prototorch_models/examples/cbc_spiral.py
Alexander Engelsberger 1c3613019b Update Examples to new initializer architecture.
Visualization still borken for some examples.
2021-05-06 14:10:09 +02:00

136 lines
3.9 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.datasets.abstract import NumpyDataset
from torch.utils.data import DataLoader
from prototorch.models.cbc import CBC
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)
def make_spirals(n_samples=500, noise=0.3):
def get_samples(n, delta_t):
points = []
for i in range(n):
r = i / n_samples * 5
t = 1.75 * i / n * 2 * np.pi + delta_t
x = r * np.sin(t) + np.random.rand(1) * noise
y = r * np.cos(t) + np.random.rand(1) * noise
points.append([x, y])
return points
n = n_samples // 2
positive = get_samples(n=n, delta_t=0)
negative = get_samples(n=n, delta_t=np.pi)
x = np.concatenate(
[np.array(positive).reshape(n, -1),
np.array(negative).reshape(n, -1)],
axis=0)
y = np.concatenate([np.zeros(n), np.ones(n)])
return x, y
if __name__ == "__main__":
# Dataset
x_train, y_train = make_spirals(n_samples=1000, noise=0.3)
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=2,
prototypes_per_class=40,
prototype_initializer="stratified_random",
lr=0.05,
)
# Initialize the model
model_class = CBC
model = model_class(hparams, data=[x_train, y_train])
# Pure-positive reasonings
new_reasoning = torch.zeros_like(
model.reasoning_layer.reasoning_probabilities)
for i, label in enumerate(model.component_layer.prototype_labels):
new_reasoning[0][0][i][int(label)] = 1.0
model.reasoning_layer.reasoning_probabilities.data = new_reasoning
# Model summary
print(model)
# Callbacks
vis = VisualizationCallback(x_train,
y_train,
prototype_model=hasattr(model, "prototypes"))
# Setup trainer
trainer = pl.Trainer(
max_epochs=500,
callbacks=[
vis,
],
)
# Training loop
trainer.fit(model, train_loader)