97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
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"""ProtoTorch CBC example using 2D Iris data."""
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import torch
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from matplotlib import pyplot as plt
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import prototorch as pt
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class CBC(torch.nn.Module):
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def __init__(self, data, **kwargs):
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super().__init__(**kwargs)
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self.components_layer = pt.components.ReasoningComponents(
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distribution=[2, 1, 2],
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components_initializer=pt.initializers.SSCI(data, noise=0.1),
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reasonings_initializer=pt.initializers.PPRI(components_first=True),
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)
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def forward(self, x):
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components, reasonings = self.components_layer()
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sims = pt.similarities.euclidean_similarity(x, components)
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probs = pt.competitions.cbcc(sims, reasonings)
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return probs
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class VisCBC2D():
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def __init__(self, model, data):
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self.model = model
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self.x_train, self.y_train = pt.utils.parse_data_arg(data)
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self.title = "Components Visualization"
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self.fig = plt.figure(self.title)
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self.border = 0.1
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self.resolution = 100
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self.cmap = "viridis"
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def on_epoch_end(self):
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x_train, y_train = self.x_train, self.y_train
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_components = self.model.components_layer._components.detach()
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.axis("off")
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ax.scatter(
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x_train[:, 0],
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x_train[:, 1],
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c=y_train,
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cmap=self.cmap,
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edgecolor="k",
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marker="o",
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s=30,
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)
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ax.scatter(
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_components[:, 0],
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_components[:, 1],
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c="w",
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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x = torch.vstack((x_train, _components))
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mesh_input, xx, yy = pt.utils.mesh2d(x, self.border, self.resolution)
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with torch.no_grad():
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y_pred = self.model(
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torch.Tensor(mesh_input).type_as(_components)).argmax(1)
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y_pred = y_pred.cpu().reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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plt.pause(0.2)
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if __name__ == "__main__":
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train_ds = pt.datasets.Iris(dims=[0, 2])
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
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model = CBC(train_ds)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
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criterion = pt.losses.MarginLoss(margin=0.1)
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vis = VisCBC2D(model, train_ds)
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for epoch in range(200):
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correct = 0.0
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for x, y in train_loader:
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y_oh = torch.eye(3)[y]
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y_pred = model(x)
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loss = criterion(y_pred, y_oh).mean(0)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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correct += (y_pred.argmax(1) == y).float().sum(0)
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acc = 100 * correct / len(train_ds)
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print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
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vis.on_epoch_end()
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