def __init__(self, path):
'''
Constructor
'''
trainMatrix = self.load_rating_file_as_matrix(path + ".train.rating")
testRatings = self.load_rating_file_as_matrix(path + ".test.rating")
testNegatives = self.load_negative_file(path + ".test.negative")
assert len(testRatings) == len(testNegatives)
self.num_users, self.num_items = trainMatrix.shape
from Base.Recommender_utils import reshapeSparse
self.URM_train = trainMatrix.tocsr()
self.URM_test = testRatings.tocsr()
shape = (max(self.URM_train.shape[0], self.URM_test.shape[0]),
max(self.URM_train.shape[1], self.URM_test.shape[1]))
self.URM_train = reshapeSparse(self.URM_train, 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(testNegatives)):
user_test_items = 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()
def _load_data_file(self, filePath, separator=" "):
URM_builder = IncrementalSparseMatrix(auto_create_row_mapper=False,
auto_create_col_mapper=False)
fileHandle = open(filePath, "r")
user_index = 0
for line in fileHandle:
if (user_index % 1000000 == 0):
print("Processed {} cells".format(user_index))
if (len(line)) > 1:
line = line.replace("\n", "")
line = line.split(separator)
if len(line) > 0:
if line[0] != "0":
line = [int(line[i]) for i in range(len(line))]
URM_builder.add_single_row(user_index,
line[1:],
data=1.0)
user_index += 1
fileHandle.close()
return URM_builder
def test_IncrementalSparseMatrix_add_rows(self):
import numpy as np
n_rows = 100
n_cols = 200
randomMatrix = sps.random(n_rows, n_cols, density=0.01, format="csr")
incrementalMatrix = IncrementalSparseMatrix(n_rows=n_rows,
n_cols=n_cols)
for row in range(n_rows):
row_data = randomMatrix.indices[randomMatrix.
indptr[row]:randomMatrix.indptr[row
+
1]]
incrementalMatrix.add_single_row(row, row_data, 5.0)
randomMatrix.data = np.ones_like(randomMatrix.data) * 5.0
randomMatrix_incremental = incrementalMatrix.get_SparseMatrix()
assert sparse_are_equals(randomMatrix, randomMatrix_incremental)
def split_train_validation_test_negative_leave_one_out_user_wise(URM_all, negative_items_per_positive=50, verbose=True,
at_least_n_train_items_test=0,
at_least_n_train_items_validation=0):
"""
This function creates a Train, Test, Validation split with negative items sampled
The split is perfomed user-wise, hold 1 out for validation and test
:param URM_all:
:param negative_items_per_positive:
:return:
"""
URM_all = sps.csr_matrix(URM_all)
n_rows, n_cols = URM_all.shape
print('Creation test...')
URM_train_all, URM_test = split_train_validation_leave_one_out_user_wise(URM_all,
at_least_n_train_items=at_least_n_train_items_test,
verbose=verbose)
print('Creation validation...')
URM_train, URM_validation = split_train_validation_leave_one_out_user_wise(URM_train_all,
at_least_n_train_items=at_least_n_train_items_validation,
verbose=verbose)
URM_negative_builder = IncrementalSparseMatrix(n_rows=n_rows, n_cols=n_cols)
all_items = np.arange(0, n_cols, dtype=np.int)
for user_index in range(URM_train_all.shape[0]):
if user_index % 10000 == 0:
print("split_data_train_validation_test_negative: user {} of {}".format(user_index, URM_all.shape[0]))
start_pos = URM_all.indptr[user_index]
end_pos = URM_all.indptr[user_index + 1]
user_profile = URM_all.indices[start_pos:end_pos]
unobserved_index = np.in1d(all_items, user_profile, assume_unique=True, invert=True)
unobserved_items = all_items[unobserved_index]
np.random.shuffle(unobserved_items)
n_test_items = URM_test.indptr[user_index + 1] - URM_test.indptr[user_index]
num_negative_items = n_test_items * negative_items_per_positive
if num_negative_items > len(unobserved_items):
print(
"split_data_train_validation_test_negative: WARNING number of negative to sample for user {} is greater than available negative items {}".format(
num_negative_items, len(unobserved_items)))
num_negative_items = min(num_negative_items, len(unobserved_items))
URM_negative_builder.add_single_row(user_index, unobserved_items[:num_negative_items], 1.0)
URM_negative = URM_negative_builder.get_SparseMatrix()
return URM_train, URM_validation, URM_test, URM_negative
def split_data_train_validation_test_negative_user_wise(URM_all, negative_items_per_positive=50):
"""
This function creates a Train, Test, Validation split with negative items sampled
The split is perfomed user-wise, 20% is test, 80% is train. Train is further divided in 90% final train and 10% validation
:param URM_all:
:param negative_items_per_positive:
:return:
"""
URM_all = sps.csr_matrix(URM_all)
n_rows, n_cols = URM_all.shape
URM_train_all, URM_test = split_train_validation_percentage_user_wise(URM_all, train_percentage=0.8)
URM_train, URM_validation = split_train_validation_percentage_user_wise(URM_train_all, train_percentage=0.9)
URM_negative_builder = IncrementalSparseMatrix(n_rows=n_rows, n_cols=n_cols)
all_items = np.arange(0, n_cols, dtype=np.int)
for user_index in range(URM_train_all.shape[0]):
if user_index % 10000 == 0:
print("split_data_train_validation_test_negative: user {} of {}".format(user_index, URM_all.shape[0]))
start_pos = URM_all.indptr[user_index]
end_pos = URM_all.indptr[user_index + 1]
user_profile = URM_all.indices[start_pos:end_pos]
unobserved_index = np.in1d(all_items, user_profile, assume_unique=True, invert=True)
unobserved_items = all_items[unobserved_index]
np.random.shuffle(unobserved_items)
n_test_items = URM_test.indptr[user_index + 1] - URM_test.indptr[user_index]
num_negative_items = n_test_items * negative_items_per_positive
if num_negative_items > len(unobserved_items):
print(
"split_data_train_validation_test_negative: WARNING number of negative to sample for user {} is greater than available negative items {}".format(
num_negative_items, len(unobserved_items)))
num_negative_items = min(num_negative_items, len(unobserved_items))
URM_negative_builder.add_single_row(user_index, unobserved_items[:num_negative_items], 1.0)
URM_negative = URM_negative_builder.get_SparseMatrix()
return URM_train, URM_validation, URM_test, URM_negative
def __init__(self):
super(Movielens100KReader, self).__init__()
pre_splitted_path = "Data_manager_split_datasets/Movielens100K/KDD/MCRec_our_interface/"
pre_splitted_filename = "splitted_data"
original_data_path = "Conferences/KDD/MCRec_github/data/"
# If directory does not exist, create
if not os.path.exists(pre_splitted_path):
os.makedirs(pre_splitted_path)
try:
print("Movielens100KReader: 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("Movielens100KReader: Pre-splitted data not found, building new one")
print("Movielens100KReader: loading URM")
from Conferences.KDD.MCRec_github.code.Dataset import Dataset
dataset = 'ml-100k'
dataset = Dataset(original_data_path + dataset)
URM_train, testRatings, testNegatives = dataset.trainMatrix, dataset.testRatings, dataset.testNegatives
# Dataset adds 1 to user and item id, removing it to restore 0 indexing
URM_train = sps.coo_matrix(URM_train)
URM_train.row -= 1
URM_train.col -= 1
self.URM_train = sps.csr_matrix((np.ones_like(URM_train.data), (URM_train.row, URM_train.col)))
num_users, num_items = self.URM_train.shape
# Build sparse matrices from lists
URM_test_builder = IncrementalSparseMatrix(n_rows=num_users, n_cols=num_items)
URM_test_negative_builder = IncrementalSparseMatrix(n_rows=num_users, n_cols=num_items)
for user_index in range(len(testRatings)):
user_id = testRatings[user_index][0]
current_user_test_items = testRatings[user_index][1:]
current_user_test_negative_items = testNegatives[user_index]
current_user_test_items = np.array(current_user_test_items) -1
current_user_test_negative_items = np.array(current_user_test_negative_items) -1
URM_test_builder.add_single_row(user_id -1, current_user_test_items, 1.0)
URM_test_negative_builder.add_single_row(user_id -1, current_user_test_negative_items, 1.0)
# the test data has repeated data, apparently
self.URM_test = URM_test_builder.get_SparseMatrix()
self.URM_test_negative = URM_test_negative_builder.get_SparseMatrix()
# Split validation from train as 10%
from Data_manager.split_functions.split_train_validation import split_train_validation_percentage_user_wise
self.URM_train, self.URM_validation = split_train_validation_percentage_user_wise(self.URM_train, train_percentage=0.9)
# Load features
data_reader = Movielens100KReader_DataManager()
data_reader.load_data()
zipFile_path = data_reader.DATASET_SPLIT_ROOT_FOLDER + data_reader.DATASET_SUBFOLDER
dataFile = zipfile.ZipFile(zipFile_path + "ml-100k.zip")
ICM_path = dataFile.extract("ml-100k/u.item", path=zipFile_path + "decompressed/")
ICM_genre = self._loadICM(ICM_path)
ICM_genre = ICM_genre.get_SparseMatrix()
shutil.rmtree(zipFile_path + "decompressed", ignore_errors=True)
self.ICM_dict = {"ICM_genre": ICM_genre}
data_dict = {
"URM_train": self.URM_train,
"URM_test": self.URM_test,
"URM_validation": self.URM_validation,
"URM_test_negative": self.URM_test_negative,
"ICM_dict": self.ICM_dict,
}
save_data_dict(data_dict, pre_splitted_path, pre_splitted_filename)
print("Movielens100KReader: loading complete")
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 split_data_on_timestamp(URM_all, URM_timestamp, negative_items_per_positive=100):
URM_all = sps.csr_matrix(URM_all)
URM_timestamp = sps.csr_matrix(URM_timestamp)
n_rows, n_cols = URM_all.shape
URM_train_builder = IncrementalSparseMatrix(n_rows=n_rows, n_cols=n_cols)
URM_test_builder = IncrementalSparseMatrix(n_rows=n_rows, n_cols=n_cols)
URM_validation_builder = IncrementalSparseMatrix(n_rows=n_rows, n_cols=n_cols)
URM_negative_builder = IncrementalSparseMatrix(n_rows=n_rows, n_cols=n_cols)
all_items = np.arange(0, n_cols, dtype=np.int)
for user_index in range(URM_all.shape[0]):
if user_index % 10000 == 0:
print("split_data_on_sequence: user {} of {}".format(user_index, URM_all.shape[0]))
start_pos = URM_all.indptr[user_index]
end_pos = URM_all.indptr[user_index+1]
user_profile = URM_all.indices[start_pos:end_pos]
user_data = URM_all.data[start_pos:end_pos]
user_sequence = URM_timestamp.data[start_pos:end_pos]
unobserved_index = np.in1d(all_items, user_profile, assume_unique=True, invert=True)
unobserved_items = all_items[unobserved_index]
np.random.shuffle(unobserved_items)
URM_negative_builder.add_single_row(user_index, unobserved_items[:negative_items_per_positive], 1.0)
if len(user_profile) >= 3:
# Test contain the first one, validation the second
min_pos = np.argmax(user_sequence)
venue_index = user_profile[min_pos]
venue_data = user_data[min_pos]
URM_test_builder.add_data_lists([user_index], [venue_index], [venue_data])
user_profile = np.delete(user_profile, min_pos)
user_data = np.delete(user_data, min_pos)
user_sequence = np.delete(user_sequence, min_pos)
min_pos = np.argmax(user_sequence)
venue_index = user_profile[min_pos]
venue_data = user_data[min_pos]
URM_validation_builder.add_data_lists([user_index], [venue_index], [venue_data])
user_profile = np.delete(user_profile, min_pos)
user_data = np.delete(user_data, min_pos)
#user_sequence = np.delete(user_sequence, min_pos)
URM_train_builder.add_data_lists([user_index]*len(user_profile), user_profile, user_data)
URM_train = URM_train_builder.get_SparseMatrix()
URM_validation = URM_validation_builder.get_SparseMatrix()
URM_test = URM_test_builder.get_SparseMatrix()
URM_negative = URM_negative_builder.get_SparseMatrix()
return URM_train, URM_validation, URM_test, URM_negative
def __init__(self, pre_splitted_path, type="original"):
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 type == "original":
# Ensure file is loaded as matrix
Dataset_github.load_rating_file_as_list = Dataset_github.load_rating_file_as_matrix
dataset = Dataset_github(
"Conferences/IJCAI/DELF_original/Data/ml-1m")
URM_train, URM_validation, URM_test, testNegatives = dataset.trainMatrix, dataset.validRatings, \
dataset.testRatings, dataset.testNegatives
URM_train = URM_train.tocsr()
URM_validation = URM_validation.tocsr()
URM_test = URM_test.tocsr()
URM_timestamp = "no"
from Base.Recommender_utils import reshapeSparse
shape = (max(URM_train.shape[0], URM_validation.shape[0],
URM_test.shape[0]),
max(URM_train.shape[1], URM_validation.shape[1],
URM_test.shape[1]))
URM_train = reshapeSparse(URM_train, shape)
URM_validation = reshapeSparse(URM_validation, 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(
)
elif type == "ours":
# create from full dataset with leave out one time wise from ORIGINAL full dateset
data_reader = Movielens1MReader_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")
# make rating implicit
URM_all.data = np.ones_like(URM_all.data)
URM_train, URM_validation, URM_test, URM_test_negative = split_data_on_timestamp(
URM_all, URM_timestamp, negative_items_per_positive=99)
else:
assert 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, 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):
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)