107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
|
"""ProtoTorch LGMLVQ example using 2D Iris data."""
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
from matplotlib import pyplot as plt
|
||
|
|
from sklearn.datasets import load_iris
|
||
|
|
from sklearn.metrics import accuracy_score
|
||
|
|
|
||
|
|
from prototorch.functions.competitions import stratified_min
|
||
|
|
from prototorch.functions.distances import lomega_distance
|
||
|
|
from prototorch.functions.init import eye_
|
||
|
|
from prototorch.modules.losses import GLVQLoss
|
||
|
|
from prototorch.modules.prototypes import Prototypes1D
|
||
|
|
|
||
|
|
# Prepare training data
|
||
|
|
x_train, y_train = load_iris(True)
|
||
|
|
x_train = x_train[:, [0, 2]]
|
||
|
|
|
||
|
|
|
||
|
|
# Define the model
|
||
|
|
class Model(torch.nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
"""Local-GMLVQ model."""
|
||
|
|
super().__init__()
|
||
|
|
self.p1 = Prototypes1D(input_dim=2,
|
||
|
|
prototype_distribution=[1, 2, 2],
|
||
|
|
prototype_initializer="stratified_random",
|
||
|
|
data=[x_train, y_train])
|
||
|
|
omegas = torch.zeros(5, 2, 2)
|
||
|
|
self.omegas = torch.nn.Parameter(omegas)
|
||
|
|
eye_(self.omegas)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
protos = self.p1.prototypes
|
||
|
|
plabels = self.p1.prototype_labels
|
||
|
|
omegas = self.omegas
|
||
|
|
dis = lomega_distance(x, protos, omegas)
|
||
|
|
return dis, plabels
|
||
|
|
|
||
|
|
|
||
|
|
# Build the model
|
||
|
|
model = Model()
|
||
|
|
|
||
|
|
# Optimize using Adam optimizer from `torch.optim`
|
||
|
|
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
|
||
|
|
criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
|
||
|
|
|
||
|
|
x_in = torch.Tensor(x_train)
|
||
|
|
y_in = torch.Tensor(y_train)
|
||
|
|
|
||
|
|
# Training loop
|
||
|
|
title = "Prototype Visualization"
|
||
|
|
fig = plt.figure(title)
|
||
|
|
for epoch in range(100):
|
||
|
|
# Compute loss
|
||
|
|
dis, plabels = model(x_in)
|
||
|
|
loss = criterion([dis, plabels], y_in)
|
||
|
|
y_pred = np.argmin(stratified_min(dis, plabels).detach().numpy(), axis=1)
|
||
|
|
acc = accuracy_score(y_train, y_pred)
|
||
|
|
log_string = f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} "
|
||
|
|
log_string += f"Acc: {acc * 100:05.02f}%"
|
||
|
|
print(log_string)
|
||
|
|
|
||
|
|
# Take a gradient descent step
|
||
|
|
optimizer.zero_grad()
|
||
|
|
loss.backward()
|
||
|
|
optimizer.step()
|
||
|
|
|
||
|
|
# Get the prototypes form the model
|
||
|
|
protos = model.p1.prototypes.data.numpy()
|
||
|
|
|
||
|
|
# Visualize the data and the prototypes
|
||
|
|
ax = fig.gca()
|
||
|
|
ax.cla()
|
||
|
|
ax.set_title(title)
|
||
|
|
ax.set_xlabel("Data dimension 1")
|
||
|
|
ax.set_ylabel("Data dimension 2")
|
||
|
|
cmap = "viridis"
|
||
|
|
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k')
|
||
|
|
ax.scatter(protos[:, 0],
|
||
|
|
protos[:, 1],
|
||
|
|
c=plabels,
|
||
|
|
cmap=cmap,
|
||
|
|
edgecolor='k',
|
||
|
|
marker='D',
|
||
|
|
s=50)
|
||
|
|
|
||
|
|
# Paint decision regions
|
||
|
|
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()]
|
||
|
|
|
||
|
|
d, plabels = model(torch.Tensor(mesh_input))
|
||
|
|
y_pred = np.argmin(stratified_min(d, plabels).detach().numpy(), axis=1)
|
||
|
|
y_pred = y_pred.reshape(xx.shape)
|
||
|
|
|
||
|
|
# Plot voronoi regions
|
||
|
|
ax.contourf(xx, yy, y_pred, cmap=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)
|