Source code for cornac.models.conv_mf.recom_convmf

# 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 os
import time
import math

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 ConvMF(Recommender): """ Parameters ---------- k: int, optional, default: 50 The dimension of the user and item latent factors. n_epochs: int, optional, default: 50 Maximum number of epochs for training. cnn_epochs: int, optional, default: 5 Number of epochs for optimizing the CNN for each overall training epoch. cnn_bs: int, optional, default: 128 Batch size for optimizing CNN. cnn_lr: float, optional, default: 0.001 Learning rate for optimizing CNN. lambda_u: float, optional, default: 1.0 The regularization hyper-parameter for user latent factor. lambda_v: float, optional, default: 100.0 The regularization hyper-parameter for item latent factor. emb_dim: int, optional, default: 200 The embedding size of each word. One word corresponds with [1 x emb_dim] vector in the embedding space max_len: int, optional, default 300 The maximum length of item's document filter_sizes: list, optional, default: [3, 4, 5] The length of filters in convolutional layer num_filters: int, optional, default: 100 The number of filters in convolutional layer hidden_dim: int, optional, default: 200 The dimension of hidden layer after the pooling of all convolutional layers dropout_rate: float, optional, default: 0.2 Dropout rate while training CNN give_item_weight: boolean, optional, default: True When True, each item will be weighted base on the number of user who have rated this item init_params: dict, optional, default: {'U':None, 'V':None, 'W': None} Initial U and V matrix and initial weight for embedding layer W 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 ---------- * Donghyun Kim1, Chanyoung Park1. ConvMF: Convolutional Matrix Factorization for Document Context-Aware Recommendation. \ In :10th ACM Conference on Recommender Systems Pages 233-240 """ def __init__( self, name="ConvMF", k=50, n_epochs=50, cnn_epochs=5, cnn_bs=128, cnn_lr=128, lambda_u=1, lambda_v=100, emb_dim=200, max_len=300, filter_sizes=[3, 4, 5], num_filters=100, hidden_dim=200, dropout_rate=0.2, give_item_weight=True, trainable=True, verbose=False, init_params=None, seed=None, ): super().__init__(name=name, trainable=trainable, verbose=verbose) self.give_item_weight = give_item_weight self.n_epochs = n_epochs self.cnn_bs = cnn_bs self.cnn_lr = cnn_lr self.lambda_u = lambda_u self.lambda_v = lambda_v self.k = k self.dropout_rate = dropout_rate self.emb_dim = emb_dim self.max_len = max_len self.filter_sizes = filter_sizes self.num_filters = num_filters self.hidden_dim = hidden_dim = name self.verbose = verbose self.cnn_epochs = cnn_epochs 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) self.W = self.init_params.get("W", None) def _init(self): rng = get_rng(self.seed) n_users, n_items = self.train_set.num_users, self.train_set.num_items vocab_size = self.train_set.item_text.vocab.size if self.U is None: self.U = xavier_uniform((n_users, self.k), rng) if self.V is None: self.V = xavier_uniform((n_items, self.k), rng) if self.W is None: self.W = xavier_uniform((vocab_size, self.emb_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_convmf() return self
@staticmethod def _build_data(csr_mat): data = [] index_list = [] rating_list = [] for i in range(csr_mat.shape[0]): j, k = csr_mat.indptr[i], csr_mat.indptr[i + 1] index_list.append(csr_mat.indices[j:k]) rating_list.append([j:k]) data.append(index_list) data.append(rating_list) return data def _fit_convmf(self): user_data = self._build_data(self.train_set.matrix) item_data = self._build_data(self.train_set.matrix.T.tocsr()) n_user = len(user_data[0]) n_item = len(item_data[0]) # R_user and R_item contain rating values R_user = user_data[1] R_item = item_data[1] if self.give_item_weight: item_weight = np.array([math.sqrt(len(i)) for i in R_item], dtype=float) item_weight = (float(n_item) / item_weight.sum()) * item_weight else: item_weight = np.ones(n_item, dtype=float) # Initialize cnn module import tensorflow.compat.v1 as tf from .convmf import CNN_module # less verbose TF os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" tf.logging.set_verbosity(tf.logging.ERROR) tf.set_random_seed(self.seed) cnn_module = CNN_module( output_dimension=self.k, dropout_rate=self.dropout_rate, emb_dim=self.emb_dim, max_len=self.max_len, filter_sizes=self.filter_sizes, num_filters=self.num_filters, hidden_dim=self.hidden_dim, seed=self.seed, init_W=self.W, learning_rate=self.cnn_lr, ) config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) # init variable document = self.train_set.item_text.batch_seq( np.arange(n_item), max_length=self.max_len ) feed_dict = {cnn_module.model_input: document} theta =, feed_dict=feed_dict) endure = 3 converge_threshold = 0.01 history = 1e-50 loss = 0 for epoch in range(1, self.n_epochs + 1): if self.verbose: print("Epoch: {}/{}".format(epoch, self.n_epochs)) tic = time.time() user_loss = 0.0 for i in range(n_user): idx_item = user_data[0][i] V_i = self.V[idx_item] R_i = R_user[i] A = self.lambda_u * np.eye(self.k) + B = (V_i * (np.tile(R_i, (self.k, 1)).T)).sum(0) self.U[i] = np.linalg.solve(A, B) user_loss += self.lambda_u *[i], self.U[i]) item_loss = 0.0 for j in range(n_item): idx_user = item_data[0][j] U_j = self.U[idx_user] R_j = R_item[j] A = self.lambda_v * item_weight[j] * np.eye(self.k) + B = (U_j * (np.tile(R_j, (self.k, 1)).T)).sum( 0 ) + self.lambda_v * item_weight[j] * theta[j] self.V[j] = np.linalg.solve(A, B) item_loss += np.square(R_j -[j])).sum() loop = trange( self.cnn_epochs, desc="Optimizing CNN", disable=not self.verbose ) for _ in loop: for batch_ids in self.train_set.item_iter( batch_size=self.cnn_bs, shuffle=True ): batch_seq = self.train_set.item_text.batch_seq( batch_ids, max_length=self.max_len ) feed_dict = { cnn_module.model_input: batch_seq, cnn_module.v: self.V[batch_ids], cnn_module.sample_weight: item_weight[batch_ids], }[cnn_module.optimizer], feed_dict=feed_dict) feed_dict = { cnn_module.model_input: document, cnn_module.v: self.V, cnn_module.sample_weight: item_weight, } theta, cnn_loss = [cnn_module.model_output, cnn_module.weighted_loss], feed_dict=feed_dict ) loss = 0.5 * (user_loss + item_loss + self.lambda_v * cnn_loss) toc = time.time() elapsed = toc - tic converge = abs((loss - history) / history) if self.verbose: print( "Loss: %.5f Elapsed: %.4fs Converge: %.6f " % (loss, elapsed, converge) ) history = loss if converge < converge_threshold: endure -= 1 if endure == 0: break 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 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_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