def __init__(self):
self.HYP = {}
self.report_counter = 60
self.writer = Writer()
# Some parameters
self.hyperparams = dict()
self.hyperparams_names = list()
self.hyperparams_values = list()
self.hyperparams_single_value = dict()
# Extractor for matricies
extractor = Extractor()
urm = extractor.get_urm_all()
self.icm = extractor.get_icm_all()
# Splitting into post-validation & testing in case of parameter tuning
matrices = loo.split_train_leave_k_out_user_wise(urm, 1, False, True)
self.urm_post_validation = matrices[0]
self.urm_test = matrices[1]
# Splitting the post-validation matrix in train & validation
# (Problem of merging train and validation again at the end => loo twice)
matrices_for_validation = loo.split_train_leave_k_out_user_wise(
self.urm_post_validation, 1, False, True)
self.urm_train = matrices_for_validation[0]
self.urm_validation = matrices_for_validation[1]
def create_validation_test_files(self, write_userf, write_itemf):
import Utils.Split.split_train_validation_leave_k_out as loo
urm = self.urm
# Splitting into post-validation & testing in case of parameter tuning
matrices = loo.split_train_leave_k_out_user_wise(urm, 1, False, True)
urm_post_validation = matrices[0]
self.my_path = self.extractor.DATA_FILE_PATH + "xL_data/post_validation.txt"
self.urm = urm_post_validation
self.create_general_file(write_userf, write_itemf)
urm_test = matrices[1]
self.my_path = self.extractor.DATA_FILE_PATH + "xL_data/test.txt"
self.urm = urm_test
self.create_general_file(write_userf, write_itemf)
# Splitting the post-validation matrix in train & validation
# (Problem of merging train and validation again at the end => loo twice)
matrices_for_validation = loo.split_train_leave_k_out_user_wise(
urm_post_validation, 1, False, True)
urm_train = matrices_for_validation[0]
self.my_path = self.extractor.DATA_FILE_PATH + "xL_data/train.txt"
self.urm = urm_train
self.create_general_file(write_userf, write_itemf)
urm_validation = matrices_for_validation[1]
self.my_path = self.extractor.DATA_FILE_PATH + "xL_data/validation.txt"
self.urm = urm_validation
self.create_general_file(write_userf, write_itemf)
def run(self, is_test, is_SSLIM):
"""
From here we start each algorithm.
:param is_test: specifies if we want to write a report or a submission
"""
self.is_test = is_test
self.is_SSLIM = is_SSLIM
if self.is_test:
extractor = Extractor()
urm = extractor.get_urm_all()
self.icm = extractor.get_icm_all()
# Splitting into post-validation & testing in case of parameter tuning
matrices = loo.split_train_leave_k_out_user_wise(
urm, 1, False, True)
self.urm_post_validation = matrices[0]
self.urm_test = matrices[1]
# Splitting the post-validation matrix in train & validation
# (Problem of merging train and validation again at the end => loo twice)
matrices_for_validation = loo.split_train_leave_k_out_user_wise(
self.urm_post_validation, 1, False, True)
self.urm_train = matrices_for_validation[0]
self.urm_validation = matrices_for_validation[1]
self.urm_train = extractor.preprocess_csr_matrix(self.urm_train)
self.write_report()
if self.is_SSLIM:
# for topK in [50, 100, 200]:
# for epochs in [10, 20, 50, 100, 200, 300]:
self.sslim_pars = WeightConstants.SLIM_BPR_ICM
slim_bpr = SLIM_BPR_Cython(self.icm.copy())
slim_bpr.fit(**self.sslim_pars)
self.icm = slim_bpr.recs.copy().tocsr()
self.evaluate()
else:
self.evaluate()
else:
extractor = Extractor()
users = extractor.get_target_users_of_recs()
self.urm_train = extractor.get_urm_all()
self.icm = extractor.get_icm_all()
self.write_submission(users)
def run(self, is_test):
self.is_test = is_test
if self.is_test:
extractor = Extractor()
builder = Builder()
urm = extractor.get_urm_all()
self.icm = extractor.get_icm_all()
# Splitting into post-validation & testing in case of parameter tuning
matrices = loo.split_train_leave_k_out_user_wise(
urm, 1, False, True)
self.urm_post_validation = matrices[0]
self.urm_test = matrices[1]
# Splitting the post-validation matrix in train & validation
# (Problem of merging train and validation again at the end => loo twice)
matrices_for_validation = loo.split_train_leave_k_out_user_wise(
self.urm_post_validation, 1, False, True)
self.urm_train = matrices_for_validation[0]
self.urm_validation = matrices_for_validation[1]
# Building the urm_per_feature lists
if self.users_per_region:
self.urm_per_region_list = builder.build_per_region_urm_train(
self.urm_train)
if self.users_per_age:
self.urm_per_age_list = builder.build_per_age_urm_train(
self.urm_train)
self.write_report()
self.evaluate()
else:
extractor = Extractor()
builder = Builder()
users = extractor.get_target_users_of_recs()
self.urm_train = extractor.get_urm_all()
self.icm = extractor.get_icm_all()
# Building the urm_per_feature lists
if self.users_per_region:
self.urm_per_region_list = builder.build_per_region_urm_train(
self.urm_train)
if self.users_per_age:
self.urm_per_age_list = builder.build_per_age_urm_train(
self.urm_train)
self.write_submission(users)
def run(self, is_test):
"""
From here we start each algorithm.
:param is_test: specifies if we want to write a report or a submission
"""
if is_test:
# CREATION OF THE VALIDATIONS FOR EACH PART OF THE TRAIN
vals = []
urms = []
target_profiles = []
for i in range(1, 5):
urm_to_predict = self.extractor.get_single_urm(i)
matrices = loo.split_train_leave_k_out_user_wise(
urm_to_predict, 1, False, True)
target_users_profile = matrices[0]
target_profiles.append(target_users_profile)
val = matrices[1]
vals.append(val)
urm = self.extractor.get_others_urm_vstack(i)
urms.append(urm)
if self.icfknn:
self.p_icfknn = ParametersTuning.ICFKNN_BEST
if self.cbfknn:
self.p_cbfknn = ParametersTuning.CBFKNN_BEST
if self.rp3b:
self.p_rp3b = ParametersTuning.RP3B_BEST
if self.slim_en:
self.p_slimen = ParametersTuning.SLIM_ELASTIC_NET_BEST
# URM splitted in 4 smaller URMs for cross-validation
for i in range(0, 4):
self.urm_validation = vals[i].copy()
self.urm_train = urms[i].copy()
self.target_users = self.extractor.get_target_users_of_specific_part(
i + 1)
# GETTING THE RECOMMENDATIONS FOR THE TRAIN DATAFRAME
user_ids, item_ids = self.evaluate(i + 1, target_profiles[i])
self.df_user_id_col.extend(user_ids)
self.df_item_id_col.extend(item_ids)
# print(self.df_user_id_col[0:100])
# print(self.df_item_id_col[0:100])
# print(len(self.df_user_id_col))
# print(len(self.df_item_id_col))
self.score_ranking()
def run(self, is_test):
"""
From here we start each algorithm.
:param is_test: specifies if we want to write a report or a submission
"""
self.is_test = is_test
if self.is_test:
extractor = Extractor
urm = extractor.get_urm_all(extractor)
self.icm = extractor.get_icm_all(extractor)
# Splitting into post-validation & testing in case of parameter tuning
matrices = loo.split_train_leave_k_out_user_wise(
urm, 1, False, True)
self.urm_post_validation = matrices[0]
self.urm_test = matrices[1]
# Splitting the post-validation matrix in train & validation
# (Problem of merging train and validation again at the end => loo twice)
matrices_for_validation = loo.split_train_leave_k_out_user_wise(
self.urm_post_validation, 1, False, True)
self.urm_train = matrices_for_validation[0]
self.urm_validation = matrices_for_validation[1]
self.write_report()
self.evaluate()
else:
extractor = Extractor
users = extractor.get_target_users_of_recs(extractor)
self.urm_train = extractor.get_urm_all(extractor)
self.icm = extractor.get_icm_all(extractor)
self.write_submission(users)
def run(self, is_test):
"""
From here we start each algorithm.
:param is_test: specifies if we want to write a report or a submission
"""
self.is_test = is_test
self.icm = self.extractor.get_icm_all()
if self.is_test:
# CREATION OF THE VALIDATIONS FOR EACH PART OF THE TRAIN
vals = []
urms = []
target_profiles = []
for i in range(1, 5):
urm_to_predict = self.extractor.get_single_urm(i)
matrices = loo.split_train_leave_k_out_user_wise(
urm_to_predict, 1, False, True)
target_users_profile = matrices[0]
target_profiles.append(target_users_profile)
val = matrices[1]
vals.append(val)
urm = self.extractor.get_others_urm_vstack(i)
urms.append(urm)
if self.icfknn:
self.p_icfknn = ParametersTuning.ICFKNN_BEST
if self.cbfknn:
self.p_cbfknn = ParametersTuning.CBFKNN_BEST
if self.rp3b:
self.p_rp3b = ParametersTuning.RP3B_BEST
if self.slim_en:
self.p_slimen = ParametersTuning.SLIM_ELASTIC_NET_BEST
# TUNING WITH THE DIFFERENT PARAMS
for params in ParametersTuning.UCFKNN:
if self.ucfknn:
self.p_ucfknn = params
self.write_report()
# URM splitted in 4 smaller URMs for cross-validation
for i in range(0, 4):
self.urm_validation = vals[i].copy()
self.urm_train = urms[i].copy()
self.target_users = self.extractor.get_target_users_of_specific_part(
i + 1)
self.evaluate(i + 1, target_profiles[i])
self.output_average_MAP()
self.output_best_params()
else:
self.p_cbfknn = ParametersTuning.CBFKNN_BEST
self.p_icfknn = ParametersTuning.ICFKNN_BEST
self.p_slimen = ParametersTuning.SLIM_ELASTIC_NET_BEST
self.p_rp3b = ParametersTuning.RP3B_BEST
users = self.extractor.get_target_users_of_recs()
self.urm_train = self.extractor.get_urm_all()
self.writer.write_header(self.writer,
sub_counter=submission_counter)
self.write_submission(users)
