
validation_preds = self.predict(self.validation)
validation_rmse = RMSE(
validation_preds, np.float16(self.validation[:, 2]))
self.train_errors.append(train_rmse)
self.validation_errors.append(validation_rmse)
print "iterations: %3d, train RMSE: %.6f, validation RMSE: %.6f " % \
(epoch + 1, train_rmse, validation_rmse)
# stop if converge
if last_rmse:
if abs(train_rmse - last_rmse) < converge:
# break
pass
last_rmse = train_rmse
def load(self, train, validation):
# self.train =\
# movielens_extractor.reviews_to_numpy_matrix(train_reviews)
self.train = train
self.validation = validation
self.num_users = movielens_extractor.get_num_users(self.train)
self.num_items = movielens_extractor.get_num_items(self.train)
self.mean_rating = movielens_extractor.get_mean_rating(self.train)
self.batch_size = int(
np.ceil(float(len(self.train) / self.num_batches)))
self.alpha = self.epsilon / self.batch_size
self.logger.debug('epsilon: %f', self.epsilon)
self.logger.debug('alpha: %f', self.alpha)
# latent variables
self.user_features = 0.1 * np.random.rand(self.num_users,
self.num_features)
self.item_features = 0.1 * np.random.rand(self.num_items,
self.num_features)
user_features_inc = np.zeros((self.num_users, self.num_features))
item_features_inc = np.zeros((self.num_items, self.num_features))
converge = 1e-4
last_rmse = None
for epoch in xrange(self.num_epochs):
# In each cycle the training vector is shuffled
np.random.shuffle(self.train)
for batch in xrange(self.num_batches):
data = self.train[batch * self.batch_size:(batch + 1) *
self.batch_size]
users = data[:, 0]
items = data[:, 1]
ratings = data[:, 2]
# Default prediction is the mean rating
ratings = ratings - self.mean_rating
# compute predictions
predicted_ratings = np.sum(
self.user_features[users, :] *
self.item_features[items, :], 1)
# compute gradients
error_list = predicted_ratings - ratings
error_matrix = np.tile(error_list, (self.num_features, 1)).T
item_gradients =\
error_matrix * self.user_features[users, :] + self.var_lambda * self.item_features[items, :]
user_gradients =\
error_matrix * self.item_features[items, :] + self.var_lambda * self.user_features[users, :]
user_feature_gradients =\
np.zeros((self.num_users, self.num_features))
item_feature_gradients =\
np.zeros((self.num_items, self.num_features))
# In the above line the gradient is calculated for every rating,
# but it has to be grouped (by summing it) for each user and
# item features
for i in xrange(self.batch_size):
user_feature_gradients[users[i], :] += user_gradients[i, :]
item_feature_gradients[items[i], :] += item_gradients[i, :]
# Update item and user features
# The update is done using the momentum technique of gradient descent
user_features_inc = self.momentum * user_features_inc +\
self.alpha * user_feature_gradients
# self.user_features = self.user_features - \
# self.alpha * user_feature_gradients
self.user_features = self.user_features - user_features_inc
item_features_inc = self.momentum * item_features_inc +\
self.alpha * item_feature_gradients
# self.item_features = self.item_features - \
# self.alpha * item_feature_gradients
self.item_features = self.item_features - item_features_inc
# compute RMSE
# train errors
train_preds = self.predict(self.train)
train_rmse = RMSE(train_preds, np.float16(self.train[:, 2]))
# validation errors
validation_preds = self.predict(self.validation)
validation_rmse = RMSE(validation_preds,
np.float16(self.validation[:, 2]))
self.train_errors.append(train_rmse)
self.validation_errors.append(validation_rmse)
print "iterations: %3d, train RMSE: %.6f, validation RMSE: %.6f " % \
(epoch + 1, train_rmse, validation_rmse)
# stop if converge
if last_rmse:
if abs(train_rmse - last_rmse) < converge:
# break
pass
last_rmse = train_rmse
