# 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 GMF(NCFBase):
"""Generalized Matrix Factorization.
Parameters
----------
num_factors: int, optional, default: 8
Embedding size of MF model.
reg: float, optional, default: 0.
Regularization (weight_decay).
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: 'GMF'
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="GMF",
num_factors=8,
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.reg = reg
########################
## TensorFlow backend ##
########################
def _build_graph_tf(self):
import tensorflow.compat.v1 as tf
from .backend_tf import gmf, loss_fn, train_fn
self.graph = tf.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 = gmf(
uid=self.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,
)
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()
def _score_tf(self, user_idx, item_idx):
feed_dict = {
self.user_id: [user_idx],
self.item_id: np.arange(self.num_items) if item_idx is None else [item_idx],
}
return self.sess.run(self.prediction, feed_dict=feed_dict)
#####################
## PyTorch backend ##
#####################
def _build_model_pt(self):
from .backend_pt import GMF
return GMF(self.num_users, self.num_items, self.num_factors)
def _score_pt(self, user_idx, item_idx):
import torch
with torch.no_grad():
users = torch.tensor(user_idx).unsqueeze(0).to(self.device)
items = (
torch.from_numpy(np.arange(self.num_items))
if item_idx is None
else torch.tensor(item_idx).unsqueeze(0)
).to(self.device)
output = self.model(users, items)
return output.squeeze().cpu().numpy()
