Source code for cornac.models.hpf.recom_hpf

# Copyright 2018 The Cornac Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ============================================================================

import numpy as np
import scipy.sparse as sp

from cornac.models.hpf import hpf
from ..recommender import Recommender
from ...exception import ScoreException

[docs]class HPF(Recommender): """Hierarchical Poisson Factorization. Parameters ---------- k: int, optional, default: 5 The dimension of the latent factors. max_iter: int, optional, default: 100 Maximum number of iterations. name: string, optional, default: 'HPF' The name of the recommender model. trainable: boolean, optional, default: True When False, the model is not trained and Cornac assumes that the model is already \ pre-trained (Theta and Beta are not None). verbose: boolean, optional, default: False When True, some running logs are displayed. hierarchical: boolean, optional, default: True When False, PF is used instead of HPF. seed: int, optional, default: None Random seed for parameters initialization. init_params: dict, optional, default: None Initial parameters of the model. 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) This represents "shape" parameters of Gamma distribution over Theta. G_r: ndarray, shape (n_users, k) This represents "rate" parameters of Gamma distribution over Theta. L_s: ndarray, shape (n_items, k) This represents "shape" parameters of Gamma distribution over Beta. L_r: ndarray, shape (n_items, k) This represents "rate" parameters of Gamma distribution over Beta. References ---------- * Gopalan, Prem, Jake M. Hofman, and David M. Blei. Scalable Recommendation with \ Hierarchical Poisson Factorization. In UAI, pp. 326-335. 2015. """ def __init__( self, k=5, max_iter=100, name="HPF", trainable=True, verbose=False, hierarchical=True, seed=None, init_params=None, ): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.max_iter = max_iter self.ll = np.full(max_iter, 0) self.etp_r = np.full(max_iter, 0) self.etp_c = np.full(max_iter, 0) self.eps = 0.000000001 self.hierarchical = hierarchical self.seed = seed # Init params if provided self.init_params = {} if init_params is None else init_params self.Theta = self.init_params.get("Theta", None) # matrix of user factors self.Beta = self.init_params.get("Beta", None) # matrix of item factors 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) if self.trainable: # 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, } X = sp.csc_matrix(self.train_set.matrix) # recover the striplet sparse format from csc sparse matrix X (needed to feed c++) (rid, cid, val) = sp.find(X) val = np.array(val, dtype="float32") 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.hierarchical: res = hpf.hpf( tX, X.shape[0], X.shape[1], self.k, self.max_iter, self.seed, init_params ) else: res = tX, X.shape[0], X.shape[1], self.k, self.max_iter, self.seed, init_params ) self.Theta = np.asarray(res["Z"]) self.Beta = np.asarray(res["W"]) # overwrite init_params for future fine-tuning self.Gs = np.asarray(res["G_s"]) self.Gr = np.asarray(res["G_r"]) self.Ls = np.asarray(res["L_s"]) self.Lr = np.asarray(res["L_r"]) 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 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 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: if self.train_set.is_unk_user(user_idx): u_representation = np.ones(self.k) else: u_representation = self.Theta[user_idx, :] known_item_scores = known_item_scores = np.array(known_item_scores, dtype="float64").flatten() 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.Beta[item_idx, :].dot(self.Theta[user_idx, :]) user_pred = np.array(user_pred, dtype="float64").flatten()[0] return user_pred