95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
"""Probabilistic GLVQ methods"""
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import torch
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from prototorch.functions.competitions import stratified_sum
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from prototorch.functions.transform import gaussian
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from .glvq import GLVQ
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def likelihood_loss(probabilities, target, prototype_labels):
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uniques = prototype_labels.unique(sorted=True).tolist()
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labels = target.tolist()
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key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
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target_indices = torch.LongTensor(list(map(key_val.get, labels)))
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whole_probability = probabilities.sum(dim=1)
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correct_probability = probabilities[torch.arange(len(probabilities)),
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target_indices]
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wrong_probability = whole_probability - correct_probability
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likelihood = correct_probability / wrong_probability
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log_likelihood = torch.log(likelihood)
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return log_likelihood
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def robust_soft_loss(probabilities, target, prototype_labels):
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uniques = prototype_labels.unique(sorted=True).tolist()
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labels = target.tolist()
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key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
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target_indices = torch.LongTensor(list(map(key_val.get, labels)))
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whole_probability = probabilities.sum(dim=1)
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correct_probability = probabilities[torch.arange(len(probabilities)),
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target_indices]
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likelihood = correct_probability / whole_probability
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log_likelihood = torch.log(likelihood)
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return log_likelihood
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class ProbabilisticLVQ(GLVQ):
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def __init__(self, hparams, rejection_confidence=1.0, **kwargs):
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super().__init__(hparams, **kwargs)
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self.conditional_distribution = gaussian
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self.rejection_confidence = rejection_confidence
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def predict(self, x):
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probabilities = self.forward(x)
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confidence, prediction = torch.max(probabilities, dim=1)
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prediction[confidence < self.rejection_confidence] = -1
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return prediction
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def forward(self, x):
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distances = self._forward(x)
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conditional = self.conditional_distribution(distances,
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self.hparams.variance)
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prior = 1.0 / torch.Tensor(self.proto_layer.distribution).sum().item()
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posterior = conditional * prior
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plabels = torch.LongTensor(self.proto_layer.component_labels)
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y_pred = stratified_sum(posterior.T, plabels)
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return y_pred
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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X, y = batch
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out = self.forward(X)
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plabels = self.proto_layer.component_labels
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batch_loss = -self.loss_fn(out, y, plabels)
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loss = batch_loss.sum(dim=0)
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return loss
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class LikelihoodRatioLVQ(ProbabilisticLVQ):
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"""Learning Vector Quantization based on Likelihood Ratios
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"""
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@property
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def loss_fn(self):
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return likelihood_loss
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class RSLVQ(ProbabilisticLVQ):
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"""Learning Vector Quantization based on Likelihood Ratios
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"""
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@property
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def loss_fn(self):
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return robust_soft_loss
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__all__ = ["LikelihoodRatioLVQ", "RSLVQ"]
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