Source code for cornac.models.mcf.recom_mcf

# 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 ..recommender import Recommender
from ...utils.common import sigmoid
from ...utils.common import scale
from ...exception import ScoreException

[docs]class MCF(Recommender): """Matrix Co-Factorization. Parameters ---------- k: int, optional, default: 5 The dimension of the latent factors. max_iter: int, optional, default: 100 Maximum number of iterations or the number of epochs for SGD. learning_rate: float, optional, default: 0.001 The learning rate for SGD_RMSProp. gamma: float, optional, default: 0.9 The weight for previous/current gradient in RMSProp. lamda: float, optional, default: 0.001 The regularization parameter. name: string, optional, default: 'MCF' The name of the recommender model. trainable: boolean, optional, default: True When False, the model is not trained and Cornac assumes that the model is already \ pre-trained (U and V are not None). item-affinity network: See "cornac/examples/" for an example of how to use \ cornac's graph modality to load and provide the ``item-affinity network'' for MCF. verbose: boolean, optional, default: False When True, some running logs are displayed. init_params: dictionary, optional, default: {} List of initial parameters, e.g., init_params = {'U':U, 'V':V}. \ U: a csc_matrix of shape (n_users,k), containing the user latent factors. \ V: a csc_matrix of shape (n_items,k), containing the item latent factors. \ Z: a csc_matrix of shape (n_items,k), containing the ``Also-Viewed'' item latent factors. seed: int, optional, default: None Random seed for parameters initialization. References ---------- * Park, Chanyoung, Donghyun Kim, Jinoh Oh, and Hwanjo Yu. "Do Also-Viewed Products Help User Rating Prediction?."\ In Proceedings of WWW, pp. 1113-1122. 2017. """ def __init__(self, k=5, max_iter=100, learning_rate=0.001, gamma=0.9, lamda=0.001, name="MCF", trainable=True, verbose=False, init_params={}, seed=None): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.init_params = init_params self.max_iter = max_iter self.learning_rate = learning_rate self.gamma = gamma self.lamda = lamda self.ll = np.full(max_iter, 0) self.eps = 0.000000001 self.U = self.init_params.get('U') # matrix of user factors self.V = self.init_params.get('V') # matrix of item factors self.Z = self.init_params.get('Z') # matrix of Also-Viewed item factors 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 contraining 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. """ from cornac.models.mcf import mcf, train_set) if self.trainable: # user-item interactions (rat_uid, rat_iid, rat_val) = train_set.uir_tuple # item-item affinity network map_iid = train_set.iid_list (net_iid, net_jid, net_val) = train_set.item_graph.get_train_triplet(map_iid, map_iid) if [self.train_set.min_rating, self.train_set.max_rating] != [0, 1]: if self.train_set.min_rating == self.train_set.max_rating: rat_val = scale(rat_val, 0., 1., 0., self.train_set.max_rating) else: rat_val = scale(rat_val, 0., 1., self.train_set.min_rating, self.train_set.max_rating) if [min(net_val), max(net_val)] != [0, 1]: if min(net_val) == max(net_val): net_val = scale(net_val, 0., 1., 0., max(net_val)) else: net_val = scale(net_val, 0., 1., min(net_val), max(net_val)) rat_val = np.array(rat_val, dtype='float32') rat_uid = np.array(rat_uid, dtype='int32') rat_iid = np.array(rat_iid, dtype='int32') net_val = np.array(net_val, dtype='float32') net_iid = np.array(net_iid, dtype='int32') net_jid = np.array(net_jid, dtype='int32') if self.verbose: print('Learning...') res = mcf.mcf(rat_uid, rat_iid, rat_val, net_iid, net_jid, net_val, k=self.k, n_users=train_set.num_users, n_items=train_set.num_items, n_ratings=len(rat_val), n_edges=len(net_val), n_epochs=self.max_iter, lamda=self.lamda, learning_rate=self.learning_rate, gamma=self.gamma, init_params=self.init_params, verbose=self.verbose, seed=self.seed) self.U = np.asarray(res['U']) self.V = np.asarray(res['V']) self.Z = np.asarray(res['Z']) if self.verbose: print('Learning completed') elif self.verbose: print('%s is trained already (trainable = False)' %
[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, :]) user_pred = sigmoid(user_pred) if self.train_set.min_rating == self.train_set.max_rating: user_pred = scale(user_pred, 0., self.train_set.max_rating, 0., 1.) else: user_pred = scale(user_pred, self.train_set.min_rating, self.train_set.max_rating, 0., 1.) return user_pred