# 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 ..recommender import Recommender
from ..recommender import ANNMixin, MEASURE_DOT
from ...utils.common import scale
from ...exception import ScoreException
[docs]
class BiVAECF(Recommender, ANNMixin):
"""Bilateral Variational AutoEncoder for Collaborative Filtering.
Parameters
----------
k: int, optional, default: 10
The dimension of the stochastic user ``theta'' and item ``beta'' factors.
encoder_structure: list, default: [20]
The number of neurons per layer of the user and item encoders for BiVAE.
For example, encoder_structure = [20], the user (item) encoder structure will be [num_items, 20, k] ([num_users, 20, k]).
act_fn: str, default: 'tanh'
Name of the activation function used between hidden layers of the auto-encoder.
Supported functions: ['sigmoid', 'tanh', 'elu', 'relu', 'relu6']
likelihood: str, default: 'pois'
The likelihood function used for modeling the observations.
Supported choices:
bern: Bernoulli likelihood
gaus: Gaussian likelihood
pois: Poisson likelihood
n_epochs: int, optional, default: 100
The number of epochs for SGD.
batch_size: int, optional, default: 100
The batch size.
learning_rate: float, optional, default: 0.001
The learning rate for Adam.
beta_kl: float, optional, default: 1.0
The weight of the KL terms as in beta-VAE.
cap_priors: dict, optional, default: {"user":False, "item":False}
When {"user":True, "item":True}, CAP priors are used (see BiVAE paper for details),\
otherwise the standard Normal is used as a Prior over the user and item latent variables.
name: string, optional, default: 'BiVAECF'
The 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.
use_gpu: boolean, optional, default: True
If True and your system supports CUDA then training is performed on GPUs.
References
----------
* Quoc-Tuan Truong, Aghiles Salah, Hady W. Lauw. " Bilateral Variational Autoencoder for Collaborative Filtering."
ACM International Conference on Web Search and Data Mining (WSDM). 2021.
"""
def __init__(
self,
name="BiVAECF",
k=10,
encoder_structure=[20],
act_fn="tanh",
likelihood="pois",
n_epochs=100,
batch_size=100,
learning_rate=0.001,
beta_kl=1.0,
cap_priors={"user": False, "item": False},
trainable=True,
verbose=False,
seed=None,
use_gpu=True,
):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.encoder_structure = encoder_structure
self.act_fn = act_fn
self.likelihood = likelihood
self.batch_size = batch_size
self.n_epochs = n_epochs
self.learning_rate = learning_rate
self.beta_kl = beta_kl
self.cap_priors = cap_priors
self.seed = seed
self.use_gpu = use_gpu
[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)
import torch
from .bivae import BiVAE, learn
self.device = (
torch.device("cuda:0")
if (self.use_gpu and torch.cuda.is_available())
else torch.device("cpu")
)
if self.trainable:
feature_dim = {"user": None, "item": None}
if self.cap_priors.get("user", False):
if train_set.user_feature is None:
raise ValueError(
"CAP priors for users is set to True but no user features are provided"
)
else:
feature_dim["user"] = train_set.user_feature.feature_dim
if self.cap_priors.get("item", False):
if train_set.item_feature is None:
raise ValueError(
"CAP priors for items is set to True but no item features are provided"
)
else:
feature_dim["item"] = train_set.item_feature.feature_dim
if self.seed is not None:
torch.manual_seed(self.seed)
torch.cuda.manual_seed(self.seed)
if not hasattr(self, "bivae"):
num_items = train_set.matrix.shape[1]
num_users = train_set.matrix.shape[0]
self.bivae = BiVAE(
k=self.k,
user_encoder_structure=[num_items] + self.encoder_structure,
item_encoder_structure=[num_users] + self.encoder_structure,
act_fn=self.act_fn,
likelihood=self.likelihood,
cap_priors=self.cap_priors,
feature_dim=feature_dim,
batch_size=self.batch_size,
).to(self.device)
learn(
self.bivae,
train_set,
n_epochs=self.n_epochs,
batch_size=self.batch_size,
learn_rate=self.learning_rate,
beta_kl=self.beta_kl,
verbose=self.verbose,
device=self.device,
)
elif self.verbose:
print("%s is trained already (trainable = False)" % (self.name))
return self
[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:
theta_u = self.bivae.mu_theta[user_idx].view(1, -1)
beta = self.bivae.mu_beta
return self.bivae.decode_user(theta_u, beta).cpu().numpy().ravel()
else:
theta_u = self.bivae.mu_theta[user_idx].view(1, -1)
beta_i = self.bivae.mu_beta[item_idx].view(1, -1)
pred = self.bivae.decode_user(theta_u, beta_i).cpu().numpy().ravel()
return scale(pred, self.min_rating, self.max_rating, 0.0, 1.0)
[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.
"""
user_vectors = self.bivae.mu_theta.detach().cpu().numpy()
return user_vectors
[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.
"""
item_vectors = self.bivae.mu_beta.detach().cpu().numpy()
return item_vectors
[docs]
def save(self, save_dir=None, save_trainset=True):
"""Save model to the filesystem.
Parameters
----------
save_dir: str, default: None
Path to a directory for the model to be stored.
save_trainset: bool, default: True
Save train_set together with the model. This is useful
if we want to deploy model later because train_set is
required for certain evaluation steps.
Returns
-------
model_file : str
Path to the model file stored on the filesystem.
"""
import torch
if save_dir is None:
return
self.bivae.to(torch.device("cpu"))
model_file = Recommender.save(
self, save_dir=save_dir, save_trainset=save_trainset
)
return model_file
[docs]
@staticmethod
def load(model_path, trainable=False):
"""Load model from the filesystem.
Parameters
----------
model_path: str, required
Path to a file or directory where the model is stored. If a directory is
provided, the latest model will be loaded.
trainable: boolean, optional, default: False
Set it to True if you would like to finetune the model. By default,
the model parameters are assumed to be fixed after being loaded.
Returns
-------
self : object
"""
import torch
model = Recommender.load(model_path, trainable)
if "cuda" in str(model.device) and torch.cuda.is_available():
model.bivae.to(model.device)
return model
