Source code for cornac.models.cdl.recom_cdl

# 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 CDL(Recommender): """Collaborative Deep Learning. Parameters ---------- name: string, default: 'CDL' The name of the recommender model. k: int, optional, default: 50 The dimension of the latent factors. max_iter: int, optional, default: 100 Maximum number of iterations or the number of epochs for SGD. autoencoder_structure: list, default: None The number of neurons of encoder/decoder layer for SDAE. For example, autoencoder_structure = [200], the SDAE structure will be [vocab_size, 200, k, 200, vocab_size] act_fn: str, default: 'relu' Name of the activation function used for the auto-encoder. Supported functions: ['sigmoid', 'tanh', 'elu', 'relu', 'relu6', 'leaky_relu', 'identity'] learning_rate: float, optional, default: 0.001 The learning rate for AdamOptimizer. vocab_size: int, default: 8000 The size of text input for the SDAE. lambda_u: float, optional, default: 0.1 The regularization parameter for users. lambda_v: float, optional, default: 10 The regularization parameter for items. lambda_w: float, optional, default: 0.1 The regularization parameter for SDAE weights. lambda_n: float, optional, default: 1000 The regularization parameter for SDAE output. a: float, optional, default: 1 The confidence of observed ratings. b: float, optional, default: 0.01 The confidence of unseen ratings. corruption_rate: float, optional, default: 0.3 The corruption ratio for input text of the SDAE. dropout_rate: float, optional, default: 0.1 The probability that each element is removed in dropout of SDAE. batch_size: int, optional, default: 128 The batch size for SGD. 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). init_params: dictionary, optional, default: None List of initial parameters, e.g., init_params = {'U':U, 'V':V} U: ndarray, shape (n_users,k) The user latent factors, optional initialization via init_params. V: ndarray, shape (n_items,k) The item latent factors, optional initialization via init_params. seed: int, optional, default: None Random seed for weight initialization. References ---------- * Hao Wang, Naiyan Wang, Dit-Yan Yeung. CDL: Collaborative Deep Learning for Recommender Systems. In : SIGKDD. 2015. p. 1235-1244. """ def __init__( self, name="CDL", k=50, autoencoder_structure=None, act_fn="relu", lambda_u=0.1, lambda_v=10, lambda_w=0.1, lambda_n=1000, a=1, b=0.01, corruption_rate=0.3, learning_rate=0.001, vocab_size=8000, dropout_rate=0.1, batch_size=128, max_iter=100, trainable=True, verbose=True, init_params=None, seed=None, ): super().__init__(name=name, trainable=trainable, verbose=verbose) self.k = k self.lambda_u = lambda_u self.lambda_v = lambda_v self.lambda_w = lambda_w self.lambda_n = lambda_n self.a = a self.b = b self.corruption_rate = corruption_rate self.dropout_rate = dropout_rate self.learning_rate = learning_rate self.vocab_size = vocab_size = name self.max_iter = max_iter self.autoencoder_structure = autoencoder_structure self.act_fn = act_fn self.batch_size = batch_size self.verbose = verbose self.seed = seed self.rng = get_rng(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): 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.k), self.rng) if self.V is None: self.V = xavier_uniform((n_items, self.k), self.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_cdl() return self
def _fit_cdl(self): import tensorflow.compat.v1 as tf from .cdl import Model R = self.train_set.csc_matrix # csc for efficient slicing over items n_users, n_items = self.train_set.num_users, self.train_set.num_items text_feature = self.train_set.item_text.batch_bow( np.arange(n_items) ) # bag of word feature text_feature = (text_feature - text_feature.min()) / ( text_feature.max() - text_feature.min() ) # normalization # Build model layer_sizes = ( [self.vocab_size] + self.autoencoder_structure + [self.k] + self.autoencoder_structure + [self.vocab_size] ) tf.set_random_seed(self.seed) model = Model( n_users=n_users, n_items=n_items, n_vocab=self.vocab_size, k=self.k, layers=layer_sizes, lambda_u=self.lambda_u, lambda_v=self.lambda_v, lambda_w=self.lambda_w, lambda_n=self.lambda_n, lr=self.learning_rate, dropout_rate=self.dropout_rate, U=self.U, V=self.V, act_fn=self.act_fn, seed=self.seed, ) # Training model config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: loop = trange(self.max_iter, disable=not self.verbose) for _ in loop: corruption_mask = self.rng.binomial( 1, 1 - self.corruption_rate, size=(n_items, self.vocab_size) ) sum_loss = 0 count = 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.text_mask: corruption_mask[batch_ids, :], model.text_input: text_feature[batch_ids], model.ratings: batch_R.A, model.C: batch_C, model.item_ids: batch_ids, }, feed_dict) # train U, V _, _loss = [model.opt2, model.loss], feed_dict ) # train SDAE sum_loss += _loss count += len(batch_ids) if i % 10 == 0: loop.set_postfix(loss=(sum_loss / count)) self.U, self.V =[model.U, model.V]) tf.reset_default_graph() if self.verbose: print("Learning completed!")
[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