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* Added RecVAE * add recvae example * update readme * update README * fix conflict * remove local ndcg func * removed unnecessary things from the source code. * Add None Seed * Remove unsued code. Fix bottleneck * Added docstring for main class * sort the imports alphabetically * fix typo * reorder import s * CSR: Compressed Sparse Row, CSC: Compressed Sparse Colum * reorder imports * Update models.rst * Update README.md --------- Co-authored-by: SEUNGHEE.oh <[email protected]> Co-authored-by: Quoc-Tuan Truong <[email protected]>
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| from .recom_recvae import RecVAE |
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| # 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. | ||
| # ============================================================================ | ||
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| import numpy as np | ||
| from tqdm.auto import trange | ||
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| from ..recommender import Recommender | ||
| from ...exception import ScoreException | ||
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| class RecVAE(Recommender): | ||
| """ | ||
| RecVAE, a recommender system based on a Variational Autoencoder. | ||
| Parameters | ||
| ---------- | ||
| name : str, optional, default: 'RecVae' | ||
| Name of the recommender model. | ||
| hidden_dim : int, optional, default: 600 | ||
| Dimension of the hidden layer in the VAE architecture. | ||
| latent_dim : int, optional, default: 200 | ||
| Dimension of the latent layer in the VAE architecture. | ||
| batch_size : int, optional, default: 500 | ||
| Size of the batches used during training. | ||
| beta : float, optional | ||
| Weighting factor for the KL divergence term in the VAE loss function. | ||
| gamma : float, optional, default: 0.005 | ||
| Weighting factor for the regularization term in the loss function. | ||
| lr : float, optional, default: 5e-4 | ||
| Learning rate for the optimizer. | ||
| n_epochs : int, optional, default: 50 | ||
| Number of epochs to train the model. | ||
| n_enc_epochs : int, optional, default: 3 | ||
| Number of epochs to train the encoder part of VAE. | ||
| n_dec_epochs : int, optional, default: 1 | ||
| Number of epochs to train the decoder part of VAE. | ||
| not_alternating : boolean, optional, default: False | ||
| If True, the model training will not alternate between encoder and decoder. | ||
| trainable : boolean, optional, default: True | ||
| When False, the model will not be re-trained, and input of pre-trained parameters are required. | ||
| verbose : boolean, optional, default: False | ||
| When True, running logs are displayed. | ||
| seed : int, optional | ||
| Random seed for weight initialization and training reproducibility. | ||
| use_gpu : boolean, optional, default: True | ||
| When True, training utilizes GPU if available. | ||
| References | ||
| ---------- | ||
| * RecVAE GitHub Repository: https://github.com/ilya-shenbin/RecVAE | ||
| * Paper Link: https://arxiv.org/abs/1912.11160 | ||
| """ | ||
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| def __init__( | ||
| self, | ||
| name="RecVae", | ||
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| hidden_dim = 600, | ||
| latent_dim = 200, | ||
| batch_size = 500, | ||
| beta = None, | ||
| gamma = 0.005, | ||
| lr = 5e-4, | ||
| n_epochs = 50, | ||
| n_enc_epochs = 3, | ||
| n_dec_epochs = 1, | ||
| not_alternating = False, | ||
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| trainable=True, | ||
| verbose=False, | ||
| seed=None, | ||
| use_gpu=True, | ||
| ): | ||
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| Recommender.__init__(self,name=name, trainable=trainable, verbose=verbose) | ||
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| self.hidden_dim = hidden_dim | ||
| self.latent_dim = latent_dim | ||
| self.batch_size = batch_size | ||
| self.beta = beta | ||
| self.gamma = gamma | ||
| self.lr = lr | ||
| self.n_epochs = n_epochs | ||
| self.n_enc_epochs = n_enc_epochs | ||
| self.n_dec_epochs = n_dec_epochs | ||
| self.not_alternating = not_alternating | ||
| self.seed = seed | ||
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| import torch | ||
| if use_gpu and torch.cuda.is_available(): | ||
| self.device = torch.device("cuda:0") | ||
| else: | ||
| self.device = torch.device("cpu") | ||
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| def run(self,model, opts, train_set, my_batch_size, n_epochs, beta, gamma, dropout_rate): | ||
| import torch | ||
| train_data = train_set.csr_matrix | ||
| model.train() | ||
| for _ in range(n_epochs): | ||
| for i, batch_ids in enumerate( | ||
| train_set.user_iter(my_batch_size, shuffle=True) | ||
| ): | ||
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| ratings = torch.Tensor((train_data[batch_ids,:]).toarray()).to(self.device) | ||
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| for optimizer in opts: | ||
| optimizer.zero_grad() | ||
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| _, loss = model(ratings, beta=beta, gamma=gamma, dropout_rate=dropout_rate) | ||
| loss.backward() | ||
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| for optimizer in opts: | ||
| optimizer.step() | ||
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| 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) | ||
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| from .recvae import VAE | ||
| import torch | ||
| from torch import optim | ||
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| from ...metrics import NDCG | ||
| from ...eval_methods import ranking_eval | ||
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| if self.trainable: | ||
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| if self.verbose: | ||
| print("Learning...") | ||
| if self.seed is not None: | ||
| np.random.seed(self.seed) | ||
| torch.manual_seed(self.seed) | ||
| torch.cuda.manual_seed(self.seed) | ||
| torch.cuda.manual_seed_all(self.seed) | ||
| torch.backends.cudnn.deterministic = True | ||
| torch.backends.cudnn.benchmark = False | ||
| torch.backends.cudnn.enabled = False | ||
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| model_kwargs = { | ||
| 'hidden_dim': self.hidden_dim, | ||
| 'latent_dim': self.latent_dim, | ||
| 'input_dim': train_set.num_items, | ||
| } | ||
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| self.recvae_model = VAE(**model_kwargs).to(self.device) | ||
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| learning_kwargs = { | ||
| 'model': self.recvae_model, | ||
| 'train_set': train_set, | ||
| 'my_batch_size': self.batch_size, | ||
| 'beta': self.beta, | ||
| 'gamma': self.gamma | ||
| } | ||
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| self.mydata = train_set | ||
| decoder_params = set(self.recvae_model.decoder.parameters()) | ||
| encoder_params = set(self.recvae_model.encoder.parameters()) | ||
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| optimizer_encoder = optim.Adam(encoder_params, lr=self.lr) | ||
| optimizer_decoder = optim.Adam(decoder_params, lr=self.lr) | ||
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| progress_bar = trange(1, self.n_epochs + 1, desc="RecVAE", disable=not self.verbose) | ||
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| for _ in progress_bar: | ||
| if self.not_alternating: | ||
| self.run(opts=[optimizer_encoder, optimizer_decoder], n_epochs=1, dropout_rate=0.5, **learning_kwargs) | ||
| else: | ||
| self.run(opts=[optimizer_encoder], n_epochs=self.n_enc_epochs, dropout_rate=0.5, **learning_kwargs) | ||
| self.recvae_model.update_prior() | ||
| self.run(opts=[optimizer_decoder], n_epochs=self.n_dec_epochs, dropout_rate=0, **learning_kwargs) | ||
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| ndcg_100 = ranking_eval( | ||
| model=self, | ||
| metrics=[NDCG(k=100)], | ||
| train_set=train_set, | ||
| test_set=train_set, | ||
| )[0][0] | ||
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| progress_bar.set_postfix(ndcg100 = ndcg_100) | ||
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| if self.verbose: | ||
| print(f"Learning completed : [{ndcg_100}]") | ||
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| elif self.verbose: | ||
| print("%s is trained already (trainable = False)" % (self.name)) | ||
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| return self | ||
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| def score(self, user_idx, item_idx=None): | ||
| """Predict the scores/ratings of a user for an item. | ||
| Parameters | ||
| ---------- | ||
| user_id: int, required | ||
| The index of the user for whom to perform score prediction. | ||
| item_id: 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 | ||
| """ | ||
| import torch | ||
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| ratings_in = self.mydata.matrix[user_idx,:] | ||
| ratings_pred = self.recvae_model(torch.Tensor(ratings_in.toarray()).to(self.device), calculate_loss=False).cpu().detach().numpy().flatten() | ||
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| if item_idx is None: | ||
| if not self.knows_user(user_idx): | ||
| raise ScoreException( | ||
| "Can't make score prediction for (user_id=%d)" % user_idx | ||
| ) | ||
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| return ratings_pred | ||
| else: | ||
| if not (self.knows_user(user_idx) and self.knows_item(item_idx)): | ||
| raise ScoreException( | ||
| "Can't make score prediction for (user_id=%d, item_id=%d)" | ||
| % (user_idx, item_idx) | ||
| ) | ||
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| return ratings_pred[item_idx] | ||
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