102 lines
2.9 KiB
Python
102 lines
2.9 KiB
Python
"""ProtoTorch GLVQ 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.preprocessing import StandardScaler
|
|
|
|
from prototorch.functions.distances import euclidean_distance
|
|
from prototorch.modules.losses import GLVQLoss
|
|
from prototorch.modules.prototypes import Prototypes1D
|
|
|
|
# Prepare and preprocess the data
|
|
scaler = StandardScaler()
|
|
x_train, y_train = load_iris(True)
|
|
x_train = x_train[:, [0, 2]]
|
|
scaler.fit(x_train)
|
|
x_train = scaler.transform(x_train)
|
|
|
|
|
|
# Define the GLVQ model
|
|
class Model(torch.nn.Module):
|
|
def __init__(self, **kwargs):
|
|
"""GLVQ model."""
|
|
super().__init__()
|
|
self.p1 = Prototypes1D(input_dim=2,
|
|
prototypes_per_class=1,
|
|
nclasses=3,
|
|
prototype_initializer='zeros')
|
|
|
|
def forward(self, x):
|
|
protos = self.p1.prototypes
|
|
plabels = self.p1.prototype_labels
|
|
dis = euclidean_distance(x, protos)
|
|
return dis, plabels
|
|
|
|
|
|
# Build the GLVQ model
|
|
model = Model()
|
|
|
|
# Optimize using SGD optimizer from `torch.optim`
|
|
optimizer = torch.optim.SGD(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(70):
|
|
# Compute loss
|
|
dis, plabels = model(x_in)
|
|
loss = criterion([dis, plabels], y_in)
|
|
print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f}')
|
|
|
|
# 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()]
|
|
|
|
torch_input = torch.Tensor(mesh_input)
|
|
d = model(torch_input)[0]
|
|
y_pred = np.argmin(d.detach().numpy(),
|
|
axis=1) # assume one prototype per class
|
|
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)
|