Source code for cornac.models.pmf.recom_pmf

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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 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: {} 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. seed: int, optional, default: None Random seed for parameters initialization. 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={}, 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.variant = variant 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.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.pmf import pmf Recommender.fit(self, train_set) if self.trainable: # converting data to the triplet format (needed for cython function pmf) (uid, iid, rat) = train_set.uir_tuple rat = np.array(rat, 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]: rat = scale(rat, 0., 1., self.train_set.min_rating, self.train_set.max_rating) uid = np.array(uid, dtype='int32') iid = np.array(iid, dtype='int32') if self.verbose: print('Learning...') if self.variant == 'linear': res = pmf.pmf_linear(uid, iid, rat, k=self.k, n_users=train_set.num_users, n_items=train_set.num_items, n_ratings=len(rat), 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) elif self.variant == 'non_linear': res = pmf.pmf_non_linear(uid, iid, rat, k=self.k, n_users=train_set.num_users, n_items=train_set.num_items, n_ratings=len(rat), 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) 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)' % (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, :]) if self.variant == "non_linear": user_pred = sigmoid(user_pred) user_pred = scale(user_pred, self.train_set.min_rating, self.train_set.max_rating, 0., 1.) return user_pred