129 lines
3.9 KiB
Python
129 lines
3.9 KiB
Python
"""Unsupervised prototype learning algorithms."""
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import warnings
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import torch
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import torchmetrics
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from prototorch.components import Components, LabeledComponents
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from prototorch.components import initializers as cinit
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from prototorch.components.initializers import ZerosInitializer, parse_data_arg
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from prototorch.functions.competitions import knnc
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from prototorch.functions.distances import euclidean_distance
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from prototorch.modules.losses import NeuralGasEnergy
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from .abstract import AbstractPrototypeModel
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class EuclideanDistance(torch.nn.Module):
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def forward(self, x, y):
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return euclidean_distance(x, y)
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class ConnectionTopology(torch.nn.Module):
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def __init__(self, agelimit, num_prototypes):
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super().__init__()
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self.agelimit = agelimit
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self.num_prototypes = num_prototypes
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self.cmat = torch.zeros((self.num_prototypes, self.num_prototypes))
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self.age = torch.zeros_like(self.cmat)
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def forward(self, d):
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order = torch.argsort(d, dim=1)
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for element in order:
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i0, i1 = element[0], element[1]
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self.cmat[i0][i1] = 1
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self.age[i0][i1] = 0
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self.age[i0][self.cmat[i0] == 1] += 1
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self.cmat[i0][self.age[i0] > self.agelimit] = 0
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def extra_repr(self):
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return f"agelimit: {self.agelimit}"
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class KNN(AbstractPrototypeModel):
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"""K-Nearest-Neighbors classification algorithm."""
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def __init__(self, hparams, **kwargs):
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super().__init__()
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self.save_hyperparameters(hparams)
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# Default Values
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self.hparams.setdefault("k", 1)
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self.hparams.setdefault("distance", euclidean_distance)
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data = kwargs.get("data")
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x_train, y_train = parse_data_arg(data)
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self.proto_layer = LabeledComponents(initialized_components=(x_train,
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y_train))
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self.train_acc = torchmetrics.Accuracy()
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@property
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def prototype_labels(self):
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return self.proto_layer.component_labels.detach()
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def forward(self, x):
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protos, _ = self.proto_layer()
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dis = self.hparams.distance(x, protos)
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return dis
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def predict(self, x):
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# model.eval() # ?!
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with torch.no_grad():
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d = self(x)
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plabels = self.proto_layer.component_labels
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y_pred = knnc(d, plabels, k=self.hparams.k)
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return y_pred
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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return 1
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def on_train_batch_start(self,
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train_batch,
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batch_idx,
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dataloader_idx=None):
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warnings.warn("k-NN has no training, skipping!")
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return -1
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def configure_optimizers(self):
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return None
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class NeuralGas(AbstractPrototypeModel):
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def __init__(self, hparams, **kwargs):
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super().__init__()
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self.save_hyperparameters(hparams)
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self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
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# Default Values
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self.hparams.setdefault("input_dim", 2)
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self.hparams.setdefault("agelimit", 10)
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self.hparams.setdefault("lm", 1)
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self.hparams.setdefault("prototype_initializer",
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ZerosInitializer(self.hparams.input_dim))
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self.proto_layer = Components(
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self.hparams.num_prototypes,
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initializer=self.hparams.prototype_initializer)
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self.distance_layer = EuclideanDistance()
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self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
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self.topology_layer = ConnectionTopology(
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agelimit=self.hparams.agelimit,
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num_prototypes=self.hparams.num_prototypes,
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)
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def training_step(self, train_batch, batch_idx):
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x = train_batch[0]
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protos = self.proto_layer()
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d = self.distance_layer(x, protos)
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cost, order = self.energy_layer(d)
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self.topology_layer(d)
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return cost
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