test: remove examples/liramlvq_tecator.py temporarily

examples/liramlvq_tecator.py

@@ -1,88 +0,0 @@
-"""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)