"weights_to_dweights": -1})
print("TEST")
print("Starting Evaluations...")
# to indicate if plotting for lenght or for pop
results_run, results_run_string, target_recommendations = evaluator.evaluateRecommender(recommender,
plot_stats=True,
onPop=onPop)
print("Algorithm: {}, results: \n{}".format([rec.__class__ for rec in recommender.recommender_list],
results_run_string))
logFile.write("Algorithm: {}, results: \n{}\n".format(recommender.__class__, results_run_string))
logFile.flush()
if not evaluate_algorithm:
target_playlist = dataReader.get_target_playlist()
md.assign_recomendations_to_correct_playlist(target_playlist, target_recommendations)
md.make_CSV_file(target_playlist, filename)
print('File {} created!'.format(filename))
except Exception as e:
traceback.print_exc()
logFile.write("Algorithm: {} - Exception: {}\n".format(recommender_class, str(e)))
logFile.flush()
if not evaluate_algorithm:
delete_previous_intermediate_computations()
def run():
evaluate_algorithm = False
delete_old_computations = False
slim_after_hybrid = False
# delete_previous_intermediate_computations()
# if not evaluate_algorithm:
# delete_previous_intermediate_computations()
# else:
# print("ATTENTION: old intermediate computations kept, pay attention if running with all_train")
# delete_previous_intermediate_computations()
filename = "hybrid_ICB_ICF_UCF_SLIM_ELASTIC_local_08052.csv"
dataReader = RS_Data_Loader(all_train=not evaluate_algorithm)
URM_train = dataReader.get_URM_train()
URM_PageRank_train = dataReader.get_page_rank_URM()
URM_validation = dataReader.get_URM_validation()
URM_test = dataReader.get_URM_test()
ICM = dataReader.get_ICM()
UCM_tfidf = dataReader.get_tfidf_artists()
# _ = dataReader.get_tfidf_album()
recommender_list1 = [
# Random,
# TopPop,
ItemKNNCBFRecommender,
# UserKNNCBRecommender,
ItemKNNCFRecommender,
UserKNNCFRecommender,
# P3alphaRecommender,
# RP3betaRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
SLIM_BPR_Cython,
# ItemKNNCFRecommenderFAKESLIM,
# PureSVDRecommender,
SLIMElasticNetRecommender
]
# ITEM CB, ITEM CF, USER CF, RP3BETA, PURE SVD
recommender_list2 = [
# Random,
# TopPop,
ItemKNNCBFRecommender,
# UserKNNCBRecommender,
# ItemKNNCFPageRankRecommender,
ItemKNNCFRecommender,
UserKNNCFRecommender,
# P3alphaRecommender,
# RP3betaRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
SLIM_BPR_Cython,
SLIMElasticNetRecommender
# PureSVDRecommender
]
from Base.Evaluation.Evaluator import SequentialEvaluator
evaluator = SequentialEvaluator(URM_test, URM_train, exclude_seen=True)
output_root_path = "result_experiments/"
# If directory does not exist, create
if not os.path.exists(output_root_path):
os.makedirs(output_root_path)
logFile = open(output_root_path + "result_all_algorithms.txt", "a")
try:
recommender_class = HybridRecommender
print("Algorithm: {}".format(recommender_class))
'''
Our optimal run
'''
recommender_list = recommender_list1 # + recommender_list2 # + recommender_list3
onPop = False
# On pop it used to choose if have dynamic weights for
recommender = recommender_class(URM_train, ICM, recommender_list, URM_PageRank_train=URM_PageRank_train,
dynamic=False, UCM_train=UCM_tfidf,
URM_validation=URM_validation, onPop=onPop)
lambda_i = 0.1
lambda_j = 0.05
old_similrity_matrix = None
num_factors = 395
l1_ratio = 1e-06
# Variabili secondo intervallo
alphaRP3_2 = 0.9223827655310622
betaRP3_2 = 0.2213306613226453
num_factors_2 = 391
recommender.fit(**
{
"topK": [10, 33, 160, 761, 490],
"shrink": [8, 26, 2, -1, -1],
"pop": [280],
"weights": [0.33804686720093335, 1.3092081994688194, 0.642288869881126, 0.18883962446529368,
1.9317211019160747],
"final_weights": [1, 1],
"force_compute_sim": False, # not evaluate_algorithm,
"feature_weighting_index": 0,
"epochs": 150,
'lambda_i': [0.0], 'lambda_j': [1.0153577332223556e-08], 'SLIM_lr': [0.1],
'alphaP3': [0.4121720883248633],
'alphaRP3': [0.8582865731462926],
'betaRP': [0.2814208416833668],
'l1_ratio': 3.020408163265306e-06,
'alpha': 0.0014681984611695231,
'tfidf': [True],
"weights_to_dweights": -1,
"filter_top_pop_len": 0})
print("TEST")
print("Starting Evaluations...")
# to indicate if plotting for lenght or for pop
results_run, results_run_string, target_recommendations = evaluator.evaluateRecommender(recommender,
plot_stats=False,
onPop=onPop)
print("Algorithm: {}, results: \n{}".format([rec.RECOMMENDER_NAME for rec in recommender.recommender_list],
results_run_string))
logFile.write("Algorithm: {}, results: \n{} time: {}".format(
[rec.RECOMMENDER_NAME for rec in recommender.recommender_list], results_run_string, time.time()))
logFile.flush()
if not evaluate_algorithm:
target_playlist = dataReader.get_target_playlist()
md.assign_recomendations_to_correct_playlist(target_playlist, target_recommendations)
md.make_CSV_file(target_playlist, filename)
print('File {} created!'.format(filename))
except Exception as e:
traceback.print_exc()
logFile.write("Algorithm: {} - Exception: {}\n".format(recommender_class, str(e)))
logFile.flush()
def run():
evaluate_algorithm = True
delete_old_computations = False
slim_after_hybrid = False
# delete_previous_intermediate_computations()
# if not evaluate_algorithm:
# delete_previous_intermediate_computations()
# else:
# print("ATTENTION: old intermediate computations kept, pay attention if running with all_train")
# delete_previous_intermediate_computations()
filename = "hybrid_ICB_ICF_UCF_IALS_SLIM_ELASTIC_local_081962.csv"
dataReader = RS_Data_Loader(all_train=not evaluate_algorithm)
URM_train = dataReader.get_URM_train()
URM_PageRank_train = dataReader.get_page_rank_URM()
URM_validation = dataReader.get_URM_validation()
URM_test = dataReader.get_URM_test()
ICM = dataReader.get_ICM()
UCM_tfidf = dataReader.get_tfidf_artists()
# _ = dataReader.get_tfidf_album()
recommender_list1 = [
# Random,
# TopPop,
ItemKNNCBFRecommender,
# UserKNNCBRecommender,
ItemKNNCFRecommender,
UserKNNCFRecommender,
# P3alphaRecommender,
# RP3betaRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
IALS_numpy,
SLIM_BPR_Cython,
# ItemKNNCFRecommenderFAKESLIM,
# PureSVDRecommender,
SLIMElasticNetRecommender
]
from Base.Evaluation.Evaluator import SequentialEvaluator
evaluator = SequentialEvaluator(URM_test, URM_train, exclude_seen=True)
output_root_path = "result_experiments/"
# If directory does not exist, create
if not os.path.exists(output_root_path):
os.makedirs(output_root_path)
logFile = open(output_root_path + "result_all_algorithms.txt", "a")
try:
recommender_class = HybridRecommender
print("Algorithm: {}".format(recommender_class))
'''
Our optimal run
'''
recommender_list = recommender_list1 # + recommender_list2 # + recommender_list3
onPop = False
# On pop it used to choose if have dynamic weights for
recommender = recommender_class(URM_train,
ICM,
recommender_list,
URM_PageRank_train=URM_PageRank_train,
dynamic=False,
UCM_train=UCM_tfidf,
URM_validation=URM_validation,
onPop=onPop)
recommender.fit(
**{
"topK": [10, 181, 82, -1, 761, 490],
"shrink": [8, 0, 3, -1, -1, -1],
"pop": [280],
"weights": [
0.47412263345597117, 1.3864620551711606,
0.6224999770898935, 1.5498327677561246, 0.1993692779443738,
2.113324096784624
],
"final_weights": [1, 1],
"force_compute_sim":
False, # not evaluate_algorithm,
"feature_weighting_index":
0,
"epochs":
150,
'lambda_i': [0.0],
'lambda_j': [1.0153577332223556e-08],
'SLIM_lr': [0.1],
'alphaP3': [0.4121720883248633],
'alphaRP3': [0.8582865731462926],
'betaRP': [0.2814208416833668],
'l1_ratio':
3.020408163265306e-06,
'alpha':
0.0014681984611695231,
'tfidf': [True],
"weights_to_dweights":
-1,
"IALS_num_factors":
290,
"IALS_reg":
0.001,
"IALS_iters":
6,
"IALS_scaling":
'log',
"IALS_alpha":
40,
"filter_top_pop_len":
0
})
print("TEST")
print("Starting Evaluations...")
# to indicate if plotting for lenght or for pop
results_run, results_run_string, target_recommendations = evaluator.evaluateRecommender(
recommender, plot_stats=False, onPop=onPop)
print("Algorithm: {}, results: \n{}".format(
[rec.RECOMMENDER_NAME for rec in recommender.recommender_list],
results_run_string))
logFile.write("Algorithm: {}, results: \n{} time: {}".format(
[rec.RECOMMENDER_NAME for rec in recommender.recommender_list],
results_run_string, time.time()))
logFile.flush()
if not evaluate_algorithm:
target_playlist = dataReader.get_target_playlist()
md.assign_recomendations_to_correct_playlist(
target_playlist, target_recommendations)
md.make_CSV_file(target_playlist, filename)
print('File {} created!'.format(filename))
except Exception as e:
traceback.print_exc()
logFile.write("Algorithm: {} - Exception: {}\n".format(
recommender_class, str(e)))
logFile.flush()
def run():
evaluate_algorithm = True
delete_old_computations = False
slim_after_hybrid = False
# delete_previous_intermediate_computations()
# if not evaluate_algorithm:
# delete_previous_intermediate_computations()
# else:
# print("ATTENTION: old intermediate computations kept, pay attention if running with all_train")
# delete_previous_intermediate_computations()
filename = "hybrid_ICB_ICF_UCF_SLIM_ELASTIC_local_08052.csv"
dataReader = RS_Data_Loader(all_train=not evaluate_algorithm)
URM_train = dataReader.get_URM_train()
URM_PageRank_train = dataReader.get_page_rank_URM()
URM_validation = dataReader.get_URM_validation()
URM_test = dataReader.get_URM_test()
ICM = dataReader.get_ICM()
UCM_tfidf = dataReader.get_tfidf_artists()
# _ = dataReader.get_tfidf_album()
recommender_list1 = [
# Random,
# TopPop,
ItemKNNCBFRecommender,
# UserKNNCBRecommender,
ItemKNNCFRecommender,
UserKNNCFRecommender,
# P3alphaRecommender,
# RP3betaRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
SLIM_BPR_Cython,
# ItemKNNCFRecommenderFAKESLIM,
# PureSVDRecommender,
SLIMElasticNetRecommender
]
# ITEM CB, ITEM CF, USER CF, RP3BETA, PURE SVD
recommender_list2 = [
# Random,
# TopPop,
ItemKNNCBFRecommender,
# UserKNNCBRecommender,
ItemKNNCFRecommender,
UserKNNCFRecommender,
# P3alphaRecommender,
# RP3betaRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
SLIM_BPR_Cython,
# ItemKNNCFRecommenderFAKESLIM,
# PureSVDRecommender,
SLIMElasticNetRecommender
]
from Base.Evaluation.Evaluator import SequentialEvaluator
evaluator = SequentialEvaluator(URM_test, URM_train, exclude_seen=True)
output_root_path = "result_experiments/"
# If directory does not exist, create
if not os.path.exists(output_root_path):
os.makedirs(output_root_path)
logFile = open(output_root_path + "result_all_algorithms.txt", "a")
try:
recommender_class = HybridRecommender
print("Algorithm: {}".format(recommender_class))
'''
Our optimal run
'''
recommender_list = recommender_list1 + recommender_list2 # + recommender_list3
d_weights = [
[0.5469789514168496, 1.5598358421050373, 1.1505851198615593, 0.2540023047558251, 0.9403502151872645] + [
0] * len(recommender_list2),
[0] * len(recommender_list1) + [0.5205017325111618, 1.6831295912149837, 1.6560707664775454,
0.3144197724407203, 1.9912784665282535]
]
onPop = False
# On pop it used to choose if have dynamic weights for
recommender = recommender_class(URM_train, ICM, recommender_list, URM_PageRank_train=URM_PageRank_train,
dynamic=True, UCM_train=UCM_tfidf, d_weights=d_weights,
URM_validation=URM_validation, onPop=onPop)
recommender.fit(**
{
"topK": [10, 33, 160, 761, 490] + [10, 33, 160, 761, 490],
"shrink": [8, 26, 2, -1, -1] + [8, 26, 2, -1, -1],
"pop": [30],
"weights": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"final_weights": [1, 1],
"force_compute_sim": False, # not evaluate_algorithm,
"feature_weighting_index": [0, 0],
"epochs": 150,
'lambda_i': [0.0, 0.0], 'lambda_j': [1.0153577332223556e-08, 1.0153577332223556e-08],
'SLIM_lr': [0.1, 0.1],
'alphaP3': [0.4121720883248633],
'alphaRP3': [0.8582865731462926],
'betaRP': [0.2814208416833668],
'l1_ratio': [3.020408163265306e-06, 3.020408163265306e-06],
'alpha': [0.0014681984611695231, 0.0014681984611695231],
'tfidf': [True, True],
"weights_to_dweights": -1,
"filter_top_pop_len": 0})
print("TEST")
print("Starting Evaluations...")
# to indicate if plotting for lenght or for pop
results_run, results_run_string, target_recommendations = evaluator.evaluateRecommender(recommender,
plot_stats=True,
onPop=onPop)
print("Algorithm: {}, results: \n{}".format([rec.RECOMMENDER_NAME for rec in recommender.recommender_list],
results_run_string))
logFile.write("Algorithm: {}, results: \n{} time: {} \n".format(
[rec.RECOMMENDER_NAME for rec in recommender.recommender_list], results_run_string, time.time()))
logFile.flush()
if not evaluate_algorithm:
target_playlist = dataReader.get_target_playlist()
md.assign_recomendations_to_correct_playlist(target_playlist, target_recommendations)
md.make_CSV_file(target_playlist, filename)
print('File {} created!'.format(filename))
except Exception as e:
traceback.print_exc()
logFile.write("Algorithm: {} - Exception: {}\n".format(recommender_class, str(e)))
logFile.flush()
