prototorch_models/examples/gtlvq_moons.py

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"""Localized-GTLVQ example using the Moons dataset."""
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import argparse
import logging
import warnings
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import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import GTLVQ, VisGLVQ2D
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
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__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
seed_everything(seed=2)
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# Dataset
train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
# Dataloaders
train_loader = DataLoader(
train_ds,
batch_size=256,
shuffle=True,
)
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# Hyperparameters
# Latent_dim should be lower than input dim.
hparams = dict(distribution=[1, 3], input_dim=2, latent_dim=1)
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# Initialize the model
model = GTLVQ(hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds))
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# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
logging.info(model)
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# Callbacks
vis = VisGLVQ2D(data=train_ds)
es = EarlyStopping(
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monitor="train_acc",
min_delta=0.001,
patience=20,
mode="max",
verbose=False,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
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)