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feat: remove old architecture

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This commit is contained in:
Alexander Engelsberger
2022-08-15 12:14:14 +02:00
parent bcf9c6bdb1
commit 5a89f24c10
44 changed files with 371 additions and 3757 deletions
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67
examples/cbc_iris.py
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"""CBC example using the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
from prototorch.models import CBC, VisCBC2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# 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 = DataLoader(train_ds, batch_size=32)
# Hyperparameters
hparams = dict(
distribution=[1, 0, 3],
margin=0.1,
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the model
model = CBC(
hparams,
components_initializer=pt.initializers.SSCI(train_ds, noise=0.1),
reasonings_initializer=pt.initializers.
PurePositiveReasoningsInitializer(),
)
# Callbacks
vis = VisCBC2D(
data=train_ds,
title="CBC Iris Example",
resolution=100,
axis_off=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
detect_anomaly=True,
log_every_n_steps=1,
max_epochs=1000,
)
# Training loop
trainer.fit(model, train_loader)

99
examples/dynamic_pruning.py
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"""Dynamically prune 'loser' prototypes in GLVQ-type models."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import (
CELVQ,
PruneLoserPrototypes,
VisGLVQ2D,
)
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
num_classes = 4
num_features = 2
num_clusters = 1
train_ds = pt.datasets.Random(
num_samples=500,
num_classes=num_classes,
num_features=num_features,
num_clusters=num_clusters,
separation=3.0,
seed=42,
)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=256)
# Hyperparameters
prototypes_per_class = num_clusters * 5
hparams = dict(
distribution=(num_classes, prototypes_per_class),
lr=0.2,
)
# Initialize the model
model = CELVQ(
hparams,
prototypes_initializer=pt.initializers.FVCI(2, 3.0),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
logging.info(model)
# Callbacks
vis = VisGLVQ2D(train_ds)
pruning = PruneLoserPrototypes(
threshold=0.01, # prune prototype if it wins less than 1%
idle_epochs=20, # pruning too early may cause problems
prune_quota_per_epoch=2, # prune at most 2 prototypes per epoch
frequency=1, # prune every epoch
verbose=True,
)
es = 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=[
vis,
pruning,
es,
],
detect_anomaly=True,
log_every_n_steps=1,
max_epochs=1000,
)
# Training loop
trainer.fit(model, train_loader)

79
examples/glvq_iris.py
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"""GLVQ example using the Iris dataset."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import GLVQ, VisGLVQ2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=PossibleUserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# 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 = DataLoader(train_ds, batch_size=64, num_workers=4)
# Hyperparameters
hparams = dict(
distribution={
"num_classes": 3,
"per_class": 4
},
lr=0.01,
)
# Initialize the model
model = GLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
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 = VisGLVQ2D(data=train_ds)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
max_epochs=100,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)
# Manual save
trainer.save_checkpoint("./glvq_iris.ckpt")
# Load saved model
new_model = GLVQ.load_from_checkpoint(
checkpoint_path="./glvq_iris.ckpt",
strict=False,
)
logging.info(new_model)

161
examples/gmlvq_iris.py
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"""GMLVQ example using the Iris dataset."""
import logging
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import GMLVQ, VisGMLVQ2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
import torchmetrics
from prototorch.core import SMCI
from prototorch.datasets import Iris
from prototorch.models.architectures.base import Steps
from prototorch.models.callbacks import (
LogTorchmetricCallback,
PlotLambdaMatrixToTensorboard,
VisGMLVQ2D,
)
from prototorch.models.library.gmlvq import GMLVQ
from pytorch_lightning.callbacks import EarlyStopping
from torch.utils.data import DataLoader, random_split
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
# ##############################################################################
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
def main():
# ------------------------------------------------------------
# DATA
# ------------------------------------------------------------
# Dataset
train_ds = pt.datasets.Iris()
full_dataset = Iris()
full_count = len(full_dataset)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=64)
train_count = int(full_count * 0.5)
val_count = int(full_count * 0.4)
test_count = int(full_count * 0.1)
# Hyperparameters
hparams = dict(
train_dataset, val_dataset, test_dataset = random_split(
full_dataset, (train_count, val_count, test_count))
# Dataloader
train_loader = DataLoader(
train_dataset,
batch_size=1,
num_workers=4,
shuffle=True,
)
val_loader = DataLoader(
val_dataset,
batch_size=1,
num_workers=4,
shuffle=False,
)
test_loader = DataLoader(
test_dataset,
batch_size=1,
num_workers=0,
shuffle=False,
)
# ------------------------------------------------------------
# HYPERPARAMETERS
# ------------------------------------------------------------
# Select Initializer
components_initializer = SMCI(full_dataset)
# Define Hyperparameters
hyperparameters = GMLVQ.HyperParameters(
lr=dict(components_layer=0.1, _omega=0),
input_dim=4,
latent_dim=4,
distribution={
"num_classes": 3,
"per_class": 2
},
proto_lr=0.01,
bb_lr=0.01,
distribution=dict(
num_classes=3,
per_class=1,
),
component_initializer=components_initializer,
)
# Initialize the model
model = GMLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
lr_scheduler=ExponentialLR,
lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
# Create Model
model = GMLVQ(hyperparameters)
# ------------------------------------------------------------
# TRAINING
# ------------------------------------------------------------
# Controlling Callbacks
recall = LogTorchmetricCallback(
'training_recall',
torchmetrics.Recall,
num_classes=3,
step=Steps.TRAINING,
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 4)
stopping_criterion = LogTorchmetricCallback(
'validation_recall',
torchmetrics.Recall,
num_classes=3,
step=Steps.VALIDATION,
)
# Callbacks
vis = VisGMLVQ2D(data=train_ds)
es = EarlyStopping(
monitor=stopping_criterion.name,
mode="max",
patience=10,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
# Visualization Callback
vis = VisGMLVQ2D(data=full_dataset)
# Define trainer
trainer = pl.Trainer(
callbacks=[
vis,
recall,
stopping_criterion,
es,
PlotLambdaMatrixToTensorboard(),
],
max_epochs=100,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)
# Train
trainer.fit(model, train_loader, val_loader)
trainer.test(model, test_loader)
# Manual save
trainer.save_checkpoint("./y_arch.ckpt")
# Load saved model
new_model = GMLVQ.load_from_checkpoint(
checkpoint_path="./y_arch.ckpt",
strict=True,
)
if __name__ == "__main__":
main()

112
examples/gmlvq_mnist.py
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"""GMLVQ example using the MNIST dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import (
ImageGMLVQ,
PruneLoserPrototypes,
VisImgComp,
)
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = MNIST(
"~/datasets",
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
]),
)
test_ds = MNIST(
"~/datasets",
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
]),
)
# Dataloaders
train_loader = DataLoader(train_ds, num_workers=4, batch_size=256)
test_loader = DataLoader(test_ds, num_workers=4, batch_size=256)
# Hyperparameters
num_classes = 10
prototypes_per_class = 10
hparams = dict(
input_dim=28 * 28,
latent_dim=28 * 28,
distribution=(num_classes, prototypes_per_class),
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the model
model = ImageGMLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
)
# Callbacks
vis = VisImgComp(
data=train_ds,
num_columns=10,
show=False,
tensorboard=True,
random_data=100,
add_embedding=True,
embedding_data=200,
flatten_data=False,
)
pruning = PruneLoserPrototypes(
threshold=0.01,
idle_epochs=1,
prune_quota_per_epoch=10,
frequency=1,
verbose=True,
)
es = EarlyStopping(
monitor="train_loss",
min_delta=0.001,
patience=15,
mode="min",
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
pruning,
es,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

94
examples/gmlvq_spiral.py
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"""GMLVQ example using the spiral dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import (
GMLVQ,
PruneLoserPrototypes,
VisGLVQ2D,
)
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Spiral(num_samples=500, noise=0.5)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=256)
# Hyperparameters
num_classes = 2
prototypes_per_class = 10
hparams = dict(
distribution=(num_classes, prototypes_per_class),
transfer_function="swish_beta",
transfer_beta=10.0,
proto_lr=0.1,
bb_lr=0.1,
input_dim=2,
latent_dim=2,
)
# Initialize the model
model = GMLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SSCI(train_ds, noise=1e-2),
)
# Callbacks
vis = VisGLVQ2D(
train_ds,
show_last_only=False,
block=False,
)
pruning = PruneLoserPrototypes(
threshold=0.01,
idle_epochs=10,
prune_quota_per_epoch=5,
frequency=5,
replace=True,
prototypes_initializer=pt.initializers.SSCI(train_ds, noise=1e-1),
verbose=True,
)
es = EarlyStopping(
monitor="train_loss",
min_delta=1.0,
patience=5,
mode="min",
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
es,
pruning,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

65
examples/gng_iris.py
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"""Growing Neural Gas example using the Iris dataset."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import GrowingNeuralGas, VisNG2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
seed_everything(seed=42)
# Prepare the data
train_ds = pt.datasets.Iris(dims=[0, 2])
train_loader = DataLoader(train_ds, batch_size=64)
# Hyperparameters
hparams = dict(
num_prototypes=5,
input_dim=2,
lr=0.1,
)
# Initialize the model
model = GrowingNeuralGas(
hparams,
prototypes_initializer=pt.initializers.ZCI(2),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Model summary
logging.info(model)
# Callbacks
vis = VisNG2D(data=train_loader)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
max_epochs=100,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

116
examples/gtlvq_mnist.py
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"""GTLVQ example using the MNIST dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import (
ImageGTLVQ,
PruneLoserPrototypes,
VisImgComp,
)
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = MNIST(
"~/datasets",
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
]),
)
test_ds = MNIST(
"~/datasets",
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
]),
)
# Dataloaders
train_loader = DataLoader(train_ds, num_workers=0, batch_size=256)
test_loader = DataLoader(test_ds, num_workers=0, batch_size=256)
# Hyperparameters
num_classes = 10
prototypes_per_class = 1
hparams = dict(
input_dim=28 * 28,
latent_dim=28,
distribution=(num_classes, prototypes_per_class),
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the model
model = ImageGTLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
#Use one batch of data for subspace initiator.
omega_initializer=pt.initializers.PCALinearTransformInitializer(
next(iter(train_loader))[0].reshape(256, 28 * 28)))
# Callbacks
vis = VisImgComp(
data=train_ds,
num_columns=10,
show=False,
tensorboard=True,
random_data=100,
add_embedding=True,
embedding_data=200,
flatten_data=False,
)
pruning = PruneLoserPrototypes(
threshold=0.01,
idle_epochs=1,
prune_quota_per_epoch=10,
frequency=1,
verbose=True,
)
es = EarlyStopping(
monitor="train_loss",
min_delta=0.001,
patience=15,
mode="min",
check_on_train_epoch_end=True,
)
# Setup trainer
# using GPUs here is strongly recommended!
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
pruning,
es,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

76
examples/gtlvq_moons.py
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@@ -1,76 +0,0 @@
"""Localized-GTLVQ example using the Moons dataset."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import GTLVQ, VisGLVQ2D
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
seed_everything(seed=2)
# Dataset
train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
# Dataloaders
train_loader = DataLoader(
train_ds,
batch_size=256,
shuffle=True,
)
# Hyperparameters
# Latent_dim should be lower than input dim.
hparams = dict(distribution=[1, 3], input_dim=2, latent_dim=1)
# Initialize the model
model = GTLVQ(hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds))
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
logging.info(model)
# Callbacks
vis = VisGLVQ2D(data=train_ds)
es = EarlyStopping(
monitor="train_acc",
min_delta=0.001,
patience=20,
mode="max",
verbose=False,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
es,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

81
examples/knn_iris.py
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@@ -1,81 +0,0 @@
"""k-NN example using the Iris dataset from scikit-learn."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import KNN, VisGLVQ2D
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
X, y = load_iris(return_X_y=True)
X = X[:, 0:3:2]
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.5,
random_state=42,
)
train_ds = pt.datasets.NumpyDataset(X_train, y_train)
test_ds = pt.datasets.NumpyDataset(X_test, y_test)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=16)
test_loader = DataLoader(test_ds, batch_size=16)
# Hyperparameters
hparams = dict(k=5)
# Initialize the model
model = KNN(hparams, data=train_ds)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
logging.info(model)
# Callbacks
vis = VisGLVQ2D(
data=(X_train, y_train),
resolution=200,
block=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
max_epochs=1,
callbacks=[
vis,
],
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
# This is only for visualization. k-NN has no training phase.
trainer.fit(model, train_loader)
# Recall
y_pred = model.predict(torch.tensor(X_train))
logging.info(y_pred)
# Test
trainer.test(model, dataloaders=test_loader)

118
examples/ksom_colors.py
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"""Kohonen Self Organizing Map."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from matplotlib import pyplot as plt
from prototorch.models import KohonenSOM
from prototorch.utils.colors import hex_to_rgb
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader, TensorDataset
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
class Vis2DColorSOM(pl.Callback):
def __init__(self, data, title="ColorSOMe", pause_time=0.1):
super().__init__()
self.title = title
self.fig = plt.figure(self.title)
self.data = data
self.pause_time = pause_time
def on_train_epoch_end(self, trainer, pl_module: KohonenSOM):
ax = self.fig.gca()
ax.cla()
ax.set_title(self.title)
h, w = pl_module._grid.shape[:2]
protos = pl_module.prototypes.view(h, w, 3)
ax.imshow(protos)
ax.axis("off")
# Overlay color names
d = pl_module.compute_distances(self.data)
wp = pl_module.predict_from_distances(d)
for i, iloc in enumerate(wp):
plt.text(
iloc[1],
iloc[0],
color_names[i],
ha="center",
va="center",
bbox=dict(facecolor="white", alpha=0.5, lw=0),
)
if trainer.current_epoch != trainer.max_epochs - 1:
plt.pause(self.pause_time)
else:
plt.show(block=True)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
seed_everything(seed=42)
# Prepare the data
hex_colors = [
"#000000", "#0000ff", "#00007f", "#1f86ff", "#5466aa", "#997fff",
"#00ff00", "#ff0000", "#00ffff", "#ff00ff", "#ffff00", "#ffffff",
"#545454", "#7f7f7f", "#a8a8a8", "#808000", "#800080", "#ffa500"
]
color_names = [
"black", "blue", "darkblue", "skyblue", "greyblue", "lilac", "green",
"red", "cyan", "magenta", "yellow", "white", "darkgrey", "mediumgrey",
"lightgrey", "olive", "purple", "orange"
]
colors = list(hex_to_rgb(hex_colors))
data = torch.Tensor(colors) / 255.0
train_ds = TensorDataset(data)
train_loader = DataLoader(train_ds, batch_size=8)
# Hyperparameters
hparams = dict(
shape=(18, 32),
alpha=1.0,
sigma=16,
lr=0.1,
)
# Initialize the model
model = KohonenSOM(
hparams,
prototypes_initializer=pt.initializers.RNCI(3),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 3)
# Model summary
logging.info(model)
# Callbacks
vis = Vis2DColorSOM(data=data)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
max_epochs=500,
callbacks=[
vis,
],
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

77
examples/lgmlvq_moons.py
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"""Localized-GMLVQ example using the Moons dataset."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import LGMLVQ, VisGLVQ2D
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
seed_everything(seed=2)
# Dataset
train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=256, shuffle=True)
# Hyperparameters
hparams = dict(
distribution=[1, 3],
input_dim=2,
latent_dim=2,
)
# Initialize the model
model = LGMLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
logging.info(model)
# Callbacks
vis = VisGLVQ2D(data=train_ds)
es = EarlyStopping(
monitor="train_acc",
min_delta=0.001,
patience=20,
mode="max",
verbose=False,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
es,
],
log_every_n_steps=1,
max_epochs=1000,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

103
examples/lvqmln_iris.py
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"""LVQMLN example using all four dimensions of the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import (
LVQMLN,
PruneLoserPrototypes,
VisSiameseGLVQ2D,
)
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.latent_size = latent_size
self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
self.activation = torch.nn.Sigmoid()
def forward(self, x):
x = self.activation(self.dense1(x))
out = self.activation(self.dense2(x))
return out
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()
# Reproducibility
seed_everything(seed=42)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=150)
# Hyperparameters
hparams = dict(
distribution=[3, 4, 5],
proto_lr=0.001,
bb_lr=0.001,
)
# Initialize the backbone
backbone = Backbone()
# Initialize the model
model = LVQMLN(
hparams,
prototypes_initializer=pt.initializers.SSCI(
train_ds,
transform=backbone,
),
backbone=backbone,
)
# Callbacks
vis = VisSiameseGLVQ2D(
data=train_ds,
map_protos=False,
border=0.1,
resolution=500,
axis_off=True,
)
pruning = 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,
pruning,
],
log_every_n_steps=1,
max_epochs=1000,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

68
examples/median_lvq_iris.py
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"""Median-LVQ example using the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import MedianLVQ, VisGLVQ2D
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# 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 = DataLoader(
train_ds,
batch_size=len(train_ds), # MedianLVQ cannot handle mini-batches
)
# Initialize the model
model = 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 = VisGLVQ2D(data=train_ds)
es = 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,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

74
examples/ng_iris.py
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"""Neural Gas example using the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import NeuralGas, VisNG2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Prepare and pre-process the dataset
x_train, y_train = load_iris(return_X_y=True)
x_train = x_train[:, 0:3:2]
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
train_ds = pt.datasets.NumpyDataset(x_train, y_train)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=150)
# Hyperparameters
hparams = dict(
num_prototypes=30,
input_dim=2,
lr=0.03,
)
# Initialize the model
model = NeuralGas(
hparams,
prototypes_initializer=pt.core.ZCI(2),
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 = VisNG2D(data=train_ds)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

68
examples/rslvq_iris.py
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@@ -1,68 +0,0 @@
"""RSLVQ example using the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import RSLVQ, VisGLVQ2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
seed_everything(seed=42)
# Dataset
train_ds = pt.datasets.Iris(dims=[0, 2])
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=64)
# Hyperparameters
hparams = dict(
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 = RSLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SSCI(train_ds, noise=0.2),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Callbacks
vis = VisGLVQ2D(data=train_ds)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
detect_anomaly=True,
max_epochs=100,
log_every_n_steps=1,
)
# Training loop
trainer.fit(model, train_loader)

83
examples/siamese_glvq_iris.py
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@@ -1,83 +0,0 @@
"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import SiameseGLVQ, VisSiameseGLVQ2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.latent_size = latent_size
self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
self.activation = torch.nn.Sigmoid()
def forward(self, x):
x = self.activation(self.dense1(x))
out = self.activation(self.dense2(x))
return out
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()
# Reproducibility
seed_everything(seed=2)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=150)
# Hyperparameters
hparams = dict(
distribution=[1, 2, 3],
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the backbone
backbone = Backbone()
# Initialize the model
model = SiameseGLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
backbone=backbone,
both_path_gradients=False,
)
# Callbacks
vis = VisSiameseGLVQ2D(data=train_ds, border=0.1)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

85
examples/siamese_gtlvq_iris.py
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"""Siamese GTLVQ example using all four dimensions of the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import SiameseGTLVQ, VisSiameseGLVQ2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.latent_size = latent_size
self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
self.activation = torch.nn.Sigmoid()
def forward(self, x):
x = self.activation(self.dense1(x))
out = self.activation(self.dense2(x))
return out
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()
# Reproducibility
seed_everything(seed=2)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=150)
# Hyperparameters
hparams = dict(
distribution=[1, 2, 3],
proto_lr=0.01,
bb_lr=0.01,
input_dim=2,
latent_dim=1,
)
# Initialize the backbone
backbone = Backbone(latent_size=hparams["input_dim"])
# Initialize the model
model = SiameseGTLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
backbone=backbone,
both_path_gradients=False,
)
# Callbacks
vis = VisSiameseGLVQ2D(data=train_ds, border=0.1)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

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examples/warm_starting.py
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"""Warm-starting GLVQ with prototypes from Growing Neural Gas."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import (
GLVQ,
KNN,
GrowingNeuralGas,
PruneLoserPrototypes,
VisGLVQ2D,
)
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# 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 = DataLoader(train_ds, batch_size=64, num_workers=0)
# Initialize the gng
gng = 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 = 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=1000,
callbacks=[
es,
],
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(gng, train_loader)
# Hyperparameters
hparams = dict(
distribution=[],
lr=0.01,
)
# Warm-start prototypes
knn = KNN(dict(k=1), data=train_ds)
prototypes = gng.prototypes
plabels = knn.predict(prototypes)
# Initialize the model
model = 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 = VisGLVQ2D(data=train_ds)
pruning = PruneLoserPrototypes(
threshold=0.02,
idle_epochs=2,
prune_quota_per_epoch=5,
frequency=1,
verbose=True,
)
es = 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,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)

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examples/y_architecture_example.py
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import logging
import prototorch as pt
import pytorch_lightning as pl
import torchmetrics
from prototorch.core import SMCI
from prototorch.y.architectures.base import Steps
from prototorch.y.callbacks import (
LogTorchmetricCallback,
PlotLambdaMatrixToTensorboard,
VisGMLVQ2D,
)
from prototorch.y.library.gmlvq import GMLVQ
from pytorch_lightning.callbacks import EarlyStopping
from torch.utils.data import DataLoader, random_split
logging.basicConfig(level=logging.INFO)
# ##############################################################################
def main():
# ------------------------------------------------------------
# DATA
# ------------------------------------------------------------
# Dataset
full_dataset = pt.datasets.Iris()
full_count = len(full_dataset)
train_count = int(full_count * 0.5)
val_count = int(full_count * 0.4)
test_count = int(full_count * 0.1)
train_dataset, val_dataset, test_dataset = random_split(
full_dataset, (train_count, val_count, test_count))
# Dataloader
train_loader = DataLoader(
train_dataset,
batch_size=1,
num_workers=4,
shuffle=True,
)
val_loader = DataLoader(
val_dataset,
batch_size=1,
num_workers=4,
shuffle=False,
)
test_loader = DataLoader(
test_dataset,
batch_size=1,
num_workers=0,
shuffle=False,
)
# ------------------------------------------------------------
# HYPERPARAMETERS
# ------------------------------------------------------------
# Select Initializer
components_initializer = SMCI(full_dataset)
# Define Hyperparameters
hyperparameters = GMLVQ.HyperParameters(
lr=dict(components_layer=0.1, _omega=0),
input_dim=4,
distribution=dict(
num_classes=3,
per_class=1,
),
component_initializer=components_initializer,
)
# Create Model
model = GMLVQ(hyperparameters)
# ------------------------------------------------------------
# TRAINING
# ------------------------------------------------------------
# Controlling Callbacks
recall = LogTorchmetricCallback(
'training_recall',
torchmetrics.Recall,
num_classes=3,
step=Steps.TRAINING,
)
stopping_criterion = LogTorchmetricCallback(
'validation_recall',
torchmetrics.Recall,
num_classes=3,
step=Steps.VALIDATION,
)
es = EarlyStopping(
monitor=stopping_criterion.name,
mode="max",
patience=10,
)
# Visualization Callback
vis = VisGMLVQ2D(data=full_dataset)
# Define trainer
trainer = pl.Trainer(
callbacks=[
vis,
recall,
stopping_criterion,
es,
PlotLambdaMatrixToTensorboard(),
],
max_epochs=100,
)
# Train
trainer.fit(model, train_loader, val_loader)
trainer.test(model, test_loader)
# Manual save
trainer.save_checkpoint("./y_arch.ckpt")
# Load saved model
new_model = GMLVQ.load_from_checkpoint(
checkpoint_path="./y_arch.ckpt",
strict=True,
)
if __name__ == "__main__":
main()