Source code for cornac.models.sorec.recom_sorec

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#     http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np
import scipy

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


[docs]class SoRec(Recommender): """Social recommendation using Probabilistic Matrix 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. lamda_c: float, optional, default: 10 The parameter balancing the information from the user-item rating matrix and the user social network. name: string, optional, default: 'SOREC' 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, V and Z are not None). verbose: boolean, optional, default: False When True, some running logs are displayed. init_params: dictionary, optional, default: {'U':None,'V':None} List of initial parameters, e.g., init_params = {'U':U, 'V':V,'Z':Z}. \ U: a ndarray of shape (n_users,k), containing the user latent factors. \ V: a ndarray of shape (n_items,k), containing the item latent factors. \ Z: a ndarray of shape (n_users,k), containing the social network latent factors. \ References ---------- * H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec:Social recommendation using probabilistic matrix factorization. \ CIKM ’08, pages 931–940, Napa Valley, USA, 2008. """ def __init__(self, name="SoRec", k=5, max_iter=100, learning_rate=0.001, lamda_c=10, lamda=0.001, gamma=0.9, trainable=True, verbose=False, init_params={'U': None, 'V': None, 'Z': 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.lamda_c = lamda_c self.lamda = lamda self.gamma = gamma self.ll = np.full(max_iter, 0) self.eps = 0.000000001 self.U = init_params['U'] # matrix of user factors self.V = init_params['V'] # matrix of item factors self.Z = init_params['V'] # matrix of social network factors if self.U is None: print("random initialize user factors") elif self.U.shape[1] != self.k: raise ValueError('initial parameters U dimension error') if self.V is None: print("random initialize item factors") elif self.V.shape[1] != self.k: raise ValueError('initial parameters V dimension error') if self.Z is None: print("random initialize social factors") elif self.Z.shape[1] != self.k: raise ValueError('initial parameters Z dimension error')
[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. """ import math from cornac.models.sorec import sorec Recommender.fit(self, train_set) if self.trainable: # user-item interactions (rat_uid, rat_iid, rat_val) = train_set.uir_tuple # user-user social network map_uid = train_set.uid_list social_net = train_set.user_graph.get_train_triplet(map_uid, map_uid) social_raw = scipy.sparse.csc_matrix((social_net[:, 2], (social_net[:, 0], social_net[:, 1])), shape=(len(map_uid), len(map_uid))) outdegree = np.array(social_raw.sum(axis=1)).flatten() indegree = np.array(social_raw.sum(axis=0)).flatten() weighted_social = [] for ui, uk, cik in social_net: i_out = outdegree[int(ui)] k_in = indegree[int(uk)] cik_weighted = math.sqrt(k_in / (k_in + i_out)) * cik weighted_social.append(cik_weighted) (net_uid, net_jid, net_val) = (social_net[:, 0], social_net[:, 1], weighted_social) 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) 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_uid = np.array(net_uid, dtype='int32') net_jid = np.array(net_jid, dtype='int32') if self.verbose: print('Learning...') res = sorec.sorec(rat_uid, rat_iid, rat_val, net_uid, 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_c=self.lamda_c, lamda=self.lamda, learning_rate=self.learning_rate, gamma=self.gamma, init_params=self.init_params, verbose=self.verbose) 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)' % self.name)
[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 = self.V.dot(self.U[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