def fit(self,
topK=50,
shrink=100,
similarity='cosine',
normalize=True,
feature_weighting="none",
**similarity_args):
self.topK = topK
self.shrink = shrink
if feature_weighting not in self.FEATURE_WEIGHTING_VALUES:
raise ValueError(
"Value for 'feature_weighting' not recognized. Acceptable values are {}, provided was '{}'"
.format(self.FEATURE_WEIGHTING_VALUES, feature_weighting))
if feature_weighting == "BM25":
self.UCM_train = self.UCM_train.astype(np.float32)
self.UCM_train = okapi_BM_25(self.UCM_train)
elif feature_weighting == "TF-IDF":
self.UCM_train = self.UCM_train.astype(np.float32)
self.UCM_train = TF_IDF(self.UCM_train)
similarity = Compute_Similarity(self.UCM_train.T,
shrink=shrink,
topK=topK,
normalize=normalize,
similarity=similarity,
**similarity_args)
self.W_sparse = similarity.compute_similarity()
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def fit(self,
show_max_performance=False,
precompute_common_features=False,
learning_rate=0.1,
positive_only_D=True,
initialization_mode_D="random",
normalize_similarity=False,
use_dropout=True,
dropout_perc=0.3,
l1_reg=0.0,
l2_reg=0.0,
epochs=50,
topK=300,
add_zeros_quota=0.0,
log_file=None,
verbose=False,
sgd_mode='adagrad',
gamma=0.9,
beta_1=0.9,
beta_2=0.999,
**earlystopping_kwargs):
if initialization_mode_D not in self.INIT_TYPE_VALUES:
raise ValueError(
"Value for 'initialization_mode_D' not recognized. Acceptable values are {}, provided was '{}'"
.format(self.INIT_TYPE_VALUES, initialization_mode_D))
# Import compiled module
from Recommenders.FeatureWeighting.Cython.CFW_D_Similarity_Cython_SGD import CFW_D_Similarity_Cython_SGD
self.show_max_performance = show_max_performance
self.normalize_similarity = normalize_similarity
self.learning_rate = learning_rate
self.add_zeros_quota = add_zeros_quota
self.l1_reg = l1_reg
self.l2_reg = l2_reg
self.epochs = epochs
self.topK = topK
self.log_file = log_file
self.verbose = verbose
self._generate_train_data()
weights_initialization_D = None
if initialization_mode_D == "random":
weights_initialization_D = np.random.normal(
0.001, 0.1, self.n_features).astype(np.float64)
elif initialization_mode_D == "one":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
elif initialization_mode_D == "zero":
weights_initialization_D = np.zeros(self.n_features,
dtype=np.float64)
elif initialization_mode_D == "BM25":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
self.ICM = self.ICM.astype(np.float32)
self.ICM = okapi_BM_25(self.ICM)
elif initialization_mode_D == "TF-IDF":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
self.ICM = self.ICM.astype(np.float32)
self.ICM = TF_IDF(self.ICM)
else:
raise ValueError(
"CFW_D_Similarity_Cython: 'init_type' not recognized")
# Instantiate fast Cython implementation
self.FW_D_Similarity = CFW_D_Similarity_Cython_SGD(
self.row_list,
self.col_list,
self.data_list,
self.n_features,
self.ICM,
precompute_common_features=precompute_common_features,
positive_only_D=positive_only_D,
weights_initialization_D=weights_initialization_D,
use_dropout=use_dropout,
dropout_perc=dropout_perc,
learning_rate=learning_rate,
l1_reg=l1_reg,
l2_reg=l2_reg,
sgd_mode=sgd_mode,
verbose=self.verbose,
gamma=gamma,
beta_1=beta_1,
beta_2=beta_2)
if self.verbose:
print(self.RECOMMENDER_NAME + ": Initialization completed")
self.D_incremental = self.FW_D_Similarity.get_weights()
self.D_best = self.D_incremental.copy()
self._train_with_early_stopping(epochs,
algorithm_name=self.RECOMMENDER_NAME,
**earlystopping_kwargs)
self.compute_W_sparse(model_to_use="best")
sys.stdout.flush()
def fit(self,
topK=300,
epochs=30,
n_factors=2,
learning_rate=1e-5,
precompute_user_feature_count=False,
initialization_mode_D="random",
positive_only_D=True,
positive_only_V=True,
l2_reg_D=0.01,
l2_reg_V=0.01,
non_negative_weights=False,
verbose=False,
sgd_mode='adam',
gamma=0.9,
beta_1=0.9,
beta_2=0.999,
**earlystopping_kwargs):
if initialization_mode_D not in self.INIT_TYPE_VALUES:
raise ValueError(
"Value for 'initialization_mode_D' not recognized. Acceptable values are {}, provided was '{}'"
.format(self.INIT_TYPE_VALUES, initialization_mode_D))
from FeatureWeighting.Cython.FBSM_Rating_Cython_SGD import FBSM_Rating_Cython_SGD
self.n_factors = n_factors
self.learning_rate = learning_rate
self.l2_reg_D = l2_reg_D
self.l2_reg_V = l2_reg_V
self.topK = topK
self.epochs = epochs
self.verbose = verbose
# For mean_init use Xavier Initialization
if self.n_factors != 0:
std_init = 1 / self.n_features / self.n_factors
else:
std_init = 0
mean_init = 0
weights_initialization_D = None
if initialization_mode_D == "random":
weights_initialization_D = np.random.normal(
0.001, 0.1, self.n_features).astype(np.float64)
elif initialization_mode_D == "one":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
elif initialization_mode_D == "zero":
weights_initialization_D = np.zeros(self.n_features,
dtype=np.float64)
elif initialization_mode_D == "BM25":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
self.ICM = self.ICM.astype(np.float32)
self.ICM = okapi_BM_25(self.ICM)
elif initialization_mode_D == "TF-IDF":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
self.ICM = self.ICM.astype(np.float32)
self.ICM = TF_IDF(self.ICM)
else:
raise ValueError(
"CFW_D_Similarity_Cython: 'init_type' not recognized")
self.FBSM_Rating = FBSM_Rating_Cython_SGD(
self.URM_train,
self.ICM,
n_factors=self.n_factors,
precompute_user_feature_count=precompute_user_feature_count,
learning_rate=self.learning_rate,
l2_reg_D=self.l2_reg_D,
l2_reg_V=self.l2_reg_V,
weights_initialization_D=weights_initialization_D,
weights_initialization_V=None,
positive_only_D=positive_only_D,
positive_only_V=positive_only_V,
verbose=self.verbose,
sgd_mode=sgd_mode,
gamma=gamma,
beta_1=beta_1,
beta_2=beta_2,
mean_init=mean_init,
std_init=std_init)
if self.verbose:
print(self.RECOMMENDER_NAME + ": Initialization completed")
self._train_with_early_stopping(epochs,
algorithm_name=self.RECOMMENDER_NAME,
**earlystopping_kwargs)
self.compute_W_sparse(model_to_use="best")
sys.stdout.flush()