Source code for cornac.models.coe.recom_coe

# 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.
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# 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

from ..recommender import Recommender
from ...exception import ScoreException

[docs]class COE(Recommender): """Collaborative Ordinal Embedding. Parameters ---------- k: int, optional, default: 20 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: 0.05 The learning rate for SGD. lamda: float, optional, default: 0.001 The regularization parameter. batch_size: int, optional, default: 100 The batch size for SGD. name: string, optional, default: 'IBRP' 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 (U and V are not None). verbose: boolean, optional, default: False When True, some running logs are displayed. init_params: dictionary, optional, default: None List of initial parameters, e.g., init_params = {'U':U, 'V':V}. U: ndarray, shape (n_users, k) The user latent factors. V: ndarray, shape (n_items, k) The item latent factors. References ---------- * Le, D. D., & Lauw, H. W. (2016, June). Euclidean co-embedding of ordinal data for multi-type visualization.\ In Proceedings of the 2016 SIAM International Conference on Data Mining (pp. 396-404). Society for Industrial and Applied Mathematics. """ def __init__( self, k=20, max_iter=100, learning_rate=0.05, lamda=0.001, batch_size=1000, name="coe", trainable=True, verbose=False, init_params=None, ): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) self.k = k self.max_iter = max_iter = name self.learning_rate = learning_rate self.lamda = lamda self.batch_size = batch_size # 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:``, 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: from .coe import coe if self.verbose: print("Learning...") res = coe( self.train_set.matrix, k=self.k, n_epochs=self.max_iter, lamda=self.lamda, learning_rate=self.learning_rate, batch_size=self.batch_size, init_params={"U": self.U, "V": self.V}, ) self.U = np.asarray(res["U"]) self.V = np.asarray(res["V"]) if self.verbose: print("Learning completed") return self
# get prefiction for a single user (predictions for one user at a time for efficiency purposes) # predictions are not stored for the same efficiency reasons"""
[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): raise ScoreException( "Can't make score prediction for (user_id=%d)" % user_idx ) known_item_scores = np.sum( np.abs(self.V - self.U[user_idx, :]) ** 2, axis=-1 ) ** (1.0 / 2) 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 = np.sum( np.abs(self.V[item_idx, :] - self.U[user_idx, :]) ** 2, axis=-1 ) ** (1.0 / 2) return user_pred