414 lines
18 KiB
Python
414 lines
18 KiB
Python
import os
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from pathlib import Path
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import random
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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 quapy.method.aggregative import *
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from quapy.util import EarlyStop
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class QuaNetTrainer(BaseQuantifier):
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"""
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Implementation of `QuaNet <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_, a neural network for
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quantification. This implementation uses `PyTorch <https://pytorch.org/>`_ and can take advantage of GPU
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for speeding-up the training phase.
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Example:
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>>> import quapy as qp
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>>> from quapy.method.meta import QuaNet
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>>> from quapy.classification.neural import NeuralClassifierTrainer, CNNnet
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>>>
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>>> # use samples of 100 elements
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>>> qp.environ['SAMPLE_SIZE'] = 100
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>>>
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>>> # load the kindle dataset as text, and convert words to numerical indexes
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>>> dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
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>>> qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
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>>>
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>>> # the text classifier is a CNN trained by NeuralClassifierTrainer
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>>> cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)
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>>> learner = NeuralClassifierTrainer(cnn, device='cuda')
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>>>
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>>> # train QuaNet (QuaNet is an alias to QuaNetTrainer)
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>>> model = QuaNet(learner, qp.environ['SAMPLE_SIZE'], device='cuda')
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>>> model.fit(dataset.training)
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>>> estim_prevalence = model.quantify(dataset.test.instances)
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:param learner: an object implementing `fit` (i.e., that can be trained on labelled data),
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`predict_proba` (i.e., that can generate posterior probabilities of unlabelled examples) and
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`transform` (i.e., that can generate embedded representations of the unlabelled instances).
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:param sample_size: integer, the sample size
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:param n_epochs: integer, maximum number of training epochs
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:param tr_iter_per_poch: integer, number of training iterations before considering an epoch complete
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:param va_iter_per_poch: integer, number of validation iterations to perform after each epoch
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:param lr: float, the learning rate
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:param lstm_hidden_size: integer, hidden dimensionality of the LSTM cells
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:param lstm_nlayers: integer, number of LSTM layers
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:param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the
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quantification embedding
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:param bidirectional: boolean, indicates whether the LSTM is bidirectional or not
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:param qdrop_p: float, dropout probability
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:param patience: integer, number of epochs showing no improvement in the validation set before stopping the
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training phase (early stopping)
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:param checkpointdir: string, a path where to store models' checkpoints
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:param checkpointname: string (optional), the name of the model's checkpoint
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:param device: string, indicate "cpu" or "cuda"
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"""
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def __init__(self,
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learner,
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sample_size,
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n_epochs=100,
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tr_iter_per_poch=500,
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va_iter_per_poch=100,
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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,
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checkpointdir='../checkpoint',
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checkpointname=None,
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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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if checkpointname is None:
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local_random = random.Random()
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random_code = '-'.join(str(local_random.randint(0, 1000000)) for _ in range(5))
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checkpointname = 'QuaNet-'+random_code
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self.checkpointdir = checkpointdir
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self.checkpoint = os.path.join(checkpointdir, checkpointname)
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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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self._classes_ = None
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def fit(self, data: LabelledCollection, fit_learner=True):
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"""
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Trains QuaNet.
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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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:return: self
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"""
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self._classes_ = data.classes_
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os.makedirs(self.checkpointdir, exist_ok=True)
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if fit_learner:
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classifier_data, unused_data = data.split_stratified(0.4)
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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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self.learner.fit(*classifier_data.Xy)
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else:
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classifier_data = None
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train_data, valid_data = data.split_stratified(0.66)
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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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# 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' original representations into embeddings
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valid_data_embed = LabelledCollection(self.learner.transform(valid_data.instances), valid_data.labels, self._classes_)
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train_data_embed = LabelledCollection(self.learner.transform(train_data.instances), train_data.labels, self._classes_)
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self.quantifiers = {
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'cc': CC(self.learner).fit(None, fit_learner=False),
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'acc': ACC(self.learner).fit(None, fit_learner=False, val_split=valid_data),
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'pcc': PCC(self.learner).fit(None, fit_learner=False),
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'pacc': PACC(self.learner).fit(None, fit_learner=False, val_split=valid_data),
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}
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if classifier_data is not None:
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self.quantifiers['emq'] = EMQ(self.learner).fit(classifier_data, fit_learner=False)
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self.status = {
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'tr-loss': -1,
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'va-loss': -1,
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'tr-mae': -1,
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'va-mae': -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_embed.instances.shape[1],
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n_classes=data.n_classes,
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stats_size=nQ*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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print(self.quanet)
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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.checkpoint
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for epoch_i in range(1, self.n_epochs):
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self._epoch(train_data_embed, train_posteriors, self.tr_iter, epoch_i, early_stop, train=True)
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self._epoch(valid_data_embed, 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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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 isinstance(quantifier, AggregativeProbabilisticQuantifier) else label_predictions
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prevs_estim.extend(quantifier.aggregate(predictions))
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# there is no real need for adding static estims like the TPR or FPR from training since those are constant
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return prevs_estim
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def quantify(self, instances):
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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)
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if self.device == torch.device('cuda'):
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prevalence = prevalence.cpu()
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prevalence = prevalence.numpy().flatten()
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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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self.quanet.train(mode=train)
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losses = []
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mae_errors = []
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if train==False:
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prevpoints = F.get_nprevpoints_approximation(iterations, self.quanet.n_classes)
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iterations = F.num_prevalence_combinations(prevpoints, self.quanet.n_classes)
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with qp.util.temp_seed(0):
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sampling_index_gen = data.artificial_sampling_index_generator(self.sample_size, prevpoints)
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else:
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sampling_index_gen = [data.sampling_index(self.sample_size, *prev) for prev in
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F.uniform_simplex_sampling(data.n_classes, iterations)]
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pbar = tqdm(sampling_index_gen, total=iterations) if train else sampling_index_gen
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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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mae = mae_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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mae = mae_loss(phat, ptrue)
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losses.append(loss.item())
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mae_errors.append(mae.item())
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mse = np.mean(losses)
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mae = np.mean(mae_errors)
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if train:
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self.status['tr-loss'] = mse
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self.status['tr-mae'] = mae
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else:
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self.status['va-loss'] = mse
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self.status['va-mae'] = mae
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if train:
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pbar.set_description(f'[QuaNet] '
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f'epoch={epoch} [it={it}/{iterations}]\t'
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f'tr-mseloss={self.status["tr-loss"]:.5f} tr-maeloss={self.status["tr-mae"]:.5f}\t'
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f'val-mseloss={self.status["va-loss"]:.5f} val-maeloss={self.status["va-mae"]:.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.items():
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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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def clean_checkpoint(self):
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"""
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Removes the checkpoint
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"""
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os.remove(self.checkpoint)
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def clean_checkpoint_dir(self):
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"""
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Removes anything contained in the checkpoint directory
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"""
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import shutil
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shutil.rmtree(self.checkpointdir, ignore_errors=True)
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@property
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def classes_(self):
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return self._classes_
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def mae_loss(output, target):
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"""
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Torch-like wrapper for the Mean Absolute Error
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:param output: predictions
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:param target: ground truth values
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:return: mean absolute error loss
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"""
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return torch.mean(torch.abs(output - target))
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class QuaNetModule(torch.nn.Module):
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"""
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Implements the `QuaNet <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_ forward pass.
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See :class:`QuaNetTrainer` for training QuaNet.
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:param doc_embedding_size: integer, the dimensionality of the document embeddings
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:param n_classes: integer, number of classes
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:param stats_size: integer, number of statistics estimated by simple quantification methods
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:param lstm_hidden_size: integer, hidden dimensionality of the LSTM cell
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:param lstm_nlayers: integer, number of LSTM layers
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:param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the
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quantification embedding
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:param bidirectional: boolean, whether or not to use bidirectional LSTM
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:param qdrop_p: float, dropout probability
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:param order_by: integer, class for which the document embeddings are to be sorted
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"""
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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=0):
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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 + n_classes)
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embeded_posteriors = embeded_posteriors.unsqueeze(0)
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self.lstm.flatten_parameters()
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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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