# 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
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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,
# 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 ..recommender import Recommender
from ...exception import ScoreException
[docs]class GMF(Recommender):
"""Generalized Matrix Factorization.
Parameters
----------
num_factors: int, optional, default: 8
Embedding size of MF model.
regs: float, optional, default: 0.
Regularization for user and item 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: '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, regs=(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)
self.num_factors = num_factors
self.regs = regs
self.num_epochs = num_epochs
self.batch_size = batch_size
self.num_neg = num_neg
self.learning_rate = lr
self.learner = learner
self.early_stopping = early_stopping
self.seed = seed
[docs] def fit(self, train_set, val_set=None):
"""Fit the model to observations.
Parameters
----------
train_set: :obj:`cornac.data.Dataset`, required
User-Item preference data as well as additional modalities.
val_set: :obj:`cornac.data.Dataset`, optional, default: None
User-Item preference data for model selection purposes (e.g., early stopping).
Returns
-------
self : object
"""
Recommender.fit(self, train_set, val_set)
if self.trainable:
self._fit_gmf()
return self
def _fit_gmf(self):
import os
import tensorflow as tf
from tqdm import trange
from .ops import gmf, loss_fn, train_fn
np.random.seed(self.seed)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
graph = tf.Graph()
with 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.train_set.num_users,
num_items=self.train_set.num_items, emb_size=self.num_factors,
reg_user=self.regs[0], reg_item=self.regs[1], 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)
train_op = train_fn(self.loss, learning_rate=self.learning_rate, learner=self.learner)
initializer = tf.global_variables_initializer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(graph=graph, config=config)
self.sess.run(initializer)
loop = trange(self.num_epochs, disable=not self.verbose)
for _ in loop:
count = 0
sum_loss = 0
for i, (batch_users, batch_items, batch_ratings) in enumerate(
self.train_set.uir_iter(self.batch_size, shuffle=True, binary=True, num_zeros=self.num_neg)):
_, _loss = self.sess.run([train_op, self.loss],
feed_dict={
self.user_id: batch_users,
self.item_id: batch_items,
self.labels: batch_ratings.reshape(-1, 1)
})
count += len(batch_ratings)
sum_loss += _loss * len(batch_ratings)
if i % 10 == 0:
loop.set_postfix(loss=(sum_loss / count))
if self.early_stopping is not None and self.early_stop(**self.early_stopping):
break
loop.close()
[docs] def monitor_value(self):
"""Calculating monitored value used for early stopping on validation set (`val_set`).
This function will be called by `early_stop()` function.
Returns
-------
res : float
Monitored value on validation set.
Return `None` if `val_set` is `None`.
"""
if self.val_set is None:
return None
from .ops import ndcg
return ndcg(self, self.train_set, self.val_set)
[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.user_id: [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.user_id: [user_idx], self.item_id: [item_idx]
})
return user_pred.ravel()