131 lines
4.2 KiB
Python
131 lines
4.2 KiB
Python
"""GLVQ example using the Iris dataset."""
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import argparse
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import numpy as np
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import pytorch_lightning as pl
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import torch
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from matplotlib import pyplot as plt
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from prototorch.models.glvq import GLVQ
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader, TensorDataset
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class NumpyDataset(TensorDataset):
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def __init__(self, *arrays):
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tensors = [torch.from_numpy(arr) for arr in arrays]
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super().__init__(*tensors)
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class GLVQIris(GLVQ):
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@staticmethod
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def add_model_specific_args(parent_parser):
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parser = argparse.ArgumentParser(parents=[parent_parser],
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add_help=False)
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parser.add_argument("--epochs", type=int, default=1)
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parser.add_argument("--lr", type=float, default=1e-1)
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parser.add_argument("--batch_size", type=int, default=150)
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parser.add_argument("--input_dim", type=int, default=2)
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parser.add_argument("--nclasses", type=int, default=3)
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parser.add_argument("--prototypes_per_class", type=int, default=3)
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parser.add_argument("--prototype_initializer",
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type=str,
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default="stratified_mean")
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return parser
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Prototype Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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def on_epoch_end(self, trainer, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50)
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x = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
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y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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ax.set_xlim(left=x_min + 0, right=x_max - 0)
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ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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plt.pause(0.1)
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if __name__ == "__main__":
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# For best-practices when using `argparse` with `pytorch_lightning`, see
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# https://pytorch-lightning.readthedocs.io/en/stable/common/hyperparameters.html
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parser = argparse.ArgumentParser()
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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x_train = x_train[:, [0, 2]]
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
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# Add model specific args
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parser = GLVQIris.add_model_specific_args(parser)
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# Callbacks
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vis = VisualizationCallback(x_train, y_train)
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# Automatically add trainer-specific-args like `--gpus`, `--num_nodes` etc.
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parser = pl.Trainer.add_argparse_args(parser)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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parser,
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max_epochs=10,
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callbacks=[
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vis, # comment this line out to disable the visualization
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],
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)
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# trainer.tune(model)
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# Initialize the model
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args = parser.parse_args()
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model = GLVQIris(args, data=[x_train, y_train])
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# Model summary
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print(model)
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# Training loop
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trainer.fit(model, train_loader)
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# Save the model manually (use `pl.callbacks.ModelCheckpoint` to automate)
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ckpt = "glvq_iris.ckpt"
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trainer.save_checkpoint(ckpt)
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# Load the checkpoint
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new_model = GLVQIris.load_from_checkpoint(checkpoint_path=ckpt)
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print(new_model)
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