refactor(api)!: merge the new api changes into dev

examples/cbc_iris.py

@@ -2,11 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -24,14 +23,18 @@ if __name__ == "__main__":
 
     # Hyperparameters
     hparams = dict(
-        distribution=[2, 2, 2],
-        proto_lr=0.1,
+        distribution=[1, 0, 3],
+        margin=0.1,
+        proto_lr=0.01,
+        bb_lr=0.01,
     )
 
     # Initialize the model
     model = pt.models.CBC(
         hparams,
-        prototype_initializer=pt.components.SSI(train_ds, noise=0.01),
+        components_initializer=pt.initializers.SSCI(train_ds, noise=0.01),
+        reasonings_iniitializer=pt.initializers.
+        PurePositiveReasoningsInitializer(),
     )
 
     # Callbacks

examples/dynamic_pruning.py

@@ -2,11 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -37,7 +36,7 @@ if __name__ == "__main__":
     # Initialize the model
     model = pt.models.CELVQ(
         hparams,
-        prototype_initializer=pt.components.Ones(2, scale=3),
+        prototypes_initializer=pt.initializers.FVCI(2, 3.0),
     )
 
     # Compute intermediate input and output sizes

examples/glvq_iris.py

@@ -2,12 +2,11 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from torch.optim.lr_scheduler import ExponentialLR
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -24,7 +23,7 @@ if __name__ == "__main__":
     hparams = dict(
         distribution={
             "num_classes": 3,
-            "prototypes_per_class": 4
+            "per_class": 4
         },
         lr=0.01,
     )
@@ -33,7 +32,7 @@ if __name__ == "__main__":
     model = pt.models.GLVQ(
         hparams,
         optimizer=torch.optim.Adam,
-        prototype_initializer=pt.components.SMI(train_ds),
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
         lr_scheduler=ExponentialLR,
         lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
     )

examples/glvq_spiral.py

@@ -2,11 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -26,7 +25,6 @@ if __name__ == "__main__":
         distribution=(num_classes, prototypes_per_class),
         transfer_function="swish_beta",
         transfer_beta=10.0,
-        # lr=0.1,
         proto_lr=0.1,
         bb_lr=0.1,
         input_dim=2,
@@ -37,7 +35,7 @@ if __name__ == "__main__":
     model = pt.models.GMLVQ(
         hparams,
         optimizer=torch.optim.Adam,
-        prototype_initializer=pt.components.SSI(train_ds, noise=1e-2),
+        prototypes_initializer=pt.initializers.SSCI(train_ds, noise=1e-2),
     )
 
     # Callbacks
@@ -47,12 +45,12 @@ if __name__ == "__main__":
         block=False,
     )
     pruning = pt.models.PruneLoserPrototypes(
-        threshold=0.02,
+        threshold=0.01,
         idle_epochs=10,
         prune_quota_per_epoch=5,
-        frequency=2,
+        frequency=5,
         replace=True,
-        initializer=pt.components.SSI(train_ds, noise=1e-2),
+        prototypes_initializer=pt.initializers.SSCI(train_ds, noise=1e-1),
         verbose=True,
     )
     es = pl.callbacks.EarlyStopping(
@@ -68,7 +66,7 @@ if __name__ == "__main__":
         args,
         callbacks=[
             vis,
-            # es,
+            # es, # FIXME
             pruning,
         ],
         terminate_on_nan=True,

examples/gmlvq_iris.py

@@ -1,59 +0,0 @@
-"""GLVQ example using the Iris dataset."""
-
-import argparse
-
-import prototorch as pt
-import pytorch_lightning as pl
-import torch
-from torch.optim.lr_scheduler import ExponentialLR
-
-if __name__ == "__main__":
-    # Command-line arguments
-    parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
-    args = parser.parse_args()
-
-    # Dataset
-    train_ds = pt.datasets.Iris()
-
-    # Dataloaders
-    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
-
-    # Hyperparameters
-    hparams = dict(
-        input_dim=4,
-        latent_dim=3,
-        distribution={
-            "num_classes": 3,
-            "prototypes_per_class": 2
-        },
-        proto_lr=0.0005,
-        bb_lr=0.0005,
-    )
-
-    # Initialize the model
-    model = pt.models.GMLVQ(
-        hparams,
-        optimizer=torch.optim.Adam,
-        prototype_initializer=pt.components.SSI(train_ds),
-        lr_scheduler=ExponentialLR,
-        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
-        omega_initializer=pt.components.PCA(train_ds.data)
-    )
-
-    # Compute intermediate input and output sizes
-    #model.example_input_array = torch.zeros(4, 2)
-
-    # Callbacks
-    vis = pt.models.VisGMLVQ2D(data=train_ds, border=0.1)
-
-    # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
-        args,
-        callbacks=[vis],
-        weights_summary="full",
-        accelerator="ddp",
-    )
-
-    # Training loop
-    trainer.fit(model, train_loader)

examples/gng_iris.py

@@ -2,11 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -30,7 +29,7 @@ if __name__ == "__main__":
     # Initialize the model
     model = pt.models.GrowingNeuralGas(
         hparams,
-        prototype_initializer=pt.components.Zeros(2),
+        prototypes_initializer=pt.initializers.ZCI(2),
     )
 
     # Compute intermediate input and output sizes

examples/ksom_colors.py

@@ -2,25 +2,11 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from matplotlib import pyplot as plt
-
-import prototorch as pt
-
-
-def hex_to_rgb(hex_values):
-    for v in hex_values:
-        v = v.lstrip('#')
-        lv = len(v)
-        c = [int(v[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)]
-        yield c
-
-
-def rgb_to_hex(rgb_values):
-    for v in rgb_values:
-        c = "%02x%02x%02x" % tuple(v)
-        yield c
+from prototorch.utils.colors import hex_to_rgb
 
 
 class Vis2DColorSOM(pl.Callback):
@@ -93,7 +79,7 @@ if __name__ == "__main__":
     # Initialize the model
     model = pt.models.KohonenSOM(
         hparams,
-        prototype_initializer=pt.components.Random(3),
+        prototypes_initializer=pt.initializers.RNCI(3),
     )
 
     # Compute intermediate input and output sizes

examples/lgmlvq_moons.py

@@ -2,23 +2,22 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
     parser = pl.Trainer.add_argparse_args(parser)
     args = parser.parse_args()
 
-    # Dataset
-    train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
-
     # Reproducibility
     pl.utilities.seed.seed_everything(seed=2)
 
+    # Dataset
+    train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
+
     # Dataloaders
     train_loader = torch.utils.data.DataLoader(train_ds,
                                                batch_size=256,
@@ -32,8 +31,10 @@ if __name__ == "__main__":
     )
 
     # Initialize the model
-    model = pt.models.LGMLVQ(hparams,
-                             prototype_initializer=pt.components.SMI(train_ds))
+    model = pt.models.LGMLVQ(
+        hparams,
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
+    )
 
     # Compute intermediate input and output sizes
     model.example_input_array = torch.zeros(4, 2)

examples/liramlvq_tecator.py

@@ -3,11 +3,10 @@
 import argparse
 
 import matplotlib.pyplot as plt
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 
 def plot_matrix(matrix):
     title = "Lambda matrix"
@@ -40,20 +39,19 @@ if __name__ == "__main__":
     hparams = dict(
         distribution={
             "num_classes": 2,
-            "prototypes_per_class": 1
+            "per_class": 1,
         },
         input_dim=100,
         latent_dim=2,
-        proto_lr=0.0001,
-        bb_lr=0.0001,
+        proto_lr=0.001,
+        bb_lr=0.001,
     )
 
     # Initialize the model
     model = pt.models.SiameseGMLVQ(
         hparams,
-        # optimizer=torch.optim.SGD,
         optimizer=torch.optim.Adam,
-        prototype_initializer=pt.components.SMI(train_ds),
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
     )
 
     # Summary

examples/lvqmln_iris.py

@@ -2,11 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 
 class Backbone(torch.nn.Module):
     def __init__(self, input_size=4, hidden_size=10, latent_size=2):
@@ -41,7 +40,7 @@ if __name__ == "__main__":
 
     # Hyperparameters
     hparams = dict(
-        distribution=[1, 2, 2],
+        distribution=[3, 4, 5],
         proto_lr=0.001,
         bb_lr=0.001,
     )
@@ -52,7 +51,10 @@ if __name__ == "__main__":
     # Initialize the model
     model = pt.models.LVQMLN(
         hparams,
-        prototype_initializer=pt.components.SSI(train_ds, transform=backbone),
+        prototypes_initializer=pt.initializers.SSCI(
+            train_ds,
+            transform=backbone,
+        ),
         backbone=backbone,
     )
 
@@ -67,11 +69,21 @@ if __name__ == "__main__":
         resolution=500,
         axis_off=True,
     )
+    pruning = pt.models.PruneLoserPrototypes(
+        threshold=0.01,
+        idle_epochs=20,
+        prune_quota_per_epoch=2,
+        frequency=10,
+        verbose=True,
+    )
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(
         args,
-        callbacks=[vis],
+        callbacks=[
+            vis,
+            pruning,
+        ],
     )
 
     # Training loop

examples/rslvq_iris.py

@@ -2,11 +2,9 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
-from torchvision.transforms import Lambda
-
-import prototorch as pt
 
 if __name__ == "__main__":
     # Command-line arguments
@@ -28,19 +26,17 @@ if __name__ == "__main__":
         distribution=[2, 2, 3],
         proto_lr=0.05,
         lambd=0.1,
+        variance=1.0,
         input_dim=2,
         latent_dim=2,
         bb_lr=0.01,
     )
 
     # Initialize the model
-    model = pt.models.probabilistic.PLVQ(
+    model = pt.models.RSLVQ(
         hparams,
         optimizer=torch.optim.Adam,
-        # prototype_initializer=pt.components.SMI(train_ds),
-        prototype_initializer=pt.components.SSI(train_ds, noise=0.2),
-        # prototype_initializer=pt.components.Zeros(2),
-        # prototype_initializer=pt.components.Ones(2, scale=2.0),
+        prototypes_initializer=pt.initializers.SSCI(train_ds, noise=0.2),
     )
 
     # Compute intermediate input and output sizes
@@ -50,7 +46,7 @@ if __name__ == "__main__":
     print(model)
 
     # Callbacks
-    vis = pt.models.VisSiameseGLVQ2D(data=train_ds)
+    vis = pt.models.VisGLVQ2D(data=train_ds)
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(

examples/siamese_glvq_iris.py

@@ -2,11 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 
 class Backbone(torch.nn.Module):
     def __init__(self, input_size=4, hidden_size=10, latent_size=2):
@@ -52,7 +51,7 @@ if __name__ == "__main__":
     # Initialize the model
     model = pt.models.SiameseGLVQ(
         hparams,
-        prototype_initializer=pt.components.SMI(train_ds),
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
         backbone=backbone,
         both_path_gradients=False,
     )

examples/warm_starting.py

@@ -0,0 +1,84 @@
+"""Warm-starting GLVQ with prototypes from Growing Neural Gas."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from torch.optim.lr_scheduler import ExponentialLR
+
+if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    # Prepare the data
+    train_ds = pt.datasets.Iris(dims=[0, 2])
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
+
+    # Initialize the gng
+    gng = pt.models.GrowingNeuralGas(
+        hparams=dict(num_prototypes=5, insert_freq=2, lr=0.1),
+        prototypes_initializer=pt.initializers.ZCI(2),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+    )
+
+    # Callbacks
+    es = pl.callbacks.EarlyStopping(
+        monitor="loss",
+        min_delta=0.001,
+        patience=20,
+        mode="min",
+        verbose=False,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer for GNG
+    trainer = pl.Trainer(
+        max_epochs=200,
+        callbacks=[es],
+        weights_summary=None,
+    )
+
+    # Training loop
+    trainer.fit(gng, train_loader)
+
+    # Hyperparameters
+    hparams = dict(
+        distribution=[],
+        lr=0.01,
+    )
+
+    # Warm-start prototypes
+    knn = pt.models.KNN(dict(k=1), data=train_ds)
+    prototypes = gng.prototypes
+    plabels = knn.predict(prototypes)
+
+    # Initialize the model
+    model = pt.models.GLVQ(
+        hparams,
+        optimizer=torch.optim.Adam,
+        prototypes_initializer=pt.initializers.LCI(prototypes),
+        labels_initializer=pt.initializers.LLI(plabels),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=train_ds)
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+        weights_summary="full",
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)