def __init__(self):
super(CiteULikeReader, self).__init__()
pre_splitted_path = "Data_manager_split_datasets/CiteULike/SIGIR/CMN_our_interface/"
pre_splitted_filename = "splitted_data"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("CiteULikeReader: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print(
"CiteULikeReader: Pre-splitted data not found, building new one"
)
print("CiteULikeReader: loading URM")
filename = "Conferences/SIGIR/CMN_github/data/citeulike-a.npz"
self.URM_train_original, self.URM_test, self.URM_test_negative = self.build_sparse_matrix(
filename)
self.URM_train, self.URM_validation = split_train_validation_leave_one_out_user_wise(
self.URM_train_original.copy())
data_dict = {
"URM_train_original": self.URM_train_original,
"URM_train": self.URM_train,
"URM_test": self.URM_test,
"URM_test_negative": self.URM_test_negative,
"URM_validation": self.URM_validation,
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("N_items {}, n_users {}".format(self.URM_train.shape[1],
self.URM_train.shape[0]))
print("CiteULikeReader: Dataset loaded")
def __init__(self, pre_splitted_path):
super(CiteULikeReader, self).__init__()
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("CiteULikeReader: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print(
"CiteULikeReader: Pre-splitted data not found, building new one"
)
print("CiteULikeReader: loading URM")
filename = "Conferences/SIGIR/CMN_github/data/citeulike-a.npz"
URM_train_original, URM_test, URM_test_negative = self.build_sparse_matrix(
filename)
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_train_original.copy())
self.URM_DICT = {
"URM_train_original": URM_train_original,
"URM_train": URM_train,
"URM_test": URM_test,
"URM_test_negative": URM_test_negative,
"URM_validation": URM_validation,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("CiteULikeReader: Dataset loaded")
def __init__(self, pre_splitted_path):
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_{}: Attempting to load pre-splitted data".format(
self.DATASET_NAME))
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("Dataset_{}: Pre-splitted data not found, building new one".
format(self.DATASET_NAME))
# create from full dataset with leave out one time wise from ORIGINAL full dateset
data_reader = Movielens100KReader_DataManager()
loaded_dataset = data_reader.load_data()
URM_all = loaded_dataset.get_URM_from_name("URM_all")
URM_timestamp = loaded_dataset.get_URM_from_name("URM_timestamp")
# use this function 2 time because the order could change slightly the number of final interactions
URM_all = filter_urm(URM_all,
user_min_number_ratings=0,
item_min_number_ratings=5)
URM_all = filter_urm(URM_all,
user_min_number_ratings=20,
item_min_number_ratings=0)
URM_timestamp = filter_urm(URM_timestamp,
user_min_number_ratings=0,
item_min_number_ratings=5)
URM_timestamp = filter_urm(URM_timestamp,
user_min_number_ratings=20,
item_min_number_ratings=0)
URM_timestamp = URM_timestamp
URM_train, URM_validation, URM_test, URM_test_negative = split_data_on_timestamp(
URM_all, URM_timestamp, negative_items_per_positive=99)
# We want the validation to be sampled at random, not as the last interaction
URM_train = URM_train + URM_validation
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_train, verbose=False)
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
"URM_test_negative": URM_test_negative,
"URM_timestamp": URM_timestamp,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("{}: Dataset loaded".format(self.DATASET_NAME))
print_stat_datareader(self)
def __init__(self, pre_splitted_path):
super(Movielens1MReader, self).__init__()
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_Movielens1M: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print(
"Dataset_Movielens1M: Pre-splitted data not found, building new one"
)
# Ensure file is loaded as matrix
Dataset_github.load_rating_file_as_list = Dataset_github.load_rating_file_as_matrix
dataset = Dataset_github("Conferences/WWW/NeuMF_github/Data/ml-1m")
URM_train_original, URM_test = dataset.trainMatrix, dataset.testRatings
URM_train_original = URM_train_original.tocsr()
URM_test = URM_test.tocsr()
from Base.Recommender_utils import reshapeSparse
shape = (max(URM_train_original.shape[0], URM_test.shape[0]),
max(URM_train_original.shape[1], URM_test.shape[1]))
URM_train_original = reshapeSparse(URM_train_original, shape)
URM_test = reshapeSparse(URM_test, shape)
URM_test_negatives_builder = IncrementalSparseMatrix(
n_rows=shape[0], n_cols=shape[1])
for user_index in range(len(dataset.testNegatives)):
user_test_items = dataset.testNegatives[user_index]
URM_test_negatives_builder.add_single_row(user_index,
user_test_items,
data=1.0)
URM_test_negative = URM_test_negatives_builder.get_SparseMatrix()
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_train_original.copy())
self.URM_DICT = {
"URM_train_original": URM_train_original,
"URM_train": URM_train,
"URM_test": URM_test,
"URM_test_negative": URM_test_negative,
"URM_validation": URM_validation,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("Dataset_Movielens1M: Dataset loaded")
def __init__(self, pre_splitted_path, original=True):
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_{}: Attempting to load pre-splitted data".format(
self.DATASET_NAME))
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("Dataset_{}: Pre-splitted data not found, building new one".
format(self.DATASET_NAME))
compressed_file_folder = "Conferences/IJCAI/ConvNCF_github/Data/"
decompressed_file_folder = "Data_manager_split_datasets/Yelp/"
# compressed_file = tarfile.open(compressed_file_folder + "yelp.test.negative.gz", "r:gz")
# compressed_file.extract("yelp.test.negative", path=decompressed_file_folder + "decompressed/")
# compressed_file.close()
#
# compressed_file = tarfile.open(compressed_file_folder + "yelp.test.rating.gz", "r:gz")
# compressed_file.extract("yelp.test.rating", path=decompressed_file_folder + "decompressed/")
# compressed_file.close()
#
# compressed_file = tarfile.open(compressed_file_folder + "yelp.train.rating.gz", "r:gz")
# compressed_file.extract("yelp.train.rating", path=decompressed_file_folder + "decompressed/")
# compressed_file.close()
# if original:
Dataset_github.load_rating_file_as_list = Dataset_github.load_training_file_as_matrix
try:
dataset = Dataset_github(compressed_file_folder + "yelp")
except FileNotFoundError as exc:
print(
"Dataset_{}: Uncompressed files not found, please manually decompress the *.gz files in this folder: '{}'"
.format(self.DATASET_NAME, compressed_file_folder))
raise exc
URM_train_original, URM_test = dataset.trainMatrix, dataset.testRatings
n_users = max(URM_train_original.shape[0], URM_test.shape[0])
n_items = max(URM_train_original.shape[1], URM_test.shape[1])
URM_train_original = sps.csr_matrix(URM_train_original,
shape=(n_users, n_items))
URM_test = sps.csr_matrix(URM_test, shape=(n_users, n_items))
URM_train_original.data = np.ones_like(URM_train_original.data)
URM_test.data = np.ones_like(URM_test.data)
URM_test_negatives_builder = IncrementalSparseMatrix(
n_rows=n_users, n_cols=n_items)
n_negative_samples = 999
for user_index in range(len(dataset.testNegatives)):
user_test_items = dataset.testNegatives[user_index]
if len(user_test_items) != n_negative_samples:
print(
"user id: {} has {} negative items instead {}".format(
user_index, len(user_test_items),
n_negative_samples))
URM_test_negatives_builder.add_single_row(user_index,
user_test_items,
data=1.0)
URM_test_negative = URM_test_negatives_builder.get_SparseMatrix()
URM_test_negative.data = np.ones_like(URM_test_negative.data)
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_train_original.copy(), verbose=False)
#
# else:
# data_reader = YelpReader_DataManager()
# loaded_dataset = data_reader.load_data()
#
# URM_all = loaded_dataset.get_URM_all()
#
# URM_timestamp = URM_all.copy()
#
# URM_all.data = np.ones_like(URM_all.data)
#
# URM_train, URM_validation, URM_test, URM_negative = split_data_on_timestamp(URM_all, URM_timestamp, negative_items_per_positive=999)
# URM_train = URM_train + URM_validation
# URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(URM_train, verbose=False)
shutil.rmtree(decompressed_file_folder + "decompressed/",
ignore_errors=True)
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
"URM_test_negative": URM_test_negative,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("{}: Dataset loaded".format(self.DATASET_NAME))
ut.print_stat_datareader(self)
def __init__(self, pre_splitted_path, type='original'):
assert type in ["original", "ours"]
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_{}: Attempting to load pre-splitted data".format(
self.DATASET_NAME))
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("Dataset_{}: Pre-splitted data not found, building new one".
format(self.DATASET_NAME))
from Conferences.IJCAI.CoupledCF_original import LoadMovieDataCnn as DatareaderOriginal
path = "Conferences/IJCAI/CoupledCF_original/ml-1m/"
n_users, gender, age, occupation = DatareaderOriginal.load_user_attributes(
path=path, split=True)
n_items, items_genres_mat = DatareaderOriginal.load_itemGenres_as_matrix(
path=path)
ratings = DatareaderOriginal.load_rating_train_as_matrix(path=path)
testRatings = DatareaderOriginal.load_rating_file_as_list(
path=path)
testNegatives = DatareaderOriginal.load_negative_file(path=path)
URM_all = ratings.tocsr()
UCM_gender = gender.tocsr()
UCM_age = age.tocsr()
UCM_occupation = occupation.tocsr()
UCM_all = sps.hstack((UCM_gender, UCM_age, UCM_occupation)).tocsr()
ICM_all = sps.csr_matrix(items_genres_mat)
testRatings = np.array(testRatings).T
URM_test_builder = IncrementalSparseMatrix(n_rows=n_users + 1,
n_cols=n_items + 1)
URM_test_builder.add_data_lists(testRatings[0], testRatings[1],
np.ones(len(testRatings[0])))
URM_test = URM_test_builder.get_SparseMatrix()
URM_test_negatives_builder = IncrementalSparseMatrix(
n_rows=n_users + 1, n_cols=n_items + 1)
# care here, the test negative start from index 0 but it refer to user index 1 (user index start from 1)
n_negative_samples = 99
for index in range(len(testNegatives)):
user_test_items = testNegatives[index]
if len(user_test_items) != n_negative_samples:
print(
"user id: {} has {} negative items instead {}".format(
index + 1, len(user_test_items),
n_negative_samples))
URM_test_negatives_builder.add_single_row(index + 1,
user_test_items,
data=1.0)
URM_test_negative = URM_test_negatives_builder.get_SparseMatrix()
URM_test_negative.data = np.ones_like(URM_test_negative.data)
if type == 'original':
URM_test = URM_test
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_all.copy(), verbose=False)
else: # redo the split
URM_full = URM_all + URM_test
URM_temp, URM_test = split_train_validation_leave_one_out_user_wise(
URM_full.copy(), verbose=False)
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_temp.copy(), verbose=False)
self.ICM_DICT = {
"UCM_gender": UCM_gender,
"UCM_occupation": UCM_occupation,
"UCM_age": UCM_age,
"UCM_all": UCM_all,
"ICM_all": ICM_all,
}
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
"URM_test_negative": URM_test_negative,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("{}: Dataset loaded".format(self.DATASET_NAME))
ut.print_stat_datareader(self)
def __init__(self, pre_splitted_path, type='original'):
assert type in ["original", "ours"]
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_{}: Attempting to load pre-splitted data".format(
self.DATASET_NAME))
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("Dataset_{}: Pre-splitted data not found, building new one".
format(self.DATASET_NAME))
from Conferences.IJCAI.CoupledCF_original import LoadTafengDataCnn as DatareaderOriginal
path = "Conferences/IJCAI/CoupledCF_original/tafeng/"
n_users, user_attributes_mat = DatareaderOriginal.load_user_attributes(
path=path)
n_items, items_genres_mat = DatareaderOriginal.load_itemGenres_as_matrix(
path=path)
ratings = DatareaderOriginal.load_rating_train_as_matrix(path=path)
testRatings = DatareaderOriginal.load_rating_file_as_list(
path=path)
testNegatives = DatareaderOriginal.load_negative_file(path=path)
URM_all = ratings.tocsr()
UCM_all = sps.csc_matrix(user_attributes_mat)
UCM_age = UCM_all[:, 0:11].tocsr()
UCM_region = UCM_all[:, 11:19].tocsr()
UCM_all = UCM_all.tocsr()
# col: 0->category, 2->asset(0-1), 1->price(0-1)
ICM_original = sps.csc_matrix(items_genres_mat)
# category could be used as matrix, not single row
ICM_sub_class = ICM_original[:, 0:1].tocsr()
max = ICM_sub_class.shape[0]
rows, cols, data = [], [], []
for idx in range(max):
# we have only index 0 as col
data_vect = ICM_sub_class.data[
ICM_sub_class.indptr[idx]:ICM_sub_class.indptr[idx + 1]]
if len(data_vect) == 0:
# handle category value 0 that in a csr matrix is not present
cols.append(int(0))
else:
cols.append(int(data_vect[0]))
rows.append(idx)
data.append(1.0)
ICM_sub_class = sps.csr_matrix((data, (rows, cols)))
ICM_asset = ICM_original[:, 1:2].tocsr()
ICM_price = ICM_original[:, 2:3].tocsr()
ICM_original = ICM_original.tocsc()
ICM_all = sps.hstack((ICM_sub_class, ICM_asset, ICM_price))
testRatings = np.array(testRatings).T
URM_test_builder = IncrementalSparseMatrix(n_rows=n_users + 1,
n_cols=n_items + 1)
URM_test_builder.add_data_lists(testRatings[0], testRatings[1],
np.ones(len(testRatings[0])))
URM_test = URM_test_builder.get_SparseMatrix()
URM_test_negatives_builder = IncrementalSparseMatrix(
n_rows=n_users + 1, n_cols=n_items + 1)
# care here, the test negative start from index 0 but it refer to user index 1 (user index start from 1)
n_negative_samples = 99
for index in range(len(testNegatives)):
user_test_items = testNegatives[index]
if len(user_test_items) != n_negative_samples:
print(
"user id: {} has {} negative items instead {}".format(
index + 1, len(user_test_items),
n_negative_samples))
URM_test_negatives_builder.add_single_row(index + 1,
user_test_items,
data=1.0)
URM_test_negative = URM_test_negatives_builder.get_SparseMatrix()
URM_test_negative.data = np.ones_like(URM_test_negative.data)
if type == 'original':
URM_test = URM_test
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_all.copy(), verbose=False)
else: # redo the split
URM_full = URM_all + URM_test
URM_temp, URM_test = split_train_validation_leave_one_out_user_wise(
URM_full.copy(), verbose=False)
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_temp.copy(), verbose=False)
self.ICM_DICT = {
"UCM_age": UCM_age,
"UCM_region": UCM_region,
"UCM_all": UCM_all,
"ICM_all": ICM_all,
"ICM_original": ICM_original,
"ICM_sub_class": ICM_sub_class,
"ICM_asset": ICM_asset,
"ICM_price": ICM_price,
}
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
"URM_test_negative": URM_test_negative,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("{}: Dataset loaded".format(self.DATASET_NAME))
ut.print_stat_datareader(self)
def __init__(self):
super(PinterestICCVReader, self).__init__()
pre_splitted_path = "Data_manager_split_datasets/PinterestICCV/WWW/NeuMF_our_interface/"
pre_splitted_filename = "splitted_data"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_Pinterest: Attempting to load pre-splitted data")
for attrib_name, attrib_object in load_data_dict(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print(
"Dataset_Pinterest: Pre-splitted data not found, building new one"
)
# Ensure file is loaded as matrix
Dataset_github.load_rating_file_as_list = Dataset_github.load_rating_file_as_matrix
dataset = Dataset_github(
"Conferences/WWW/NeuMF_github/Data/pinterest-20")
self.URM_train_original, self.URM_test = dataset.trainMatrix, dataset.testRatings
self.URM_train_original = self.URM_train_original.tocsr()
self.URM_test = self.URM_test.tocsr()
from Base.Recommender_utils import reshapeSparse
shape = (max(self.URM_train_original.shape[0],
self.URM_test.shape[0]),
max(self.URM_train_original.shape[1],
self.URM_test.shape[1]))
self.URM_train_original = reshapeSparse(self.URM_train_original,
shape)
self.URM_test = reshapeSparse(self.URM_test, shape)
URM_test_negatives_builder = IncrementalSparseMatrix(
n_rows=shape[0], n_cols=shape[1])
for user_index in range(len(dataset.testNegatives)):
user_test_items = dataset.testNegatives[user_index]
URM_test_negatives_builder.add_single_row(user_index,
user_test_items,
data=1.0)
self.URM_test_negative = URM_test_negatives_builder.get_SparseMatrix(
)
self.URM_train, self.URM_validation = split_train_validation_leave_one_out_user_wise(
self.URM_train_original.copy())
data_dict = {
"URM_train_original": self.URM_train_original,
"URM_train": self.URM_train,
"URM_test": self.URM_test,
"URM_test_negative": self.URM_test_negative,
"URM_validation": self.URM_validation,
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("Dataset_Pinterest: Dataset loaded")
print("N_items {}, n_users {}".format(self.URM_train.shape[1],
self.URM_train.shape[0]))
def __init__(self, pre_splitted_path, original=True):
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_{}: Attempting to load pre-splitted data".format(
self.DATASET_NAME))
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("Dataset_{}: Pre-splitted data not found, building new one".
format(self.DATASET_NAME))
if original:
URM_path = 'Conferences/IJCAI/DMF_original/data_www/Amazon_ratings_Digital_Music_pruned.txt'
#
# dataFile = open(URM_path, "r")
#
# # textData = dataFile.readlines()
# dataFile.close()
#
# u_map = {}
# discarded = 0
# for line in tqdm(textData):
# line = line.split(' ')
# u, i, rating, new_time = int(line[0]), int(line[1]), float(line[2]), int(line[3])
#
# # convert u id and i id in integer starting from 0 and initialize u_map
# if u not in u_map:
# u_map[u] = {}
#
# if i not in u_map[u]:
# u_map[u][i] = [rating, new_time]
# else: # rating already exist, keep the most recent timestamp
# discarded += 1
# current_time = u_map[u][i][1]
# if new_time > current_time:
# u_map[u][i] = [rating, new_time]
#
# print('Merged {} interactions, kept the most recent timestamps'.format(discarded))
#
# UTM_builder = IncrementalSparseMatrix()
# URM_builder = IncrementalSparseMatrix()
#
# for u in u_map:
# items, ratings, timestamps = [], [], []
# for i in u_map[u]:
# items.append(i)
# timestamps.append(u_map[u][i][1])
# ratings.append(u_map[u][i][0])
# UTM_builder.add_data_lists(row_list_to_add=np.full(len(items), int(u)), col_list_to_add=items, data_list_to_add=timestamps)
# URM_builder.add_data_lists(row_list_to_add=np.full(len(items), int(u)), col_list_to_add=items, data_list_to_add=ratings)
#
URM_rating_builder = IncrementalSparseMatrix(
auto_create_col_mapper=True, auto_create_row_mapper=True)
URM_timestamp_builder = IncrementalSparseMatrix(
auto_create_col_mapper=True, auto_create_row_mapper=True)
# URM_duplicate_assert_builder = IncrementalSparseMatrix( auto_create_col_mapper = True, auto_create_row_mapper = True)
df_original = pd.read_csv(filepath_or_buffer=URM_path,
sep=" ",
header=None,
dtype={
0: int,
1: int,
2: float,
3: int
})
df_original.columns = [
'userId', 'itemId', 'rating', 'timestamp'
]
userId_list = df_original['userId'].values
itemId_list = df_original['itemId'].values
rating_list = df_original['rating'].values
timestamp_list = df_original['timestamp'].values
URM_rating_builder.add_data_lists(userId_list, itemId_list,
rating_list)
URM_timestamp_builder.add_data_lists(userId_list, itemId_list,
timestamp_list)
# URM_duplicate_assert_builder.add_data_lists(userId_list, itemId_list, np.ones_like(rating_list))
# URM_duplicate_assert = URM_duplicate_assert_builder.get_SparseMatrix()
#
# assert np.all(URM_duplicate_assert.data == 1.0), "Duplicates detected"
# Check if duplicates exist
num_unique_user_item_ids = df_original.drop_duplicates(
['userId', 'itemId'], keep='first', inplace=False).shape[0]
assert num_unique_user_item_ids == len(
userId_list), "Duplicate (user, item) values found"
URM_timestamp = URM_timestamp_builder.get_SparseMatrix()
URM_all = URM_rating_builder.get_SparseMatrix()
URM_train, URM_validation, URM_test, URM_test_negative = split_data_on_timestamp(
URM_all, URM_timestamp, negative_items_per_positive=99)
# We want the validation to be sampled at random, not as the last interaction
URM_train = URM_train + URM_validation
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_train, verbose=False)
else:
# create from full dataset with leave out one time wise from ORIGINAL full dateset
data_reader = AmazonMusicReader_DataManager()
loaded_dataset = data_reader.load_data()
URM_all = loaded_dataset.get_URM_from_name("URM_all")
URM_timestamp = loaded_dataset.get_URM_from_name(
"URM_timestamp")
# use this function 2 time because the order could change slightly the number of final interactions
URM_all = filter_urm(URM_all,
user_min_number_ratings=1,
item_min_number_ratings=5)
URM_all = filter_urm(URM_all,
user_min_number_ratings=20,
item_min_number_ratings=1)
URM_timestamp = filter_urm(URM_timestamp,
user_min_number_ratings=1,
item_min_number_ratings=5)
URM_timestamp = filter_urm(URM_timestamp,
user_min_number_ratings=20,
item_min_number_ratings=1)
URM_timestamp = URM_timestamp
URM_train, URM_validation, URM_test, URM_test_negative = split_data_on_timestamp(
URM_all, URM_timestamp, negative_items_per_positive=99)
# We want the validation to be sampled at random, not as the last interaction
URM_train = URM_train + URM_validation
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_train, verbose=False)
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
"URM_test_negative": URM_test_negative,
"URM_timestamp": URM_timestamp,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("{}: Dataset loaded".format(self.DATASET_NAME))
print_stat_datareader(self)
def __init__(self, pre_splitted_path):
pre_splitted_path += "data_split/"
pre_splitted_filename = "splitted_data_"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Dataset_{}: Attempting to load pre-splitted data".format(
self.DATASET_NAME))
for attrib_name, attrib_object in load_data_dict_zip(
pre_splitted_path, pre_splitted_filename).items():
self.__setattr__(attrib_name, attrib_object)
except FileNotFoundError:
print("Dataset_{}: Pre-splitted data not found, building new one".
format(self.DATASET_NAME))
compressed_file_folder = "Conferences/IJCAI/ConvNCF_github/Data/"
decompressed_file_folder = "Data_manager_split_datasets/Gowalla/"
# compressed_file = tarfile.open(compressed_file_folder + "gowalla.test.negative.gz", "r:gz")
# compressed_file.extract("yelp.test.negative", path=decompressed_file_folder + "decompressed/")
# compressed_file.close()
#
# compressed_file = tarfile.open(compressed_file_folder + "gowalla.test.rating.gz", "r:gz")
# compressed_file.extract("yelp.test.rating", path=decompressed_file_folder + "decompressed/")
# compressed_file.close()
#
# compressed_file = tarfile.open(compressed_file_folder + "gowalla.train.rating.gz", "r:gz")
# compressed_file.extract("yelp.train.rating", path=decompressed_file_folder + "decompressed/")
# compressed_file.close()
# if original:
Dataset_github.load_rating_file_as_list = Dataset_github.load_training_file_as_matrix
try:
dataset = Dataset_github(compressed_file_folder + "gowalla")
except FileNotFoundError as exc:
print(
"Dataset_{}: Gowalla files not found, please download them and put them in this folder '{}', url: {}"
.format(self.DATASET_NAME, compressed_file_folder,
self.DATASET_URL))
print(
"Dataset_{}: Uncompressed files not found, please manually decompress the *.gz files in this folder: '{}'"
.format(self.DATASET_NAME, compressed_file_folder))
raise exc
URM_train_original, URM_test = dataset.trainMatrix, dataset.testRatings
n_users = max(URM_train_original.shape[0], URM_test.shape[0])
n_items = max(URM_train_original.shape[1], URM_test.shape[1])
URM_train_original = sps.csr_matrix(URM_train_original,
shape=(n_users, n_items))
URM_test = sps.csr_matrix(URM_test, shape=(n_users, n_items))
URM_train_original.data = np.ones_like(URM_train_original.data)
URM_test.data = np.ones_like(URM_test.data)
URM_test_negatives_builder = IncrementalSparseMatrix(
n_rows=n_users, n_cols=n_items)
n_negative_samples = 999
for user_index in range(len(dataset.testNegatives)):
user_test_items = dataset.testNegatives[user_index]
if len(user_test_items) != n_negative_samples:
print(
"user id: {} has {} negative items instead {}".format(
user_index, len(user_test_items),
n_negative_samples))
URM_test_negatives_builder.add_single_row(user_index,
user_test_items,
data=1.0)
URM_test_negative = URM_test_negatives_builder.get_SparseMatrix(
).tocsr()
URM_test_negative.data = np.ones_like(URM_test_negative.data)
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(
URM_train_original.copy(), verbose=False)
#
#
# # NOT USED
# # elif not time_split: #create from full dataset with random leave one out from LINKED dateset in the article since timestamp is not present.
# #
# # data_reader = GowallaGithubReader_DataManager()
# # loaded_dataset = data_reader.load_data()
# #
# # URM_all = loaded_dataset.get_URM_all()
# #
# # URM_all.eliminate_zeros()
# #
# # URM_all.data = np.ones_like(URM_all.data)
# #
# # #use this function 2 time because the order could change slightly the number of final interactions
# # #with this order we get the same number of interactions as in the paper
# # URM_all = filter_urm(URM_all, user_min_number_ratings=0, item_min_number_ratings=10)
# # URM_all = filter_urm(URM_all, user_min_number_ratings=2, item_min_number_ratings=0)
# #
# # URM_train, URM_validation, URM_test, URM_negative = split_train_validation_test_negative_leave_one_out_user_wise(URM_all, negative_items_per_positive=999,
# # at_least_n_train_items_test=0, at_least_n_train_items_validation=0,
# # verbose=True)
# # URM_timestamp = sps.csc_matrix(([],([],[])), shape=URM_train.shape)
#
# else: # create from full dataset with leave out one time wise from ORIGINAL full dateset
# data_reader = GowallaReader_DataManager()
# loaded_dataset = data_reader.load_data()
#
# URM_all = loaded_dataset.get_URM_all()
#
# # use this function 2 time because the order could change slightly the number of final interactions
# # with this order we get the same number of interactions as in the paper
# URM_all = filter_urm(URM_all, user_min_number_ratings=0, item_min_number_ratings=10)
# URM_all = filter_urm(URM_all, user_min_number_ratings=2, item_min_number_ratings=0)
#
# URM_timestamp = URM_all.copy()
# URM_all.data = np.ones_like(URM_all.data)
#
# URM_train, URM_validation, URM_test, URM_negative = split_data_on_timestamp(URM_all, URM_timestamp, negative_items_per_positive=999)
# URM_train = URM_train + URM_validation
# URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(URM_train, verbose=False)
self.URM_DICT = {
"URM_train": URM_train,
"URM_test": URM_test,
"URM_validation": URM_validation,
"URM_test_negative": URM_test_negative,
}
save_data_dict_zip(self.URM_DICT, self.ICM_DICT, pre_splitted_path,
pre_splitted_filename)
print("{}: Dataset loaded".format(self.DATASET_NAME))
ut.print_stat_datareader(self)
