def main():
args = get_arguments()
# Data loading
root_data_path = args.reader_path
data_reader = RecSys2019Reader(root_data_path)
data_reader = New_DataSplitter_leave_k_out(data_reader, k_out_value=K_OUT, allow_cold_users=ALLOW_COLD_USERS,
use_validation_set=False, force_new_split=True, seed=args.seed)
data_reader.load_data()
URM_train, URM_test = data_reader.get_holdout_split()
# Remove interactions to users that has len == 1 to URM_train
len_1_users_mask = np.ediff1d(URM_train.tocsr().indptr) == 1
len_1_users = np.arange(URM_train.shape[0])[len_1_users_mask]
URM_train = URM_train.tolil()
URM_train[len_1_users, :] = 0
URM_train = URM_train.tocsr()
# Remove interactions to users that has len == 1 to URM_test
len_1_users_mask = np.ediff1d(URM_test.tocsr().indptr) == 1
len_1_users = np.arange(URM_test.shape[0])[len_1_users_mask]
URM_test = URM_test.tolil()
URM_test[len_1_users, :] = 0
URM_test = URM_test.tocsr()
UCM_all = get_UCM_train_cold(data_reader)
ignore_users = get_ignore_users(URM_train, data_reader.get_original_user_id_to_index_mapper(),
lower_threshold=args.lower_threshold, upper_threshold=args.upper_threshold,
ignore_non_target_users=args.exclude_non_target)
ignore_users = np.concatenate([ignore_users, len_1_users])
# Setting evaluator
cutoff_list = [10]
evaluator = EvaluatorHoldout(URM_test, cutoff_list=cutoff_list, ignore_users=ignore_users)
# HP tuning
print("Start tuning...")
version_path = "../../report/hp_tuning/{}/".format(args.recommender_name)
now = datetime.now().strftime('%b%d_%H-%M-%S')
now = now + "_k_out_value_{}/".format(K_OUT)
version_path = version_path + "/" + now
run_parameter_search_user_demographic(URM_train=URM_train, UCM_object=UCM_all, UCM_name="UCM_all",
recommender_class=RECOMMENDER_CLASS_DICT[args.recommender_name],
evaluator_validation=evaluator,
metric_to_optimize="MAP",
output_folder_path=version_path,
parallelizeKNN=True,
n_cases=int(args.n_cases),
n_random_starts=int(args.n_random_starts))
print("...tuning ended")
def main():
args = get_arguments()
# Data loading
data_reader = RecSys2019Reader(args.reader_path)
data_reader = New_DataSplitter_leave_k_out(data_reader, k_out_value=3, use_validation_set=False,
force_new_split=True, seed=args.seed)
data_reader.load_data()
URM_train, URM_test = data_reader.get_holdout_split()
if args.recommender_name == "sslim_bpr":
ICM_all = get_ICM_train(data_reader)
URM_train = sps.vstack([URM_train, ICM_all.T], format="csr")
if args.recommender_name == "rp3beta_side":
ICM_all = get_ICM_train(data_reader)
URM_train = sps.vstack([URM_train, ICM_all.T], format="csr")
URM_train = TF_IDF(URM_train).tocsr()
if args.recommender_name == "pure_svd":
URM_train = TF_IDF(URM_train).tocsr()
if args.recommender_name == "pure_svd_side":
ICM_all = get_ICM_train(data_reader)
URM_train = sps.vstack([URM_train, ICM_all.T], format="csr")
# Setting evaluator
exclude_cold_users = args.exclude_users
h = int(args.focus_on_high)
fol = int(args.focus_on_low)
if h != 0:
print("Excluding users with less than {} interactions".format(h))
ignore_users_mask = np.ediff1d(URM_train.tocsr().indptr) < h
ignore_users = np.arange(URM_train.shape[0])[ignore_users_mask]
elif fol != 0:
print("Excluding users with more than {} interactions".format(fol))
warm_users_mask = np.ediff1d(URM_train.tocsr().indptr) > fol
ignore_users = np.arange(URM_train.shape[0])[warm_users_mask]
if exclude_cold_users:
cold_user_mask = np.ediff1d(URM_train.tocsr().indptr) == 0
cold_users = np.arange(URM_train.shape[0])[cold_user_mask]
ignore_users = np.unique(np.concatenate((cold_users, ignore_users)))
elif exclude_cold_users:
print("Excluding cold users...")
cold_user_mask = np.ediff1d(URM_train.tocsr().indptr) == 0
ignore_users = np.arange(URM_train.shape[0])[cold_user_mask]
else:
ignore_users = None
cutoff_list = [10]
evaluator = EvaluatorHoldout(URM_test, cutoff_list=cutoff_list, ignore_users=ignore_users)
# HP tuning
print("Start tuning...")
version_path = "../../report/hp_tuning/{}/".format(args.recommender_name)
now = datetime.now().strftime('%b%d_%H-%M-%S')
now = now + "_k_out_value_3/"
version_path = version_path + "/" + now
runParameterSearch_Collaborative(URM_train=URM_train,
recommender_class=RECOMMENDER_CLASS_DICT[args.recommender_name],
evaluator_validation=evaluator,
metric_to_optimize="MAP",
output_folder_path=version_path,
n_cases=int(args.n_cases),
n_random_starts=int(args.n_random_starts))
print("...tuning ended")
from src.data_management.New_DataSplitter_leave_k_out import *
from src.data_management.RecSys2019Reader import RecSys2019Reader
from src.data_management.RecSys2019Reader_utils import merge_UCM, get_ICM_numerical
from src.data_management.data_getter import get_warmer_UCM
from src.model import best_models
from src.utils.general_utility_functions import get_split_seed
if __name__ == '__main__':
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
# Data loading
data_reader = RecSys2019Reader("../data/")
data_reader = New_DataSplitter_leave_k_out(data_reader,
k_out_value=3,
use_validation_set=False,
force_new_split=True,
seed=get_split_seed())
data_reader.load_data()
URM_train, URM_test = data_reader.get_holdout_split()
# Build ICMs
ICM_numerical, _ = get_ICM_numerical(data_reader.dataReader_object)
ICM = data_reader.get_ICM_from_name("ICM_sub_class")
ICM_all, _ = merge_ICM(ICM, URM_train.transpose(), {}, {})
# Build UCMs
URM_all = data_reader.dataReader_object.get_URM_all()
UCM_age = data_reader.dataReader_object.get_UCM_from_name("UCM_age")
UCM_region = data_reader.dataReader_object.get_UCM_from_name("UCM_region")
UCM_age_region, _ = merge_UCM(UCM_age, UCM_region, {}, {})
from src.model import new_best_models
from src.model.FactorizationMachine.FieldAwareFMRecommender import FieldAwareFMRecommender
from src.utils.general_utility_functions import get_split_seed, get_project_root_path
if __name__ == '__main__':
set_env_variables()
# Data loading
root_data_path = "../data/"
data_reader = RecSys2019Reader(root_data_path)
data_reader = DataPreprocessingRemoveColdUsersItems(data_reader,
threshold_users=25,
threshold_items=20)
data_reader = New_DataSplitter_leave_k_out(data_reader,
k_out_value=1,
use_validation_set=False,
force_new_split=True,
seed=get_split_seed())
data_reader.load_data()
URM_train, URM_test = data_reader.get_holdout_split()
# Build ICMs
ICM_all, item_feature_fields = get_ICM_with_fields(data_reader)
# Build UCMs: do not change the order of ICMs and UCMs
UCM_all, user_feature_fields = get_UCM_with_fields(data_reader)
cold_users_mask = np.ediff1d(URM_train.tocsr().indptr) == 0
cold_users = np.arange(URM_train.shape[0])[cold_users_mask]
cutoff_list = [10]
all_models[
'ItemCBF_CF'] = best_models_lower_threshold_23.ItemCBF_CF.get_model(
URM_train=URM_train, ICM_train=ICM_all)
all_models['ItemCBF_FW'] = new_best_models.ItemCBF_all_FW.get_model(
URM_train=URM_train, ICM_train=ICM_all)
return all_models
if __name__ == '__main__':
# Data loading
root_data_path = "../../data/"
data_reader = RecSys2019Reader(root_data_path)
data_reader = New_DataSplitter_leave_k_out(data_reader,
k_out_value=K_OUT,
use_validation_set=False,
force_new_split=True,
seed=get_split_seed())
data_reader.load_data()
URM_train, URM_test = data_reader.get_holdout_split()
# Build ICMs
ICM_all = get_ICM_train(data_reader)
# Build UCMs
UCM_all = get_UCM_train(data_reader)
model = HybridWeightedAverageRecommender(URM_train, normalize=NORMALIZE)
all_models = _get_all_models(URM_train=URM_train,
UCM_all=UCM_all,
from src.data_management.New_DataSplitter_leave_k_out import *
from src.data_management.RecSys2019Reader import RecSys2019Reader
from src.data_management.data_reader import get_UCM_train, get_ICM_train_new
from src.data_management.dataframe_preprocessing import get_preprocessed_dataframe
from src.feature.demographics_content import get_user_demographic
from src.model import new_best_models, k_1_out_best_models
from src.model.KNN.ItemKNNCBFCFRecommender import ItemKNNCBFCFRecommender
from src.plots.recommender_plots import *
from src.utils.general_utility_functions import get_split_seed
if __name__ == '__main__':
root_data_path = "../../data"
k_out = 1
data_reader = RecSys2019Reader(root_data_path)
data_reader = New_DataSplitter_leave_k_out(data_reader, k_out_value=k_out, use_validation_set=False,
force_new_split=True, seed=get_split_seed())
data_reader.load_data()
URM_train, URM_test = data_reader.get_holdout_split()
ICM_all, _ = get_ICM_train_new(data_reader)
UCM_all = get_UCM_train(data_reader)
item_cbf_cf_parm = new_best_models.ItemCBF_CF.get_best_parameters()
item_cbf_cf_parm['topK'] = 5
model = ItemKNNCBFCFRecommender(URM_train=URM_train, ICM_train=ICM_all)
model.fit(**item_cbf_cf_parm)
model.RECOMMENDER_NAME = "ItemCBFCF"
sub4 = k_1_out_best_models.HybridNormWeightedAvgAll.get_model(URM_train, ICM_all, UCM_all)
recommender_list = [model, sub4]