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✨ Support for individuals in EL models
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,156 @@ | ||
| from mowl.base_models.elmodel import EmbeddingELModel | ||
| from mowl.nn import ELBEModule | ||
| from tqdm import trange, tqdm | ||
| import torch as th | ||
| import numpy as np | ||
| from deprecated.sphinx import deprecated | ||
| import logging | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
| handler = logging.StreamHandler() | ||
| logger.addHandler(handler) | ||
| logger.setLevel(logging.INFO) | ||
|
|
||
| class ELBE(EmbeddingELModel): | ||
| """ | ||
| Implementation based on [peng2020]_. | ||
| """ | ||
|
|
||
| def __init__(self, | ||
| dataset, | ||
| embed_dim=50, | ||
| margin=0, | ||
| reg_norm=1, | ||
| learning_rate=0.001, | ||
| epochs=1000, | ||
| batch_size=4096 * 8, | ||
| model_filepath=None, | ||
| device='cpu' | ||
| ): | ||
| super().__init__(dataset, embed_dim, batch_size, extended=True, model_filepath=model_filepath) | ||
|
|
||
| self.margin = margin | ||
| self.reg_norm = reg_norm | ||
| self.learning_rate = learning_rate | ||
| self.epochs = epochs | ||
| self.device = device | ||
| self._loaded = False | ||
| self.extended = False | ||
| self.init_module() | ||
|
|
||
| def init_module(self): | ||
| self.module = ELBEModule( | ||
| len(self.class_index_dict), | ||
| len(self.object_property_index_dict), | ||
| len(self.individual_index_dict), | ||
| embed_dim=self.embed_dim, | ||
| margin=self.margin | ||
| ).to(self.device) | ||
|
|
||
| def train(self, epochs=None, validate_every=1): | ||
| logger.warning('You are using the default training method. If you want to use a cutomized training method (e.g., different negative sampling, etc.), please reimplement the train method in a subclass.') | ||
|
|
||
| points_per_dataset = {k: len(v) for k, v in self.training_datasets.items()} | ||
| string = "Training datasets: \n" | ||
| for k, v in points_per_dataset.items(): | ||
| string += f"\t{k}: {v}\n" | ||
|
|
||
| logger.info(string) | ||
|
|
||
| optimizer = th.optim.Adam(self.module.parameters(), lr=self.learning_rate) | ||
| criterion = th.nn.MSELoss() | ||
| best_loss = float('inf') | ||
|
|
||
| all_classes_ids = list(self.class_index_dict.values()) | ||
| all_inds_ids = list(self.individual_index_dict.values()) | ||
|
|
||
| if epochs is None: | ||
| epochs = self.epochs | ||
|
|
||
| for epoch in trange(epochs): | ||
| self.module.train() | ||
|
|
||
| train_loss = 0 | ||
| loss = 0 | ||
|
|
||
| for gci_name, gci_dataset in self.training_datasets.items(): | ||
| if len(gci_dataset) == 0: | ||
| continue | ||
|
|
||
| scores = th.mean(self.module(gci_dataset[:], gci_name)) | ||
| loss += criterion(scores, th.zeros_like(scores, requires_grad=False)) | ||
|
|
||
| if gci_name == "gci2": | ||
| idxs_for_negs = np.random.choice(all_classes_ids, size=len(gci_dataset), replace=True) | ||
| rand_index = th.tensor(idxs_for_negs).to(self.device) | ||
| data = gci_dataset[:] | ||
| neg_data = th.cat([data[:, :2], rand_index.unsqueeze(1)], dim=1) | ||
| scores = th.mean(self.module(neg_data, gci_name, neg=True)) | ||
| loss += criterion(scores, th.ones_like(scores, requires_grad=False)) | ||
|
|
||
| if gci_name == "object_property_assertion": | ||
| idxs_for_negs = np.random.choice(all_inds_ids, size=len(gci_dataset), replace=True) | ||
| rand_index = th.tensor(idxs_for_negs).to(self.device) | ||
| data = gci_dataset[:] | ||
| neg_data = th.cat([data[:, :2], rand_index.unsqueeze(1)], dim=1) | ||
| scores = th.mean(self.module(neg_data, gci_name, neg=True)) | ||
| loss += criterion(scores, th.ones_like(scores, requires_grad=False)) | ||
|
|
||
| optimizer.zero_grad() | ||
| loss.backward() | ||
| optimizer.step() | ||
| train_loss += loss.detach().item() | ||
|
|
||
| loss = 0 | ||
|
|
||
| if (epoch + 1) % validate_every == 0: | ||
| if self.dataset.validation is not None: | ||
| with th.no_grad(): | ||
| self.module.eval() | ||
| valid_loss = 0 | ||
| gci2_data = self.validation_datasets["gci2"][:] | ||
| loss = th.mean(self.module(gci2_data, "gci2")) | ||
| valid_loss += loss.detach().item() | ||
|
|
||
|
|
||
| if valid_loss < best_loss: | ||
| best_loss = valid_loss | ||
| th.save(self.module.state_dict(), self.model_filepath) | ||
| print(f'Epoch {epoch+1}: Train loss: {train_loss} Valid loss: {valid_loss}') | ||
| else: | ||
| print(f'Epoch {epoch+1}: Train loss: {train_loss}') | ||
|
|
||
| def eval_method(self, data): | ||
| return self.module.gci2_loss(data) | ||
|
|
||
| def get_embeddings(self): | ||
| self.init_module() | ||
|
|
||
| print('Load the best model', self.model_filepath) | ||
| self.load_best_model() | ||
|
|
||
| ent_embeds = { | ||
| k: v for k, v in zip(self.class_index_dict.keys(), | ||
| self.module.class_embed.weight.cpu().detach().numpy())} | ||
| rel_embeds = { | ||
| k: v for k, v in zip(self.object_property_index_dict.keys(), | ||
| self.module.rel_embed.weight.cpu().detach().numpy())} | ||
| if self.module.ind_embed is not None: | ||
| ind_embeds = { | ||
| k: v for k, v in zip(self.individual_index_dict.keys(), | ||
| self.module.ind_embed.weight.cpu().detach().numpy())} | ||
| else: | ||
| ind_embeds = None | ||
| return ent_embeds, rel_embeds, ind_embeds | ||
|
|
||
| def load_best_model(self): | ||
| self.init_module() | ||
| self.module.load_state_dict(th.load(self.model_filepath)) | ||
| self.module.eval() | ||
|
|
||
|
|
||
| @deprecated(version='1.0.2', reason="Use ELBoxEmbeddings instead.") | ||
| class ELBoxEmbeddings(ELBE): | ||
| def __init__(self, *args, **kwargs): | ||
| super().__init__(*args, **kwargs) | ||
|
|
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