48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
"""ProtoTorch KNN model."""
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import warnings
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from ..core.competitions import KNNC
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from ..core.components import LabeledComponents
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from ..core.initializers import (
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LiteralCompInitializer,
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LiteralLabelsInitializer,
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)
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from ..utils.utils import parse_data_arg
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from .abstract import SupervisedPrototypeModel
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class KNN(SupervisedPrototypeModel):
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"""K-Nearest-Neighbors classification algorithm."""
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, skip_proto_layer=True, **kwargs)
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# Default hparams
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self.hparams.setdefault("k", 1)
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data = kwargs.get("data", None)
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if data is None:
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raise ValueError("KNN requires data, but was not provided!")
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data, targets = parse_data_arg(data)
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# Layers
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self.proto_layer = LabeledComponents(
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distribution=len(data) * [1],
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components_initializer=LiteralCompInitializer(data),
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labels_initializer=LiteralLabelsInitializer(targets))
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self.competition_layer = KNNC(k=self.hparams.k)
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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return 1 # skip training step
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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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