prototorch_models/examples/median_lvq_iris.py

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"""Median-LVQ example using the Iris dataset."""
import argparse
import warnings
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import prototorch as pt
import pytorch_lightning as pl
import torch
from lightning_fabric.utilities.seed import seed_everything
from prototorch.models import MedianLVQ, VisGLVQ2D
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
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if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
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# Command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--gpus", type=int, default=0)
parser.add_argument("--fast_dev_run", type=bool, default=False)
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args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Iris(dims=[0, 2])
# Dataloaders
train_loader = DataLoader(
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train_ds,
batch_size=len(train_ds), # MedianLVQ cannot handle mini-batches
)
# Initialize the model
model = MedianLVQ(
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hparams=dict(distribution=(3, 2), lr=0.01),
prototypes_initializer=pt.initializers.SSCI(train_ds),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Callbacks
vis = VisGLVQ2D(data=train_ds)
es = EarlyStopping(
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monitor="train_acc",
min_delta=0.01,
patience=5,
mode="max",
verbose=True,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer(
accelerator="cuda" if args.gpus else "cpu",
devices=args.gpus if args.gpus else "auto",
fast_dev_run=args.fast_dev_run,
callbacks=[
vis,
es,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
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)
# Training loop
trainer.fit(model, train_loader)