Source code for cornac.models.cdr.recom_cdr

# 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 CDR(Recommender): """Collaborative Deep Ranking. Parameters ---------- 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. 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. corruption_rate: float, optional, default: 0.3 The corruption ratio for 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. name: string, optional, default: 'CDR' The name of the recommender model. 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. Reference: Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback Ying H., Chen L., Xiong Y., Wu J. (2016) """ def __init__(self, name="CDR", k=50, autoencoder_structure=None, act_fn='relu', lambda_u=0.1, lambda_v=100, lambda_w=0.1, lambda_n=1000, corruption_rate=0.3, learning_rate=0.001, dropout_rate=0.1, batch_size=128, max_iter=100, trainable=True, verbose=True, vocab_size=8000, 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.corruption_rate = corruption_rate self.dropout_rate = dropout_rate self.learning_rate = learning_rate = name self.max_iter = max_iter self.ae_structure = autoencoder_structure self.act_fn = act_fn self.batch_size = batch_size self.verbose = verbose self.vocab_size = vocab_size self.init_params = init_params if init_params is not None else {} self.seed = seed # fit the recommender model to the traning data
[docs] def fit(self, train_set): """Fit the model to observations. Parameters ---------- train_set: object of type TrainSet, required An object containing the user-item preference in csr scipy sparse format,\ as well as some useful attributes such as mappings to the original user/item ids.\ Please refer to the class TrainSet in the "data" module for details. """, train_set) from ...utils import get_rng from ...utils.init_utils import xavier_uniform self.seed = get_rng(self.seed) self.U = self.init_params.get('U', xavier_uniform((self.train_set.num_users, self.k), self.seed)) self.V = self.init_params.get('V', xavier_uniform((self.train_set.num_items, self.k), self.seed)) if self.trainable: self._fit_cdr()
def _fit_cdr(self): import tensorflow as tf from tqdm import trange from .model import Model n_users = self.train_set.num_users n_items = 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.ae_structure + [self.k] + self.ae_structure + [self.vocab_size] 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 = np.random.binomial(1, 1 - self.corruption_rate, (n_items, self.vocab_size)) sum_loss = 0 count = 0 batch_count = 0 for batch_u, batch_i, batch_j in self.train_set.uij_iter(batch_size=self.batch_size, shuffle=True): feed_dict = { model.mask_input: corruption_mask[batch_i, :], model.text_input: text_feature[batch_i, :], model.batch_u: batch_u, model.batch_i: batch_i, model.batch_j: batch_j }, feed_dict) # train U, V _, _loss =[model.opt2, model.loss], feed_dict) # train SDAE sum_loss += _loss count += len(batch_u) batch_count += 1 if batch_count % 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('\nLearning completed')
[docs] def score(self, user_id, item_id=None): """Predict the scores/ratings of a user for an item. Parameters ---------- user_id: int, required The index of the user for whom to perform score prediction. item_id: int, optional, default: None The index of the item for that 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_id is None: if self.train_set.is_unk_user(user_id): raise ScoreException("Can't make score prediction for (user_id=%d)" % user_id) known_item_scores =[user_id, :]) return known_item_scores else: if self.train_set.is_unk_user(user_id) or self.train_set.is_unk_item(item_id): raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_id, item_id)) user_pred = self.V[item_id, :].dot(self.U[user_id, :]) return user_pred