def main():
# Data loading
root_data_path = os.path.join(get_project_root_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,
allow_cold_users=ALLOW_COLD_USERS,
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_new(data_reader)
# Ignoring users
ignore_users = get_ignore_users(
URM_train,
data_reader.get_original_user_id_to_index_mapper(),
lower_threshold=LOWER_THRESHOLD,
upper_threshold=UPPER_THRESHOLD,
ignore_non_target_users=IGNORE_NON_TARGET_USERS)
evaluator = EvaluatorHoldout(URM_test,
cutoff_list=[CUTOFF],
ignore_users=ignore_users)
# Model evaluation
model = get_model(URM_train, ICM_all, UCM_all)
print(evaluator.evaluateRecommender(model))
def read_split_load_data(k_out, allow_cold_users, seed):
root_data_path = os.path.join(get_project_root_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,
allow_cold_users=allow_cold_users,
force_new_split=True,
seed=seed)
data_reader.load_data()
return data_reader
from course_lib.Base.Evaluation.Evaluator import EvaluatorHoldout
from src.data_management.New_DataSplitter_leave_k_out import New_DataSplitter_leave_k_out
from src.data_management.RecSys2019Reader import RecSys2019Reader
from src.data_management.dataframe_preprocesser import get_preprocessed_dataframe
from src.tuning.holdout_validation.run_parameter_search_advanced_top_pop import run_parameter_search_advanced_top_pop
from src.utils.general_utility_functions import get_split_seed
if __name__ == '__main__':
# 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()
mapper = data_reader.get_original_user_id_to_index_mapper()
df = get_preprocessed_dataframe("../../data/", keep_warm_only=True)
# Setting evaluator
# warm_users_mask = np.ediff1d(URM_train.tocsr().indptr) > 0
# warm_users = np.arange(URM_train.shape[0])[warm_users_mask]
# ignore_users = warm_users
cutoff_list = [10]
evaluator = EvaluatorHoldout(URM_test, cutoff_list=cutoff_list)
# HP tuning
print("Start tuning...")
version_path = "../../report/hp_tuning/advanced_top_pop/"
now = datetime.now().strftime('%b%d_%H-%M-%S')
import numpy as np
import xlearn as xl
from src.data_management.New_DataSplitter_leave_k_out import New_DataSplitter_leave_k_out
from src.data_management.RecSys2019Reader import RecSys2019Reader
from src.data_management.data_preprocessing_fm import format_URM_positive_non_compressed, \
uniform_sampling_strategy, format_URM_positive_user_compressed, \
format_URM_negative_sampling_user_compressed, mix_URM, add_ICM_info
from src.utils.general_utility_functions import get_split_seed, get_project_root_path
if __name__ == '__main__':
dataset = RecSys2019Reader("../../data/")
dataset = New_DataSplitter_leave_k_out(dataset, k_out_value=3, use_validation_set=False, force_new_split=True,
seed=get_split_seed())
dataset.load_data()
URM_train, URM_test = dataset.get_holdout_split()
ICM_all = dataset.get_ICM_from_name("ICM_all")
URM_positive_FM_matrix = format_URM_positive_user_compressed(URM_train)
URM_negative_FM_matrix = format_URM_negative_sampling_user_compressed(URM_train, negative_rate=1,
sampling_function=uniform_sampling_strategy,
check_replacement=True)
URM_FM_matrix = mix_URM(URM_positive_FM_matrix, URM_negative_FM_matrix)[:, :-1]
URM_FM_matrix = add_ICM_info(URM_FM_matrix, ICM_all, URM_train.shape[0])
root_path = get_project_root_path()
fm_data_path = os.path.join(root_path, "resources", "fm_data")
# Prepare train sparse matrix and labels for dumping to file
FM_sps_matrix = URM_FM_matrix.copy()