# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
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: float, optional, default: 0.
Regularization (weight_decay).
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
backend: str, optional, default: 'tensorflow'
Backend used for model training: tensorflow, pytorch
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=0.0,
num_epochs=20,
batch_size=256,
num_neg=4,
lr=0.001,
learner="adam",
backend="tensorflow",
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,
backend=backend,
early_stopping=early_stopping,
seed=seed,
)
self.num_factors = num_factors
self.layers = layers
self.act_fn = act_fn
self.reg = reg
self.pretrained = False
self.ignored_attrs.extend(
[
"gmf_user_id",
"mlp_user_id",
"pretrained_gmf",
"pretrained_mlp",
"alpha",
]
)
[docs]
def from_pretrained(self, pretrained_gmf, pretrained_mlp, alpha=0.5):
"""Provide pre-trained GMF and MLP models. Section 3.4.1 of the paper.
Parameters
----------
pretrained_gmf: object of type GMF, required
Reference to trained/fitted GMF model.
pretrained_mlp: object of type MLP, required
Reference to trained/fitted MLP 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.pretrained_gmf = pretrained_gmf
self.pretrained_mlp = pretrained_mlp
self.alpha = alpha
return self
########################
## TensorFlow backend ##
########################
def _build_graph_tf(self):
import tensorflow.compat.v1 as tf
from .backend_tf import gmf, mlp, loss_fn, train_fn
self.graph = tf.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,
reg_item=self.reg,
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] * len(self.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_tf()
if self.pretrained:
gmf_kernel = self.pretrained_gmf.sess.run(
self.pretrained_gmf.sess.graph.get_tensor_by_name("logits/kernel:0")
)
gmf_bias = self.pretrained_gmf.sess.run(
self.pretrained_gmf.sess.graph.get_tensor_by_name("logits/bias:0")
)
mlp_kernel = self.pretrained_mlp.sess.run(
self.pretrained_mlp.sess.graph.get_tensor_by_name("logits/kernel:0")
)
mlp_bias = self.pretrained_mlp.sess.run(
self.pretrained_mlp.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.pretrained_gmf.sess
self.sess.run(
tf.assign(v, sess.run(sess.graph.get_tensor_by_name(v.name)))
)
elif v.name.startswith("MLP"):
sess = self.pretrained_mlp.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 _get_feed_dict(self, batch_users, batch_items, batch_ratings):
return {
self.gmf_user_id: batch_users,
self.mlp_user_id: batch_users,
self.item_id: batch_items,
self.labels: batch_ratings.reshape(-1, 1),
}
def _score_tf(self, user_idx, item_idx):
if item_idx is None:
feed_dict = {
self.gmf_user_id: [user_idx],
self.mlp_user_id: np.ones(self.num_items) * user_idx,
self.item_id: np.arange(self.num_items),
}
else:
feed_dict = {
self.gmf_user_id: [user_idx],
self.mlp_user_id: [user_idx],
self.item_id: [item_idx],
}
return self.sess.run(self.prediction, feed_dict=feed_dict)
#####################
## PyTorch backend ##
#####################
def _build_model_pt(self):
from .backend_pt import NeuMF
model = NeuMF(
num_users=self.num_users,
num_items=self.num_items,
layers=self.layers,
act_fn=self.act_fn,
)
if self.pretrained:
model.from_pretrained(
self.pretrained_gmf.model, self.pretrained_mlp.model, self.alpha
)
return model
def _score_pt(self, user_idx, item_idx):
import torch
with torch.no_grad():
if item_idx is None:
users = torch.from_numpy(np.ones(self.num_items, dtype=int) * user_idx)
items = (torch.from_numpy(np.arange(self.num_items))).to(self.device)
else:
users = torch.tensor(user_idx).unsqueeze(0)
items = torch.tensor(item_idx).unsqueeze(0)
gmf_users = torch.tensor(user_idx).unsqueeze(0).to(self.device)
output = self.model(
users.to(self.device), items.to(self.device), gmf_users.to(self.device)
)
return output.squeeze().cpu().numpy()
