Source code for cornac.models.cvae.recom_cvae

# 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.
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# 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
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# ============================================================================

import numpy as np
from import trange

from ..recommender import Recommender
from ...exception import ScoreException
from ...utils import get_rng
from ...utils.init_utils import xavier_uniform

[docs]class CVAE(Recommender): """ Collaborative Variational Autoencoder Parameters ---------- z_dim: int, optional, default: 50 The dimension of the user and item latent factors. n_epochs: int, optional, default: 100 Maximum number of epochs for training. lambda_u: float, optional, default: 1e-4 The regularization hyper-parameter for user latent factor. lambda_v: float, optional, default: 0.001 The regularization hyper-parameter for item latent factor. lambda_r: float, optional, default: 10.0 Parameter that balance the focus on content or ratings lambda_w: float, optional, default: 1e-4 The regularization for VAE weights lr: float, optional, default: 0.001 Learning rate in the auto-encoder training a: float, optional, default: 1 The confidence of observed ratings. b: float, optional, default: 0.01 The confidence of unseen ratings. input_dim: int, optional, default: 8000 The size of input vector vae_layers: list, optional, default: [200, 100] The list containing size of each layers in neural network structure act_fn: str, default: 'sigmoid' Name of the activation function used for the variational auto-encoder. Supported functions: ['sigmoid', 'tanh', 'elu', 'relu', 'relu6', 'leaky_relu', 'identity'] loss_type: String, optional, default: "cross-entropy" Either "cross-entropy" or "rmse" The type of loss function in the last layer batch_size: int, optional, default: 128 The batch size for SGD. init_params: dict, optional, default: {'U':None, 'V':None} Initial U and V latent matrix trainable: boolean, optional, default: True When False, the model is not trained and Cornac assumes that the model already \ pre-trained (U and V are not None). References ---------- Collaborative Variational Autoencoder for Recommender Systems X. Li and J. She ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 """ def __init__( self, name="CVAE", z_dim=50, n_epochs=100, lambda_u=1e-4, lambda_v=0.001, lambda_r=10, lambda_w=1e-4, lr=0.001, a=1, b=0.01, input_dim=8000, vae_layers=[200, 100], act_fn="sigmoid", loss_type="cross-entropy", batch_size=128, init_params=None, trainable=True, seed=None, verbose=True, ): super().__init__(name=name, trainable=trainable, verbose=verbose) self.lambda_u = lambda_u self.lambda_v = lambda_v self.lambda_r = lambda_r self.lambda_w = lambda_w self.a = a self.b = b self.n_epochs = n_epochs self.input_dim = input_dim self.vae_layers = vae_layers self.z_dim = z_dim self.loss_type = loss_type self.act_fn = act_fn = lr self.batch_size = batch_size self.seed = seed # Init params if provided self.init_params = {} if init_params is None else init_params self.U = self.init_params.get("U", None) self.V = self.init_params.get("V", None) def _init(self): rng = get_rng(self.seed) n_users, n_items = self.train_set.num_users, self.train_set.num_items if self.U is None: self.U = xavier_uniform((n_users, self.z_dim), rng) if self.V is None: self.V = xavier_uniform((n_items, self.z_dim), rng)
[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) self._init() if self.trainable: self._fit_cvae() return self
def _fit_cvae(self): R = self.train_set.csc_matrix # csc for efficient slicing over items document = self.train_set.item_text.batch_bow( np.arange(self.train_set.num_items) ) # bag of word feature document = (document - document.min()) / ( document.max() - document.min() ) # normalization # VAE initialization from .cvae import Model import tensorflow as tf tf.set_random_seed(self.seed) model = Model( n_users=self.train_set.num_users, n_items=self.train_set.num_items, input_dim=self.input_dim, U=self.U, V=self.V, n_z=self.z_dim, lambda_u=self.lambda_u, lambda_v=self.lambda_v, lambda_r=self.lambda_r, lambda_w=self.lambda_w, layers=self.vae_layers, loss_type=self.loss_type, act_fn=self.act_fn, seed=self.seed,, ) config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) # init variable loop = trange(self.n_epochs, disable=not self.verbose) for _ in loop: cf_loss, vae_loss, count = 0, 0, 0 for i, batch_ids in enumerate( self.train_set.item_iter(self.batch_size, shuffle=True) ): batch_R = R[:, batch_ids] batch_C = np.ones(batch_R.shape) * self.b batch_C[batch_R.nonzero()] = self.a feed_dict = { model.x: document[batch_ids], model.ratings: batch_R.A, model.C: batch_C, model.item_ids: batch_ids, } _, _vae_los =[model.vae_update, model.vae_loss], feed_dict) _, _cf_loss =[model.cf_update, model.cf_loss], feed_dict) cf_loss += _cf_loss vae_loss += _vae_los count += len(batch_ids) if i % 10 == 0: loop.set_postfix( vae_loss=(vae_loss / count), cf_loss=(cf_loss / count) ) self.U, self.V =[model.U, model.V]) tf.reset_default_graph()
[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 """ 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 ) known_item_scores =[user_idx, :]) 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) ) user_pred = self.V[item_idx, :].dot(self.U[user_idx, :]) return user_pred