def __init__(self,
aws_access_key_id=None,
aws_secret_access_key=None,
aws_bucket_name='cvae-insights',
local_data_store=False,
deployment_prefix='dev',
model_version='2019-01-03'):
"""Create an instance of GetData."""
self.aws_access_key_id = os.environ.get('AWS_S3_ACCESS_KEY_ID', '')
self.aws_secret_access_key = os.environ.get('AWS_S3_SECRET_ACCESS_KEY',
'')
self.github_token = os.environ.get('GITHUB_TOKEN', '')
self.bucket_name = aws_bucket_name
self.deployment_prefix = deployment_prefix
self.version_name = model_version
if local_data_store:
self.s3_client = LocalDataStore('tests/test_data')
else:
self.s3_object = AmazonS3(
bucket_name=self.bucket_name,
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key)
self.s3_client = self.load_S3()
self.utility = Utility()
def __init__(self, data_obj, df_=pd.DataFrame()):
"""Create an instance for PreprocessData."""
self.get_data_obj = data_obj
self.utility_obj = Utility()
self.get_keywords_obj = GetKeywords(data_obj)
self.df_ = df_
self.existing_data = self.get_data_obj.load_existing_data()
self.pkg_kwd_df = self.fetch_package_keywords()
def __init__(self,
df_=pd.DataFrame(),
dict_=dict(),
local_data_store=False):
"""Create an instance for GetKeywords."""
self.df_ = df_
self.dict_ = dict_
self.get_data = GetData(local_data_store=local_data_store)
self.utility = Utility()
def __init__(self,
aws_access_key_id='',
aws_secret_access_key='',
aws_bucket_name='cvae-insights',
model_version='',
num_train_per_user=5):
"""Create an instance for GetPreprocessData."""
self.obj_ = GetData(aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_bucket_name=aws_bucket_name,
model_version=model_version,
num_train_per_user=num_train_per_user)
self.keyword_obj_ = GetKeywords(self.obj_)
self.preprocess_data_obj = PreprocessData(data_obj=self.obj_)
self.utils = Utility()
self.num_users = num_train_per_user
def __init__(self,
aws_access_key_id,
aws_secret_access_key,
num_train_per_user,
aws_bucket_name,
model_version):
"""Create an instance of GetData."""
self.aws_access_key_id = os.environ.get('AWS_S3_ACCESS_KEY_ID', aws_access_key_id)
self.aws_secret_access_key = os.environ.get('AWS_S3_SECRET_ACCESS_KEY',
aws_secret_access_key)
self.github_token = os.environ.get('GITHUB_TOKEN', '')
self.bucket_name = aws_bucket_name
self.version_name = model_version
self.s3_object = AmazonS3(bucket_name=self.bucket_name,
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key
)
self.num_train_per_user = num_train_per_user
self.s3_client = self.load_s3()
self.utility = Utility()
class PreprocessData:
"""This class defines the PreprocessData functions."""
def __init__(self, data_obj, df_=pd.DataFrame()):
"""Create an instance for PreprocessData."""
self.get_data_obj = data_obj
self.utility_obj = Utility()
self.get_keywords_obj = GetKeywords(data_obj)
self.df_ = df_
self.existing_data = self.get_data_obj.load_existing_data()
self.pkg_kwd_df = self.fetch_package_keywords()
def add_dependencies_resolved_column(self, df_, dependencies_list):
"""Return a binary value for dependency resoled column."""
dependencies = [dep.lower() for dep in dependencies_list]
pkg_with_tags = df_.loc[df_['name'].isin(dependencies)]
if len(pkg_with_tags) == 0:
return 0
elif len(set(dependencies) - set(pkg_with_tags['name'])) == 0:
return 1
else:
return 0
def check_resolved_dependencies(self, df_):
"""Add a column all dependencies resolved and assign the binary value."""
df_['all_deps_resolved'] = [
self.add_dependencies_resolved_column(self.pkg_kwd_df, i)
for i in df_['dependencies']
]
df_ = df_.loc[df_['all_deps_resolved'] == 0]
return df_
def fetch_package_keywords(self):
"""Fetch the keywords for raw data's package list."""
raw_data = self.get_data_obj.load_raw_data()
manifest_data = raw_data.get('package_dict', {})
all_manifest = manifest_data.get('user_input_stack', []) + \
manifest_data.get('bigquery_data', [])
try:
package_keyword_df = self.get_keywords_obj.find_keywords(
self.existing_data, all_manifest)
return package_keyword_df
except Exception:
raise ValueError("Unable to fetch keywords.")
def make_necessary_df(self, limit_manifest, limit_keywords):
"""Create two dataframes for dependencies and keywords respectively.."""
filtered_pkg_kwd_df = self.df_
manifest_df = self.df_
if 'dependencies' in self.pkg_kwd_df.columns:
manifest_df = self.utility_obj.make_manifest_df(
self.pkg_kwd_df, limit_manifest)
else:
raise KeyError("Dependency is not present")
if 'keywords' in self.pkg_kwd_df.columns:
filtered_pkg_kwd_df = self.utility_obj.make_filtered_pkg_kwd_df(
self.pkg_kwd_df, limit_keywords)
else:
raise KeyError("Keywords are not present")
return (list([manifest_df, filtered_pkg_kwd_df]))
def extract_unique_packages(self):
"""Return all unique packages from filtered package keyword dataframe."""
filtered_pkg_kwd_df = self.make_necessary_df(5, 0)[1]
data_with_dep_check = self.check_resolved_dependencies(
filtered_pkg_kwd_df)
unique_packages, manifest_user_data = self.utility_obj.extract_package_manifest_lst(
data_with_dep_check)
manifest_user_data = self.utility_obj.make_user_data(
manifest_user_data, unique_packages)
return unique_packages, manifest_user_data
def create_df_and_dictionaries(self):
"""Create all the necessary dataframes and dictionaries."""
self.unique_packages, self.manifest_user_data = self.extract_unique_packages(
)
self.keyword_df, self.dependencies_df = self.utility_obj.make_kwd_dependencies_df(
self.pkg_kwd_df, self.unique_packages)
self.package_tag_map, self.vocabulary = self.utility_obj.create_pkg_tag_map(
self.keyword_df)
self.package_dep_map, self.first_level_deps = self.utility_obj.create_pkg_dep_map(
self.dependencies_df)
def create_extended_pkg_tag_map(self):
"""Create the package tag map according to all first level dependencies."""
self.create_df_and_dictionaries()
self.extended_ptm = dict()
keywords_df_deps = self.pkg_kwd_df.loc[
self.pkg_kwd_df['name'].isin(self.first_level_deps),
['name', 'keywords']]
for k, g in keywords_df_deps.groupby("name"):
try:
self.extended_ptm[k] = self.utility_obj.clean_set(
self.package_dep_map.get(k, set()).union(
set(g["keywords"].tolist()[0])))
except Exception:
pass
return self.extended_ptm, self.manifest_user_data, self.unique_packages
def update_pkg_tag_map(self):
"""Update the existing package tag map."""
extended_ptm, manifest_user_data, unique_packages = self.create_extended_pkg_tag_map(
)
for package_name in self.package_tag_map.keys():
more_keywords = set()
for dependency in self.package_dep_map[package_name]:
more_keywords = more_keywords.union(
set(extended_ptm.get(dependency, [])))
self.package_tag_map[package_name] = self.package_tag_map.get(
package_name).union(more_keywords)
self.vocabulary = self.vocabulary.union(more_keywords)
return self.package_tag_map, self.vocabulary, manifest_user_data, unique_packages
class GetData:
"""This class defines the S3 Connections viz fetching and storing data."""
def __init__(self,
aws_access_key_id,
aws_secret_access_key,
num_train_per_user,
aws_bucket_name,
model_version):
"""Create an instance of GetData."""
self.aws_access_key_id = os.environ.get('AWS_S3_ACCESS_KEY_ID', aws_access_key_id)
self.aws_secret_access_key = os.environ.get('AWS_S3_SECRET_ACCESS_KEY',
aws_secret_access_key)
self.github_token = os.environ.get('GITHUB_TOKEN', '')
self.bucket_name = aws_bucket_name
self.version_name = model_version
self.s3_object = AmazonS3(bucket_name=self.bucket_name,
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key
)
self.num_train_per_user = num_train_per_user
self.s3_client = self.load_s3()
self.utility = Utility()
def load_s3(self):
"""Establish the connection with S3."""
self.s3_object.connect()
if self.s3_object.is_connected():
logger.info("S3 connection established.")
return self.s3_object
else:
raise Exception
def load_raw_data(self):
"""Load the raw data from S3 bucket."""
NPM_raw_data_path = os.path.join(self.version_name,
"data/manifest.json")
logger.info("Reading raw data from {}".format(self.version_name))
if (self.s3_client.object_exists(NPM_raw_data_path)):
try:
raw_data_dict_ = self.s3_client.read_json_file(NPM_raw_data_path)
logger.info("Size of Raw Manifest file is: {}".format(len(raw_data_dict_)))
return raw_data_dict_
except Exception:
raise Exception
def load_existing_data(self):
"""Load the node registry dump from S3 bucket."""
NPM_clean_json_data_path = os.path.join("training-utils",
"node-package-details-with-url.json")
if self.s3_client.object_exists(NPM_clean_json_data_path):
try:
logger.info("Reading dump data from training-utils folder.")
existing_data = self.s3_client.read_generic_file(NPM_clean_json_data_path)
existing_df = self.utility.read_json_file(existing_data)
logger.info("Size of Raw df with url is: {}".format(len(existing_df)))
return existing_df
except Exception:
raise Exception("S3 connection error")
else:
raise ValueError("Given Path is not present.")
def load_user_item_data(self):
"""Load the manifest file."""
NPM_manifest_user_data_path = os.path.join(TEMPORARY_PATH, "manifest_user_data.dat")
try:
with open(NPM_manifest_user_data_path, 'rb') as f:
user_item_data = f.read()
return user_item_data
except Exception:
raise Exception("S3 could not read the file.")
def create_package_train_user_data(self):
"""Create package train user data."""
self.package_train_user_data = list()
for user_id in range(self.num_users):
this_user_items = self.pairs_train[self.pairs_train[:, 0] == user_id, 1]
items_str = " ".join(str(x) for x in this_user_items)
self.package_train_user_data.append([len(this_user_items), items_str])
return self.package_train_user_data
def create_package_train_item_data(self):
"""Create package train item data."""
self.package_train_item_data = list()
for item_id in range(self.num_items):
this_item_users = self.pairs_train[self.pairs_train[:, 1] == item_id, 0]
users_str = " ".join(str(x) for x in this_item_users)
self.package_train_item_data.append([len(this_item_users), users_str])
return self.package_train_item_data
def create_package_test_user_data(self):
"""Create package test user data."""
self.package_test_user_data = list()
for user_id in range(self.num_users):
this_user_items = self.pairs_test[self.pairs_test[:, 0] == user_id, 1]
items_str = " ".join(str(x) for x in this_user_items)
self.package_test_user_data.append([len(this_user_items), items_str])
return self.package_test_user_data
def create_package_test_item_data(self):
"""Create package test item data."""
self.package_test_item_data = list()
for item_id in range(self.num_items):
this_item_users = self.pairs_test[self.pairs_test[:, 1] == item_id, 0]
users_str = " ".join(str(x) for x in this_item_users)
self.package_test_item_data.append([len(this_item_users), users_str])
return self.package_test_item_data
def train_test_data(self):
"""Create the training testing data for PMF."""
data_list = self.split_training_testing_data()
self.pairs_train = data_list[0]
self.pairs_test = data_list[1]
self.num_users = data_list[2]
self.num_items = data_list[3]
packagedata_train_users = self.create_package_train_user_data()
packagedata_train_items = self.create_package_train_item_data()
packagedata_test_users = self.create_package_test_user_data()
packagedata_test_items = self.create_package_test_item_data()
return packagedata_train_users, packagedata_train_items, \
packagedata_test_users, packagedata_test_items
def split_training_testing_data(self):
"""Split data into training and testing."""
data_in_bytes = self.load_user_item_data()
data = data_in_bytes.decode("utf-8")
data_list = data.split('\n')
pairs_train = []
pairs_test = []
user_id = 0
np.random.seed(int(time.time()))
logger.info("Splitting data into training and testing.")
for line in data_list:
arr = line.strip().split()
arr = np.asarray([int(x) for x in arr[1:]])
n = len(arr)
idx = np.random.permutation(n)
for i in range(min(self.num_train_per_user, n)):
pairs_train.append((user_id, arr[idx[i]]))
if n > self.num_train_per_user:
for i in range(self.num_train_per_user, n):
pairs_test.append((user_id, arr[idx[i]]))
user_id += 1
num_users = user_id
pairs_train = np.asarray(pairs_train)
pairs_test = np.asarray(pairs_test)
num_items = np.maximum(np.max(pairs_train[:, 1]), np.max(pairs_test[:, 1])) + 1
logger.info("Number of users and items are respectively {},"
" {}".format(num_users, num_items))
return [pairs_train, pairs_test, num_users, num_items]
def check_path(self, path):
"""Check the given datastore path."""
logger.info("Given path is: {}".format(path))
try:
if not os.path.exists(path):
os.makedirs(path)
return path
except Exception as e:
raise e
def save_file_temporary(self, content, filename, datastore):
"""Store data file in temporary storage."""
path = self.check_path(datastore)
try:
with open(os.path.join(path, filename), 'w') as f:
for lst in content:
ele_str = " ".join([str(x) for x in lst[1:]])
f.write("{} {}\n".format(lst[0], ele_str))
logger.info("File has been stored successfully.")
except Exception as e:
raise e
def save_manifest_file_temporary(self, content, filename, datastore):
"""Store manifest file in temporary storage."""
path = self.check_path(datastore)
try:
with open(os.path.join(path, filename), 'w') as f:
for lst in content:
f.write("{} {}\n".format(lst[0], " ".join(str(x) for x in lst[1:])))
logger.info("Manifest File has been stored successfully.")
except Exception as e:
raise e
def save_numpy_matrix_temporary(self, content, filename, datastore):
"""Store numpy matrix in temporary storage."""
path = self.check_path(datastore)
try:
np.savez(os.path.join(path, filename), matrix=content)
logger.info("Numpy matrix has been stored successfully.")
except Exception as e:
raise e
def save_json_file_temporary(self, content, filename, datastore):
"""Store JSON file in temporary storage."""
path = self.check_path(datastore)
try:
with open(os.path.join(path, filename), 'w') as f:
json.dump(content, f)
logger.info("JSON file has been stored successfully.")
except Exception as e:
raise e
def save_on_s3(self, folder_path):
"""Store all the contents on S3."""
try:
if os.path.exists(folder_path):
if 'intermediate-model' in folder_path:
self.s3_client.s3_upload_folder(folder_path=folder_path,
prefix=self.version_name + '/intermediate-model'
)
else:
self.s3_client.s3_upload_folder(folder_path=folder_path,
prefix=self.version_name + '')
logger.info("Folders are successfully saved on S3.")
else:
logger.error("Folder path doesn't exist.")
except Exception as e:
raise e
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import json
import unittest
from training.datastore.utils import Utility
import pandas as pd
utils_obj = Utility()
with open('tests/test_data/npm/dev/2019-01-03/data/test-node-package-details-with-url.json') as f:
test_data_df = pd.DataFrame(json.load(f))
class TestUtility(unittest.TestCase):
"""This class tests the Utility Class."""
def test_flatten_list(self):
"""Test Flatten list function."""
test_list_ = [[1, 2], [3, 4]]
test_flatten_list_output = utils_obj.flatten_list(test_list_)
assert type(test_flatten_list_output[0]) != list
def test_make_list_from_series(self):
class GetKeywords:
"""This class defines the S3 Connections viz fetching and storing data."""
def __init__(self,
df_=pd.DataFrame(),
dict_=dict(),
local_data_store=False):
"""Create an instance for GetKeywords."""
self.df_ = df_
self.dict_ = dict_
self.get_data = GetData(local_data_store=local_data_store)
self.utility = Utility()
def from_existing_df(self, df_, package):
"""Find the keywords from existing dump."""
if not df_.empty:
data_lst = df_.loc[
df_['name'] == str(package),
['name', 'description', 'keywords', 'dependencies']].iloc[0]
return data_lst
else:
logger.error("Node Package details Dataframe is not existed.")
return self.df_
def from_npm_registry(self, package):
"""Find the keywords from NPM registry(through api)."""
data_dict = self.dict_
api_url = "https://registry.npmjs.org/" + str(package)
try:
api_data = requests.get(api_url).text
json_data = json.loads(api_data)
data_dict['name'] = json_data.get('name', '')
data_dict['description'] = json_data.get('description', '')
data_dict['keywords'] = json_data.get('keywords', [])
data_dict['dependencies'] = self.get_dependencies(json_data)
return data_dict
except Exception:
logger.error("Can't fetch the keywords from NPM Registry")
return data_dict
def get_version(self, api_data):
"""Give the latest version for the package."""
if api_data:
try:
latest_version = api_data['dist-tags']['latest']
return latest_version
except Exception:
logger.info("Unable to fetch latest version from API data.")
return ''
else:
logger.error("API Data is not available.")
return ''
def get_dependencies(self, api_data):
"""Give the dependencies for latest version of package."""
version = self.get_version(api_data)
logger.info("Latest_version is: {}".format(version))
versions_dict = api_data.get('versions', dict())
try:
if versions_dict:
latest_version_data_dict = versions_dict.get(version, dict())
if latest_version_data_dict:
latest_dependencies = latest_version_data_dict.get(
'dependencies', list())
return list(latest_dependencies.keys())
except Exception:
return list()
def clean_response(self, response_json):
"""Clean the api response json."""
topic_lst = response_json['data']['organization']['repository'][
'repositoryTopics']['nodes']
topic_name_lst = [dict(i.get('topic')).get('name') for i in topic_lst]
return list(topic_name_lst)
def from_github(self, package, url_df, api_url, api_token):
"""Find the keywords from the Github Graph QL."""
url_ = self.utility.get_url(url_df, package)
logger.info("Repo URL is {}".format(url_))
keywords = list()
query_params = self.utility.get_query_params(url_)
logger.info("Query Parameters are: {}, {}".format(
query_params[0], query_params[1]))
json = {
'query':
'{{organization(login: "******"){{name url repository(name: "{1}")\
{{name url description repositoryTopics(first: 10){{nodes{{topic {{name}}}}}}}}}}}}'
.format(str(query_params[0]), str(query_params[1]))
}
headers = {'Authorization': 'token %s' % api_token}
try:
response = requests.post(url=api_url, json=json, headers=headers)
keywords = list(self.clean_response(response.json()))
return keywords
except Exception:
logger.error("Github tokens are not present.")
def find_keywords(self, df_, list_):
"""Find the keywords for given list of list of raw data."""
package_lst = self.utility.flatten_list(list_)
out_lst = list()
for i in package_lst:
pkg_kwd_lst = list()
pkg_kwd_lst = self.utility.make_list_from_series(
self.from_existing_df(df_, i))
if not pkg_kwd_lst or type(pkg_kwd_lst[2]) != list:
logger.info("Finding from the NPM repository.")
pkg_kwd_dict = self.from_npm_registry(i)
pkg_kwd_lst = list(pkg_kwd_dict.values())
if len(pkg_kwd_lst[2]) == 0:
logger.info("Trying to fetch from Github")
api_url = 'https://api.github.com/graphql'
api_token = self.get_data.github_token
pkg_kwd_lst[2] = self.from_github(i, df_, api_url,
api_token)
out_lst.append(pkg_kwd_lst)
return pd.DataFrame(
out_lst,
columns=['name', 'description', 'keywords', 'dependencies'])
def __init__(self, df_=pd.DataFrame(), dict_=dict()):
"""Create an instance for GetKeywords."""
self.df_ = df_
self.dict_ = dict_
self.get_data = GetData()
self.utility = Utility()
class GetData:
"""This class defines the S3 Connections viz fetching and storing data."""
def __init__(self,
aws_access_key_id=None,
aws_secret_access_key=None,
aws_bucket_name='cvae-insights',
local_data_store=False,
deployment_prefix='dev',
model_version='2019-01-03'):
"""Create an instance of GetData."""
self.aws_access_key_id = os.environ.get('AWS_S3_ACCESS_KEY_ID', '')
self.aws_secret_access_key = os.environ.get('AWS_S3_SECRET_ACCESS_KEY',
'')
self.github_token = os.environ.get('GITHUB_TOKEN', '')
self.bucket_name = aws_bucket_name
self.deployment_prefix = deployment_prefix
self.version_name = model_version
if local_data_store:
self.s3_client = LocalDataStore('tests/test_data')
else:
self.s3_object = AmazonS3(
bucket_name=self.bucket_name,
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key)
self.s3_client = self.load_S3()
self.utility = Utility()
def load_S3(self):
"""Establish the connection with S3."""
self.s3_object.connect()
if self.s3_object.is_connected():
logger.info("S3 connection established.")
return self.s3_object
else:
raise Exception
def load_raw_data(self):
"""Load the raw data from S3 bucket."""
NPM_raw_data_path = os.path.join(self.version_name,
"data/manifest.json")
try:
raw_data_dict_ = self.s3_client.read_json_file(NPM_raw_data_path)
logger.info("Size of Raw Manifest file is: {}".format(
len(raw_data_dict_)))
return raw_data_dict_
except Exception:
raise Exception
def load_existing_data(self):
"""Load the node registry dump from S3 bucket."""
NPM_clean_json_data_path = os.path.join(
self.version_name, "data/node-package-details-with-url.json")
try:
logger.info("Path Existed")
existing_data = self.s3_client.read_generic_file(
NPM_clean_json_data_path)
existing_df = self.utility.read_json_file(existing_data)
logger.info("Size of Raw df with url is: {}".format(
len(existing_df)))
return existing_df
except Exception:
raise Exception("S3 connection error")
class GetPreprocessData:
"""This class processes raw data and converts into the input data for models."""
def __init__(self,
aws_access_key_id='',
aws_secret_access_key='',
aws_bucket_name='cvae-insights',
model_version='',
num_train_per_user=5):
"""Create an instance for GetPreprocessData."""
self.obj_ = GetData(aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_bucket_name=aws_bucket_name,
model_version=model_version,
num_train_per_user=num_train_per_user)
self.keyword_obj_ = GetKeywords(self.obj_)
self.preprocess_data_obj = PreprocessData(data_obj=self.obj_)
self.utils = Utility()
self.num_users = num_train_per_user
def preprocess_data(self):
"""Preprocesses the data and save into temporary storage."""
package_tag_map, vocabulary, manifest_user_data, unique_packages = \
self.preprocess_data_obj.update_pkg_tag_map()
package_tag_map = {k: list(v) for k, v in package_tag_map.items()}
self.obj_.save_manifest_file_temporary(manifest_user_data,
'manifest_user_data.dat',
TEMPORARY_DATA_PATH)
package_id_map = self.utils.create_package_map(unique_packages)
id_package_map = dict(
zip(range(len(unique_packages)), list(unique_packages)))
user_train_data, item_train_data, user_test_data, item_test_data = \
self.obj_.train_test_data()
content_matrix = self.utils.create_content_matrix(
package_tag_map, unique_packages, vocabulary)
self.obj_.save_json_file_temporary(package_id_map,
'package_to_index_map.json',
TEMPORARY_PATH)
self.obj_.save_json_file_temporary(id_package_map,
'index_to_package_map.json',
TEMPORARY_PATH)
self.obj_.save_json_file_temporary(package_tag_map,
'package_tag_map.json',
TEMPORARY_PATH)
self.obj_.save_file_temporary(
user_train_data,
"packagedata-train-" + str(self.num_users) + "-users.dat",
TEMPORARY_DATA_PATH)
self.obj_.save_file_temporary(
user_test_data,
"packagedata-test-" + str(self.num_users) + "-users.dat",
TEMPORARY_DATA_PATH)
self.obj_.save_file_temporary(
item_train_data,
"packagedata-train-" + str(self.num_users) + "-items.dat",
TEMPORARY_DATA_PATH)
self.obj_.save_file_temporary(
item_test_data,
"packagedata-test-" + str(self.num_users) + "-items.dat",
TEMPORARY_DATA_PATH)
self.obj_.save_numpy_matrix_temporary(content_matrix,
'content_matrix.npz',
TEMPORARY_DATA_PATH)
logger.info("All items are saved successfully in temporary location.")
