# 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.
# ============================================================================
from ..recommender import Recommender
from ..recommender import ANNMixin, MEASURE_DOT
from ...exception import ScoreException
from tqdm.auto import tqdm, trange
[docs]
class NGCF(Recommender, ANNMixin):
"""
Neural Graph Collaborative Filtering
Parameters
----------
name: string, default: 'NGCF'
The name of the recommender model.
emb_size: int, default: 64
Size of the node embeddings.
layer_sizes: list, default: [64, 64, 64]
Size of the output of convolution layers.
dropout_rates: list, default: [0.1, 0.1, 0.1]
Dropout rate for each of the convolution layers.
- Number of values should be the same as 'layer_sizes'
num_epochs: int, default: 1000
Maximum number of iterations or the number of epochs.
learning_rate: float, default: 0.001
The learning rate that determines the step size at each iteration.
batch_size: int, default: 1024
Mini-batch size used for training.
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.
lambda_reg: float, default: 1e-4
Weight decay for the L2 normalization.
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: 2020
Random seed for parameters initialization.
References
----------
* Wang, Xiang, et al. "Neural graph collaborative filtering." Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. 2019.
"""
def __init__(
self,
name="NGCF",
emb_size=64,
layer_sizes=[64, 64, 64],
dropout_rates=[0.1, 0.1, 0.1],
num_epochs=1000,
learning_rate=0.001,
batch_size=1024,
early_stopping=None,
lambda_reg=1e-4,
trainable=True,
verbose=False,
seed=2020,
):
super().__init__(name=name, trainable=trainable, verbose=verbose)
self.emb_size = emb_size
self.layer_sizes = layer_sizes
self.dropout_rates = dropout_rates
self.num_epochs = num_epochs
self.learning_rate = learning_rate
self.batch_size = batch_size
self.early_stopping = early_stopping
self.lambda_reg = lambda_reg
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 not self.trainable:
return self
# model setup
import torch
from .ngcf import Model
from .ngcf import construct_graph
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if self.seed is not None:
torch.manual_seed(self.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(self.seed)
graph = construct_graph(train_set, self.total_users, self.total_items).to(
self.device
)
model = Model(
graph,
self.emb_size,
self.layer_sizes,
self.dropout_rates,
self.lambda_reg,
).to(self.device)
optimizer = torch.optim.Adam(model.parameters(), lr=self.learning_rate)
# model training
pbar = trange(
self.num_epochs,
desc="Training",
unit="iter",
position=0,
leave=False,
disable=not self.verbose,
)
for _ in pbar:
model.train()
accum_loss = 0.0
for batch_u, batch_i, batch_j in tqdm(
train_set.uij_iter(
batch_size=self.batch_size,
shuffle=True,
),
desc="Epoch",
total=train_set.num_batches(self.batch_size),
leave=False,
position=1,
disable=not self.verbose,
):
u_g_embeddings, pos_i_g_embeddings, neg_i_g_embeddings = model(
graph, batch_u, batch_i, batch_j
)
batch_loss, batch_bpr_loss, batch_reg_loss = model.loss_fn(
u_g_embeddings, pos_i_g_embeddings, neg_i_g_embeddings
)
accum_loss += batch_loss.cpu().item() * len(batch_u)
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
accum_loss /= len(train_set.uir_tuple[0]) # normalize over all observations
pbar.set_postfix(loss=accum_loss)
# store user and item embedding matrices for prediction
model.eval()
u_embs, i_embs, _ = model(graph)
# we will use numpy for faster prediction in the score function, no need torch
self.U = u_embs.cpu().detach().numpy()
self.V = i_embs.cpu().detach().numpy()
if self.early_stopping is not None and self.early_stop(
train_set, val_set, **self.early_stopping
):
break
[docs]
def monitor_value(self, train_set, val_set):
"""Calculating monitored value used for early stopping on validation set (`val_set`).
This function will be called by `early_stop()` function.
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
-------
res : float
Monitored value on validation set.
Return `None` if `val_set` is `None`.
"""
if val_set is None:
return None
from ...metrics import Recall
from ...eval_methods import ranking_eval
recall_20 = ranking_eval(
model=self,
metrics=[Recall(k=20)],
train_set=train_set,
test_set=val_set,
verbose=True,
)[0][0]
return recall_20 # Section 4.2.3 in the paper
[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 self.is_unknown_user(user_idx):
raise ScoreException("Can't make score prediction for user %d" % user_idx)
if item_idx is not None and self.is_unknown_item(item_idx):
raise ScoreException("Can't make score prediction for item %d" % item_idx)
if item_idx is None:
return self.V.dot(self.U[user_idx, :])
return self.V[item_idx, :].dot(self.U[user_idx, :])
[docs]
def get_vector_measure(self):
"""Getting a valid choice of vector measurement in ANNMixin._measures.
Returns
-------
measure: MEASURE_DOT
Dot product aka. inner product
"""
return MEASURE_DOT
[docs]
def get_user_vectors(self):
"""Getting a matrix of user vectors serving as query for ANN search.
Returns
-------
out: numpy.array
Matrix of user vectors for all users available in the model.
"""
return self.U
[docs]
def get_item_vectors(self):
"""Getting a matrix of item vectors used for building the index for ANN search.
Returns
-------
out: numpy.array
Matrix of item vectors for all items available in the model.
"""
return self.V
