Source code for cornac.models.ctr.recom_ctr

# 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 xavier_uniform

[docs]class CTR(Recommender): """Collaborative Topic Regression. Parameters ---------- name: string, default: 'CTR' The name of the recommender model. k: int, optional, default: 200 The dimension of the latent factors. max_iter: int, optional, default: 100 Maximum number of iterations or the number of epochs for SGD. lambda_u: float, optional, default: 0.01 The regularization parameter for users. lambda_v: float, optional, default: 0.01 The regularization parameter for items. a: float, optional, default: 1 The confidence of observed ratings. b: float, optional, default: 0.01 The confidence of unseen ratings. eta: float, optional, default: 0.01 Added value for smoothing phi. 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 ---------- Wang, Chong, and David M. Blei. "Collaborative topic modeling for recommending scientific articles." Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011. """ def __init__( self, name="CTR", k=200, lambda_u=0.01, lambda_v=0.01, eta=0.01, a=1, b=0.01, 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.a = a self.b = b self.eta = eta = name self.max_iter = max_iter self.verbose = verbose 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) self.n_item = self.train_set.num_items self.n_user = self.train_set.num_users if self.U is None: self.U = xavier_uniform((self.n_user, self.k), rng) if self.V is None: self.V = xavier_uniform((self.n_item, self.k), 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_ctr() 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_ctr(self,): from .ctr import Model user_data = self._build_data(self.train_set.matrix) item_data = self._build_data(self.train_set.matrix.T.tocsr()) bow_mat = self.train_set.item_text.batch_bow( np.arange(self.n_item), keep_sparse=True ) doc_ids, doc_cnt = self._build_data(bow_mat) # bag of word feature self.model = Model( n_user=self.n_user, n_item=self.n_item, U=self.U, V=self.V, k=self.k, n_vocab=self.train_set.item_text.vocab.size, lambda_u=self.lambda_u, lambda_v=self.lambda_v, a=self.a, b=self.b, max_iter=self.max_iter, seed=self.seed, ) loop = trange(self.max_iter, disable=not self.verbose) for _ in loop: cf_loss = self.model.update_cf( user_data=user_data, item_data=item_data ) # u and v updating lda_loss = self.model.update_theta(doc_ids=doc_ids, doc_cnt=doc_cnt) self.model.update_beta() loop.set_postfix(cf_loss=cf_loss, lda_likelihood=-lda_loss) 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 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