prototorch_models/prototorch/models/probabilistic.py

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"""Probabilistic GLVQ methods"""
import torch
from .glvq import GLVQ
# HELPER
# TODO: Refactor into general files, if useful
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def probability(distance, variance):
return torch.exp(-(distance * distance) / (2 * variance))
def grouped_sum(value: torch.Tensor,
labels: torch.LongTensor) -> (torch.Tensor, torch.LongTensor):
"""Group-wise average for (sparse) grouped tensors
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Args:
value (torch.Tensor): values to average (# samples, latent dimension)
labels (torch.LongTensor): labels for embedding parameters (# samples,)
Returns:
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result (torch.Tensor): (# unique labels, latent dimension)
new_labels (torch.LongTensor): (# unique labels,)
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Examples:
>>> samples = torch.Tensor([
[0.15, 0.15, 0.15], #-> group / class 1
[0.2, 0.2, 0.2 ], #-> group / class 3
[0.4, 0.4, 0.4 ], #-> group / class 3
[0.0, 0.0, 0.0 ] #-> group / class 0
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])
>>> labels = torch.LongTensor([1, 5, 5, 0])
>>> result, new_labels = groupby_mean(samples, labels)
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>>> result
tensor([[0.0000, 0.0000, 0.0000],
[0.1500, 0.1500, 0.1500],
[0.3000, 0.3000, 0.3000]])
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>>> new_labels
tensor([0, 1, 5])
"""
uniques = labels.unique(sorted=True).tolist()
labels = labels.tolist()
key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
labels = torch.LongTensor(list(map(key_val.get, labels)))
labels = labels.view(labels.size(0), 1).expand(-1, value.size(1))
unique_labels = labels.unique(dim=0)
result = torch.zeros_like(unique_labels, dtype=torch.float).scatter_add_(
0, labels, value)
return result.T
def likelihood_loss(probabilities, target, prototype_labels):
uniques = prototype_labels.unique(sorted=True).tolist()
labels = target.tolist()
key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
target_indices = torch.LongTensor(list(map(key_val.get, labels)))
whole_probability = probabilities.sum(dim=1)
correct_probability = probabilities[torch.arange(len(probabilities)),
target_indices]
wrong_probability = whole_probability - correct_probability
likelihood = correct_probability / wrong_probability
log_likelihood = torch.log(likelihood)
return log_likelihood
def robust_soft_loss(probabilities, target, prototype_labels):
uniques = prototype_labels.unique(sorted=True).tolist()
labels = target.tolist()
key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
target_indices = torch.LongTensor(list(map(key_val.get, labels)))
whole_probability = probabilities.sum(dim=1)
correct_probability = probabilities[torch.arange(len(probabilities)),
target_indices]
likelihood = correct_probability / whole_probability
log_likelihood = torch.log(likelihood)
return log_likelihood
class LikelihoodRatioLVQ(GLVQ):
"""Learning Vector Quantization based on Likelihood Ratios
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.conditional_distribution = probability
def forward(self, x):
distances = self._forward(x)
conditional = self.conditional_distribution(distances,
self.hparams.variance)
prior = 1.0 / torch.Tensor(self.proto_layer.distribution).sum().item()
posterior = conditional * prior
plabels = torch.LongTensor(self.proto_layer.component_labels)
y_pred = grouped_sum(posterior.T, plabels)
return y_pred
def training_step(self, batch, batch_idx, optimizer_idx=None):
X, y = batch
out = self.forward(X)
plabels = self.proto_layer.component_labels
batch_loss = -likelihood_loss(out, y, prototype_labels=plabels)
loss = batch_loss.sum(dim=0)
return loss
def predict(self, x):
probabilities = self.forward(x)
confidence, prediction = torch.max(probabilities, dim=1)
prediction[confidence < 0.1] = -1
return prediction
class RSLVQ(GLVQ):
"""Learning Vector Quantization based on Likelihood Ratios
"""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.conditional_distribution = probability
def forward(self, x):
distances = self._forward(x)
conditional = self.conditional_distribution(distances,
self.hparams.variance)
prior = 1.0 / torch.Tensor(self.proto_layer.distribution).sum().item()
posterior = conditional * prior
plabels = torch.LongTensor(self.proto_layer.component_labels)
y_pred = grouped_sum(posterior.T, plabels)
return y_pred
def training_step(self, batch, batch_idx, optimizer_idx=None):
X, y = batch
out = self.forward(X)
plabels = self.proto_layer.component_labels
batch_loss = -robust_soft_loss(out, y, prototype_labels=plabels)
loss = batch_loss.sum(dim=0)
return loss
def predict(self, x):
probabilities = self.forward(x)
confidence, prediction = torch.max(probabilities, dim=1)
#prediction[confidence < 0.1] = -1
return prediction
__all__ = ["LikelihoodRatioLVQ", "probability", "grouped_sum"]