Source code for ontolearn.nces_trainer

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"""Trainer for NCES instances"""
import numpy as np
import copy
import torch
from torch.utils.data import DataLoader
from tqdm import trange
import os
import json
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.nn import functional as F
from torch.nn.utils import clip_grad_value_
from torch.nn.utils.rnn import pad_sequence
import time
from collections import defaultdict
from ontolearn.data_struct import NCESDataset, ROCESDataset, TriplesDataset

[docs] def before_pad(arg): arg_temp = [] for atm in arg: if atm == 'PAD': break arg_temp.append(atm) if len(set(arg_temp)) == 3 and ('⊓' in arg_temp or '⊔' in arg_temp): return arg_temp[0] return arg_temp
[docs] class NCESTrainer: """Trainer for neural class expression synthesizers, i.e., NCES, NCES2, ROCES.""" def __init__(self, synthesizer, epochs=300, batch_size=128, learning_rate=1e-4, tmax=20, eta_min=1e-5, clip_value=5.0, num_workers=8, storage_path="./"): self.synthesizer = synthesizer self.epochs = epochs self.batch_size = batch_size self.learning_rate = learning_rate self.tmax = tmax self.eta_min = eta_min self.clip_value = clip_value self.num_workers = num_workers self.storage_path = storage_path
[docs] @staticmethod def compute_accuracy(prediction, target): def soft(arg1, arg2): arg1_ = arg1 arg2_ = arg2 if isinstance(arg1_, str): arg1_ = set(before_pad(NCESDataset.decompose(arg1_))) else: arg1_ = set(before_pad(arg1_)) if isinstance(arg2_, str): arg2_ = set(before_pad(NCESDataset.decompose(arg2_))) else: arg2_ = set(before_pad(arg2_)) return 100*float(len(arg1_.intersection(arg2_)))/len(arg1_.union(arg2_)) def hard(arg1, arg2): arg1_ = arg1 arg2_ = arg2 if isinstance(arg1_, str): arg1_ = before_pad(NCESDataset.decompose(arg1_)) else: arg1_ = before_pad(arg1_) if isinstance(arg2_, str): arg2_ = before_pad(NCESDataset.decompose(arg2_)) else: arg2_ = before_pad(arg2_) return 100*float(sum(map(lambda x, y: x == y, arg1_, arg2_)))/max(len(arg1_), len(arg2_)) soft_acc = sum(map(soft, prediction, target))/len(target) hard_acc = sum(map(hard, prediction, target))/len(target) return soft_acc, hard_acc
[docs] def get_optimizer(self, model, emb_model=None, optimizer='Adam'): # pragma: no cover if emb_model is not None: parameters = list(model.parameters()) + list(emb_model.parameters()) else: parameters = model.parameters() if optimizer == 'Adam': return torch.optim.Adam(parameters, lr=self.learning_rate) elif optimizer == 'SGD': return torch.optim.SGD(parameters, lr=self.learning_rate) elif optimizer == 'RMSprop': return torch.optim.RMSprop(parameters, lr=self.learning_rate) else: raise ValueError print('Unsupported optimizer')
[docs] def get_data_idxs(self): data_idxs = [(self.synthesizer.triples_data.entity2idx.loc[t[0]].values[0], self.synthesizer.triples_data.relation2idx.loc[t[1]].values[0], self.synthesizer.triples_data.entity2idx.loc[t[2]].values[0]) for t in self.synthesizer.triples_data.triples] return data_idxs
[docs] def get_er_vocab(self): er_vocab = defaultdict(list) data_idxs = self.get_data_idxs() for triple in data_idxs: er_vocab[(triple[0], triple[1])].append(triple[2]) return er_vocab
[docs] @staticmethod def show_num_trainable_params(synthesizer): size_emb_model = 0 # If training NCES there is no embedding model to train size_model = sum([p.numel() for p in synthesizer["model"].parameters()]) if synthesizer["emb_model"]: size_emb_model = sum([p.numel() for p in synthesizer["emb_model"].parameters()]) print("#"*30+"Trainable model size"+"#"*30) print("Synthesizer: ", size_model) print("Embedding model: ", size_emb_model) print("#"*30+"Trainable model size"+"#"*30)
[docs] def collate_batch(self, batch): # pragma: no cover pos_emb_list = [] neg_emb_list = [] target_labels = [] for pos_emb, neg_emb, label in batch: if pos_emb.ndim != 2: pos_emb = pos_emb.reshape(1, -1) if neg_emb.ndim != 2: neg_emb = neg_emb.reshape(1, -1) pos_emb_list.append(pos_emb) neg_emb_list.append(neg_emb) target_labels.append(label) pos_emb_list[0] = F.pad(pos_emb_list[0], (0, 0, 0, self.synthesizer.num_examples - pos_emb_list[0].shape[0]), "constant", 0) pos_emb_list = pad_sequence(pos_emb_list, batch_first=True, padding_value=0) neg_emb_list[0] = F.pad(neg_emb_list[0], (0, 0, 0, self.synthesizer.num_examples - neg_emb_list[0].shape[0]), "constant", 0) neg_emb_list = pad_sequence(neg_emb_list, batch_first=True, padding_value=0) target_labels = pad_sequence(target_labels, batch_first=True, padding_value=-100) return pos_emb_list, neg_emb_list, target_labels
[docs] def map_to_token(self, idx_array): return self.synthesizer.inv_vocab[idx_array]
[docs] def train_step(self, batch, model, emb_model, optimizer, device, triples_dataloader=None): if emb_model: try: triples_batch = next(triples_dataloader) except: triples_dataloader = iter(DataLoader(TriplesDataset(er_vocab=self.er_vocab, num_e=len(self.synthesizer.triples_data.entities)), batch_size=2*self.batch_size, num_workers=self.num_workers, shuffle=True)) triples_batch = next(triples_dataloader) x_pos, x_neg, labels = batch target_sequence = self.map_to_token(labels) if device.type == "cuda": x_pos, x_neg, labels = x_pos.cuda(), x_neg.cuda(), labels.cuda() pred_sequence, scores = model(x_pos, x_neg) loss = model.loss(scores, labels) # Forward triples to embedding model if emb_model is not None: e1_idx, r_idx, emb_targets = triples_batch if device.type == "cuda": emb_targets = emb_targets.cuda() r_idx = r_idx.cuda() e1_idx = e1_idx.cuda() loss_ = emb_model.forward_head_and_loss(e1_idx, r_idx, emb_targets) loss = loss + loss_ s_acc, h_acc = self.compute_accuracy(pred_sequence, target_sequence) optimizer.zero_grad() loss.backward() clip_grad_value_(model.parameters(), clip_value=self.clip_value) if emb_model is not None: clip_grad_value_(emb_model.parameters(), clip_value=self.clip_value) optimizer.step() return loss.item(), s_acc, h_acc
[docs] def train(self, data, shuffle_examples=False, example_sizes=None, save_model=True, optimizer='Adam', record_runtime=True): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for model_name in self.synthesizer.model: self.show_num_trainable_params(self.synthesizer.model[model_name]) if device.type == "cpu": print("Training on CPU, it may take long...") else: print("GPU available !") print() print("#"*80) print() model = copy.deepcopy(self.synthesizer.model[model_name]) if model["model"].name == "SetTransformer": start_message = "{}: {} ({} inducing points) starts training... \n".format(self.synthesizer.name, model["model"].name, model["model"].m) else: start_message = "{}: {} starts training... \n".format(self.synthesizer.name, model["model"].name) print(start_message) print("#"*80, "\n") desc = model["model"].name if model["model"].name != model_name: desc += "_" + model_name if device.type == "cuda": model["model"].cuda() if model["emb_model"] is not None: model["emb_model"].cuda() optim_algo = self.get_optimizer(model=model["model"], emb_model=model["emb_model"], optimizer=optimizer) self.scheduler = CosineAnnealingLR(optim_algo, T_max=self.tmax, eta_min=self.eta_min) if model["emb_model"] is not None: # When there is no embedding_model, then we are training NCES2 or ROCES and we need to repeatedly query the embedding model for the updated embeddings train_dataset = ROCESDataset(data, self.synthesizer.triples_data, k=self.synthesizer.k if hasattr(self.synthesizer, 'k') else None, vocab=self.synthesizer.vocab, inv_vocab=self.synthesizer.inv_vocab, max_length=self.synthesizer.max_length, num_examples=self.synthesizer.num_examples, sampling_strategy=self.synthesizer.sampling_strategy) train_dataset.load_embeddings(model["emb_model"]) # Load embeddings the first time train_dataloader = DataLoader(train_dataset, batch_size=self.batch_size, num_workers=self.num_workers, collate_fn=self.collate_batch, shuffle=True) # Get dataloader for the embedding model self.er_vocab = self.get_er_vocab() triples_dataloader = iter(DataLoader(TriplesDataset(er_vocab=self.er_vocab, num_e=len(self.synthesizer.triples_data.entities)), batch_size=2*self.batch_size, num_workers=self.num_workers, shuffle=True)) else: assert hasattr(self.synthesizer, "instance_embeddings"), "If no embedding model is available, `instance_embeddings` must be an attribute of the synthesizer since you are probably training NCES" train_dataloader = DataLoader(NCESDataset(data, embeddings=self.synthesizer.instance_embeddings, num_examples=self.synthesizer.num_examples, vocab=self.synthesizer.vocab, inv_vocab=self.synthesizer.inv_vocab, shuffle_examples=shuffle_examples, max_length=self.synthesizer.max_length, example_sizes=example_sizes), batch_size=self.batch_size, num_workers=self.num_workers, collate_fn=self.collate_batch, shuffle=True) Train_loss = [] Train_acc = defaultdict(list) best_score = 0 best_weights = (None, None) s_acc, h_acc = 0, 0 if record_runtime: t0 = time.time() Epochs = trange(self.epochs, desc=f'Loss: {np.nan}, Soft Acc: {s_acc}%, Hard Acc: {h_acc}%, Lr: {self.learning_rate}', leave=True, colour='green') for e in Epochs: soft_acc, hard_acc = [], [] train_losses = [] batch_count = 0 num_batches = len(data) // self.batch_size if len(data) % self.batch_size == 0 else len(data) // self.batch_size + 1 batch_data = trange(num_batches, desc=f'Train: <Batch: {batch_count}/{num_batches}, Loss: {np.nan}, Soft Acc: {s_acc}%, Hard Acc: {h_acc}%>', leave=False) if model["emb_model"] is not None: for _, batch in zip(batch_data, train_dataloader): loss, s_acc, h_acc = self.train_step(batch, model["model"], model["emb_model"], optim_algo, device, triples_dataloader) batch_count += 1 batch_data.set_description('Train: <Batch: {}/{}, Loss: {:.4f}, Soft Acc: {:.2f}%, Hard Acc: {:.2f}%>'.format(batch_count, num_batches, loss, s_acc, h_acc)) batch_data.refresh() soft_acc.append(s_acc) hard_acc.append(h_acc) train_losses.append(loss) # Load currently learned embeddings train_dataset.load_embeddings(model["emb_model"]) else: # When an embedding model is None, then we are training NCES for _, batch in zip(batch_data, train_dataloader): loss, s_acc, h_acc = self.train_step(batch, model["model"], model["emb_model"], optim_algo, device) batch_count += 1 batch_data.set_description('Train: <Batch: {}/{}, Loss: {:.4f}, Soft Acc: {:.2f}%, Hard Acc: {:.2f}%>'.format(batch_count, num_batches, loss, s_acc, h_acc)) batch_data.refresh() soft_acc.append(s_acc) hard_acc.append(h_acc) train_losses.append(loss) train_soft_acc, train_hard_acc = np.mean(soft_acc), np.mean(hard_acc) Train_loss.append(np.mean(train_losses)) Train_acc['soft'].append(train_soft_acc) Train_acc['hard'].append(train_hard_acc) self.scheduler.step() last_lr = self.scheduler.get_last_lr()[0] Epochs.set_description('<Epoch: {}/{}> Loss: {:.4f}, Soft Acc: {:.2f}%, Hard Acc: {:.2f}(%), Lr: {:.6f}'.format(e+1, self.epochs, Train_loss[-1], train_soft_acc, train_hard_acc, last_lr)) Epochs.refresh() model_weights = copy.deepcopy(model["model"].state_dict()) emb_model_weights = None if model["emb_model"] is not None: emb_model_weights = copy.deepcopy(model["emb_model"].state_dict()) if Train_acc['hard'] and Train_acc['hard'][-1] > best_score: best_score = Train_acc['hard'][-1] best_weights = (model_weights, emb_model_weights) model["model"].load_state_dict(best_weights[0]) if model["emb_model"] is not None: model["emb_model"].load_state_dict(best_weights[1]) if record_runtime: # pragma: no cover duration = time.time()-t0 runtime_info = {"Architecture": model["model"].name, "Number of Epochs": self.epochs, "Runtime (s)": duration} if not os.path.exists(self.storage_path+"/runtime/"): os.mkdir(self.storage_path+"/runtime/") with open(self.storage_path+"/runtime/runtime"+"_"+desc+".json", "w") as file: json.dump(runtime_info, file, indent=3) results_dict = dict() print("Top performance: loss: {:.4f}, soft accuracy: {:.2f}% ... " "hard accuracy: {:.2f}%".format(min(Train_loss), max(Train_acc['soft']), max(Train_acc['hard']))) print() results_dict.update({"Train Max Soft Acc": max(Train_acc['soft']), "Train Max Hard Acc": max(Train_acc['hard']), "Train Min Loss": min(Train_loss)}) if save_model: # pragma: no cover if not os.path.exists(self.storage_path+"/results/"): os.mkdir(self.storage_path+"/results/") with open(self.storage_path+"/results/"+"results"+"_"+desc+".json", "w") as file: json.dump(results_dict, file, indent=3) if not os.path.exists(self.storage_path+"/trained_models/"): os.mkdir(self.storage_path+"/trained_models/") model_file_name = "trained_" + model["model"].name if model["model"].name != model_name: model_file_name += "_" + model_name if model["emb_model"] is not None: model_file_name += "_" + model["emb_model"].name torch.save(model["model"].state_dict(), self.storage_path+"/trained_models/"+model_file_name+".pt") with open(self.storage_path+"/trained_models/config.json", "w") as f: config = {"max_length": self.synthesizer.max_length, "proj_dim": self.synthesizer.proj_dim, "num_heads": self.synthesizer.num_heads, "num_seeds": self.synthesizer.num_seeds} if hasattr(self.synthesizer, "rnn_n_layers"): config.update({"rnn_n_layers": self.synthesizer.rnn_n_layers}) if hasattr(self.synthesizer, "k"): config.update({"k": self.synthesizer.k}) json.dump(config, f) # save common config file with open(self.storage_path+"/trained_models/vocab.json", "w") as f: json.dump(self.synthesizer.vocab, f) # save vocabulary of tokens np.save(self.storage_path+"/trained_models/inv_vocab.npy", self.synthesizer.inv_vocab) # save inverse vocabulary if model["emb_model"] is not None: torch.save(model["emb_model"].state_dict(), self.storage_path+"/trained_models/"+model_file_name+"_emb.pt") with open(self.storage_path+"/trained_models/embedding_config.json", "w") as f: json.dump({"embedding_dim": self.synthesizer.embedding_dim, "num_entities": self.synthesizer.num_entities, "num_relations": self.synthesizer.num_relations, "kernel_size": self.synthesizer.kernel_size, "num_of_output_channels": self.synthesizer.num_of_output_channels}, f) print("{} saved".format(model["model"].name)) if not os.path.exists(self.storage_path+"/metrics/"): os.mkdir(self.storage_path+"/metrics/") with open(self.storage_path+"/metrics/"+"metrics_"+desc+".json", "w") as plot_file: json.dump({"soft acc": Train_acc['soft'], "hard acc": Train_acc['hard'], "loss": Train_loss}, plot_file, indent=3)