Source code for cornac.models.ncf.recom_neumf

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import numpy as np

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


[docs]class NeuMF(NCFBase): """Neural Matrix Factorization. Parameters ---------- num_factors: int, optional, default: 8 Embedding size of MF model. 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_mf: float, optional, default: 0. Regularization for MF embeddings. 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: 'NeuMF' 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="NeuMF", num_factors=8, layers=(64, 32, 16, 8), act_fn="relu", reg_mf=0.0, 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.num_factors = num_factors self.layers = layers self.act_fn = act_fn self.reg_mf = reg_mf self.reg_layers = reg_layers self.pretrained = False self.ignored_attrs.extend( [ "gmf_user_id", "mlp_user_id", "gmf_model", "mlp_model", "alpha", ] )
[docs] def pretrain(self, gmf_model, mlp_model, alpha=0.5): """Provide pre-trained GMF and MLP models. Section 3.4.1 of the paper. Parameters ---------- gmf_model: object of type GMF, required Reference to trained/fitted GMF model. gmf_model: object of type GMF, required Reference to trained/fitted GMF model. alpha: float, optional, default: 0.5 Hyper-parameter determining the trade-off between the two pre-trained models. Details are described in the section 3.4.1 of the paper. """ self.pretrained = True self.gmf_model = gmf_model self.mlp_model = mlp_model self.alpha = alpha return self
def _build_graph(self): import tensorflow.compat.v1 as tf from .ops import gmf, mlp, loss_fn, train_fn super()._build_graph() with self.graph.as_default(): tf.set_random_seed(self.seed) self.gmf_user_id = tf.placeholder( shape=[None], dtype=tf.int32, name="gmf_user_id" ) self.mlp_user_id = tf.placeholder( shape=[None], dtype=tf.int32, name="mlp_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" ) gmf_feat = gmf( uid=self.gmf_user_id, iid=self.item_id, num_users=self.num_users, num_items=self.num_items, emb_size=self.num_factors, reg_user=self.reg_mf, reg_item=self.reg_mf, seed=self.seed, ) mlp_feat = mlp( uid=self.mlp_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, ) self.interaction = tf.concat([gmf_feat, mlp_feat], axis=-1) 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, learning_rate=self.lr, learner=self.learner ) self.initializer = tf.global_variables_initializer() self.saver = tf.train.Saver() self._sess_init() if self.pretrained: gmf_kernel = self.gmf_model.sess.run( self.gmf_model.sess.graph.get_tensor_by_name("logits/kernel:0") ) gmf_bias = self.gmf_model.sess.run( self.gmf_model.sess.graph.get_tensor_by_name("logits/bias:0") ) mlp_kernel = self.mlp_model.sess.run( self.mlp_model.sess.graph.get_tensor_by_name("logits/kernel:0") ) mlp_bias = self.mlp_model.sess.run( self.mlp_model.sess.graph.get_tensor_by_name("logits/bias:0") ) logits_kernel = np.concatenate( [self.alpha * gmf_kernel, (1 - self.alpha) * mlp_kernel] ) logits_bias = self.alpha * gmf_bias + (1 - self.alpha) * mlp_bias for v in self.graph.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES): if v.name.startswith("GMF"): sess = self.gmf_model.sess self.sess.run( tf.assign(v, sess.run(sess.graph.get_tensor_by_name(v.name))) ) elif v.name.startswith("MLP"): sess = self.mlp_model.sess self.sess.run( tf.assign(v, sess.run(sess.graph.get_tensor_by_name(v.name))) ) elif v.name.startswith("logits/kernel"): self.sess.run(tf.assign(v, logits_kernel)) elif v.name.startswith("logits/bias"): self.sess.run(tf.assign(v, logits_bias)) def _step_update(self, batch_users, batch_items, batch_ratings): _, _loss = self.sess.run( [self.train_op, self.loss], feed_dict={ self.gmf_user_id: batch_users, self.mlp_user_id: batch_users, self.item_id: batch_items, self.labels: batch_ratings.reshape(-1, 1), }, ) return _loss
[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.sess.run( self.prediction, feed_dict={ self.gmf_user_id: [user_idx], self.mlp_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.sess.run( self.prediction, feed_dict={ self.gmf_user_id: [user_idx], self.mlp_user_id: [user_idx], self.item_id: [item_idx], }, ) return user_pred.ravel()