Update Examples to new initializer architecture.

Visualization still borken for some examples.

examples/cbc_circle.py

@@ -4,13 +4,12 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
+from prototorch.components import initializers as cinit
+from prototorch.datasets.abstract import NumpyDataset
 from sklearn.datasets import make_circles
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.callbacks.visualization import VisPointProtos
 from prototorch.models.cbc import CBC, euclidean_similarity
-from prototorch.models.glvq import GLVQ
 
 
 class VisualizationCallback(pl.Callback):
@@ -32,7 +31,7 @@ class VisualizationCallback(pl.Callback):
 
     def on_epoch_end(self, trainer, pl_module):
         if self.prototype_model:
-            protos = pl_module.prototypes
+            protos = pl_module.components
             color = pl_module.prototype_labels
         else:
             protos = pl_module.components
@@ -83,8 +82,8 @@ if __name__ == "__main__":
     hparams = dict(
         input_dim=x_train.shape[1],
         nclasses=len(np.unique(y_train)),
-        prototypes_per_class=5,
+        num_components=5,
-        prototype_initializer="randn",
+        component_initializer=cinit.RandomInitializer(x_train.shape[1]),
         lr=0.01,
     )
 
@@ -95,31 +94,15 @@ if __name__ == "__main__":
         similarity=euclidean_similarity,
     )
 
-    model = GLVQ(hparams, data=[x_train, y_train])
-
-    # Fix the component locations
-    # model.proto_layer.requires_grad_(False)
-
-    # import sys
-    # sys.exit()
-
-    # Model summary
-    print(model)
-
     # Callbacks
-    dvis = VisPointProtos(
+    dvis = VisualizationCallback(x_train,
-        data=(x_train, y_train),
+                                 y_train,
-        save=True,
+                                 prototype_model=False,
-        snap=False,
+                                 title="CBC Circle Example")
-        voronoi=True,
-        resolution=50,
-        pause_time=0.1,
-        make_gif=True,
-    )
 
     # Setup trainer
     trainer = pl.Trainer(
-        max_epochs=10,
+        max_epochs=50,
         callbacks=[
             dvis,
         ],

examples/cbc_iris.py

@@ -4,30 +4,38 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
+from prototorch.components import initializers as cinit
+from prototorch.datasets.abstract import NumpyDataset
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.cbc import CBC, euclidean_similarity
-from prototorch.models.cbc import CBC
 
 
 class VisualizationCallback(pl.Callback):
-    def __init__(self,
+    def __init__(
-                 x_train,
+        self,
-                 y_train,
+        x_train,
-                 title="Prototype Visualization",
+        y_train,
-                 cmap="viridis"):
+        prototype_model=True,
+        title="Prototype Visualization",
+        cmap="viridis",
+    ):
         super().__init__()
         self.x_train = x_train
         self.y_train = y_train
         self.title = title
         self.fig = plt.figure(self.title)
         self.cmap = cmap
+        self.prototype_model = prototype_model
 
     def on_epoch_end(self, trainer, pl_module):
-        # protos = pl_module.prototypes
+        if self.prototype_model:
-        protos = pl_module.components
+            protos = pl_module.components
-        # plabels = pl_module.prototype_labels
+            color = pl_module.prototype_labels
+        else:
+            protos = pl_module.components
+            color = "k"
         ax = self.fig.gca()
         ax.cla()
         ax.set_title(self.title)
@@ -37,8 +45,7 @@ class VisualizationCallback(pl.Callback):
         ax.scatter(
             protos[:, 0],
             protos[:, 1],
-            # c=plabels,
+            c=color,
-            c="k",
             cmap=self.cmap,
             edgecolor="k",
             marker="D",
@@ -71,44 +78,33 @@ if __name__ == "__main__":
     # Hyperparameters
     hparams = dict(
         input_dim=x_train.shape[1],
-        nclasses=3,
+        nclasses=len(np.unique(y_train)),
-        prototypes_per_class=3,
+        num_components=9,
-        prototype_initializer="stratified_mean",
+        component_initializer=cinit.StratifiedMeanInitializer(
+            torch.Tensor(x_train), torch.Tensor(y_train)),
         lr=0.01,
     )
 
     # Initialize the model
-    model = CBC(hparams, data=[x_train, y_train])
+    model = CBC(
-
+        hparams,
-    # Fix the component locations
+        data=[x_train, y_train],
-    # model.proto_layer.requires_grad_(False)
+        similarity=euclidean_similarity,
-
+    )
-    # Pure-positive reasonings
-    ncomps = 3
-    nclasses = 3
-    rmat = torch.stack(
-        [0.9 * torch.eye(ncomps),
-         torch.zeros(ncomps, nclasses)], dim=0)
-    # model.reasoning_layer.load_state_dict({"reasoning_probabilities": rmat},
-    #                                       strict=True)
-
-    print(model.reasoning_layer.reasoning_probabilities)
-    # import sys
-    # sys.exit()
-
-    # Model summary
-    print(model)
 
     # Callbacks
-    vis = VisualizationCallback(x_train, y_train)
+    dvis = VisualizationCallback(x_train,
+                                 y_train,
+                                 prototype_model=False,
+                                 title="CBC Iris Example")
 
     # Setup trainer
     trainer = pl.Trainer(
-        max_epochs=100,
+        max_epochs=50,
         callbacks=[
-            vis,
+            dvis,
         ],
     )
 
     # Training loop
-    trainer.fit(model, train_loader)
+    trainer.fit(model, train_loader)

examples/cbc_spiral.py

@@ -4,9 +4,9 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
+from prototorch.datasets.abstract import NumpyDataset
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
 from prototorch.models.cbc import CBC
 
 
@@ -110,7 +110,7 @@ if __name__ == "__main__":
     # Pure-positive reasonings
     new_reasoning = torch.zeros_like(
         model.reasoning_layer.reasoning_probabilities)
-    for i, label in enumerate(model.proto_layer.prototype_labels):
+    for i, label in enumerate(model.component_layer.prototype_labels):
         new_reasoning[0][0][i][int(label)] = 1.0
 
     model.reasoning_layer.reasoning_probabilities.data = new_reasoning

examples/cbc_spiral_with_glvq_start.py

@@ -8,9 +8,9 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
+from prototorch.datasets.abstract import NumpyDataset
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
 from prototorch.models.cbc import CBC
 from prototorch.models.glvq import GLVQ
 
@@ -132,11 +132,12 @@ if __name__ == "__main__":
     train(glvq_model, x_train, y_train, train_loader, epochs=10)
 
     # Transfer Prototypes
-    cbc_model.proto_layer.load_state_dict(glvq_model.proto_layer.state_dict())
+    cbc_model.component_layer.load_state_dict(
+        glvq_model.proto_layer.state_dict())
     # Pure-positive reasonings
     new_reasoning = torch.zeros_like(
         cbc_model.reasoning_layer.reasoning_probabilities)
-    for i, label in enumerate(cbc_model.proto_layer.prototype_labels):
+    for i, label in enumerate(cbc_model.component_layer.prototype_labels):
         new_reasoning[0][0][i][int(label)] = 1.0
         new_reasoning[1][0][i][1 - int(label)] = 1.0
 

examples/glvq_iris.py

@@ -1,86 +1,16 @@
 """GLVQ example using the Iris dataset."""
 
-import argparse
-
-import numpy as np
 import pytorch_lightning as pl
 import torch
-from matplotlib import pyplot as plt
+from prototorch.components import initializers as cinit
+from prototorch.datasets.abstract import NumpyDataset
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
 
-from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.callbacks.visualization import VisGLVQ2D
 from prototorch.models.glvq import GLVQ
 
-
-class GLVQIris(GLVQ):
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = argparse.ArgumentParser(parents=[parent_parser],
-                                         add_help=False)
-        parser.add_argument("--epochs", type=int, default=1)
-        parser.add_argument("--lr", type=float, default=1e-1)
-        parser.add_argument("--batch_size", type=int, default=150)
-        parser.add_argument("--input_dim", type=int, default=2)
-        parser.add_argument("--nclasses", type=int, default=3)
-        parser.add_argument("--prototypes_per_class", type=int, default=3)
-        parser.add_argument("--prototype_initializer",
-                            type=str,
-                            default="stratified_mean")
-        return parser
-
-
-class VisualizationCallback(pl.Callback):
-    def __init__(self,
-                 x_train,
-                 y_train,
-                 title="Prototype Visualization",
-                 cmap="viridis"):
-        super().__init__()
-        self.x_train = x_train
-        self.y_train = y_train
-        self.title = title
-        self.fig = plt.figure(self.title)
-        self.cmap = cmap
-
-    def on_epoch_end(self, trainer, pl_module):
-        protos = pl_module.prototypes
-        plabels = pl_module.prototype_labels
-        ax = self.fig.gca()
-        ax.cla()
-        ax.set_title(self.title)
-        ax.set_xlabel("Data dimension 1")
-        ax.set_ylabel("Data dimension 2")
-        ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-        ax.scatter(
-            protos[:, 0],
-            protos[:, 1],
-            c=plabels,
-            cmap=self.cmap,
-            edgecolor="k",
-            marker="D",
-            s=50,
-        )
-        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()]
-        y_pred = pl_module.predict(torch.Tensor(mesh_input))
-        y_pred = y_pred.reshape(xx.shape)
-
-        ax.contourf(xx, yy, y_pred, cmap=self.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)
-
-
 if __name__ == "__main__":
-    # For best-practices when using `argparse` with `pytorch_lightning`, see
-    # https://pytorch-lightning.readthedocs.io/en/stable/common/hyperparameters.html
-    parser = argparse.ArgumentParser()
-
     # Dataset
     x_train, y_train = load_iris(return_X_y=True)
     x_train = x_train[:, [0, 2]]
@@ -89,43 +19,23 @@ if __name__ == "__main__":
     # Dataloaders
     train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
 
-    # Add model specific args
+    # Hyperparameters
-    parser = GLVQIris.add_model_specific_args(parser)
+    hparams = dict(
-
+        nclasses=3,
-    # Callbacks
+        prototypes_per_class=2,
-    vis = VisualizationCallback(x_train, y_train)
+        prototype_initializer=cinit.StratifiedMeanInitializer(
-
+            torch.Tensor(x_train), torch.Tensor(y_train)),
-    # Automatically add trainer-specific-args like `--gpus`, `--num_nodes` etc.
+        lr=0.01,
-    parser = pl.Trainer.add_argparse_args(parser)
-
-    # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
-        parser,
-        max_epochs=10,
-        callbacks=[
-            vis,
-        ],  # comment this line out to disable the visualization
     )
-    # trainer.tune(model)
 
     # Initialize the model
-    args = parser.parse_args()
+    model = GLVQ(hparams)
-    model = GLVQIris(args, data=[x_train, y_train])
 
-    # Model summary
+    # Setup trainer
-    print(model)
+    trainer = pl.Trainer(
+        max_epochs=50,
+        callbacks=[VisGLVQ2D(x_train, y_train)],
+    )
 
     # Training loop
     trainer.fit(model, train_loader)
-
-    # Save the model manually (use `pl.callbacks.ModelCheckpoint` to automate)
-    ckpt = "glvq_iris.ckpt"
-    trainer.save_checkpoint(ckpt)
-
-    # Load the checkpoint
-    new_model = GLVQIris.load_from_checkpoint(checkpoint_path=ckpt)
-
-    print(new_model)
-
-    # Continue training
-    trainer.fit(new_model, train_loader)  # TODO See why this fails!

examples/glvq_iris_v1.py

@@ -1,40 +0,0 @@
-"""GLVQ example using the Iris dataset."""
-
-import pytorch_lightning as pl
-import torch
-from prototorch.components import initializers as cinit
-from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.callbacks.visualization import VisGLVQ2D
-from prototorch.models.glvq import GLVQ
-from sklearn.datasets import load_iris
-from torch.utils.data import DataLoader
-
-if __name__ == "__main__":
-    # Dataset
-    x_train, y_train = load_iris(return_X_y=True)
-    x_train = x_train[:, [0, 2]]
-    train_ds = NumpyDataset(x_train, y_train)
-
-    # Dataloaders
-    train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
-
-    # Hyperparameters
-    hparams = dict(
-        nclasses=3,
-        prototypes_per_class=2,
-        prototype_initializer=cinit.StratifiedMeanInitializer(
-            torch.Tensor(x_train), torch.Tensor(y_train)),
-        lr=0.01,
-    )
-
-    # Initialize the model
-    model = GLVQ(hparams)
-
-    # Setup trainer
-    trainer = pl.Trainer(
-        max_epochs=50,
-        callbacks=[VisGLVQ2D(x_train, y_train)],
-    )
-
-    # Training loop
-    trainer.fit(model, train_loader)

examples/glvq_mnist.py

@@ -7,6 +7,7 @@ import argparse
 
 import pytorch_lightning as pl
 import torchvision
+from prototorch.components import initializers as cinit
 from torch.utils.data import DataLoader
 from torchvision import transforms
 from torchvision.datasets import MNIST
@@ -92,12 +93,12 @@ if __name__ == "__main__":
         input_dim=28 * 28,
         nclasses=10,
         prototypes_per_class=1,
-        prototype_initializer="stratified_mean",
+        prototype_initializer=cinit.StratifiedMeanInitializer(x, y),
         lr=args.lr,
     )
 
     # Initialize the model
-    model = ImageGLVQ(hparams, data=[x, y])
+    model = ImageGLVQ(hparams)
 
     # Model summary
     print(model)

examples/glvq_spiral.py

@@ -5,9 +5,10 @@ import torch
 from prototorch.components import initializers as cinit
 from prototorch.datasets.abstract import NumpyDataset
 from prototorch.datasets.spiral import make_spiral
+from torch.utils.data import DataLoader
+
 from prototorch.models.callbacks.visualization import VisGLVQ2D
 from prototorch.models.glvq import GLVQ
-from torch.utils.data import DataLoader
 
 
 class StopOnNaN(pl.Callback):

examples/gmlvq_iris.py

@@ -4,11 +4,12 @@ import pytorch_lightning as pl
 import torch
 from prototorch.components import initializers as cinit
 from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
-from prototorch.models.glvq import GMLVQ
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
 
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import GMLVQ
+
 if __name__ == "__main__":
     # Dataset
     x_train, y_train = load_iris(return_X_y=True)

examples/gmlvq_tecator.py

@@ -1,12 +1,12 @@
 """GMLVQ example using the Tecator dataset."""
 
 import pytorch_lightning as pl
-import torch
 from prototorch.components import initializers as cinit
 from prototorch.datasets.tecator import Tecator
+from torch.utils.data import DataLoader
+
 from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
 from prototorch.models.glvq import GMLVQ
-from torch.utils.data import DataLoader
 
 if __name__ == "__main__":
     # Dataset

examples/ng_iris.py

@@ -1,15 +1,14 @@
 """Neural Gas example using the Iris dataset."""
 
-import numpy as np
 import pytorch_lightning as pl
-from matplotlib import pyplot as plt
 from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.callbacks.visualization import VisNG2D
-from prototorch.models.neural_gas import NeuralGas
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
 from torch.utils.data import DataLoader
 
+from prototorch.models.callbacks.visualization import VisNG2D
+from prototorch.models.neural_gas import NeuralGas
+
 if __name__ == "__main__":
     # Dataset
     x_train, y_train = load_iris(return_X_y=True)

examples/siamese_glvq_iris.py

@@ -2,14 +2,16 @@
 
 import pytorch_lightning as pl
 import torch
-from prototorch.components import (StratifiedMeanInitializer,
+from prototorch.components import (
-                                   StratifiedSelectionInitializer)
+    StratifiedMeanInitializer
+)
 from prototorch.datasets.abstract import NumpyDataset
-from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
-from prototorch.models.glvq import SiameseGLVQ
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
 
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import SiameseGLVQ
+
 
 class Backbone(torch.nn.Module):
     def __init__(self, input_size=4, hidden_size=10, latent_size=2):

prototorch/models/cbc.py

@@ -1,10 +1,9 @@
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-
+from prototorch.components.components import Components
 from prototorch.functions.distances import euclidean_distance
 from prototorch.functions.similarities import cosine_similarity
-from prototorch.modules.prototypes import Prototypes1D
 
 
 def rescaled_cosine_similarity(x, y):
@@ -93,12 +92,8 @@ class CBC(pl.LightningModule):
         super().__init__()
         self.save_hyperparameters(hparams)
         self.margin = margin
-        self.proto_layer = Prototypes1D(
+        self.component_layer = Components(self.hparams.num_components,
-            input_dim=self.hparams.input_dim,
+                                          self.hparams.component_initializer)
-            nclasses=self.hparams.nclasses,
-            prototypes_per_class=self.hparams.prototypes_per_class,
-            prototype_initializer=self.hparams.prototype_initializer,
-            **kwargs)
         # self.similarity = CosineSimilarity()
         self.similarity = similarity
         self.backbone = backbone_class()
@@ -110,7 +105,7 @@ class CBC(pl.LightningModule):
 
     @property
     def components(self):
-        return self.proto_layer.prototypes.detach().cpu()
+        return self.component_layer.components.detach().cpu()
 
     @property
     def reasonings(self):
@@ -126,7 +121,7 @@ class CBC(pl.LightningModule):
 
     def forward(self, x):
         self.sync_backbones()
-        protos, _ = self.proto_layer()
+        protos = self.component_layer()
 
         latent_x = self.backbone(x)
         latent_protos = self.backbone_dependent(protos)
@@ -167,4 +162,4 @@ class ImageCBC(CBC):
     """
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
         # super().on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)
-        self.proto_layer.prototypes.data.clamp_(0.0, 1.0)
+        self.component_layer.prototypes.data.clamp_(0.0, 1.0)

prototorch/models/glvq.py

@@ -1,4 +1,3 @@
-import pytorch_lightning as pl
 import torch
 import torchmetrics
 from prototorch.components import LabeledComponents
@@ -7,7 +6,6 @@ from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import (euclidean_distance,
                                             squared_euclidean_distance)
 from prototorch.functions.losses import glvq_loss
-from prototorch.modules.prototypes import Prototypes1D
 
 from .abstract import AbstractPrototypeModel
 
@@ -55,7 +53,6 @@ class GLVQ(AbstractPrototypeModel):
         with torch.no_grad():
             preds = wtac(dis, plabels)
         # `.int()` because FloatTensors are assumed to be class probabilities
-        self.train_acc(preds.int(), y.int())
 
         # Logging
         self.log("train_loss", loss)

prototorch/models/neural_gas.py

@@ -1,9 +1,7 @@
-import pytorch_lightning as pl
 import torch
 from prototorch.components import Components
 from prototorch.components import initializers as cinit
 from prototorch.functions.distances import euclidean_distance
-from prototorch.modules import Prototypes1D
 from prototorch.modules.losses import NeuralGasEnergy
 
 from .abstract import AbstractPrototypeModel