Source code for cornac.models.pmf.recom_pmf

# -*- coding: utf-8 -*-

@author: Aghiles Salah

import numpy as np
import scipy.sparse as sp
import pmf
from ..recommender import Recommender
from ...utils.generic_utils import sigmoid
from ...utils.generic_utils import map_to
from ...utils.generic_utils import intersects
from ...exception import ScoreException

[docs]class PMF(Recommender): """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. name: string, optional, default: 'PMF' The name of the recommender model. variant: {"linear","non_linear"}, optional, default: 'non_linear' Pmf variant. If 'non_linear', the Gaussian mean is the output of a Sigmoid function.\ If 'linear' the Gaussian mean is the output of the identity function. 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). 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}. \ 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. References ---------- * Mnih, Andriy, and Ruslan R. Salakhutdinov. Probabilistic matrix factorization. \ In NIPS, pp. 1257-1264. 2008. """ def __init__(self, k=5, max_iter=100, learning_rate=0.001, gamma=0.9, lamda=0.001, name="PMF", variant='non_linear', trainable=True, verbose=False, init_params={'U': None, 'V': 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.variant = variant 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 # 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. """, train_set) X = self.train_set.matrix if self.trainable: # converting data to the triplet format (needed for cython function pmf) (rid, cid, val) = sp.find(X) val = np.array(val, dtype='float32') if self.variant == 'non_linear': # need to map the ratings to [0,1] if [self.train_set.min_rating, self.train_set.max_rating] != [0, 1]: if self.train_set.min_rating == self.train_set.max_rating: val = map_to(val, 0., 1., 0., self.train_set.max_rating) else: val = map_to(val, 0., 1., self.train_set.min_rating, self.train_set.max_rating) rid = np.array(rid, dtype='int32') cid = np.array(cid, dtype='int32') tX = np.concatenate((np.concatenate(([rid], [cid]), axis=0).T, val.reshape((len(val), 1))), axis=1) del rid, cid, val if self.verbose: print('Learning...') if self.variant == 'linear': res = pmf.pmf_linear(tX, k=self.k, n_X=X.shape[0], d_X=X.shape[1], n_epochs=self.max_iter, lamda=self.lamda, learning_rate=self.learning_rate, gamma=self.gamma, init_params=self.init_params) elif self.variant == 'non_linear': res = pmf.pmf_non_linear(tX, k=self.k, n_X=X.shape[0], d_X=X.shape[1], n_epochs=self.max_iter, lamda=self.lamda, learning_rate=self.learning_rate, gamma=self.gamma, init_params=self.init_params) else: raise ValueError('variant must be one of {"linear","non_linear"}') self.U = np.asarray(res['U']) self.V = np.asarray(res['V']) if self.verbose: print('Learning completed') elif self.verbose: print('%s is trained already (trainable = False)' % (
[docs] def score(self, user_id, item_id): """Predict the scores/ratings of a user for a list of items. Parameters ---------- user_id: int, required The index of the user for whom to perform score predictions. item_id: int, required The index of the item to be scored by the user. Returns ------- A scalar The estimated score (e.g., rating) for the user and item of interest """ 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, :]) if self.variant == "non_linear": user_pred = sigmoid(user_pred) if self.train_set.min_rating == self.train_set.max_rating: user_pred = map_to(user_pred, 0., self.train_set.max_rating, 0., 1.) else: user_pred = map_to(user_pred, self.train_set.min_rating, self.train_set.max_rating, 0., 1.) return user_pred
[docs] def rank(self, user_id, candidate_item_ids=None): """Rank all test items for a given user. Parameters ---------- user_id: int, required The index of the user for whom to perform item raking. candidate_item_ids: 1d array, optional, default: None A list of item indices to be ranked by the user. If `None`, list of ranked known item indices will be returned Returns ------- Numpy 1d array Array of item indices sorted (in decreasing order) relative to some user preference scores. """ if self.train_set.is_unk_user(user_id): return self.default_rank(candidate_item_ids) known_item_scores =[user_id, :]) #if self.variant == "non_linear": # known_item_scores = sigmoid(known_item_scores) # known_item_scores = map_to(known_item_scores, self.train_set.min_rating, self.train_set.max_rating, 0., 1.) if candidate_item_ids is None: ranked_item_ids = known_item_scores.argsort()[::-1] return ranked_item_ids else: num_items = max(self.train_set.num_items, max(candidate_item_ids) + 1) user_pref_scores = np.zeros(num_items) # you can use default score if any: user_pref_scores = np.ones(num_items) * self.default_score() user_pref_scores[:self.train_set.num_items] = known_item_scores ranked_item_ids = user_pref_scores.argsort()[::-1] ranked_item_ids = intersects(ranked_item_ids, candidate_item_ids, assume_unique=True) return ranked_item_ids