104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
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"""ProtoTorch GLVQ example using 2D Iris data"""
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import numpy as np
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import torch
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from matplotlib import pyplot as plt
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from sklearn.datasets import load_iris
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from sklearn.preprocessing import StandardScaler
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from prototorch.functions.distances import euclidean_distance
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from prototorch.modules.losses import GLVQLoss
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from prototorch.modules.prototypes import AddPrototypes1D
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# Prepare and preprocess the data
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scaler = StandardScaler()
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x_train, y_train = load_iris(True)
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x_train = x_train[:, [0, 2]]
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scaler.fit(x_train)
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x_train = scaler.transform(x_train)
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# Define the GLVQ model
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class Model(torch.nn.Module):
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def __init__(self, **kwargs):
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super().__init__()
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self.p1 = AddPrototypes1D(input_dim=2,
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prototypes_per_class=1,
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nclasses=3,
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prototype_initializer='zeros')
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def forward(self, x):
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protos = self.p1.prototypes
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plabels = self.p1.prototype_labels
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dis = euclidean_distance(x, protos)
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return dis, plabels
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# Build the GLVQ model
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model = Model()
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# Optimize using SGD optimizer from `torch.optim`
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
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# Training loop
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fig = plt.figure('Prototype Visualization')
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for epoch in range(70):
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# Compute loss.
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distances, plabels = model(torch.tensor(x_train))
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loss = criterion([distances, plabels], torch.tensor(y_train))
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print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():02.02f}')
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# Take a gradient descent step
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# Get the prototypes form the model
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protos = model.p1.prototypes.data.numpy()
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# Visualize the data and the prototypes
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ax = fig.gca()
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ax.cla()
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cmap = 'viridis'
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k')
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ax.scatter(protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=cmap,
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edgecolor='k',
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marker='D',
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s=50)
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# Paint decision regions
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border = 1
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resolution = 50
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x = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min(), x[:, 0].max()
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y_min, y_max = x[:, 1].min(), x[:, 1].max()
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x_min, x_max = x_min - border, x_max + border
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y_min, y_max = y_min - border, y_max + border
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try:
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1.0 / resolution),
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np.arange(y_min, y_max, 1.0 / resolution))
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except ValueError as ve:
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print(ve)
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raise ValueError(f'x_min: {x_min}, x_max: {x_max}. '
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f'x_min - x_max is {x_max - x_min}.')
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except MemoryError as me:
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print(me)
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raise ValueError('Too many points. ' 'Try reducing the resolution.')
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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torch_input = torch.from_numpy(mesh_input)
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d = model(torch_input)[0]
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y_pred = np.argmin(d.detach().numpy(), axis=1)
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y_pred = y_pred.reshape(xx.shape)
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# Plot voronoi regions
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ax.contourf(xx, yy, y_pred, cmap=cmap, alpha=0.35)
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ax.set_xlim(left=x_min + 0, right=x_max - 0)
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ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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plt.pause(0.1)
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