Source code for cornac.models.vaecf.recom_vaecf

# Copyright 2018 The Cornac Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import numpy as np

from ..recommender import Recommender
from ...exception import ScoreException

[docs]class VAECF(Recommender): """Variational Autoencoder for Collaborative Filtering. Parameters ---------- k: int, optional, default: 10 The dimension of the stochastic user factors ``z''. autoencoder_structure: list, default: [20] The number of neurons of encoder/decoder layer for VAE. For example, autoencoder_structure = [200], the VAE structure will be [num_items, 200, k, 200, num_items]. act_fn: str, default: 'tanh' Name of the activation function used between hidden layers of the auto-encoder. Supported functions: ['sigmoid', 'tanh', 'elu', 'relu', 'relu6'] likelihood: str, default: 'mult' Name of the likelihood function used for modeling the observations. Supported choices: mult: Multinomial likelihood bern: Bernoulli likelihood gaus: Gaussian likelihood pois: Poisson likelihood n_epochs: int, optional, default: 100 The number of epochs for SGD. batch_size: int, optional, default: 100 The batch size. learning_rate: float, optional, default: 0.001 The learning rate for Adam. beta: float, optional, default: 1.0 The weight of the KL term as in beta-VAE. name: string, optional, default: 'VAECF' The name of the recommender model. trainable: boolean, optional, default: True When False, the model is not trained and Cornac assumes that the model is already \ pre-trained. verbose: boolean, optional, default: False When True, some running logs are displayed. seed: int, optional, default: None Random seed for parameters initialization. use_gpu: boolean, optional, default: False If True and your system supports CUDA then training is performed on GPUs. References ---------- * Liang, Dawen, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. "Variational autoencoders for collaborative filtering." \ In Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 689-698. """ def __init__( self, name="VAECF", k=10, autoencoder_structure=[20], act_fn="tanh", likelihood="mult", n_epochs=100, batch_size=100, learning_rate=0.001, beta=1.0, trainable=True, verbose=False, seed=None, use_gpu=False, ): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.autoencoder_structure = autoencoder_structure self.act_fn = act_fn self.likelihood = likelihood self.batch_size = batch_size self.n_epochs = n_epochs self.learning_rate = learning_rate self.beta = beta self.seed = seed self.use_gpu = use_gpu
[docs] def fit(self, train_set, val_set=None): """Fit the model to observations. Parameters ---------- train_set: :obj:``, required User-Item preference data as well as additional modalities. val_set: :obj:``, optional, default: None User-Item preference data for model selection purposes (e.g., early stopping). Returns ------- self : object """, train_set, val_set) import torch from .vaecf import VAE, learn self.device = ( torch.device("cuda:0") if (self.use_gpu and torch.cuda.is_available()) else torch.device("cpu") ) if self.trainable: if self.seed is not None: torch.manual_seed(self.seed) torch.cuda.manual_seed(self.seed) if not hasattr(self, "vae"): data_dim = train_set.matrix.shape[1] self.vae = VAE( self.k, [data_dim] + self.autoencoder_structure, self.act_fn, self.likelihood, ).to(self.device) learn( self.vae, self.train_set, n_epochs=self.n_epochs, batch_size=self.batch_size, learn_rate=self.learning_rate, beta=self.beta, verbose=self.verbose, device=self.device, ) elif self.verbose: print("%s is trained already (trainable = False)" % ( return self
[docs] def score(self, user_idx, item_idx=None): """Predict the scores/ratings of a user for an item. Parameters ---------- user_idx: int, required The index of the user for whom to perform score prediction. item_idx: int, optional, default: None The index of the item for which to perform score prediction. If None, scores for all known items will be returned. Returns ------- res : A scalar or a Numpy array Relative scores that the user gives to the item or to all known items """ import torch if item_idx is None: if self.train_set.is_unk_user(user_idx): raise ScoreException( "Can't make score prediction for (user_id=%d)" % user_idx ) x_u = self.train_set.matrix[user_idx].copy() = np.ones(len( z_u, _ = self.vae.encode( torch.tensor(x_u.A, dtype=torch.float32, device=self.device) ) known_item_scores = self.vae.decode(z_u).data.cpu().numpy().flatten() return known_item_scores else: if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item( item_idx ): raise ScoreException( "Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx) ) x_u = self.train_set.matrix[user_idx].copy() = np.ones(len( z_u, _ = self.vae.encode( torch.tensor(x_u.A, dtype=torch.float32, device=self.device) ) user_pred = ( self.vae.decode(z_u).data.cpu().numpy().flatten()[item_idx] ) # Fix me I am not efficient return user_pred