Use Components instead of Prototypes and refactor old examples
This commit is contained in:
Jensun Ravichandran
@@ -1,63 +1,14 @@
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"""GLVQ example using the Iris dataset."""
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import numpy as np
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import pytorch_lightning as pl
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import torch
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from matplotlib import pyplot as plt
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from prototorch.components import initializers as cinit
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.callbacks.visualization import VisGLVQ2D
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from prototorch.models.glvq import GLVQ
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.glvq import GLVQ
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Prototype Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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def on_epoch_end(self, trainer, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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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.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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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 = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
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y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.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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if __name__ == "__main__":
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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@@ -69,24 +20,21 @@ if __name__ == "__main__":
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# Hyperparameters
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hparams = dict(
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input_dim=x_train.shape[1],
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nclasses=3,
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prototypes_per_class=3,
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prototype_initializer="stratified_mean",
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lr=0.1,
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prototypes_per_class=2,
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prototype_initializer=cinit.StratifiedMeanInitializer(
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torch.Tensor(x_train), torch.Tensor(y_train)),
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lr=0.01,
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)
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# Initialize the model
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model = GLVQ(hparams, data=[x_train, y_train])
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# Model summary
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print(model)
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# Callbacks
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vis = VisualizationCallback(x_train, y_train)
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# Setup trainer
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trainer = pl.Trainer(max_epochs=50, callbacks=[vis])
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trainer = pl.Trainer(
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max_epochs=50,
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callbacks=[VisGLVQ2D(x_train, y_train)],
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)
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# Training loop
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trainer.fit(model, train_loader)
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@@ -3,63 +3,13 @@
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import numpy as np
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import pytorch_lightning as pl
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from matplotlib import pyplot as plt
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.callbacks.visualization import VisNG2D
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from prototorch.models.neural_gas import NeuralGas
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from sklearn.datasets import load_iris
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from sklearn.preprocessing import StandardScaler
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from torch.utils.data import DataLoader
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.neural_gas import NeuralGas
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Neural Gas Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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def on_epoch_end(self, trainer, pl_module: NeuralGas):
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protos = pl_module.proto_layer.prototypes.detach().cpu().numpy()
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cmat = pl_module.topology_layer.cmat.cpu().numpy()
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# Visualize the data and the prototypes
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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.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(self.x_train[:, 0],
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self.x_train[:, 1],
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c=self.y_train,
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edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c="k",
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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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# Draw connections
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for i in range(len(protos)):
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for j in range(len(protos)):
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if cmat[i][j]:
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ax.plot(
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[protos[i, 0], protos[j, 0]],
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[protos[i, 1], protos[j, 1]],
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"k-",
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)
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plt.pause(0.01)
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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@@ -68,7 +18,6 @@ if __name__ == "__main__":
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scaler.fit(x_train)
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x_train = scaler.transform(x_train)
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y_single_class = np.zeros_like(y_train)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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@@ -77,20 +26,18 @@ if __name__ == "__main__":
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# Hyperparameters
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hparams = dict(
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input_dim=x_train.shape[1],
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nclasses=1,
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prototypes_per_class=30,
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prototype_initializer="rand",
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lr=0.1,
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num_prototypes=30,
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lr=0.01,
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)
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# Initialize the model
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model = NeuralGas(hparams, data=[x_train, y_single_class])
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model = NeuralGas(hparams)
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# Model summary
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print(model)
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# Callbacks
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vis = VisualizationCallback(x_train, y_train)
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vis = VisNG2D(x_train, y_train)
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# Setup trainer
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trainer = pl.Trainer(
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@@ -1,70 +1,15 @@
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"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
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import numpy as np
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import pytorch_lightning as pl
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import torch
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from matplotlib import pyplot as plt
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from prototorch.components import (StratifiedMeanInitializer,
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StratifiedSelectionInitializer)
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
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from prototorch.models.glvq import SiameseGLVQ
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.glvq import SiameseGLVQ
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Prototype Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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def on_epoch_end(self, trainer, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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x_train = pl_module.backbone(torch.Tensor(x_train)).detach()
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protos = pl_module.backbone(torch.Tensor(protos)).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(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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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 = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 0.2, x[:, 0].max() + 0.2
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y_min, y_max = x[:, 1].min() - 0.2, x[:, 1].max() + 0.2
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict_latent(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.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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tb = pl_module.logger.experiment
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tb.add_figure(
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tag=f"{self.title}",
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figure=self.fig,
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global_step=trainer.current_epoch,
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close=False,
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)
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plt.pause(0.1)
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class Backbone(torch.nn.Module):
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def __init__(self, input_size=4, hidden_size=10, latent_size=2):
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@@ -90,23 +35,24 @@ if __name__ == "__main__":
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# Hyperparameters
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hparams = dict(
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input_dim=x_train.shape[1],
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nclasses=3,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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prototype_initializer=StratifiedMeanInitializer(
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torch.Tensor(x_train), torch.Tensor(y_train)),
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lr=0.01,
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)
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# Initialize the model
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model = SiameseGLVQ(hparams,
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backbone_module=Backbone,
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data=[x_train, y_train])
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model = SiameseGLVQ(
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hparams,
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backbone_module=Backbone,
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)
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# Model summary
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print(model)
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# Callbacks
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vis = VisualizationCallback(x_train, y_train)
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vis = VisSiameseGLVQ2D(x_train, y_train)
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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