refactor: minor changes in probabilistic.py

Should fix the problem with EarlyStopping callback.

.bumpversion.cfg

@@ -1,10 +1,13 @@
 [bumpversion]
-current_version = 0.1.8
+current_version = 0.2.0
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
 serialize = {major}.{minor}.{patch}
+message = build: bump version {current_version} → {new_version}
 
 [bumpversion:file:setup.py]
 
 [bumpversion:file:./prototorch/models/__init__.py]
+
+[bumpversion:file:./docs/source/conf.py]

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Steps to reproduce the behavior**
+1. ...
+2. Run script '...' or this snippet:
+```python
+import prototorch as pt
+
+...
+```
+3. See errors
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Observed behavior**
+A clear and concise description of what actually happened.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**System and version information**
+- OS: [e.g. Ubuntu 20.10]
+- ProtoTorch Version: [e.g. 0.4.0]
+- Python Version: [e.g. 3.9.5]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.gitignore

@@ -128,14 +128,19 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
-# Datasets
-datasets/
-
-# PyTorch-Lightning
-lightning_logs/
-
 .vscode/
 
+# Vim
+*~
+*.swp
+*.swo
+
 #  Pytorch Models or Weights
 #  If necessary make exceptions for single pretrained models
 *.pt
+
+# Artifacts created by ProtoTorch Models
+datasets/
+lightning_logs/
+examples/_*.py
+examples/_*.ipynb

.pre-commit-config.yaml

@@ -1,54 +1,53 @@
 # See https://pre-commit.com for more information
 # See https://pre-commit.com/hooks.html for more hooks
-repos:
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
-    hooks:
-    -   id: trailing-whitespace
-    -   id: end-of-file-fixer
-    -   id: check-yaml
-    -   id: check-added-large-files
-    -   id: check-ast
-    -   id: check-case-conflict
 
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.0.1
+  hooks:
+  - id: trailing-whitespace
+  - id: end-of-file-fixer
+  - id: check-yaml
+  - id: check-added-large-files
+  - id: check-ast
+  - id: check-case-conflict
 
 - repo: https://github.com/myint/autoflake
   rev: v1.4
   hooks:
-  -   id: autoflake
+  - id: autoflake
 
 - repo: http://github.com/PyCQA/isort
   rev: 5.8.0
   hooks:
-  -   id: isort
+  - id: isort
 
--   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: 'v0.902'
-    hooks:
-    -   id: mypy
-        files: prototorch
-        additional_dependencies: [types-pkg_resources]
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: v0.902
+  hooks:
+  - id: mypy
+    files: prototorch
+    additional_dependencies: [types-pkg_resources]
 
--   repo: https://github.com/pre-commit/mirrors-yapf
-    rev: 'v0.31.0'  # Use the sha / tag you want to point at
-    hooks:
-    -   id: yapf
+- repo: https://github.com/pre-commit/mirrors-yapf
+  rev: v0.31.0
+  hooks:
+  - id: yapf
 
--   repo: https://github.com/pre-commit/pygrep-hooks
-    rev: v1.9.0  # Use the ref you want to point at
-    hooks:
-    -   id: python-use-type-annotations
-    -   id: python-no-log-warn
-    -   id: python-check-blanket-noqa
+- repo: https://github.com/pre-commit/pygrep-hooks
+  rev: v1.9.0
+  hooks:
+  - id: python-use-type-annotations
+  - id: python-no-log-warn
+  - id: python-check-blanket-noqa
 
+- repo: https://github.com/asottile/pyupgrade
+  rev: v2.19.4
+  hooks:
+  - id: pyupgrade
 
--   repo: https://github.com/asottile/pyupgrade
-    rev: v2.19.4
-    hooks:
-    -   id: pyupgrade
-
--   repo: https://github.com/jorisroovers/gitlint
-    rev: "v0.15.1"
-    hooks:
-    -   id: gitlint
-        args: [--contrib=CT1, --ignore=B6, --msg-filename]
+- repo: https://github.com/si-cim/gitlint
+  rev: v0.15.2-unofficial
+  hooks:
+  - id: gitlint
+    args: [--contrib=CT1, --ignore=B6, --msg-filename]

README.md

@@ -20,23 +20,6 @@ pip install prototorch_models
 of** [ProtoTorch](https://github.com/si-cim/prototorch). The plugin should then
 be available for use in your Python environment as `prototorch.models`.
 
-## Contribution
-
-This repository contains definition for [git hooks](https://githooks.com).
-[Pre-commit](https://pre-commit.com) is automatically installed as development
-dependency with prototorch or you can install it manually with `pip install
-pre-commit`.
-
-Please install the hooks by running:
-```bash
-pre-commit install
-pre-commit install --hook-type commit-msg
-```
-before creating the first commit.
-
-The commit will fail if the commit message does not follow the specification
-provided [here](https://www.conventionalcommits.org/en/v1.0.0/#specification).
-
 ## Available models
 
 ### LVQ Family
@@ -53,6 +36,7 @@ provided [here](https://www.conventionalcommits.org/en/v1.0.0/#specification).
 - Soft Learning Vector Quantization (SLVQ)
 - Robust Soft Learning Vector Quantization (RSLVQ)
 - Probabilistic Learning Vector Quantization (PLVQ)
+- Median-LVQ
 
 ### Other
 
@@ -68,7 +52,6 @@ provided [here](https://www.conventionalcommits.org/en/v1.0.0/#specification).
 
 ## Planned models
 
-- Median-LVQ
 - Generalized Tangent Learning Vector Quantization (GTLVQ)
 - Self-Incremental Learning Vector Quantization (SILVQ)
 
@@ -103,6 +86,23 @@ To assist in the development process, you may also find it useful to install
 please avoid installing Tensorflow in this environment. It is known to cause
 problems with PyTorch-Lightning.**
 
+## Contribution
+
+This repository contains definition for [git hooks](https://githooks.com).
+[Pre-commit](https://pre-commit.com) is automatically installed as development
+dependency with prototorch or you can install it manually with `pip install
+pre-commit`.
+
+Please install the hooks by running:
+```bash
+pre-commit install
+pre-commit install --hook-type commit-msg
+```
+before creating the first commit.
+
+The commit will fail if the commit message does not follow the specification
+provided [here](https://www.conventionalcommits.org/en/v1.0.0/#specification).
+
 ## FAQ
 
 ### How do I update the plugin?

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.4.4"
+release = "0.2.0"
 
 # -- General configuration ---------------------------------------------------
 

examples/binnam_tecator.py

@@ -0,0 +1,81 @@
+"""Neural Additive Model (NAM) example for binary classification."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from matplotlib import pyplot as plt
+
+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.Tecator("~/datasets")
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
+
+    # Hyperparameters
+    hparams = dict(lr=0.1)
+
+    # Define the feature extractor
+    class FE(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.modules_list = torch.nn.ModuleList([
+                torch.nn.Linear(1, 3),
+                torch.nn.Sigmoid(),
+                torch.nn.Linear(3, 1),
+                torch.nn.Sigmoid(),
+            ])
+
+        def forward(self, x):
+            for m in self.modules_list:
+                x = m(x)
+            return x
+
+    # Initialize the model
+    model = pt.models.BinaryNAM(
+        hparams,
+        extractors=torch.nn.ModuleList([FE() for _ in range(100)]),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 100)
+
+    # Callbacks
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_loss",
+        min_delta=0.001,
+        patience=20,
+        mode="min",
+        verbose=True,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[
+            es,
+        ],
+        terminate_on_nan=True,
+        weights_summary=None,
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)
+
+    # Visualize extractor shape functions
+    fig, axes = plt.subplots(10, 10)
+    for i, ax in enumerate(axes.flat):
+        x = torch.linspace(-2, 2, 100)  # TODO use min/max from data
+        y = model.extractors[i](x.view(100, 1)).squeeze().detach()
+        ax.plot(x, y)
+        ax.set(title=f"Feature {i + 1}", xticklabels=[], yticklabels=[])
+    plt.show()

examples/binnam_xor.py

@@ -0,0 +1,86 @@
+"""Neural Additive Model (NAM) example for binary classification."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from matplotlib import pyplot as plt
+
+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.XOR()
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=256)
+
+    # Hyperparameters
+    hparams = dict(lr=0.001)
+
+    # Define the feature extractor
+    class FE(torch.nn.Module):
+        def __init__(self, hidden_size=10):
+            super().__init__()
+            self.modules_list = torch.nn.ModuleList([
+                torch.nn.Linear(1, hidden_size),
+                torch.nn.ReLU(),
+                torch.nn.Linear(hidden_size, 1),
+                torch.nn.ReLU(),
+            ])
+
+        def forward(self, x):
+            for m in self.modules_list:
+                x = m(x)
+            return x
+
+    # Initialize the model
+    model = pt.models.BinaryNAM(
+        hparams,
+        extractors=torch.nn.ModuleList([FE(20) for _ in range(2)]),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Summary
+    print(model)
+
+    # Callbacks
+    vis = pt.models.Vis2D(data=train_ds)
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_loss",
+        min_delta=0.001,
+        patience=50,
+        mode="min",
+        verbose=False,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[
+            vis,
+            es,
+        ],
+        terminate_on_nan=True,
+        weights_summary="full",
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)
+
+    # Visualize extractor shape functions
+    fig, axes = plt.subplots(2)
+    for i, ax in enumerate(axes.flat):
+        x = torch.linspace(0, 1, 100)  # TODO use min/max from data
+        y = model.extractors[i](x.view(100, 1)).squeeze().detach()
+        ax.plot(x, y)
+        ax.set(title=f"Feature {i + 1}")
+    plt.show()

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/cli/gmlvq.py

@@ -1,12 +1,11 @@
 """GMLVQ example using the MNIST dataset."""
 
-import torch
-from pytorch_lightning.utilities.cli import LightningCLI
-
 import prototorch as pt
+import torch
 from prototorch.models import ImageGMLVQ
 from prototorch.models.abstract import PrototypeModel
 from prototorch.models.data import MNISTDataModule
+from pytorch_lightning.utilities.cli import LightningCLI
 
 
 class ExperimentClass(ImageGMLVQ):

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,
             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/gmlvq_mnist.py

@@ -2,13 +2,12 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from torchvision import transforms
 from torchvision.datasets import MNIST
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -56,7 +55,7 @@ if __name__ == "__main__":
     model = pt.models.ImageGMLVQ(
         hparams,
         optimizer=torch.optim.Adam,
-        prototype_initializer=pt.components.SMI(train_ds),
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
     )
 
     # Callbacks
@@ -95,7 +94,7 @@ if __name__ == "__main__":
         ],
         terminate_on_nan=True,
         weights_summary=None,
-        accelerator="ddp",
+        # accelerator="ddp",
     )
 
     # Training loop

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/knn_iris.py

@@ -2,12 +2,11 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from sklearn.datasets import load_iris
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()

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

@@ -1,90 +0,0 @@
-"""Limited Rank Matrix LVQ example using the Tecator dataset."""
-
-import argparse
-
-import matplotlib.pyplot as plt
-import pytorch_lightning as pl
-import torch
-
-import prototorch as pt
-
-
-def plot_matrix(matrix):
-    title = "Lambda matrix"
-    plt.figure(title)
-    plt.title(title)
-    plt.imshow(matrix, cmap="gray")
-    plt.axis("off")
-    plt.colorbar()
-    plt.show(block=True)
-
-
-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.Tecator(root="~/datasets/", train=True)
-    test_ds = pt.datasets.Tecator(root="~/datasets/", train=False)
-
-    # Reproducibility
-    pl.utilities.seed.seed_everything(seed=10)
-
-    # Dataloaders
-    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
-    test_loader = torch.utils.data.DataLoader(test_ds, batch_size=32)
-
-    # Hyperparameters
-    hparams = dict(
-        distribution={
-            "num_classes": 2,
-            "prototypes_per_class": 1
-        },
-        input_dim=100,
-        latent_dim=2,
-        proto_lr=0.0001,
-        bb_lr=0.0001,
-    )
-
-    # Initialize the model
-    model = pt.models.SiameseGMLVQ(
-        hparams,
-        # optimizer=torch.optim.SGD,
-        optimizer=torch.optim.Adam,
-        prototype_initializer=pt.components.SMI(train_ds),
-    )
-
-    # Summary
-    print(model)
-
-    # Callbacks
-    vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)
-    es = pl.callbacks.EarlyStopping(monitor="val_loss",
-                                    min_delta=0.001,
-                                    patience=50,
-                                    verbose=False,
-                                    mode="min")
-
-    # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
-        args,
-        callbacks=[vis, es],
-        weights_summary=None,
-    )
-
-    # Training loop
-    trainer.fit(model, train_loader, test_loader)
-
-    # Save the model
-    torch.save(model, "liramlvq_tecator.pt")
-
-    # Load a saved model
-    saved_model = torch.load("liramlvq_tecator.pt")
-
-    # Display the Lambda matrix
-    plot_matrix(saved_model.lambda_matrix)
-
-    # Testing
-    trainer.test(model, test_dataloaders=test_loader)

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/median_lvq_iris.py

@@ -0,0 +1,52 @@
+"""Median-LVQ example using the Iris dataset."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+
+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(dims=[0, 2])
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(
+        train_ds,
+        batch_size=len(train_ds),  # MedianLVQ cannot handle mini-batches
+    )
+
+    # Initialize the model
+    model = pt.models.MedianLVQ(
+        hparams=dict(distribution=(3, 2), lr=0.01),
+        prototypes_initializer=pt.initializers.SSCI(train_ds),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=train_ds)
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_acc",
+        min_delta=0.01,
+        patience=5,
+        mode="max",
+        verbose=True,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis, es],
+        weights_summary="full",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)

examples/ng_iris.py

@@ -2,14 +2,13 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
 from torch.optim.lr_scheduler import ExponentialLR
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -38,7 +37,7 @@ if __name__ == "__main__":
     # Initialize the model
     model = pt.models.NeuralGas(
         hparams,
-        prototype_initializer=pt.components.Zeros(2),
+        prototypes_initializer=pt.core.ZCI(2),
         lr_scheduler=ExponentialLR,
         lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
     )

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 +1,103 @@
+"""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=100,
+        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)
+    pruning = pt.models.PruneLoserPrototypes(
+        threshold=0.02,
+        idle_epochs=2,
+        prune_quota_per_epoch=5,
+        frequency=1,
+        verbose=True,
+    )
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_loss",
+        min_delta=0.001,
+        patience=10,
+        mode="min",
+        verbose=True,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[
+            vis,
+            pruning,
+            es,
+        ],
+        weights_summary="full",
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)

prototorch/models/__init__.py

@@ -4,12 +4,24 @@ from importlib.metadata import PackageNotFoundError, version
 
 from .callbacks import PrototypeConvergence, PruneLoserPrototypes
 from .cbc import CBC, ImageCBC
-from .glvq import (GLVQ, GLVQ1, GLVQ21, GMLVQ, GRLVQ, LGMLVQ, LVQMLN,
-                   ImageGLVQ, ImageGMLVQ, SiameseGLVQ, SiameseGMLVQ)
+from .glvq import (
+    GLVQ,
+    GLVQ1,
+    GLVQ21,
+    GMLVQ,
+    GRLVQ,
+    LGMLVQ,
+    LVQMLN,
+    ImageGLVQ,
+    ImageGMLVQ,
+    SiameseGLVQ,
+    SiameseGMLVQ,
+)
 from .knn import KNN
 from .lvq import LVQ1, LVQ21, MedianLVQ
+from .nam import BinaryNAM
 from .probabilistic import CELVQ, PLVQ, RSLVQ, SLVQ
 from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
 from .vis import *
 
-__version__ = "0.1.8"
+__version__ = "0.2.0"

prototorch/models/abstract.py

@@ -5,25 +5,17 @@ from typing import Final, final
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-from prototorch.components import Components, LabeledComponents
-from prototorch.functions.distances import euclidean_distance
-from prototorch.modules import WTAC, LambdaLayer
 
-
-class ProtoTorchMixin(object):
-    pass
+from ..core.competitions import WTAC
+from ..core.components import Components, LabeledComponents
+from ..core.distances import euclidean_distance
+from ..core.initializers import LabelsInitializer
+from ..core.pooling import stratified_min_pooling
+from ..nn.wrappers import LambdaLayer
 
 
 class ProtoTorchBolt(pl.LightningModule):
     """All ProtoTorch models are ProtoTorch Bolts."""
-    def __repr__(self):
-        surep = super().__repr__()
-        indented = "".join([f"\t{line}\n" for line in surep.splitlines()])
-        wrapped = f"ProtoTorch Bolt(\n{indented})"
-        return wrapped
-
-
-class PrototypeModel(ProtoTorchBolt):
     def __init__(self, hparams, **kwargs):
         super().__init__()
 
@@ -38,6 +30,34 @@ class PrototypeModel(ProtoTorchBolt):
         self.lr_scheduler = kwargs.get("lr_scheduler", None)
         self.lr_scheduler_kwargs = kwargs.get("lr_scheduler_kwargs", dict())
 
+    def configure_optimizers(self):
+        optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
+        if self.lr_scheduler is not None:
+            scheduler = self.lr_scheduler(optimizer,
+                                          **self.lr_scheduler_kwargs)
+            sch = {
+                "scheduler": scheduler,
+                "interval": "step",
+            }  # called after each training step
+            return [optimizer], [sch]
+        else:
+            return optimizer
+
+    @final
+    def reconfigure_optimizers(self):
+        self.trainer.accelerator.setup_optimizers(self.trainer)
+
+    def __repr__(self):
+        surep = super().__repr__()
+        indented = "".join([f"\t{line}\n" for line in surep.splitlines()])
+        wrapped = f"ProtoTorch Bolt(\n{indented})"
+        return wrapped
+
+
+class PrototypeModel(ProtoTorchBolt):
+    def __init__(self, hparams, **kwargs):
+        super().__init__(hparams, **kwargs)
+
         distance_fn = kwargs.get("distance_fn", euclidean_distance)
         self.distance_layer = LambdaLayer(distance_fn)
 
@@ -54,23 +74,6 @@ class PrototypeModel(ProtoTorchBolt):
         """Only an alias for the prototypes."""
         return self.prototypes
 
-    def configure_optimizers(self):
-        optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
-        if self.lr_scheduler is not None:
-            scheduler = self.lr_scheduler(optimizer,
-                                          **self.lr_scheduler_kwargs)
-            sch = {
-                "scheduler": scheduler,
-                "interval": "step",
-            }  # called after each training step
-            return [optimizer], [sch]
-        else:
-            return optimizer
-
-    @final
-    def reconfigure_optimizers(self):
-        self.trainer.accelerator_backend.setup_optimizers(self.trainer)
-
     def add_prototypes(self, *args, **kwargs):
         self.proto_layer.add_components(*args, **kwargs)
         self.reconfigure_optimizers()
@@ -85,13 +88,11 @@ class UnsupervisedPrototypeModel(PrototypeModel):
         super().__init__(hparams, **kwargs)
 
         # Layers
-        prototype_initializer = kwargs.get("prototype_initializer", None)
-        initialized_prototypes = kwargs.get("initialized_prototypes", None)
-        if prototype_initializer is not None or initialized_prototypes is not None:
+        prototypes_initializer = kwargs.get("prototypes_initializer", None)
+        if prototypes_initializer is not None:
             self.proto_layer = Components(
                 self.hparams.num_prototypes,
-                initializer=prototype_initializer,
-                initialized_components=initialized_prototypes,
+                initializer=prototypes_initializer,
             )
 
     def compute_distances(self, x):
@@ -109,23 +110,24 @@ class SupervisedPrototypeModel(PrototypeModel):
         super().__init__(hparams, **kwargs)
 
         # Layers
-        prototype_initializer = kwargs.get("prototype_initializer", None)
-        initialized_prototypes = kwargs.get("initialized_prototypes", None)
-        if prototype_initializer is not None or initialized_prototypes is not None:
+        prototypes_initializer = kwargs.get("prototypes_initializer", None)
+        labels_initializer = kwargs.get("labels_initializer",
+                                        LabelsInitializer())
+        if prototypes_initializer is not None:
             self.proto_layer = LabeledComponents(
                 distribution=self.hparams.distribution,
-                initializer=prototype_initializer,
-                initialized_components=initialized_prototypes,
+                components_initializer=prototypes_initializer,
+                labels_initializer=labels_initializer,
             )
         self.competition_layer = WTAC()
 
     @property
     def prototype_labels(self):
-        return self.proto_layer.component_labels.detach().cpu()
+        return self.proto_layer.labels.detach().cpu()
 
     @property
     def num_classes(self):
-        return len(self.proto_layer.distribution)
+        return self.proto_layer.num_classes
 
     def compute_distances(self, x):
         protos, _ = self.proto_layer()
@@ -134,15 +136,14 @@ class SupervisedPrototypeModel(PrototypeModel):
 
     def forward(self, x):
         distances = self.compute_distances(x)
-        y_pred = self.predict_from_distances(distances)
-        # TODO
-        y_pred = torch.eye(self.num_classes, device=self.device)[
-            y_pred.long()]  # depends on labels {0,...,num_classes}
+        plabels = self.proto_layer.labels
+        winning = stratified_min_pooling(distances, plabels)
+        y_pred = torch.nn.functional.softmin(winning)
         return y_pred
 
     def predict_from_distances(self, distances):
         with torch.no_grad():
-            plabels = self.proto_layer.component_labels
+            plabels = self.proto_layer.labels
             y_pred = self.competition_layer(distances, plabels)
         return y_pred
 
@@ -165,6 +166,11 @@ class SupervisedPrototypeModel(PrototypeModel):
                  logger=True)
 
 
+class ProtoTorchMixin(object):
+    """All mixins are ProtoTorchMixins."""
+    pass
+
+
 class NonGradientMixin(ProtoTorchMixin):
     """Mixin for custom non-gradient optimization."""
     def __init__(self, *args, **kwargs):

prototorch/models/callbacks.py

@@ -4,8 +4,9 @@ import logging
 
 import pytorch_lightning as pl
 import torch
-from prototorch.components import Components
 
+from ..core.components import Components
+from ..core.initializers import LiteralCompInitializer
 from .extras import ConnectionTopology
 
 
@@ -16,7 +17,7 @@ class PruneLoserPrototypes(pl.Callback):
                  prune_quota_per_epoch=-1,
                  frequency=1,
                  replace=False,
-                 initializer=None,
+                 prototypes_initializer=None,
                  verbose=False):
         self.threshold = threshold  # minimum win ratio
         self.idle_epochs = idle_epochs  # epochs to wait before pruning
@@ -24,7 +25,7 @@ class PruneLoserPrototypes(pl.Callback):
         self.frequency = frequency
         self.replace = replace
         self.verbose = verbose
-        self.initializer = initializer
+        self.prototypes_initializer = prototypes_initializer
 
     def on_epoch_end(self, trainer, pl_module):
         if (trainer.current_epoch + 1) < self.idle_epochs:
@@ -55,8 +56,9 @@ class PruneLoserPrototypes(pl.Callback):
                 if self.verbose:
                     print(f"Re-adding pruned prototypes...")
                     print(f"{distribution=}")
-                pl_module.add_prototypes(distribution=distribution,
-                                         initializer=self.initializer)
+                pl_module.add_prototypes(
+                    distribution=distribution,
+                    components_initializer=self.prototypes_initializer)
             new_num_protos = pl_module.num_prototypes
             if self.verbose:
                 print(f"`num_prototypes` changed from {cur_num_protos} "
@@ -116,7 +118,8 @@ class GNGCallback(pl.Callback):
 
             # Add component
             pl_module.proto_layer.add_components(
-                initialized_components=new_component.unsqueeze(0))
+                None,
+                initializer=LiteralCompInitializer(new_component.unsqueeze(0)))
 
             # Adjust Topology
             topology.add_prototype()

prototorch/models/cbc.py

@@ -1,49 +1,54 @@
 import torch
 import torchmetrics
 
+from ..core.competitions import CBCC
+from ..core.components import ReasoningComponents
+from ..core.initializers import RandomReasoningsInitializer
+from ..core.losses import MarginLoss
+from ..core.similarities import euclidean_similarity
+from ..nn.wrappers import LambdaLayer
 from .abstract import ImagePrototypesMixin
-from .extras import (CosineSimilarity, MarginLoss, ReasoningLayer,
-                     euclidean_similarity, rescaled_cosine_similarity,
-                     shift_activation)
 from .glvq import SiameseGLVQ
 
 
 class CBC(SiameseGLVQ):
     """Classification-By-Components."""
-    def __init__(self, hparams, margin=0.1, **kwargs):
+    def __init__(self, hparams, **kwargs):
         super().__init__(hparams, **kwargs)
-        self.margin = margin
-        self.similarity_fn = kwargs.get("similarity_fn", euclidean_similarity)
-        num_components = self.components.shape[0]
-        self.reasoning_layer = ReasoningLayer(num_components=num_components,
-                                              num_classes=self.num_classes)
-        self.component_layer = self.proto_layer
 
-    @property
-    def components(self):
-        return self.prototypes
+        similarity_fn = kwargs.get("similarity_fn", euclidean_similarity)
+        components_initializer = kwargs.get("components_initializer", None)
+        reasonings_initializer = kwargs.get("reasonings_initializer",
+                                            RandomReasoningsInitializer())
+        self.components_layer = ReasoningComponents(
+            self.hparams.distribution,
+            components_initializer=components_initializer,
+            reasonings_initializer=reasonings_initializer,
+        )
+        self.similarity_layer = LambdaLayer(similarity_fn)
+        self.competition_layer = CBCC()
 
-    @property
-    def reasonings(self):
-        return self.reasoning_layer.reasonings.cpu()
+        # Namespace hook
+        self.proto_layer = self.components_layer
+
+        self.loss = MarginLoss(self.hparams.margin)
 
     def forward(self, x):
-        components, _ = self.component_layer()
+        components, reasonings = self.components_layer()
         latent_x = self.backbone(x)
         self.backbone.requires_grad_(self.both_path_gradients)
         latent_components = self.backbone(components)
         self.backbone.requires_grad_(True)
-        detections = self.similarity_fn(latent_x, latent_components)
-        probs = self.reasoning_layer(detections)
+        detections = self.similarity_layer(latent_x, latent_components)
+        probs = self.competition_layer(detections, reasonings)
         return probs
 
     def shared_step(self, batch, batch_idx, optimizer_idx=None):
         x, y = batch
-        # x = x.view(x.size(0), -1)
         y_pred = self(x)
-        num_classes = self.reasoning_layer.num_classes
+        num_classes = self.num_classes
         y_true = torch.nn.functional.one_hot(y.long(), num_classes=num_classes)
-        loss = MarginLoss(self.margin)(y_pred, y_true).mean(dim=0)
+        loss = self.loss(y_pred, y_true).mean()
         return y_pred, loss
 
     def training_step(self, batch, batch_idx, optimizer_idx=None):
@@ -70,7 +75,3 @@ class ImageCBC(ImagePrototypesMixin, CBC):
     """CBC model that constrains the components to the range [0, 1] by
     clamping after updates.
     """
-    def __init__(self, hparams, **kwargs):
-        super().__init__(hparams, **kwargs)
-        # Namespace hook
-        self.proto_layer = self.component_layer

prototorch/models/data.py

@@ -5,13 +5,12 @@ Mainly used for PytorchLightningCLI configurations.
 """
 from typing import Any, Optional, Type
 
+import prototorch as pt
 import pytorch_lightning as pl
 from torch.utils.data import DataLoader, Dataset, random_split
 from torchvision import transforms
 from torchvision.datasets import MNIST
 
-import prototorch as pt
-
 
 # MNIST
 class MNISTDataModule(pl.LightningDataModule):

prototorch/models/extras.py

@@ -5,23 +5,32 @@ Modules not yet available in prototorch go here temporarily.
 """
 
 import torch
-from prototorch.functions.distances import euclidean_distance
-from prototorch.functions.similarities import cosine_similarity
+
+from ..core.similarities import gaussian
 
 
-def rescaled_cosine_similarity(x, y):
-    """Cosine Similarity rescaled to [0, 1]."""
-    similarities = cosine_similarity(x, y)
-    return (similarities + 1.0) / 2.0
+def rank_scaled_gaussian(distances, lambd):
+    order = torch.argsort(distances, dim=1)
+    ranks = torch.argsort(order, dim=1)
+    return torch.exp(-torch.exp(-ranks / lambd) * distances)
 
 
-def shift_activation(x):
-    return (x + 1.0) / 2.0
+class GaussianPrior(torch.nn.Module):
+    def __init__(self, variance):
+        super().__init__()
+        self.variance = variance
+
+    def forward(self, distances):
+        return gaussian(distances, self.variance)
 
 
-def euclidean_similarity(x, y, variance=1.0):
-    d = euclidean_distance(x, y)
-    return torch.exp(-(d * d) / (2 * variance))
+class RankScaledGaussianPrior(torch.nn.Module):
+    def __init__(self, lambd):
+        super().__init__()
+        self.lambd = lambd
+
+    def forward(self, distances):
+        return rank_scaled_gaussian(distances, self.lambd)
 
 
 class ConnectionTopology(torch.nn.Module):
@@ -79,64 +88,3 @@ class ConnectionTopology(torch.nn.Module):
 
     def extra_repr(self):
         return f"(agelimit): ({self.agelimit})"
-
-
-class CosineSimilarity(torch.nn.Module):
-    def __init__(self, activation=shift_activation):
-        super().__init__()
-        self.activation = activation
-
-    def forward(self, x, y):
-        epsilon = torch.finfo(x.dtype).eps
-        normed_x = (x / x.pow(2).sum(dim=tuple(range(
-            1, x.ndim)), keepdim=True).clamp(min=epsilon).sqrt()).flatten(
-                start_dim=1)
-        normed_y = (y / y.pow(2).sum(dim=tuple(range(
-            1, y.ndim)), keepdim=True).clamp(min=epsilon).sqrt()).flatten(
-                start_dim=1)
-        # normed_x = (x / torch.linalg.norm(x, dim=1))
-        diss = torch.inner(normed_x, normed_y)
-        return self.activation(diss)
-
-
-class MarginLoss(torch.nn.modules.loss._Loss):
-    def __init__(self,
-                 margin=0.3,
-                 size_average=None,
-                 reduce=None,
-                 reduction="mean"):
-        super().__init__(size_average, reduce, reduction)
-        self.margin = margin
-
-    def forward(self, input_, target):
-        dp = torch.sum(target * input_, dim=-1)
-        dm = torch.max(input_ - target, dim=-1).values
-        return torch.nn.functional.relu(dm - dp + self.margin)
-
-
-class ReasoningLayer(torch.nn.Module):
-    def __init__(self, num_components, num_classes, num_replicas=1):
-        super().__init__()
-        self.num_replicas = num_replicas
-        self.num_classes = num_classes
-        probabilities_init = torch.zeros(2, 1, num_components,
-                                         self.num_classes)
-        probabilities_init.uniform_(0.4, 0.6)
-        # TODO Use `self.register_parameter("param", Paramater(param))` instead
-        self.reasoning_probabilities = torch.nn.Parameter(probabilities_init)
-
-    @property
-    def reasonings(self):
-        pk = self.reasoning_probabilities[0]
-        nk = (1 - pk) * self.reasoning_probabilities[1]
-        ik = 1 - pk - nk
-        img = torch.cat([pk, nk, ik], dim=0).permute(1, 0, 2)
-        return img.unsqueeze(1)
-
-    def forward(self, detections):
-        pk = self.reasoning_probabilities[0].clamp(0, 1)
-        nk = (1 - pk) * self.reasoning_probabilities[1].clamp(0, 1)
-        numerator = (detections @ (pk - nk)) + nk.sum(1)
-        probs = numerator / (pk + nk).sum(1)
-        probs = probs.squeeze(0)
-        return probs

prototorch/models/glvq.py

@@ -1,16 +1,14 @@
 """Models based on the GLVQ framework."""
 
 import torch
-from prototorch.functions.activations import get_activation
-from prototorch.functions.competitions import wtac
-from prototorch.functions.distances import (lomega_distance, omega_distance,
-                                            squared_euclidean_distance)
-from prototorch.functions.helper import get_flat
-from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
-from prototorch.components import LinearMapping
-from prototorch.modules import LambdaLayer, LossLayer
 from torch.nn.parameter import Parameter
 
+from ..core.competitions import wtac
+from ..core.distances import lomega_distance, omega_distance, squared_euclidean_distance
+from ..core.initializers import EyeTransformInitializer
+from ..core.losses import GLVQLoss, lvq1_loss, lvq21_loss
+from ..core.transforms import LinearTransform
+from ..nn.wrappers import LambdaLayer, LossLayer
 from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
 
 
@@ -20,18 +18,16 @@ class GLVQ(SupervisedPrototypeModel):
         super().__init__(hparams, **kwargs)
 
         # Default hparams
+        self.hparams.setdefault("margin", 0.0)
         self.hparams.setdefault("transfer_fn", "identity")
         self.hparams.setdefault("transfer_beta", 10.0)
 
-        # Layers
-        transfer_fn = get_activation(self.hparams.transfer_fn)
-        self.transfer_layer = LambdaLayer(transfer_fn)
-
         # Loss
-        self.loss = LossLayer(glvq_loss)
-
-        # Prototype metrics
-        self.initialize_prototype_win_ratios()
+        self.loss = GLVQLoss(
+            margin=self.hparams.margin,
+            transfer_fn=self.hparams.transfer_fn,
+            beta=self.hparams.transfer_beta,
+        )
 
     def initialize_prototype_win_ratios(self):
         self.register_buffer(
@@ -59,10 +55,8 @@ class GLVQ(SupervisedPrototypeModel):
     def shared_step(self, batch, batch_idx, optimizer_idx=None):
         x, y = batch
         out = self.compute_distances(x)
-        plabels = self.proto_layer.component_labels
-        mu = self.loss(out, y, prototype_labels=plabels)
-        batch_loss = self.transfer_layer(mu, beta=self.hparams.transfer_beta)
-        loss = batch_loss.sum(dim=0)
+        plabels = self.proto_layer.labels
+        loss = self.loss(out, y, plabels)
         return out, loss
 
     def training_step(self, batch, batch_idx, optimizer_idx=None):
@@ -135,7 +129,7 @@ class SiameseGLVQ(GLVQ):
 
     def compute_distances(self, x):
         protos, _ = self.proto_layer()
-        x, protos = get_flat(x, protos)
+        x, protos = [arr.view(arr.size(0), -1) for arr in (x, protos)]
         latent_x = self.backbone(x)
         self.backbone.requires_grad_(self.both_path_gradients)
         latent_protos = self.backbone(protos)
@@ -213,18 +207,22 @@ class SiameseGMLVQ(SiameseGLVQ):
         super().__init__(hparams, **kwargs)
 
         # Override the backbone
-        self.backbone = torch.nn.Linear(self.hparams.input_dim,
-                                        self.hparams.latent_dim,
-                                        bias=False)
+        omega_initializer = kwargs.get("omega_initializer",
+                                       EyeTransformInitializer())
+        self.backbone = LinearTransform(
+            self.hparams.input_dim,
+            self.hparams.output_dim,
+            initializer=omega_initializer,
+        )
 
     @property
     def omega_matrix(self):
-        return self.backbone.weight.detach().cpu()
+        return self.backbone.weights
 
     @property
     def lambda_matrix(self):
-        omega = self.backbone.weight  # (latent_dim, input_dim)
-        lam = omega.T @ omega
+        omega = self.backbone.weight  # (input_dim, latent_dim)
+        lam = omega @ omega.T
         return lam.detach().cpu()
 
 
@@ -240,18 +238,14 @@ class GMLVQ(GLVQ):
         super().__init__(hparams, distance_fn=distance_fn, **kwargs)
 
         # Additional parameters
-        omega_initializer = kwargs.get("omega_initializer", None)
-        initialized_omega = kwargs.get("initialized_omega", None)
-        if omega_initializer is not None or initialized_omega is not None:
-            self.omega_layer = LinearMapping(
-                mapping_shape=(self.hparams.input_dim, self.hparams.latent_dim),
-                initializer=omega_initializer,
-                initialized_linearmapping=initialized_omega,
-            )
+        omega_initializer = kwargs.get("omega_initializer",
+                                       EyeTransformInitializer())
+        omega = omega_initializer.generate(self.hparams.input_dim,
+                                           self.hparams.latent_dim)
+        self.register_parameter("_omega", Parameter(omega))
+        self.backbone = LambdaLayer(lambda x: x @ self._omega,
+                                    name="omega matrix")
 
-        self.register_parameter("_omega", Parameter(self.omega_layer.mapping))
-        self.backbone = LambdaLayer(lambda x: x @ self._omega, name = "omega matrix")
-       
     @property
     def omega_matrix(self):
         return self._omega.detach().cpu()
@@ -264,24 +258,6 @@ class GMLVQ(GLVQ):
     def extra_repr(self):
         return f"(omega): (shape: {tuple(self._omega.shape)})"
 
-    def predict_latent(self, x, map_protos=True):
-        """Predict `x` assuming it is already embedded in the latent space.
-
-        Only the prototypes are embedded in the latent space using the
-        backbone.
- 
-        """
-        self.eval()
-        with torch.no_grad():
-            protos, plabels = self.proto_layer()
-            if map_protos:
-                protos = self.backbone(protos)
-            d = squared_euclidean_distance(x, protos)
-            y_pred = wtac(d, plabels)
-        return y_pred
-
-
-
 
 class LGMLVQ(GMLVQ):
     """Localized and Generalized Matrix Learning Vector Quantization."""

prototorch/models/knn.py

@@ -2,9 +2,10 @@
 
 import warnings
 
-from prototorch.components import LabeledComponents
-from prototorch.modules import KNNC
-
+from ..core.competitions import KNNC
+from ..core.components import LabeledComponents
+from ..core.initializers import LiteralCompInitializer, LiteralLabelsInitializer
+from ..utils.utils import parse_data_arg
 from .abstract import SupervisedPrototypeModel
 
 
@@ -19,9 +20,13 @@ class KNN(SupervisedPrototypeModel):
         data = kwargs.get("data", None)
         if data is None:
             raise ValueError("KNN requires data, but was not provided!")
+        data, targets = parse_data_arg(data)
 
         # Layers
-        self.proto_layer = LabeledComponents(initialized_components=data)
+        self.proto_layer = LabeledComponents(
+            distribution=[],
+            components_initializer=LiteralCompInitializer(data),
+            labels_initializer=LiteralLabelsInitializer(targets))
         self.competition_layer = KNNC(k=self.hparams.k)
 
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):

prototorch/models/lvq.py

@@ -1,7 +1,8 @@
 """LVQ models that are optimized using non-gradient methods."""
 
-from prototorch.functions.losses import _get_dp_dm
-
+from ..core.losses import _get_dp_dm
+from ..nn.activations import get_activation
+from ..nn.wrappers import LambdaLayer
 from .abstract import NonGradientMixin
 from .glvq import GLVQ
 
@@ -10,7 +11,7 @@ class LVQ1(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         protos = self.proto_layer.components
-        plabels = self.proto_layer.component_labels
+        plabels = self.proto_layer.labels
 
         x, y = train_batch
         dis = self.compute_distances(x)
@@ -29,6 +30,8 @@ class LVQ1(NonGradientMixin, GLVQ):
             self.proto_layer.load_state_dict({"_components": updated_protos},
                                              strict=False)
 
+        print(f"{dis=}")
+        print(f"{y=}")
         # Logging
         self.log_acc(dis, y, tag="train_acc")
 
@@ -39,7 +42,7 @@ class LVQ21(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 2.1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         protos = self.proto_layer.components
-        plabels = self.proto_layer.component_labels
+        plabels = self.proto_layer.labels
 
         x, y = train_batch
         dis = self.compute_distances(x)
@@ -65,4 +68,61 @@ class LVQ21(NonGradientMixin, GLVQ):
 
 
 class MedianLVQ(NonGradientMixin, GLVQ):
-    """Median LVQ"""
+    """Median LVQ
+
+    # TODO Avoid computing distances over and over
+
+    """
+    def __init__(self, hparams, verbose=True, **kwargs):
+        self.verbose = verbose
+        super().__init__(hparams, **kwargs)
+
+        self.transfer_layer = LambdaLayer(
+            get_activation(self.hparams.transfer_fn))
+
+    def _f(self, x, y, protos, plabels):
+        d = self.distance_layer(x, protos)
+        dp, dm = _get_dp_dm(d, y, plabels)
+        mu = (dp - dm) / (dp + dm)
+        invmu = -1.0 * mu
+        f = self.transfer_layer(invmu, beta=self.hparams.transfer_beta) + 1.0
+        return f
+
+    def expectation(self, x, y, protos, plabels):
+        f = self._f(x, y, protos, plabels)
+        gamma = f / f.sum()
+        return gamma
+
+    def lower_bound(self, x, y, protos, plabels, gamma):
+        f = self._f(x, y, protos, plabels)
+        lower_bound = (gamma * f.log()).sum()
+        return lower_bound
+
+    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+        protos = self.proto_layer.components
+        plabels = self.proto_layer.labels
+
+        x, y = train_batch
+        dis = self.compute_distances(x)
+
+        for i, _ in enumerate(protos):
+            # Expectation step
+            gamma = self.expectation(x, y, protos, plabels)
+            lower_bound = self.lower_bound(x, y, protos, plabels, gamma)
+
+            # Maximization step
+            _protos = protos + 0
+            for k, xk in enumerate(x):
+                _protos[i] = xk
+                _lower_bound = self.lower_bound(x, y, _protos, plabels, gamma)
+                if _lower_bound > lower_bound:
+                    if self.verbose:
+                        print(f"Updating prototype {i} to data {k}...")
+                    self.proto_layer.load_state_dict({"_components": _protos},
+                                                     strict=False)
+                    break
+
+        # Logging
+        self.log_acc(dis, y, tag="train_acc")
+
+        return None

prototorch/models/nam.py

@@ -0,0 +1,58 @@
+"""ProtoTorch Neural Additive Model."""
+
+import torch
+import torchmetrics
+
+from .abstract import ProtoTorchBolt
+
+
+class BinaryNAM(ProtoTorchBolt):
+    """Neural Additive Model for binary classification.
+
+    Paper: https://arxiv.org/abs/2004.13912
+    Official implementation: https://github.com/google-research/google-research/tree/master/neural_additive_models
+
+    """
+    def __init__(self, hparams: dict, extractors: torch.nn.ModuleList,
+                 **kwargs):
+        super().__init__(hparams, **kwargs)
+
+        # Default hparams
+        self.hparams.setdefault("threshold", 0.5)
+
+        self.extractors = extractors
+        self.linear = torch.nn.Linear(in_features=len(extractors),
+                                      out_features=1,
+                                      bias=True)
+
+    def extract(self, x):
+        """Apply the local extractors batch-wise on features."""
+        out = torch.zeros_like(x)
+        for j in range(x.shape[1]):
+            out[:, j] = self.extractors[j](x[:, j].unsqueeze(1)).squeeze()
+        return out
+
+    def forward(self, x):
+        x = self.extract(x)
+        x = self.linear(x)
+        return torch.sigmoid(x)
+
+    def training_step(self, batch, batch_idx, optimizer_idx=None):
+        x, y = batch
+        preds = self(x).squeeze()
+        train_loss = torch.nn.functional.binary_cross_entropy(preds, y.float())
+        self.log("train_loss", train_loss)
+        accuracy = torchmetrics.functional.accuracy(preds.int(), y.int())
+        self.log("train_acc",
+                 accuracy,
+                 on_step=False,
+                 on_epoch=True,
+                 prog_bar=True,
+                 logger=True)
+        return train_loss
+
+    def predict(self, x):
+        out = self(x)
+        pred = torch.zeros_like(out, device=self.device)
+        pred[out > self.hparams.threshold] = 1
+        return pred

prototorch/models/probabilistic.py

@@ -1,13 +1,10 @@
 """Probabilistic GLVQ methods"""
-
 import torch
-from prototorch.functions.losses import nllr_loss, rslvq_loss
-from prototorch.functions.pooling import (stratified_min_pooling,
-                                          stratified_sum_pooling)
-from prototorch.functions.transforms import (GaussianPrior,
-                                             RankScaledGaussianPrior)
-from prototorch.modules import LambdaLayer, LossLayer
 
+from ..core.losses import nllr_loss, rslvq_loss
+from ..core.pooling import stratified_min_pooling, stratified_sum_pooling
+from ..nn.wrappers import LambdaLayer, LossLayer
+from .extras import GaussianPrior, RankScaledGaussianPrior
 from .glvq import GLVQ, SiameseGMLVQ
 
 
@@ -22,11 +19,11 @@ class CELVQ(GLVQ):
     def shared_step(self, batch, batch_idx, optimizer_idx=None):
         x, y = batch
         out = self.compute_distances(x)  # [None, num_protos]
-        plabels = self.proto_layer.component_labels
+        plabels = self.proto_layer.labels
         winning = stratified_min_pooling(out, plabels)  # [None, num_classes]
         probs = -1.0 * winning
         batch_loss = self.loss(probs, y.long())
-        loss = batch_loss.sum(dim=0)
+        loss = batch_loss.sum()
         return out, loss
 
 
@@ -34,7 +31,7 @@ class ProbabilisticLVQ(GLVQ):
     def __init__(self, hparams, rejection_confidence=0.0, **kwargs):
         super().__init__(hparams, **kwargs)
 
-        self.conditional_distribution = None
+        self.conditional_distribution = GaussianPrior(self.hparams.variance)
         self.rejection_confidence = rejection_confidence
 
     def forward(self, x):
@@ -56,10 +53,11 @@ class ProbabilisticLVQ(GLVQ):
     def training_step(self, batch, batch_idx, optimizer_idx=None):
         x, y = batch
         out = self.forward(x)
-        plabels = self.proto_layer.component_labels
+        plabels = self.proto_layer.labels
         batch_loss = self.loss(out, y, plabels)
-        loss = batch_loss.sum(dim=0)
-        return loss
+        train_loss = batch_loss.sum()
+        self.log("train_loss", train_loss)
+        return train_loss
 
 
 class SLVQ(ProbabilisticLVQ):
@@ -67,7 +65,6 @@ class SLVQ(ProbabilisticLVQ):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.loss = LossLayer(nllr_loss)
-        self.conditional_distribution = GaussianPrior(self.hparams.variance)
 
 
 class RSLVQ(ProbabilisticLVQ):
@@ -75,7 +72,6 @@ class RSLVQ(ProbabilisticLVQ):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.loss = LossLayer(rslvq_loss)
-        self.conditional_distribution = GaussianPrior(self.hparams.variance)
 
 
 class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
@@ -89,11 +85,10 @@ class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
             self.hparams.lambd)
         self.loss = torch.nn.KLDivLoss()
 
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
-        x, y = batch
-        out = self.forward(x)
-        y_dist = torch.nn.functional.one_hot(
-            y.long(), num_classes=self.num_classes).float()
-        batch_loss = self.loss(out, y_dist)
-        loss = batch_loss.sum(dim=0)
-        return loss
+    # FIXME
+    # def training_step(self, batch, batch_idx, optimizer_idx=None):
+    #     x, y = batch
+    #     y_pred = self(x)
+    #     batch_loss = self.loss(y_pred, y)
+    #     loss = batch_loss.sum()
+    #     return loss

prototorch/models/unsupervised.py

@@ -2,11 +2,11 @@
 
 import numpy as np
 import torch
-from prototorch.functions.competitions import wtac
-from prototorch.functions.distances import squared_euclidean_distance
-from prototorch.modules import LambdaLayer
-from prototorch.modules.losses import NeuralGasEnergy
 
+from ..core.competitions import wtac
+from ..core.distances import squared_euclidean_distance
+from ..core.losses import NeuralGasEnergy
+from ..nn.wrappers import LambdaLayer
 from .abstract import NonGradientMixin, UnsupervisedPrototypeModel
 from .callbacks import GNGCallback
 from .extras import ConnectionTopology
@@ -132,7 +132,7 @@ class GrowingNeuralGas(NeuralGas):
         mask[torch.arange(len(mask)), winner] = 1.0
         dp = d * mask
 
-        self.errors += torch.sum(dp * dp, dim=0)
+        self.errors += torch.sum(dp * dp)
         self.errors *= self.hparams.step_reduction
 
         self.topology_layer(d)

prototorch/models/vis.py

@@ -7,6 +7,8 @@ import torchvision
 from matplotlib import pyplot as plt
 from torch.utils.data import DataLoader, Dataset
 
+from ..utils.utils import mesh2d
+
 
 class Vis2DAbstract(pl.Callback):
     def __init__(self,
@@ -73,23 +75,7 @@ class Vis2DAbstract(pl.Callback):
             ax.axis("off")
         return ax
 
-    def get_mesh_input(self, x):
-        x_shift = self.border * np.ptp(x[:, 0])
-        y_shift = self.border * np.ptp(x[:, 1])
-        x_min, x_max = x[:, 0].min() - x_shift, x[:, 0].max() + x_shift
-        y_min, y_max = x[:, 1].min() - y_shift, x[:, 1].max() + y_shift
-        xx, yy = np.meshgrid(np.linspace(x_min, x_max, self.resolution),
-                             np.linspace(y_min, y_max, self.resolution))
-        mesh_input = np.c_[xx.ravel(), yy.ravel()]
-        return mesh_input, xx, yy
-
-    def perform_pca_2D(self, data):
-        (_, eigVal, eigVec) = torch.pca_lowrank(data, q=2)
-        return data @ eigVec
-
-    def plot_data(self, ax, x, y, pca=False):
-        if pca:
-            x = self.perform_pca_2D(x)
+    def plot_data(self, ax, x, y):
         ax.scatter(
             x[:, 0],
             x[:, 1],
@@ -100,9 +86,7 @@ class Vis2DAbstract(pl.Callback):
             s=30,
         )
 
-    def plot_protos(self, ax, protos, plabels, pca=False):
-        if pca:
-            protos = self.perform_pca_2D(protos)
+    def plot_protos(self, ax, protos, plabels):
         ax.scatter(
             protos[:, 0],
             protos[:, 1],
@@ -133,6 +117,24 @@ class Vis2DAbstract(pl.Callback):
         plt.close()
 
 
+class Vis2D(Vis2DAbstract):
+    def on_epoch_end(self, trainer, pl_module):
+        if not self.precheck(trainer):
+            return True
+
+        x_train, y_train = self.x_train, self.y_train
+        ax = self.setup_ax(xlabel="Data dimension 1",
+                           ylabel="Data dimension 2")
+        self.plot_data(ax, x_train, y_train)
+        mesh_input, xx, yy = mesh2d(x_train, self.border, self.resolution)
+        mesh_input = torch.from_numpy(mesh_input).type_as(x_train)
+        y_pred = pl_module.predict(mesh_input)
+        y_pred = y_pred.cpu().reshape(xx.shape)
+        ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
+
+        self.log_and_display(trainer, pl_module)
+
+
 class VisGLVQ2D(Vis2DAbstract):
     def on_epoch_end(self, trainer, pl_module):
         if not self.precheck(trainer):
@@ -146,7 +148,7 @@ class VisGLVQ2D(Vis2DAbstract):
         self.plot_data(ax, x_train, y_train)
         self.plot_protos(ax, protos, plabels)
         x = np.vstack((x_train, protos))
-        mesh_input, xx, yy = self.get_mesh_input(x)
+        mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
         _components = pl_module.proto_layer._components
         mesh_input = torch.from_numpy(mesh_input).type_as(_components)
         y_pred = pl_module.predict(mesh_input)
@@ -181,9 +183,9 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
         if self.show_protos:
             self.plot_protos(ax, protos, plabels)
             x = np.vstack((x_train, protos))
-            mesh_input, xx, yy = self.get_mesh_input(x)
+            mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
         else:
-            mesh_input, xx, yy = self.get_mesh_input(x_train)
+            mesh_input, xx, yy = mesh2d(x_train, self.border, self.resolution)
         _components = pl_module.proto_layer._components
         mesh_input = torch.Tensor(mesh_input).type_as(_components)
         y_pred = pl_module.predict_latent(mesh_input,
@@ -194,50 +196,6 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
         self.log_and_display(trainer, pl_module)
 
 
-class VisGMLVQ2D(Vis2DAbstract):
-    def __init__(self, *args, map_protos=True, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.map_protos = map_protos
-
-    def on_epoch_end(self, trainer, pl_module):
-        if not self.precheck(trainer):
-            return True
-
-        protos = pl_module.prototypes
-        plabels = pl_module.prototype_labels
-        x_train, y_train = self.x_train, self.y_train
-        device = pl_module.device
-        with torch.no_grad():
-            x_train = pl_module.backbone(torch.Tensor(x_train).to(device))
-            x_train = x_train.cpu().detach()
-        if self.map_protos:
-            with torch.no_grad():
-                protos = pl_module.backbone(torch.Tensor(protos).to(device))
-                protos = protos.cpu().detach()
-        ax = self.setup_ax()
-        if x_train.shape[1] > 2:
-            self.plot_data(ax, x_train, y_train, pca=True)
-        else:
-            self.plot_data(ax, x_train, y_train, pca=False)
-        if self.show_protos:
-            if protos.shape[1] > 2:
-                self.plot_protos(ax, protos, plabels, pca=True)
-            else:
-                self.plot_protos(ax, protos, plabels, pca=False)
-        ### something to work on: meshgrid with pca
-        #    x = np.vstack((x_train, protos))
-        #    mesh_input, xx, yy = self.get_mesh_input(x)
-        #else:
-        #    mesh_input, xx, yy = self.get_mesh_input(x_train)
-        #_components = pl_module.proto_layer._components
-        #mesh_input = torch.Tensor(mesh_input).type_as(_components)
-        #y_pred = pl_module.predict_latent(mesh_input,
-        #                                  map_protos=self.map_protos)
-        #y_pred = y_pred.cpu().reshape(xx.shape)
-        #ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
-        self.log_and_display(trainer, pl_module)
-
-
 class VisCBC2D(Vis2DAbstract):
     def on_epoch_end(self, trainer, pl_module):
         if not self.precheck(trainer):
@@ -250,8 +208,8 @@ class VisCBC2D(Vis2DAbstract):
         self.plot_data(ax, x_train, y_train)
         self.plot_protos(ax, protos, "w")
         x = np.vstack((x_train, protos))
-        mesh_input, xx, yy = self.get_mesh_input(x)
-        _components = pl_module.component_layer._components
+        mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
+        _components = pl_module.components_layer._components
         y_pred = pl_module.predict(
             torch.Tensor(mesh_input).type_as(_components))
         y_pred = y_pred.cpu().reshape(xx.shape)

setup.cfg

@@ -0,0 +1,8 @@
+[isort]
+profile = hug
+src_paths = isort, test
+
+[yapf]
+based_on_style = pep8
+spaces_before_comment = 2
+split_before_logical_operator = true

setup.py

@@ -22,7 +22,7 @@ with open("README.md", "r") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
-    "prototorch>=0.5.0,<0.6.0",
+    "prototorch>=0.6.0",
     "pytorch_lightning>=1.3.5",
     "torchmetrics",
 ]
@@ -53,7 +53,7 @@ ALL = CLI + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name=safe_name("prototorch_" + PLUGIN_NAME),
-    version="0.1.8",
+    version="0.2.0",
     description="Pre-packaged prototype-based "
     "machine learning models using ProtoTorch and PyTorch-Lightning.",
     long_description=long_description,

tests/test_examples.sh

@@ -5,13 +5,15 @@ failed=0
 for example in $(find $1 -maxdepth 1 -name "*.py")
 do
     echo  -n "$x" $example '... '
-    export DISPLAY= && python $example --fast_dev_run 1 &> /dev/null
+    export DISPLAY= && python $example --fast_dev_run 1 &> run_log.txt
     if [[ $? -ne 0 ]]; then
         echo "FAILED!!"
+        cat run_log.txt
         failed=1
     else
         echo "SUCCESS!"
     fi
+    rm run_log.txt
 done
 
 exit $failed