prototorch/examples/glvq_iris.py
blackfly ab285ffcd4 Add examples/glvq_iris.py
This example demonstrates how to use the prototype module and the distance
functions from ProtoTorch together with the `GLVQLoss` module to implement
a GLVQ model and train it on the Iris dataset from scikit-learn.
2020-04-06 16:43:59 +02:00

104 lines
3.2 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 AddPrototypes1D
# 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):
super().__init__()
self.p1 = AddPrototypes1D(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)
# Training loop
fig = plt.figure('Prototype Visualization')
for epoch in range(70):
# Compute loss.
distances, plabels = model(torch.tensor(x_train))
loss = criterion([distances, plabels], torch.tensor(y_train))
print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():02.02f}')
# 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()
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
border = 1
resolution = 50
x = np.vstack((x_train, protos))
x_min, x_max = x[:, 0].min(), x[:, 0].max()
y_min, y_max = x[:, 1].min(), x[:, 1].max()
x_min, x_max = x_min - border, x_max + border
y_min, y_max = y_min - border, y_max + border
try:
xx, yy = np.meshgrid(np.arange(x_min, x_max, 1.0 / resolution),
np.arange(y_min, y_max, 1.0 / resolution))
except ValueError as ve:
print(ve)
raise ValueError(f'x_min: {x_min}, x_max: {x_max}. '
f'x_min - x_max is {x_max - x_min}.')
except MemoryError as me:
print(me)
raise ValueError('Too many points. ' 'Try reducing the resolution.')
mesh_input = np.c_[xx.ravel(), yy.ravel()]
torch_input = torch.from_numpy(mesh_input)
d = model(torch_input)[0]
y_pred = np.argmin(d.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)