Source code for cornac.models.pcrl.recom_pcrl

# Copyright 2018 The Cornac Authors. All Rights Reserved.
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import numpy as np
from ..recommender import Recommender

[docs]class PCRL(Recommender): """Probabilistic Collaborative Representation Learning. Parameters ---------- k: int, optional, default: 100 The dimension of the latent factors. z_dims: Numpy 1d array, optional, default: [300] The dimensions of the hidden intermdiate layers 'z' in the order \ [dim(z_L), ...,dim(z_1)], please refer to Figure 1 in the orginal paper for more details. max_iter: int, optional, default: 300 Maximum number of iterations (number of epochs) for variational PCRL. batch_size: int, optional, default: 300 The batch size for SGD. learning_rate: float, optional, default: 0.001 The learning rate for SGD. aux_info: see "cornac/examples/" in the GitHub repo for an example of how to use \ cornac's graph modality provide item auxiliary data (e.g., context, text, etc.) for PCRL. name: string, optional, default: 'PCRL' 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 (Theta, Beta and Xi are not None). w_determinist: boolean, optional, default: True When True, determinist wheights "W" are used for the generator network, \ otherwise "W" is stochastic as in the original paper. init_params: dictionary, optional, default: None List of initial parameters, e.g., init_params = {'G_s':G_s, 'G_r':G_r, 'L_s':L_s, 'L_r':L_r}. Theta: ndarray, shape (n_users, k) The expected user latent factors. Beta: ndarray, shape (n_items, k) The expected item latent factors. G_s: ndarray, shape (n_users, k) Represent the "shape" parameters of Gamma distribution over Theta. G_r: ndarray, shape (n_users, k) Represent the "rate" parameters of Gamma distribution over Theta. L_s: ndarray, shape (n_items, k) Represent the "shape" parameters of Gamma distribution over Beta. L_r: ndarray, shape (n_items, k) Represent the "rate" parameters of Gamma distribution over Beta. References ---------- * Salah, Aghiles, and Hady W. Lauw. Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. \ In UAI 2018. """ def __init__( self, k=100, z_dims=[300], max_iter=300, batch_size=300, learning_rate=0.001, name="PCRL", trainable=True, verbose=False, w_determinist=True, init_params=None, ): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.z_dims = ( z_dims ) # the dimension of the second hidden layer (we consider a 2-layers PCRL) self.max_iter = max_iter self.batch_size = batch_size self.learning_rate = learning_rate self.w_determinist = w_determinist # Init params if provided self.init_params = {} if init_params is None else init_params self.Theta = self.init_params.get("Theta", None) self.Beta = self.init_params.get("Beta", None) self.Gs = self.init_params.get("G_s", None) self.Gr = self.init_params.get("G_r", None) self.Ls = self.init_params.get("L_s", None) self.Lr = self.init_params.get("L_r", None)
[docs] def fit(self, train_set, val_set=None): """Fit the model to observations. Parameters ---------- train_set: :obj:``, required User-Item preference data as well as additional modalities. val_set: :obj:``, optional, default: None User-Item preference data for model selection purposes (e.g., early stopping). Returns ------- self : object """, train_set, val_set) # X = sp.csc_matrix(self.train_set.matrix) if self.trainable: from .pcrl import PCRL_ # use pre-trained params if exists, otherwise from constructor init_params = { "G_s": self.Gs, "G_r": self.Gr, "L_s": self.Ls, "L_r": self.Lr, } # instanciate pcrl # train_aux_info = train_set.item_graph.matrix[:self.train_set.num_items, :self.train_set.num_items] pcrl_ = PCRL_( train_set=train_set, k=self.k, z_dims=self.z_dims, n_epoch=self.max_iter, batch_size=self.batch_size, learning_rate=self.learning_rate, B=1, w_determinist=self.w_determinist, init_params=init_params, ).learn() self.Theta = np.array(pcrl_.Gs) / np.array(pcrl_.Gr) self.Beta = np.array(pcrl_.Ls) / np.array(pcrl_.Lr) # overwrite init_params for future fine-tuning self.Gs = pcrl_.Gs self.Gr = pcrl_.Gr self.Ls = pcrl_.Ls self.Lr = pcrl_.Lr elif self.verbose: print("%s is trained already (trainable = False)" % ( return self
[docs] def score(self, user_idx, item_idx=None): """Predict the scores/ratings of a user for a list of items. 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 which 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: user_pred = self.Beta * self.Theta[user_idx, :].T else: user_pred = self.Beta[item_idx, :] * self.Theta[user_idx, :].T # transform user_pred to a flatten array user_pred = np.array(user_pred, dtype="float64").flatten() return user_pred