prototorch/examples/glvq_iris.py
2020-09-23 15:29:26 +02:00

107 lines
3.1 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(return_X_y=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.proto_layer = Prototypes1D(
input_dim=2,
prototypes_per_class=3,
nclasses=3,
prototype_initializer='stratified_random',
data=[x_train, y_train])
def forward(self, x):
protos = self.proto_layer.prototypes
plabels = self.proto_layer.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.proto_layer.prototypes.data.numpy()
if np.isnan(np.sum(protos)):
print(f'Stopping because of `nan` in prototypes.')
break
# 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]
w_indices = torch.argmin(d, dim=1)
y_pred = torch.index_select(plabels, 0, w_indices)
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)