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. lambda_reg: float, optional, default: 0.001 The regularization coefficient. 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: dict, optional, default: None List of initial parameters, e.g., init_params = {'U':U, 'V':V}. U: ndarray, shape (n_users, k) User latent factors. V: ndarray, shape (n_items, k) 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, lambda_reg=0.001, name="PMF", variant="non_linear", trainable=True, verbose=False, init_params=None, seed=None, ): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.max_iter = max_iter self.learning_rate = learning_rate self.gamma = gamma self.lambda_reg = lambda_reg self.variant = variant self.seed = seed self.ll = np.full(max_iter, 0) self.eps = 0.000000001 # Init params if provided self.init_params = {} if init_params is None else init_params self.U = self.init_params.get("U", None) # matrix of user factors self.V = self.init_params.get("V", None) # matrix of item factors
[docs] def fit(self, train_set, val_set=None): """Fit the model to observations. Parameters ---------- train_set: :obj:`cornac.data.Dataset`, required User-Item preference data as well as additional modalities. val_set: :obj:`cornac.data.Dataset`, optional, default: None User-Item preference data for model selection purposes (e.g., early stopping). Returns ------- self : object """ Recommender.fit(self, train_set) from cornac.models.pmf import pmf 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, 0.0, 1.0, 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...") # use pre-trained params if exists, otherwise from constructor init_params = {"U": self.U, "V": self.V} 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, lambda_reg=self.lambda_reg, learning_rate=self.learning_rate, gamma=self.gamma, init_params=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, lambda_reg=self.lambda_reg, learning_rate=self.learning_rate, gamma=self.gamma, init_params=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)) return self
[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 = self.V.dot(self.U[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, :]) 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, 0.0, 1.0, ) return user_pred