122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
|
"""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.models.cbc import CBC, rescaled_cosine_similarity, euclidean_similarity
|
||
|
|
from prototorch.models.glvq import GLVQ
|
||
|
|
from sklearn.datasets import make_circles
|
||
|
|
from torch.utils.data import DataLoader, TensorDataset
|
||
|
|
|
||
|
|
|
||
|
|
class NumpyDataset(TensorDataset):
|
||
|
|
def __init__(self, *arrays):
|
||
|
|
# tensors = [torch.from_numpy(arr) for arr in arrays]
|
||
|
|
tensors = [torch.Tensor(arr) for arr in arrays]
|
||
|
|
super().__init__(*tensors)
|
||
|
|
|
||
|
|
|
||
|
|
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.prototypes
|
||
|
|
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)),
|
||
|
|
prototypes_per_class=5,
|
||
|
|
prototype_initializer="randn",
|
||
|
|
lr=0.01,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Initialize the model
|
||
|
|
model = CBC(
|
||
|
|
hparams,
|
||
|
|
data=[x_train, y_train],
|
||
|
|
similarity=euclidean_similarity,
|
||
|
|
)
|
||
|
|
|
||
|
|
#model = GLVQ(hparams, data=[x_train, y_train])
|
||
|
|
|
||
|
|
# Fix the component locations
|
||
|
|
# model.proto_layer.requires_grad_(False)
|
||
|
|
|
||
|
|
# import sys
|
||
|
|
# sys.exit()
|
||
|
|
|
||
|
|
# Model summary
|
||
|
|
print(model)
|
||
|
|
|
||
|
|
# Callbacks
|
||
|
|
vis = VisualizationCallback(x_train, y_train, prototype_model=False)
|
||
|
|
|
||
|
|
# Setup trainer
|
||
|
|
trainer = pl.Trainer(
|
||
|
|
max_epochs=500,
|
||
|
|
callbacks=[
|
||
|
|
vis,
|
||
|
|
],
|
||
|
|
)
|
||
|
|
|
||
|
|
# Training loop
|
||
|
|
trainer.fit(model, train_loader)
|