prototorch_models/examples/cbc_circle.py

"""CBC example using the Iris dataset."""

import numpy as np
import pytorch_lightning as pl
import torch
from matplotlib import pyplot as plt
from prototorch.components import initializers as cinit
from prototorch.datasets.abstract import NumpyDataset
from sklearn.datasets import make_circles
from torch.utils.data import DataLoader

from prototorch.models.cbc import CBC, euclidean_similarity


class VisualizationCallback(pl.Callback):
    def __init__(
        self,
        x_train,
        y_train,
        prototype_model=True,
        title="Prototype Visualization",
        cmap="viridis",
    ):
        super().__init__()
        self.x_train = x_train
        self.y_train = y_train
        self.title = title
        self.fig = plt.figure(self.title)
        self.cmap = cmap
        self.prototype_model = prototype_model

    def on_epoch_end(self, trainer, pl_module):
        if self.prototype_model:
            protos = pl_module.components
            color = pl_module.prototype_labels
        else:
            protos = pl_module.components
            color = "k"
        ax = self.fig.gca()
        ax.cla()
        ax.set_title(self.title)
        ax.set_xlabel("Data dimension 1")
        ax.set_ylabel("Data dimension 2")
        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
        ax.scatter(
            protos[:, 0],
            protos[:, 1],
            c=color,
            cmap=self.cmap,
            edgecolor="k",
            marker="D",
            s=50,
        )
        x = np.vstack((x_train, protos))
        x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
        y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
                             np.arange(y_min, y_max, 1 / 50))
        mesh_input = np.c_[xx.ravel(), yy.ravel()]
        y_pred = pl_module.predict(torch.Tensor(mesh_input))
        y_pred = y_pred.reshape(xx.shape)

        ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
        ax.set_xlim(left=x_min + 0, right=x_max - 0)
        ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
        plt.pause(0.1)


if __name__ == "__main__":
    # Dataset
    x_train, y_train = make_circles(n_samples=300,
                                    shuffle=True,
                                    noise=0.05,
                                    random_state=None,
                                    factor=0.5)
    train_ds = NumpyDataset(x_train, y_train)

    # Dataloaders
    train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)

    # Hyperparameters
    hparams = dict(
        input_dim=x_train.shape[1],
        nclasses=len(np.unique(y_train)),
        num_components=5,
        component_initializer=cinit.RandomInitializer(x_train.shape[1]),
        lr=0.01,
    )

    # Initialize the model
    model = CBC(
        hparams,
        data=[x_train, y_train],
        similarity=euclidean_similarity,
    )

    # Callbacks
    dvis = VisualizationCallback(x_train,
                                 y_train,
                                 prototype_model=False,
                                 title="CBC Circle Example")

    # Setup trainer
    trainer = pl.Trainer(
        max_epochs=50,
        callbacks=[
            dvis,
        ],
    )

    # Training loop
    trainer.fit(model, train_loader)