from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Hybrid.HybridGen2Recommender import HybridGen2Recommender
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_BPR_Cython
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_AsySVD_Cython
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_FunkSVD_Cython
from Hybrid.HybridNormRecommender import HybridNormRecommender
from GraphBased.RP3betaRecommender import RP3betaRecommender
from SLIM_ElasticNet.SLIMElasticNetRecommender2 import MultiThreadSLIM_ElasticNet
Data = DataManager()
urm_train, urm_test = split_train_leave_k_out_user_wise(Data.get_urm(),
threshold=10,
temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train,
threshold=10,
temperature='valid')
urm_valid = Data.create_test_warm_users(urm_valid, threshold=3)
evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
recommender = MultiThreadSLIM_ElasticNet
# recommender = MatrixFactorization_FunkSVD_Cython
# recommender = MatrixFactorization_AsySVD_Cython
# recommender = MatrixFactorization_BPR_Cython
# recommender_1 = RP3betaRecommender(urm_train)
# recommender_1.fit(topK=16, alpha=0.03374950051351756, beta=0.24087176329409027, normalize_similarity=True)
#
# result, str_result = evaluator_valid.evaluateRecommender(recommender_1)
# print('Il valid iniziale è : {}'.format(result[10]['MAP']))
#
# result, str_result = evaluator_test.evaluateRecommender(recommender_1)
data_folder = Path(__file__).parent.absolute()
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Hybrid.HybridNorm3Recommender import HybridNorm3Recommender
from MatrixFactorization.ALSRecommender import ALSRecommender
from MatrixFactorization.BPRRecommender import BPRRecommender
import similaripy as sim
data = DataManager()
urm_train = data.get_urm()
urm_train, urm_test = split_train_leave_k_out_user_wise(data.get_urm(),
temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train,
temperature='valid2')
urm_train_warm = data.create_test_warm_users(urm_train, threshold=10)
urm_test_warm = data.create_test_warm_users(urm_test, threshold=10)
evaluator_test_warm = EvaluatorHoldout(urm_test_warm, cutoff_list=[10])
recommender = UserKNNCFRecommender(urm_train)
recommender.fit(shrink=2, topK=600, normalize=True)
recommender_warm = UserKNNCFRecommender(urm_train_warm)
recommender_warm.fit(shrink=2, topK=500, normalize=True)
result, str_result = evaluator_test_warm.evaluateRecommender(recommender)
print('The Map of test of urm normal is : {}'.format(result[10]['MAP']))
result, str_result = evaluator_test_warm.evaluateRecommender(recommender_warm)
print('The Map of test of urm warm is : {}'.format(result[10]['MAP']))
