def feature_training_folding(self,
initialize_model=False,
handle_bias=False,
verbose=False):
# Initialize the model with previous results if available
if initialize_model:
self.svd_v = np.zeros([self.dimensionality, self.nbr_users
]) + self.feature_init
self.svd_u = np.zeros([self.dimensionality, self.nbr_items
]) + self.feature_init
ratings_index, ratings = self.get_ratings(randomize_order=True)
if handle_bias:
estimator_loop_with_bias(self.min_epochs, self.max_epochs,
self.min_improvement, self.dimensionality,
self.feature_init, self.learning_rate,
self.K, self.overall_bias, self.svd_u,
self.svd_v, ratings_index, ratings,
self.items_bias, self.users_bias,
self.nbr_users, self.nbr_items,
int(verbose))
else:
estimator_loop_without_bias(self.min_epochs, self.max_epochs,
self.min_improvement,
self.dimensionality, self.feature_init,
self.learning_rate, self.K, self.svd_u,
self.svd_v, ratings_index, ratings,
self.nbr_users, self.nbr_items,
int(verbose))
self.compute_components_mean()
def feature_training(self,
initialize_model=True,
handle_bias=False,
verbose=False):
'''
Compute each features using a Gradient Descent approach. This method is the core of the recommender. Once we have
the matrix containing the rating of the users, we run this method for training the recommendation engine. More precisely,
this method use a Stochastic Gradient Descent (SGD) for determining the right model parameters. Those model parameters
are then used by the predictor for computing the ratings for each users-items pair.
The loop intensive part of the code is done in the estimator_loop_without_bias() function, which is a optimize with Cython.
* initialize_model: If True, the model parameters are initialized to zero before the training. If False
the model parameters already stored are used as the initial value for the training process. [True]
* handle_bias: Handle the bias if True [False]
* verbose: Print some info on the terminal if True [False]
'''
# Initialize the model with previous results if available
if initialize_model:
self.svd_v = np.zeros([self.dimensionality, self.nbr_users
]) + self.feature_init
self.svd_u = np.zeros([self.dimensionality, self.nbr_items
]) + self.feature_init
ratings_index, ratings = self.get_ratings(randomize_order=True)
if handle_bias:
#self.items_bias = np.zeros(self.nbr_items) + 0.1
#self.users_bias = np.zeros(self.nbr_users) + 0.1
self.compute_overall_avg()
self.compute_items_bias_bk()
self.compute_users_bias_bk()
estimator_loop_with_bias(
self.min_epochs, self.max_epochs, self.min_improvement,
self.dimensionality, self.feature_init, self.learning_rate,
self.learning_rate_users, self.learning_rate_items, self.K,
self.overall_bias, self.svd_u, self.svd_v, ratings_index,
ratings, self.items_bias, self.users_bias, self.nbr_users,
self.nbr_items, int(verbose))
else:
estimator_loop_without_bias(self.min_epochs, self.max_epochs,
self.min_improvement,
self.dimensionality, self.feature_init,
self.learning_rate, self.K, self.svd_u,
self.svd_v, ratings_index, ratings,
self.nbr_users, self.nbr_items,
int(verbose))
def feature_training_folding(self, initialize_model = False, handle_bias = False, verbose = False):
# Initialize the model with previous results if available
if initialize_model:
self.svd_v = np.zeros([self.dimensionality, self.nbr_users]) + self.feature_init
self.svd_u = np.zeros([self.dimensionality, self.nbr_items]) + self.feature_init
ratings_index, ratings = self.get_ratings(randomize_order = True)
if handle_bias:
estimator_loop_with_bias(self.min_epochs, self.max_epochs, self.min_improvement, self.dimensionality, self.feature_init, self.learning_rate,
self.K, self.overall_bias, self.svd_u, self.svd_v, ratings_index, ratings, self.items_bias, self.users_bias, self.nbr_users,
self.nbr_items, int(verbose))
else:
estimator_loop_without_bias(self.min_epochs, self.max_epochs, self.min_improvement, self.dimensionality, self.feature_init, self.learning_rate,
self.K, self.svd_u, self.svd_v, ratings_index, ratings, self.nbr_users,
self.nbr_items, int(verbose))
self.compute_components_mean()
def feature_training(self, initialize_model = True, handle_bias = False, verbose = False):
'''
Compute each features using a Gradient Descent approach. This method is the core of the recommender. Once we have
the matrix containing the rating of the users, we run this method for training the recommendation engine. More precisely,
this method use a Stochastic Gradient Descent (SGD) for determining the right model parameters. Those model parameters
are then used by the predictor for computing the ratings for each users-items pair.
The loop intensive part of the code is done in the estimator_loop_without_bias() function, which is a optimize with Cython.
* initialize_model: If True, the model parameters are initialized to zero before the training. If False
the model parameters already stored are used as the initial value for the training process. [True]
* handle_bias: Handle the bias if True [False]
* verbose: Print some info on the terminal if True [False]
'''
# Initialize the model with previous results if available
if initialize_model:
self.svd_v = np.zeros([self.dimensionality, self.nbr_users]) + self.feature_init
self.svd_u = np.zeros([self.dimensionality, self.nbr_items]) + self.feature_init
ratings_index, ratings = self.get_ratings(randomize_order = True)
if handle_bias:
#self.items_bias = np.zeros(self.nbr_items) + 0.1
#self.users_bias = np.zeros(self.nbr_users) + 0.1
self.compute_overall_avg()
self.compute_items_bias_bk()
self.compute_users_bias_bk()
estimator_loop_with_bias(self.min_epochs, self.max_epochs, self.min_improvement, self.dimensionality, self.feature_init, self.learning_rate,
self.learning_rate_users, self.learning_rate_items, self.K, self.overall_bias, self.svd_u, self.svd_v, ratings_index,
ratings, self.items_bias, self.users_bias, self.nbr_users, self.nbr_items, int(verbose))
else:
estimator_loop_without_bias(self.min_epochs, self.max_epochs, self.min_improvement, self.dimensionality, self.feature_init, self.learning_rate,
self.K, self.svd_u, self.svd_v, ratings_index, ratings, self.nbr_users,
self.nbr_items, int(verbose))
