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