Source code for cornac.models.ncf.recom_mlp

# Copyright 2018 The Cornac Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
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# ============================================================================

import numpy as np

from .recom_ncf_base import NCFBase
from ...exception import ScoreException

[docs]class MLP(NCFBase): """Multi-Layer Perceptron. Parameters ---------- layers: list, optional, default: [64, 32, 16, 8] MLP layers. Note that the first layer is the concatenation of user and item embeddings. So layers[0]/2 is the embedding size. act_fn: str, default: 'relu' Name of the activation function used for the MLP layers. Supported functions: ['sigmoid', 'tanh', 'elu', 'relu', 'selu, 'relu6', 'leaky_relu'] reg_layers: list, optional, default: [0., 0., 0., 0.] Regularization for each MLP layer, reg_layers[0] is the regularization for embeddings. num_epochs: int, optional, default: 20 Number of epochs. batch_size: int, optional, default: 256 Batch size. num_neg: int, optional, default: 4 Number of negative instances to pair with a positive instance. lr: float, optional, default: 0.001 Learning rate. learner: str, optional, default: 'adam' Specify an optimizer: adagrad, adam, rmsprop, sgd early_stopping: {min_delta: float, patience: int}, optional, default: None If `None`, no early stopping. Meaning of the arguments: - `min_delta`: the minimum increase in monitored value on validation set to be considered as improvement, \ i.e. an increment of less than min_delta will count as no improvement. - `patience`: number of epochs with no improvement after which training should be stopped. name: string, optional, default: 'MLP' 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. verbose: boolean, optional, default: False When True, some running logs are displayed. seed: int, optional, default: None Random seed for parameters initialization. References ---------- * He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. \ In Proceedings of the 26th international conference on world wide web (pp. 173-182). """ def __init__( self, name="MLP", layers=(64, 32, 16, 8), act_fn="relu", reg_layers=(0.0, 0.0, 0.0, 0.0), num_epochs=20, batch_size=256, num_neg=4, lr=0.001, learner="adam", early_stopping=None, trainable=True, verbose=True, seed=None, ): super().__init__( name=name, trainable=trainable, verbose=verbose, num_epochs=num_epochs, batch_size=batch_size, num_neg=num_neg, lr=lr, learner=learner, early_stopping=early_stopping, seed=seed, ) self.layers = layers self.act_fn = act_fn self.reg_layers = reg_layers def _build_graph(self): import tensorflow.compat.v1 as tf from .ops import mlp, loss_fn, train_fn super()._build_graph() with self.graph.as_default(): tf.set_random_seed(self.seed) self.user_id = tf.placeholder(shape=[None], dtype=tf.int32, name="user_id") self.item_id = tf.placeholder(shape=[None], dtype=tf.int32, name="item_id") self.labels = tf.placeholder( shape=[None, 1], dtype=tf.float32, name="labels" ) self.interaction = mlp( uid=self.user_id, iid=self.item_id, num_users=self.num_users, num_items=self.num_items, layers=self.layers, reg_layers=self.reg_layers, act_fn=self.act_fn, seed=self.seed, ) logits = tf.layers.dense( self.interaction, units=1, name="logits", kernel_initializer=tf.initializers.lecun_uniform(self.seed), ) self.prediction = tf.nn.sigmoid(logits) self.loss = loss_fn(labels=self.labels, logits=logits) self.train_op = train_fn( self.loss,, learner=self.learner ) self.initializer = tf.global_variables_initializer() self.saver = tf.train.Saver() self._sess_init()
[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 = self.prediction, feed_dict={ self.user_id: np.ones(self.train_set.num_items) * user_idx, self.item_id: np.arange(self.train_set.num_items), }, ) return known_item_scores.ravel() 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.prediction, feed_dict={self.user_id: [user_idx], self.item_id: [item_idx]}, ) return user_pred.ravel()