prototorch_models/examples/ng_iris.py

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"""Neural Gas example using the Iris dataset."""
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import argparse
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import pytorch_lightning as pl
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import torch
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from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
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import prototorch as pt
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if __name__ == "__main__":
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# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
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# Prepare and pre-process the dataset
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x_train, y_train = load_iris(return_X_y=True)
x_train = x_train[:, [0, 2]]
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
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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, batch_size=150)
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# Hyperparameters
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hparams = dict(num_prototypes=30, lr=0.03)
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# Initialize the model
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model = pt.models.NeuralGas(hparams)
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# Model summary
print(model)
# Callbacks
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vis = pt.models.VisNG2D(data=train_ds)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[vis],
)
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# Training loop
trainer.fit(model, train_loader)