75 lines
1.7 KiB
Python
75 lines
1.7 KiB
Python
"""LVQ1 example using the Iris dataset."""
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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if __name__ == "__main__":
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# Acquire data
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from sklearn.datasets import load_iris
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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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# Relabel classes
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# y_train[y_train == 0] = 3
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# y_train[y_train == 1] = 4
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# y_train[y_train == 2] = 6
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# Dataset
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train_ds = pt.datasets.NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, shuffle=True)
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# Hyperparameters
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num_classes = 3
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prototypes_per_class = 10
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hparams = dict(
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distribution={
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# class_label: num_prototypes
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# 3: 1,
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# 4: 2,
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# 6: 3,
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0: 1,
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2: 2,
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3: 3,
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},
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lr=0.001,
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)
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# Initialize the model
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model = pt.models.LVQ1(
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hparams,
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prototypes_initializer=pt.initializers.SMCI(train_ds),
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)
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# Check if `num_classes` is correct
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print(f"{model.num_classes=}")
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assert model.num_classes == 3
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# Compute intermediate input and output sizes
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model.example_input_array = torch.zeros(4, 2)
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# Model summary
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print(model)
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# Callbacks
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vis = pt.models.VisGLVQ2D(data=(x_train, y_train),
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cmap="viridis",
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resolution=200,
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block=False)
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# Setup trainer
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trainer = pl.Trainer(
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gpus=0,
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max_epochs=50,
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callbacks=[vis],
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# fast_dev_run=1,
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)
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# Get prototype labels
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print(f"Protoype Labels are: ", model.prototype_labels.tolist())
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# Training loop
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trainer.fit(model, train_loader)
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