def rating_prediction(self):
dict_file = dict()
d_feedback = dict()
list_feedback = list()
check_error_file(self.file_read)
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
self.number_interactions += 1
user, item, feedback = int(inline[0]), int(inline[1]), float(inline[2])
d_feedback.setdefault(user, {}).update({item: feedback})
self.triple_dataset.append((user, item, feedback))
self.dict_users.setdefault(user, set()).add(item)
self.dict_items.setdefault(item, set()).add(user)
self.list_users.add(user)
self.list_items.add(item)
self.mean_feedback += feedback
list_feedback.append(feedback)
self.triple_dataset = sorted(self.triple_dataset)
self.mean_feedback /= float(self.number_interactions)
self.list_users = sorted(list(self.list_users))
self.list_items = sorted(list(self.list_items))
dict_file.update({'feedback': d_feedback, 'users': self.list_users, 'items': self.list_items,
'du': self.dict_users, 'di': self.dict_items, 'mean_rates': self.mean_feedback,
'list_feedback': self.triple_dataset, 'ni': self.number_interactions,
'max': max(list_feedback), 'min': min(list_feedback)})
return dict_file
def simple_evaluation(self, file_result, file_test):
# Verify that the files are valid
check_error_file(file_result)
check_error_file(file_test)
predict = ReadFile(file_result, space_type=self.space_type)
predict.main_information()
test = ReadFile(file_test, space_type=self.space_type)
test.main_information()
rmse = 0
mae = 0
count_comp = 0
for user in test.list_users:
for item in test.dict_users[user]:
try:
rui_predict = float(predict.dict_users[user][item])
rui_test = float(test.dict_users[user][item])
rmse += math.pow((rui_predict - rui_test), 2)
mae += math.fabs(rui_predict - rui_test)
count_comp += 1
except KeyError:
pass
if count_comp != 0:
rmse = math.sqrt(float(rmse) / float(count_comp))
mae = math.sqrt(float(mae) / float(count_comp))
return rmse, mae
def triple_information(self):
check_error_file(self.file_read)
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
self.number_interactions += 1
user, item, feedback = int(inline[0]), int(inline[1]), inline[2].replace("\n", "")
self.triple_dataset.append([user, item, feedback])
def all_but_one_evaluation(self, file_result, file_test, n_ranks=list([1, 3, 5, 10])):
check_error_file(file_result)
check_error_file(file_test)
predict = ReadFile(file_result, space_type=self.space_type)
predict.main_information_item_recommendation()
test = ReadFile(file_test, space_type=self.space_type)
test.main_information_item_recommendation()
num_user = len(test.list_users)
final_values = list()
for user in test.list_users:
test.dict_users[user] = [test.dict_users[user][0]]
for i, n in enumerate(n_ranks):
if n < 1:
print('Error: N must >= 1.')
sys.exit()
partial_precision = list()
partial_recall = list()
avg_prec_total = list()
for user in test.list_users:
num_user_interactions = len(test.dict_users[user])
hit_cont = 0
avg_prec_sum = 0
try:
# Generate user intersection list between the recommended items and test.
intersection = list(set(predict.dict_users[user][:n]).intersection(
set(test.dict_users[user])))
if len(intersection) > 0:
partial_precision.append((float(len(intersection)) / float(n)))
partial_recall.append((float(len(intersection)) / float(num_user_interactions)))
for item in intersection:
hit_cont += 1
avg_prec_sum += (float(hit_cont) / float(test.dict_users[user].index(item) + 1))
avg_prec_total.append(float(avg_prec_sum) / float(num_user_interactions))
except KeyError:
pass
if not self.only_map:
final_precision = sum(partial_precision) / float(num_user)
final_values.append(final_precision)
final_recall = sum(partial_recall) / float(num_user)
final_values.append(final_recall)
final_map = sum(avg_prec_total) / float(num_user)
final_values.append(final_map)
return final_values
def simple_evaluation(self, file_result, file_test):
# Verify that the files are valid
check_error_file(file_result)
check_error_file(file_test)
predict = ReadFile(file_result, space_type=self.space_type)
predict.main_information_item_recommendation()
test = ReadFile(file_test, space_type=self.space_type)
test.main_information_item_recommendation()
return self.default_evaluation(predict.dict_users, test)
def read_matrix(self):
matrix = list()
check_error_file(self.file_read)
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
inline = np.array(inline)
inline = np.delete(inline, len(inline)-1)
matrix.append(inline.astype(float))
return np.array(matrix)
def read_metadata(self, l_items):
dict_file = dict()
d_feedback = dict()
list_feedback = list()
map_user = dict()
map_index_user = dict()
map_item = dict()
map_index_item = dict()
check_error_file(self.file_read)
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
self.number_interactions += 1
user, item, feedback = int(inline[0]), int(inline[1]), float(inline[2])
d_feedback.setdefault(user, {}).update({item: feedback})
self.triple_dataset.append((user, item, feedback))
self.dict_users.setdefault(user, set()).add(item)
self.dict_items.setdefault(item, set()).add(user)
self.list_items.add(item)
self.mean_feedback += feedback
list_feedback.append(feedback)
self.triple_dataset = sorted(self.triple_dataset)
self.mean_feedback /= float(self.number_interactions)
self.list_users = sorted(list(l_items))
self.list_items = sorted(list(self.list_items))
for u, user in enumerate(self.list_users):
map_user[user] = u
map_index_user[u] = user
for i, item in enumerate(self.list_items):
map_item[item] = i
map_index_item[i] = item
matrix = np.zeros((len(self.list_users), len(self.list_items)))
for user in self.list_users:
try:
for item in d_feedback[user]:
matrix[map_user[user]][map_item[item]] = d_feedback[user][item]
except KeyError:
pass
dict_file.update({'feedback': d_feedback, 'items': self.list_users, 'metadata': self.list_items,
'di': self.dict_users, 'dm': self.dict_items, 'mean_rates': self.mean_feedback,
'list_feedback': self.triple_dataset, 'ni': self.number_interactions,
'max': max(list_feedback), 'min': min(list_feedback), 'matrix': matrix})
return dict_file
def read_rankings(self):
list_feedback = list()
check_error_file(self.file_read)
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
user, item, feedback = int(inline[0]), int(inline[1]), float(inline[2])
self.dict_users.setdefault(user, {}).update({item: feedback})
list_feedback.append(feedback)
self.average_scores[user] = self.average_scores.get(user, 0) + feedback
self.num_user_interactions[user] = self.num_user_interactions.get(user, 0) + 1
return self.dict_users, list_feedback
def split_dataset(self):
for i, feedback in enumerate(self.file_read):
self.dict_users = dict()
check_error_file(feedback)
with open(feedback) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
self.number_interactions += 1
user, item, feedback = int(inline[0]), int(inline[1]), float(inline[2])
self.triple_dataset.append((user, item))
self.dict_users.setdefault(user, {}).update({item: feedback})
self.individual_interaction.append(self.dict_users)
def return_matrix(self, implicit=False):
check_error_file(self.file_read)
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
self.number_interactions += 1
user, item, feedback = int(inline[0]), int(inline[1]), float(inline[2])
self.list_users.add(user)
self.list_items.add(item)
self.dict_users.setdefault(user, {}).update({item: feedback})
map_user = dict()
map_index_user = dict()
self.list_users = sorted(list(self.list_users))
for u, user in enumerate(self.list_users):
map_user[user] = u
map_index_user[u] = user
map_item = dict()
map_index_item = dict()
self.list_items = sorted(list(self.list_items))
for i, item in enumerate(self.list_items):
map_item[item] = i
map_index_item[i] = item
matrix = np.zeros((len(self.list_users), len(self.list_items)))
for user in self.list_users:
for item in self.dict_users[user]:
if implicit:
matrix[map_user[user]][map_item[item]] = 1
else:
matrix[map_user[user]][map_item[item]] = self.dict_users[user][item]
self.dict_items.setdefault(map_item[item], set()).add(map_user[user])
return {"matrix": matrix, "map_user": map_index_user, "map_item": map_index_item,
"number_interactions": self.number_interactions, "di": self.dict_items, "mu": map_user,
"users": self.list_users, "items": self.list_items, "feedback": self.dict_users}
def main_information_item_recommendation(self):
check_error_file(self.file_read)
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
self.number_interactions += 1
try:
user, item = int(inline[0]), int(inline[1])
except ValueError:
print('Error: Space type is invalid!')
sys.exit()
self.num_user_interactions[user] = self.num_user_interactions.get(user, 0) + 1
self.num_items_interactions[item] = self.num_items_interactions.get(item, 0) + 1
self.list_users.add(user)
self.list_items.add(item)
self.dict_users.setdefault(user, []).append(item)
self.list_users = sorted(self.list_users)
self.list_items = sorted(self.list_items)
def return_bprmf(self):
check_error_file(self.file_read)
not_seen = dict()
with open(self.file_read) as infile:
for line in infile:
if line.strip():
inline = line.split(self.space_type)
self.number_interactions += 1
user, item, feedback = int(inline[0]), int(inline[1]), float(inline[2])
self.list_users.add(user)
self.list_items.add(item)
self.dict_users.setdefault(user, list()).append(item)
self.list_users = sorted(list(self.list_users))
self.list_items = sorted(list(self.list_items))
for user in self.list_users:
not_seen[user] = list(set(self.list_items) - set(self.dict_users[user]))
return {"users": self.list_users, "items": self.list_items, "feedback": self.dict_users, "not_seen": not_seen,
"number_interactions": self.number_interactions}
