Source code for cornac.models.hft.recom_hft

# 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
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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 normal

[docs]class HFT(Recommender): """Hidden Factors and Hidden Topics Parameters ---------- name: string, default: 'HFT' The name of the recommender model. k: int, optional, default: 10 The dimension of the latent factors. max_iter: int, optional, default: 50 Maximum number of iterations for EM. grad_iter: int, optional, default: 50 Maximum number of iterations for L-BFGS. lambda_text: float, default: 0.1 Weight of corpus likelihood in objective function. l2_reg: float, default: 0.001 Regularization for user item latent factors. vocab_size: int, optional, default: 8000 Size of vocabulary for review text. init_params: dictionary, optional, default: None List of initial parameters, e.g., init_params = {'alpha': alpha, 'beta_u': beta_u, 'beta_i': beta_i, 'gamma_u': gamma_u, 'gamma_v': gamma_v} alpha: float Model offset, optional initialization via init_params. beta_u: ndarray. shape (n_user, 1) User biases, optional initialization via init_params. beta_u: ndarray. shape (n_item, 1) Item biases, optional initialization via init_params. gamma_u: ndarray, shape (n_users,k) The user latent factors, optional initialization via init_params. gamma_v: ndarray, shape (n_items,k) The item latent factors, optional initialization via init_params. trainable: boolean, optional, default: True When False, the model will not be re-trained, and input of pre-trained parameters are required. verbose: boolean, optional, default: True When True, some running logs are displayed. seed: int, optional, default: None Random seed for weight initialization. References ---------- Julian McAuley, Jure Leskovec. "Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text" RecSys '13 Proceedings of the 7th ACM conference on Recommender systems Pages 165-172 """ def __init__( self, name="HFT", k=10, max_iter=50, grad_iter=50, lambda_text=0.1, l2_reg=0.001, vocab_size=8000, init_params=None, trainable=True, verbose=True, seed=None, ): super().__init__(name=name, trainable=trainable, verbose=verbose) self.k = k self.lambda_text = lambda_text self.l2_reg = l2_reg self.grad_iter = grad_iter = name self.max_iter = max_iter self.verbose = verbose self.seed = seed self.vocab_size = vocab_size # Init params if provided self.init_params = {} if init_params is None else init_params self.alpha = self.init_params.get("alpha", None) self.beta_u = self.init_params.get("beta_u", None) self.beta_i = self.init_params.get("beta_i", None) self.gamma_u = self.init_params.get("gamma_u", None) self.gamma_i = self.init_params.get("gamma_i", None) def _init(self): rng = get_rng(self.seed) self.n_item = self.train_set.num_items self.n_user = self.train_set.num_users if self.alpha is None: self.alpha = self.train_set.global_mean if self.beta_u is None: self.beta_u = normal(self.n_user, std=0.01, random_state=rng) if self.beta_i is None: self.beta_i = normal(self.n_item, std=0.01, random_state=rng) if self.gamma_u is None: self.gamma_u = normal((self.n_user, self.k), std=0.01, random_state=rng) if self.gamma_i is None: self.gamma_i = normal((self.n_item, self.k), std=0.01, random_state=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_hft() return self
@staticmethod def _build_data(csr_mat): 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]) return index_list, rating_list def _fit_hft(self): from .hft import Model # document data bow_mat = self.train_set.item_text.batch_bow( np.arange(self.n_item), keep_sparse=True ) documents, _ = self._build_data(bow_mat) # bag of word feature # Rating data user_data = self._build_data(self.train_set.matrix) item_data = self._build_data(self.train_set.matrix.T.tocsr()) model = Model( n_user=self.n_user, n_item=self.n_item, alpha=self.alpha, beta_u=self.beta_u, beta_i=self.beta_i, gamma_u=self.gamma_u, gamma_i=self.gamma_i, n_vocab=self.vocab_size, k=self.k, lambda_text=self.lambda_text, l2_reg=self.l2_reg, grad_iter=self.grad_iter, ) model.init_count(docs=documents) # training loop = trange(self.max_iter, disable=not self.verbose) for _ in loop: model.assign_word_topics(docs=documents) loss = model.update_params(rating_data=(user_data, item_data)) loop.set_postfix(loss=loss) self.alpha, self.beta_u, self.beta_i, self.gamma_u, self.gamma_i = ( model.get_parameter() ) 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 = ( self.alpha + self.beta_u[user_idx] + self.beta_i +[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.alpha + self.beta_u[user_idx] + self.beta_i[item_idx] + self.gamma_i[item_idx, :].dot(self.gamma_u[user_idx, :]) ) return user_pred