63 lines
1.7 KiB
Python
63 lines
1.7 KiB
Python
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"""GMLVQ example using all four dimensions of the Iris dataset."""
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import pytorch_lightning as pl
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import torch
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from prototorch.components import initializers as cinit
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from prototorch.datasets.abstract import NumpyDataset
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
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from prototorch.models.glvq import GRLVQ
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from sklearn.preprocessing import StandardScaler
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class PrintRelevanceCallback(pl.Callback):
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def on_epoch_end(self, trainer, pl_module: GRLVQ):
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print(pl_module.relevance_profile)
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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x_train = x_train[:, [0, 2]]
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scaler = StandardScaler()
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scaler.fit(x_train)
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x_train = scaler.transform(x_train)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds,
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num_workers=0,
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batch_size=50,
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shuffle=True)
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# Hyperparameters
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hparams = dict(
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nclasses=3,
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prototypes_per_class=1,
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#prototype_initializer=cinit.SMI(torch.Tensor(x_train),
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# torch.Tensor(y_train)),
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prototype_initializer=cinit.UniformInitializer(2),
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input_dim=x_train.shape[1],
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lr=0.1,
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#transfer_function="sigmoid_beta",
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)
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# Initialize the model
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model = GRLVQ(hparams)
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# Model summary
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print(model)
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# Callbacks
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vis = VisSiameseGLVQ2D(x_train, y_train)
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debug = PrintRelevanceCallback()
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# Setup trainer
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trainer = pl.Trainer(max_epochs=200, callbacks=[vis, debug])
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# Training loop
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trainer.fit(model, train_loader)
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