def execute(self):
# methods
print("[Case Recommender: Rating Prediction > ItemKNN Algorithm]\n")
print("training data:: " + str(len(self.train_set['users'])) + " users and " + str(len(
self.train_set['items'])) + " items and " + str(self.train_set['ni']) + " interactions")
print("test data:: " + str(len(self.test_set['users'])) + " users and " + str(len(self.test_set['items'])) +
" items and " + str(self.test_set['ni']) + " interactions")
# training baselines bui
self.fill_matrix()
print("training time:: " + str(timed(self.train_baselines))) + " sec"
self.compute_similarity()
print("prediction_time:: " + str(timed(self.predict))) + " sec\n"
self.evaluate(self.predictions)
def execute(self):
print("[Case Recommender: Item Recommendation > UserKNN Algorithm]\n")
print("training data:: " + str(len(self.train_set["map_user"])) + " users and " + str(len(
self.train_set["map_item"])) + " items and " + str(self.train_set["number_interactions"]) + " interactions")
if self.test_file is not None:
test_set = ReadFile(self.test_file).return_matrix()
print("test data:: " + str(len(test_set["map_user"])) + " users and " + str(len(test_set["map_item"])) +
" items and " + str(test_set["number_interactions"]) + " interactions")
del test_set
print("training time:: " + str(timed(self.compute_similarity))) + " sec"
print("prediction_time:: " + str(timed(self.predict))) + " sec\n"
if self.test_file is not None:
self.evaluate()
def execute(self):
# methods
print("[Case Recommender: Rating Prediction > Item Attribute KNN Algorithm]\n")
print("training data:: " + str(len(self.train_set['users'])) + " users and " + str(len(
self.train_set['items'])) + " items and " + str(self.train_set['ni']) + " interactions")
print("test data:: " + str(len(self.test_set['users'])) + " users and " + str(len(self.test_set['items'])) +
" items and " + str(self.test_set['ni']) + " interactions")
# training baselines bui
print("training time:: " + str(timed(self.train_baselines))) + " sec"
if self.similarity_matrix_file is not None:
print("compute similarity:: " + str(timed(self.read_matrix))) + " sec"
else:
print("compute similarity time:: " + str(timed(self.compute_similarity))) + " sec"
print("prediction_time:: " + str(timed(self.predict))) + " sec\n"
self.evaluate(self.predictions)
def execute(self):
# methods
print("[Case Recommender: Item Recommendation > BPR MF Algorithm]\n")
print("training data:: " + str(self.number_users) + " users and " + str(self.number_items) + " items and " +
str(self.train_set["number_interactions"]) + " interactions")
if self.test_file is not None:
test_set = ReadFile(self.test_file).return_matrix()
print("test data:: " + str(len(test_set["map_user"])) + " users and " + str(len(test_set["map_item"])) +
" items and " + str(test_set["number_interactions"]) + " interactions")
del test_set
self._create_factors()
print("training time:: " + str(timed(self.train_model))) + " sec"
print("prediction_time:: " + str(timed(self.predict))) + " sec\n"
if self.test_file is not None:
self.evaluate()
def execute(self):
# methods
print("[Case Recommender: Rating Prediction > User NSVD1]\n")
print("training data:: " + str(len(self.train['users'])) + " users and " + str(len(
self.train['items'])) + " items and " + str(self.train['ni']) + " interactions")
print("test data:: " + str(len(self.test['users'])) + " users and " + str(len(self.test['items'])) +
" items and " + str(self.test['ni']) + " interactions")
print("metadata:: " + str(len(self.metadata['items'])) + " users and " + str(len(self.metadata['metadata'])) +
" metadata and " + str(self.metadata['ni']) + " interactions")
self._create_factors()
if self.batch:
print("training time:: " + str(timed(self.train_batch_model))) + " sec"
else:
print("training time:: " + str(timed(self.train_model))) + " sec"
print("prediction_time:: " + str(timed(self.predict))) + " sec\n"
self.evaluate(self.predictions)