def shape(self): nb_users = global_parameters(self.sets_parameters)[0] nb_movies = global_parameters(self.sets_parameters)[1] if self.sets_parameters['learning_type'] == 'V': nb_users, nb_movies = permute(nb_users, nb_movies) shape = (nb_users, nb_movies) return shape
def __init__(self, dataset, sets_parameters, stability_parameters): super().__init__(dataset) self.nb_users, self.nb_movies = global_parameters( sets_parameters=sets_parameters)[0:2] self.differences = stability_parameters['differences'] self.probability = stability_parameters['probability'] self.rmse = stability_parameters['rmse'] print('check self.rmse different from zero:') print(self.rmse) self.subsets_number = stability_parameters['subsets_number'] self.landa_array = stability_parameters['landa_array'] assert np.size(self.landa_array) == self.subsets_number + 1 self.coefficients = self.run() self.category_indices = {'user': 0, 'rmse': 1, 'coefficients': 2} self.category_matrix = { 'user': self.ratings, 'rmse': self.ratings, 'coefficients': self.coefficients } self.category_permute = { 'user': self.permute, 'rmse': self.identity, 'coefficients': self.permute }
def __init__(self, dataset, sets_parameters, stability_parameters): super().__init__(dataset) self.nb_users, self.nb_movies = global_parameters(sets_parameters=sets_parameters)[0:2] self.differences = stability_parameters['differences'] self.probability = stability_parameters['probability'] self.rmse = stability_parameters['rmse'] print('check self.rmse different from zero:') print(self.rmse) self.subsets_number = stability_parameters['subsets_number'] self.landa_array = stability_parameters['landa_array'] print(self.landa_array) assert np.size(self.landa_array) == self.subsets_number + 1 self.coefficients = self.run() self.category_indices = {'user': 0, 'rmse': 1, 'coefficients': 2} self.category_matrix = {'user': self.ratings, 'rmse': self.ratings, 'coefficients': self.coefficients} self.category_permute = {'user': self.permute, 'rmse': self.identity, 'coefficients': self.permute}
def __init__(self, sets_parameters, Train_set, batch_size, learning_rate0, learning_decay): self.nb_users, self.nb_movies = global_parameters(sets_parameters=sets_parameters)[0:2] self.Train_set = Train_set self.batch_size = batch_size self.learning_rate0 = learning_rate0 self.learning_rate = learning_rate0 self.learning_decay = learning_decay
def __init__(self, parameters, sets): self.database = parameters['sets']['database_id'] self.hidden1_units = parameters['autoencoder']['hidden1_units'] self.regularisation = parameters['autoencoder']['regularisation'] self.learning_rate0 = parameters['autoencoder']['learning_rate0'] self.learning_decay = parameters['autoencoder']['learning_decay'] self.batch_size_evaluate = parameters['autoencoder']['batch_size_evaluate'] self.batch_size_train = parameters['autoencoder']['batch_size_train'] self.is_test = parameters['autoencoder']['is_test'] self.nb_users, self.nb_movies = global_parameters(sets_parameters=parameters['sets'])[0:2] self.difference_matrix = 0 self.rmse = 0 self.rmse_train = 0 self.epoch_steps = int(self.nb_users / self.batch_size_train) self.nb_steps = parameters['autoencoder']['nb_epoch'] * self.epoch_steps self.Train_set = Dataset(sets['autoencoder'][0]) self.Validation_set = Dataset(sets['autoencoder'][1]) self.Test_set = Dataset(sets['autoencoder'][2]) self.Loss = Loss() self.Train = Train(sets_parameters=parameters['sets'], Train_set=self.Train_set, batch_size=self.batch_size_train, learning_decay=self.learning_decay, learning_rate0=self.learning_rate0) self.Evaluation = Evaluation(sets_parameters=parameters['sets'], batch_size_evaluate=self.batch_size_evaluate, Train_set=self.Train_set)
def __init__(self, sets_parameters): self.sets_parameters = sets_parameters self.validation_ratio = sets_parameters['validation_ratio'] self.test_ratio = sets_parameters['test_ratio'] self.nb_users, self.nb_movies = global_parameters(sets_parameters=sets_parameters)[0:2] self.database_id = sets_parameters['database_id'] self.learning_type = sets_parameters['learning_type'] self.train_val, self.test = self.first_split()
def __init__(self, factorisation_sets, factorisation_parameters, sets_parameters): if sets_parameters['learning_type'] == 'U': self.nb_users, self.nb_movies = global_parameters(sets_parameters=sets_parameters)[0:2] else: self.nb_movies, self.nb_users = global_parameters(sets_parameters=sets_parameters)[0:2] self.dimension = factorisation_parameters['dimension'] self.iterations = factorisation_parameters['iterations'] self.landa = factorisation_parameters['landa'] self.train_set = factorisation_sets[0] self.TrainSet_movies = factorisation_sets[0].transpose(copy=True).tocsr() self.validation_set = factorisation_sets[1] self.R = np.empty((self.nb_users, self.nb_movies)) self.U = np.random.rand(self.dimension, self.nb_users) self.V = np.random.rand(self.nb_movies, self.dimension) self.rmse, self.difference_matrix = self.run()
def __init__(self, factorisation_sets, factorisation_parameters, sets_parameters): if sets_parameters['learning_type'] == 'U': self.nb_users, self.nb_movies = global_parameters( sets_parameters=sets_parameters)[0:2] else: self.nb_movies, self.nb_users = global_parameters( sets_parameters=sets_parameters)[0:2] self.dimension = factorisation_parameters['dimension'] self.iterations = factorisation_parameters['iterations'] self.landa = factorisation_parameters['landa'] self.train_set = factorisation_sets[0] self.TrainSet_movies = factorisation_sets[0].transpose( copy=True).tocsr() self.validation_set = factorisation_sets[1] self.R = np.empty((self.nb_users, self.nb_movies)) self.U = np.random.rand(self.dimension, self.nb_users) self.V = np.random.rand(self.nb_movies, self.dimension) self.rmse, self.difference_matrix = self.run()
def __init__(self, parameters, sets): self.database = parameters['sets']['database_id'] self.hidden1_units = parameters['autoencoder']['hidden1_units'] self.regularisation = parameters['autoencoder']['regularisation'] self.learning_rate0 = parameters['autoencoder']['learning_rate0'] self.learning_decay = parameters['autoencoder']['learning_decay'] self.batch_size_evaluate = parameters['autoencoder'][ 'batch_size_evaluate'] self.batch_size_train = parameters['autoencoder']['batch_size_train'] self.is_test = parameters['autoencoder']['is_test'] self.nb_users, self.nb_movies = global_parameters( sets_parameters=parameters['sets'])[0:2] self.difference_matrix = 0 self.rmse = 0 self.rmse_train = 0 self.epoch_steps = int(self.nb_users / self.batch_size_train) self.nb_steps = parameters['autoencoder']['nb_epoch'] * self.epoch_steps self.Train_set = Dataset(sets['autoencoder'][0]) self.Validation_set = Dataset(sets['autoencoder'][1]) self.Test_set = Dataset(sets['autoencoder'][2]) self.Loss = Loss() self.Train = Train(sets_parameters=parameters['sets'], Train_set=self.Train_set, batch_size=self.batch_size_train, learning_decay=self.learning_decay, learning_rate0=self.learning_rate0) self.Evaluation = Evaluation( sets_parameters=parameters['sets'], batch_size_evaluate=self.batch_size_evaluate, Train_set=self.Train_set)
def __init__(self, batch_size_evaluate, sets_parameters, Train_set): self.batch_size_evaluate = batch_size_evaluate self.Train_set = Train_set self.nb_users, self.nb_movies = global_parameters(sets_parameters=sets_parameters)[0:2]
def __init__(self, batch_size_evaluate, sets_parameters, Train_set): self.batch_size_evaluate = batch_size_evaluate self.Train_set = Train_set self.nb_users, self.nb_movies = global_parameters(sets_parameters=sets_parameters)[0:2]
def full_import(sets_parameters): data_file = global_parameters(sets_parameters)[3] database = np.genfromtxt(data_file, delimiter=',')[:, 0:3] database[:, 0:2] -= 1 return database