test: remove examples/liramlvq_tecator.py temporarily

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Jensun Ravichandran 2021-06-21 16:13:41 +02:00
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"""Limited Rank Matrix LVQ example using the Tecator dataset."""
import argparse
import matplotlib.pyplot as plt
import prototorch as pt
import pytorch_lightning as pl
import torch
def plot_matrix(matrix):
title = "Lambda matrix"
plt.figure(title)
plt.title(title)
plt.imshow(matrix, cmap="gray")
plt.axis("off")
plt.colorbar()
plt.show(block=True)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Tecator(root="~/datasets/", train=True)
test_ds = pt.datasets.Tecator(root="~/datasets/", train=False)
# Reproducibility
pl.utilities.seed.seed_everything(seed=10)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
test_loader = torch.utils.data.DataLoader(test_ds, batch_size=32)
# Hyperparameters
hparams = dict(
distribution={
"num_classes": 2,
"per_class": 1,
},
input_dim=100,
latent_dim=2,
proto_lr=0.001,
bb_lr=0.001,
)
# Initialize the model
model = pt.models.SiameseGMLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
)
# Summary
print(model)
# Callbacks
vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)
es = pl.callbacks.EarlyStopping(monitor="val_loss",
min_delta=0.001,
patience=50,
verbose=False,
mode="min")
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[vis, es],
weights_summary=None,
)
# Training loop
trainer.fit(model, train_loader, test_loader)
# Save the model
torch.save(model, "liramlvq_tecator.pt")
# Load a saved model
saved_model = torch.load("liramlvq_tecator.pt")
# Display the Lambda matrix
plot_matrix(saved_model.lambda_matrix)
# Testing
trainer.test(model, test_dataloaders=test_loader)