import os from pathlib import Path import torch from torch.nn import MSELoss from torch.nn.functional import relu from quapy.method.aggregative import * from quapy.util import EarlyStop class QuaNetTrainer(BaseQuantifier): def __init__(self, learner, sample_size, n_epochs=500, tr_iter_per_poch=200, va_iter_per_poch=21, lr=1e-3, lstm_hidden_size=64, lstm_nlayers=1, ff_layers=[1024, 512], bidirectional=True, qdrop_p=0.5, patience=10, checkpointpath='../checkpoint/quanet.dat', device='cuda'): assert hasattr(learner, 'transform'), \ f'the learner {learner.__class__.__name__} does not seem to be able to produce document embeddings ' \ f'since it does not implement the method "transform"' assert hasattr(learner, 'predict_proba'), \ f'the learner {learner.__class__.__name__} does not seem to be able to produce posterior probabilities ' \ f'since it does not implement the method "predict_proba"' self.learner = learner self.sample_size = sample_size self.n_epochs = n_epochs self.tr_iter = tr_iter_per_poch self.va_iter = va_iter_per_poch self.lr = lr self.quanet_params = { 'lstm_hidden_size': lstm_hidden_size, 'lstm_nlayers': lstm_nlayers, 'ff_layers': ff_layers, 'bidirectional': bidirectional, 'qdrop_p': qdrop_p } self.patience = patience self.checkpointpath = checkpointpath os.makedirs(Path(checkpointpath).parent, exist_ok=True) self.device = torch.device(device) self.__check_params_colision(self.quanet_params, self.learner.get_params()) def fit(self, data: LabelledCollection, fit_learner=True, *args): """ :param data: the training data on which to train QuaNet. If fit_learner=True, the data will be split in 40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If fit_learner=False, the data will be split in 66/34 for training QuaNet and validating it, respectively. :param fit_learner: if true, trains the classifier on a split containing 40% of the data :param args: unused :return: self """ # split: 40% for training classification, 40% for training quapy, and 20% for validating quapy self.learner, unused_data = \ training_helper(self.learner, data, fit_learner, ensure_probabilistic=True, val_split=0.6) train_data, valid_data = unused_data.split_stratified(0.66) # 0.66 split of 60% makes 40% and 20% # compute the posterior probabilities of the instances valid_posteriors = self.learner.predict_proba(valid_data.instances) train_posteriors = self.learner.predict_proba(train_data.instances) # turn instances' indexes into embeddings valid_data.instances = self.learner.transform(valid_data.instances) train_data.instances = self.learner.transform(train_data.instances) # estimate the hard and soft stats tpr and fpr of the classifier self.tr_prev = data.prevalence() self.quantifiers = { 'cc': CC(self.learner).fit(data, fit_learner=False), 'acc': ACC(self.learner).fit(data, fit_learner=False), 'pcc': PCC(self.learner).fit(data, fit_learner=False), 'pacc': PACC(self.learner).fit(data, fit_learner=False), 'emq': EMQ(self.learner).fit(data, fit_learner=False), } self.status = { 'tr-loss': -1, 'va-loss': -1, } nQ = len(self.quantifiers) nC = data.n_classes self.quanet = QuaNetModule( doc_embedding_size=train_data.instances.shape[1], n_classes=data.n_classes, stats_size=nQ*nC + 2*nC*nC, order_by=0 if data.binary else None, **self.quanet_params ).to(self.device) self.optim = torch.optim.Adam(self.quanet.parameters(), lr=self.lr) early_stop = EarlyStop(self.patience, lower_is_better=True) checkpoint = self.checkpointpath for epoch_i in range(1, self.n_epochs): self.epoch(train_data, train_posteriors, self.tr_iter, epoch_i, early_stop, train=True) self.epoch(valid_data, valid_posteriors, self.va_iter, epoch_i, early_stop, train=False) early_stop(self.status['va-loss'], epoch_i) if early_stop.IMPROVED: torch.save(self.quanet.state_dict(), checkpoint) elif early_stop.STOP: print(f'training ended by patience exhausted; loading best model parameters in {checkpoint} ' f'for epoch {early_stop.best_epoch}') self.quanet.load_state_dict(torch.load(checkpoint)) self.epoch(valid_data, valid_posteriors, self.va_iter, epoch_i, early_stop, train=True) break return self def get_aggregative_estims(self, posteriors): label_predictions = np.argmax(posteriors, axis=-1) prevs_estim = [] for quantifier in self.quantifiers.values(): predictions = posteriors if isprobabilistic(quantifier) else label_predictions prevs_estim.extend(quantifier.aggregate(predictions)) # add the class-conditional predictions P(y'i|yj) from ACC and PACC prevs_estim.extend(self.quantifiers['acc'].Pte_cond_estim_.flatten()) prevs_estim.extend(self.quantifiers['pacc'].Pte_cond_estim_.flatten()) return prevs_estim def quantify(self, instances, *args): posteriors = self.learner.predict_proba(instances) embeddings = self.learner.transform(instances) quant_estims = self.get_aggregative_estims(posteriors) self.quanet.eval() with torch.no_grad(): prevalence = self.quanet.forward(embeddings, posteriors, quant_estims).item() return prevalence def epoch(self, data: LabelledCollection, posteriors, iterations, epoch, early_stop, train): mse_loss = MSELoss() prevpoints = F.get_nprevpoints_approximation(iterations, self.quanet.n_classes) self.quanet.train(mode=train) losses = [] pbar = tqdm(data.artificial_sampling_index_generator(self.sample_size, prevpoints)) for it, index in enumerate(pbar): sample_data = data.sampling_from_index(index) sample_posteriors = posteriors[index] quant_estims = self.get_aggregative_estims(sample_posteriors) ptrue = torch.as_tensor([sample_data.prevalence()], dtype=torch.float, device=self.device) if train: self.optim.zero_grad() phat = self.quanet.forward(sample_data.instances, sample_posteriors, quant_estims) loss = mse_loss(phat, ptrue) loss.backward() self.optim.step() else: with torch.no_grad(): phat = self.quanet.forward(sample_data.instances, sample_posteriors, quant_estims) loss = mse_loss(phat, ptrue) losses.append(loss.item()) self.status['tr-loss' if train else 'va-loss'] = np.mean(losses[-10:]) pbar.set_description(f'[QuaNet][{"training" if train else "validating"}] ' f'epoch={epoch} [it={it}/{iterations}]\t' f'tr-loss={self.status["tr-loss"]:.5f} ' f'val-loss={self.status["va-loss"]:.5f} ' f'patience={early_stop.patience}/{early_stop.PATIENCE_LIMIT}') def get_params(self, deep=True): return {**self.learner.get_params(), **self.quanet_params} def set_params(self, **parameters): learner_params={} for key, val in parameters: if key in self.quanet_params: self.quanet_params[key]=val else: learner_params[key] = val self.learner.set_params(**learner_params) def __check_params_colision(self, quanet_params, learner_params): quanet_keys = set(quanet_params.keys()) learner_keys = set(learner_params.keys()) intersection = quanet_keys.intersection(learner_keys) if len(intersection) > 0: raise ValueError(f'the use of parameters {intersection} is ambiguous sine those can refer to ' f'the parameters of QuaNet or the learner {self.learner.__class__.__name__}') class QuaNetModule(torch.nn.Module): def __init__(self, doc_embedding_size, n_classes, stats_size, lstm_hidden_size=64, lstm_nlayers=1, ff_layers=[1024, 512], bidirectional=True, qdrop_p=0.5, order_by=None): super().__init__() self.n_classes = n_classes self.order_by = order_by self.hidden_size = lstm_hidden_size self.nlayers = lstm_nlayers self.bidirectional = bidirectional self.ndirections = 2 if self.bidirectional else 1 self.qdrop_p = qdrop_p self.lstm = torch.nn.LSTM(doc_embedding_size + n_classes, # +n_classes stands for the posterior probs. (concatenated) lstm_hidden_size, lstm_nlayers, bidirectional=bidirectional, dropout=qdrop_p, batch_first=True) self.dropout = torch.nn.Dropout(self.qdrop_p) lstm_output_size = self.hidden_size * self.ndirections ff_input_size = lstm_output_size + stats_size prev_size = ff_input_size self.ff_layers = torch.nn.ModuleList() for lin_size in ff_layers: self.ff_layers.append(torch.nn.Linear(prev_size, lin_size)) prev_size = lin_size self.output = torch.nn.Linear(prev_size, n_classes) @property def device(self): return torch.device('cuda') if next(self.parameters()).is_cuda else torch.device('cpu') def init_hidden(self): directions = 2 if self.bidirectional else 1 var_hidden = torch.zeros(self.nlayers * directions, 1, self.hidden_size) var_cell = torch.zeros(self.nlayers * directions, 1, self.hidden_size) if next(self.lstm.parameters()).is_cuda: var_hidden, var_cell = var_hidden.cuda(), var_cell.cuda() return var_hidden, var_cell def forward(self, doc_embeddings, doc_posteriors, statistics): device = self.device doc_embeddings = torch.as_tensor(doc_embeddings, dtype=torch.float, device=device) doc_posteriors = torch.as_tensor(doc_posteriors, dtype=torch.float, device=device) statistics = torch.as_tensor(statistics, dtype=torch.float, device=device) if self.order_by is not None: order = torch.argsort(doc_posteriors[:, self.order_by]) doc_embeddings = doc_embeddings[order] doc_posteriors = doc_posteriors[order] embeded_posteriors = torch.cat((doc_embeddings, doc_posteriors), dim=-1) # the entire set represents only one instance in quapy contexts, and so the batch_size=1 # the shape should be (1, number-of-instances, embedding-size + 1) embeded_posteriors = embeded_posteriors.unsqueeze(0) _, (rnn_hidden,_) = self.lstm(embeded_posteriors, self.init_hidden()) rnn_hidden = rnn_hidden.view(self.nlayers, self.ndirections, -1, self.hidden_size) quant_embedding = rnn_hidden[0].view(-1) quant_embedding = torch.cat((quant_embedding, statistics)) abstracted = quant_embedding.unsqueeze(0) for linear in self.ff_layers: abstracted = self.dropout(relu(linear(abstracted))) logits = self.output(abstracted).view(1, -1) prevalence = torch.softmax(logits, -1) return prevalence