63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
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"""The popular K-Nearest-Neighbors classification algorithm."""
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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 LabeledComponents
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from prototorch.components.initializers import parse_init_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 .abstract import AbstractPrototypeModel
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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_init_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().cpu()
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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.numpy()
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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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