diff --git a/examples/UCI_benchmark_intRVFL.py b/examples/UCI_benchmark_intRVFL.py
index 82ba3539..b84a9c13 100644
--- a/examples/UCI_benchmark_intRVFL.py
+++ b/examples/UCI_benchmark_intRVFL.py
@@ -308,7 +308,9 @@ def fit(
# Obtain the classifier for the model
model.fit(train_loader)
- accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)
+ accuracy = torchmetrics.Accuracy(
+ task="multiclass", top_k=1, num_classes=num_classes
+ )
with torch.no_grad():
for samples, targets in tqdm(test_loader, desc="Testing"):
@@ -317,6 +319,7 @@ def fit(
predictions = model(samples)
accuracy.update(predictions.cpu(), targets)
+ print(f"Accuracy: {(accuracy.compute().item() * 100):.3f}%")
benchmark.report(dataset, accuracy.compute().item())
# Returns a dictionary with names of the datasets and their respective accuracy that is averaged over folds (if applicable) and repeats
diff --git a/examples/UCI_benchmark_intRVFL_GLVQ.py b/examples/UCI_benchmark_intRVFL_GLVQ.py
new file mode 100644
index 00000000..c70301d5
--- /dev/null
+++ b/examples/UCI_benchmark_intRVFL_GLVQ.py
@@ -0,0 +1,311 @@
+import copy
+import warnings
+import torch
+import torch.nn as nn
+import torch.utils.data as data
+from torch import Tensor
+from tqdm import tqdm
+
+# Note: this example requires the torchmetrics library: https://torchmetrics.readthedocs.io
+import torchmetrics
+import torchhd
+from torchhd.datasets import UCIClassificationBenchmark
+
+# Note: this example requires the prototorch library: https://github.com/si-cim/prototorch
+import prototorch as pt
+
+# Note: this example requires the prototorch-models library: https://github.com/si-cim/prototorch_models
+from prototorch.models import GLVQ
+
+# Note: this example requires the pytorch-lightning library: https://www.pytorchlightning.ai
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.warnings import PossibleUserWarning
+from torch.optim.lr_scheduler import ExponentialLR
+from pytorch_lightning.callbacks import TQDMProgressBar
+
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", category=PossibleUserWarning)
+
+
+# Function for performing min-max normalization of the input data samples
+def create_min_max_normalize(min: Tensor, max: Tensor):
+ def normalize(input: Tensor) -> Tensor:
+ return torch.nan_to_num((input - min) / (max - min))
+
+ return normalize
+
+
+# Specify a model to be evaluated
+class IntRVFLGLVQ(nn.Module):
+ """Class implementing integer random vector functional link network (intRVFL) model with Generalized Learning Vector Quantization (GLVQ) classifier as described in `Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing <https://doi.org/10.1109/IJCNN52387.2021.9533316>`_.
+
+ Args:
+ dataset (torchhd.datasets.CollectionDataset): Specifies a dataset to be evaluted by the model.
+ num_feat (int): Number of features in the dataset.
+ max_epochs (int, optional): Number of epochs to train the classifier.
+ device (torch.device, optional): Specifies device to be used for Torch.
+ """
+
+ # These values of hyperparameters were found via the grid search for intRVFL model with GLVQ classifier as described in the paper.
+ INT_RVFL_GLVQ_HYPER = {
+ "abalone": (1450, 15, 15, 1),
+ "acute-inflammation": (50, 1, 5, 1),
+ "acute-nephritis": (50, 1, 2, 1),
+ "adult": (1150, 3, 15, 1),
+ "annealing": (1150, 7, 15, 1),
+ "arrhythmia": (1400, 7, 12, 1),
+ "audiology-std": (950, 3, 13, 1),
+ "balance-scale": (50, 7, 15, 1),
+ "balloons": (50, 1, 7, 1),
+ "bank": (200, 7, 14, 1),
+ "blood": (50, 7, 10, 1),
+ "breast-cancer": (50, 1, 13, 1),
+ "breast-cancer-wisc": (650, 3, 3, 1),
+ "breast-cancer-wisc-diag": (1500, 3, 15, 1),
+ "breast-cancer-wisc-prog": (1450, 3, 7, 1),
+ "breast-tissue": (1300, 1, 2, 1),
+ "car": (250, 3, 15, 1),
+ "cardiotocography-10clases": (1350, 3, 15, 1),
+ "cardiotocography-3clases": (900, 15, 15, 1),
+ "chess-krvk": (800, 1, 15, 1),
+ "chess-krvkp": (1350, 3, 14, 1),
+ "congressional-voting": (100, 15, 1, 1),
+ "conn-bench-sonar-mines-rocks": (1100, 3, 13, 1),
+ "conn-bench-vowel-deterding": (1350, 3, 15, 1),
+ "connect-4": (1100, 3, 1, 1),
+ "contrac": (50, 7, 15, 1),
+ "credit-approval": (200, 7, 13, 1),
+ "cylinder-bands": (1100, 7, 15, 1),
+ "dermatology": (900, 3, 15, 1),
+ "echocardiogram": (250, 15, 15, 1),
+ "ecoli": (350, 3, 9, 1),
+ "energy-y1": (650, 3, 15, 1),
+ "energy-y2": (1000, 7, 13, 1),
+ "fertility": (150, 7, 12, 1),
+ "flags": (900, 15, 10, 1),
+ "glass": (1400, 3, 13, 1),
+ "haberman-survival": (100, 3, 1, 1),
+ "hayes-roth": (50, 1, 8, 1),
+ "heart-cleveland": (50, 15, 4, 1),
+ "heart-hungarian": (50, 15, 3, 1),
+ "heart-switzerland": (50, 15, 4, 1),
+ "heart-va": (1350, 15, 6, 1),
+ "hepatitis": (1300, 1, 6, 1),
+ "hill-valley": (150, 1, 11, 1),
+ "horse-colic": (850, 1, 12, 1),
+ "ilpd-indian-liver": (1200, 7, 14, 1),
+ "image-segmentation": (650, 1, 15, 1),
+ "ionosphere": (1150, 1, 14, 1),
+ "iris": (50, 3, 1, 1),
+ "led-display": (50, 7, 1, 1),
+ "lenses": (50, 1, 5, 1),
+ "letter": (1500, 1, 15, 1),
+ "libras": (1250, 3, 13, 1),
+ "low-res-spect": (1400, 7, 14, 1),
+ "lung-cancer": (450, 1, 4, 1),
+ "lymphography": (1150, 1, 9, 1),
+ "magic": (800, 3, 15, 1),
+ "mammographic": (150, 7, 14, 1),
+ "miniboone": (650, 15, 15, 1),
+ "molec-biol-promoter": (1250, 1, 11, 1),
+ "molec-biol-splice": (1000, 15, 15, 1),
+ "monks-1": (50, 3, 8, 1),
+ "monks-2": (400, 1, 14, 1),
+ "monks-3": (50, 15, 12, 1),
+ "mushroom": (150, 3, 12, 1),
+ "musk-1": (1300, 7, 15, 1),
+ "musk-2": (1150, 7, 15, 1),
+ "nursery": (1000, 3, 15, 1),
+ "oocytes_merluccius_nucleus_4d": (1500, 7, 15, 1),
+ "oocytes_merluccius_states_2f": (1500, 7, 15, 1),
+ "oocytes_trisopterus_nucleus_2f": (1450, 3, 15, 1),
+ "oocytes_trisopterus_states_5b": (1450, 7, 15, 1),
+ "optical": (1100, 7, 15, 1),
+ "ozone": (50, 1, 1, 1),
+ "page-blocks": (800, 1, 15, 1),
+ "parkinsons": (1200, 1, 13, 1),
+ "pendigits": (1500, 1, 15, 1),
+ "pima": (50, 1, 13, 1),
+ "pittsburg-bridges-MATERIAL": (100, 1, 9, 1),
+ "pittsburg-bridges-REL-L": (1200, 1, 7, 1),
+ "pittsburg-bridges-SPAN": (450, 7, 7, 1),
+ "pittsburg-bridges-T-OR-D": (1000, 1, 8, 1),
+ "pittsburg-bridges-TYPE": (50, 7, 5, 1),
+ "planning": (50, 1, 1, 1),
+ "plant-margin": (1350, 7, 8, 1),
+ "plant-shape": (1450, 3, 14, 1),
+ "plant-texture": (1500, 7, 10, 1),
+ "post-operative": (50, 15, 1, 1),
+ "primary-tumor": (950, 3, 1, 1),
+ "ringnorm": (1500, 3, 14, 1),
+ "seeds": (550, 1, 12, 1),
+ "semeion": (1400, 15, 14, 1),
+ "soybean": (850, 3, 5, 1),
+ "spambase": (1350, 15, 15, 1),
+ "spect": (50, 1, 8, 1),
+ "spectf": (1100, 15, 15, 1),
+ "statlog-australian-credit": (200, 15, 1, 1),
+ "statlog-german-credit": (500, 15, 12, 1),
+ "statlog-heart": (50, 7, 14, 1),
+ "statlog-image": (950, 1, 15, 1),
+ "statlog-landsat": (1500, 3, 15, 1),
+ "statlog-shuttle": (100, 7, 15, 1),
+ "statlog-vehicle": (1450, 7, 14, 1),
+ "steel-plates": (1500, 3, 14, 1),
+ "synthetic-control": (1350, 3, 14, 1),
+ "teaching": (400, 3, 14, 1),
+ "thyroid": (300, 7, 15, 1),
+ "tic-tac-toe": (750, 1, 15, 1),
+ "titanic": (50, 1, 13, 1),
+ "trains": (100, 1, 7, 1),
+ "twonorm": (1100, 15, 14, 1),
+ "vertebral-column-2clases": (250, 3, 14, 1),
+ "vertebral-column-3clases": (200, 15, 14, 1),
+ "wall-following": (1200, 3, 15, 1),
+ "waveform": (1400, 7, 15, 1),
+ "waveform-noise": (1300, 15, 14, 1),
+ "wine": (850, 1, 15, 1),
+ "wine-quality-red": (1100, 1, 13, 1),
+ "wine-quality-white": (950, 3, 14, 1),
+ "yeast": (1350, 1, 5, 1),
+ "zoo": (400, 7, 1, 1),
+ }
+
+ def __init__(
+ self,
+ dataset: torchhd.datasets.CollectionDataset,
+ num_feat: int,
+ max_epochs: int = 100,
+ device: torch.device = None,
+ ):
+ super(IntRVFLGLVQ, self).__init__()
+ self.device = device
+ self.num_feat = num_feat
+ self.max_epochs = max_epochs
+ # Fetch the hyperparameters for the corresponding dataset
+ hyper_param = self.INT_RVFL_GLVQ_HYPER[dataset.name]
+ # Dimensionality of vectors used when transforming input data
+ self.dimensions = hyper_param[0]
+ # Parameter of the clipping function used as the part of transforming input data
+ self.kappa = hyper_param[1]
+ self.transfer_beta = hyper_param[2]
+ self.prototypes_per_class = hyper_param[3]
+ # Number of classes in the dataset
+ self.num_classes = len(dataset.classes)
+ # Set up the encoding for the model as specified in "Density"
+ self.hypervector_encoding = torchhd.embeddings.Density(
+ self.num_feat, self.dimensions
+ )
+
+ # Specify encoding function for data samples
+ def encode(self, x):
+ return self.hypervector_encoding(x).clipping(self.kappa)
+
+ # Specify how to make an inference step and issue a prediction
+ def forward(self, x):
+ # Make encodings for all data samples in the batch
+ encodings = self.encode(x)
+ # Classify with GLVQ classifier
+ predictions = self.classifier.predict(encodings)
+ return predictions
+
+ # Train the classfier
+ def fit(
+ self,
+ train_ld: torch.utils.data.dataloader.DataLoader,
+ ):
+ # Hyperparameters for GLVQ
+ hparams = dict(
+ distribution={
+ "num_classes": self.num_classes,
+ "per_class": self.prototypes_per_class,
+ },
+ transfer_beta=self.transfer_beta,
+ lr=0.1,
+ )
+ # Initialize the GLVQ classifier
+ self.classifier = GLVQ(
+ hparams,
+ optimizer=torch.optim.Adam,
+ prototypes_initializer=pt.initializers.SMCI(train_loader.dataset),
+ lr_scheduler=ExponentialLR,
+ lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+ )
+
+ # Setup trainer
+ trainer = pl.Trainer(
+ callbacks=[
+ TQDMProgressBar(refresh_rate=3),
+ ],
+ max_epochs=self.max_epochs,
+ detect_anomaly=True,
+ )
+
+ # Training loop
+ trainer.fit(self.classifier, train_ld)
+
+
+# Specify device to be used for Torch.
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print("Using {} device".format(device))
+# Specifies batch size to be used for the model.
+batch_size = 200
+# Specifies how many random initializations of the model to evaluate for each dataset in the collection.
+repeats = 5
+
+
+# Get an instance of the UCI benchmark
+benchmark = UCIClassificationBenchmark("../data", download=True)
+# Perform evaluation
+for dataset in benchmark.datasets():
+ print(dataset.name)
+
+ # Number of features in the dataset.
+ num_feat = dataset.train[0][0].size(-1)
+ # Number of classes in the dataset.
+ num_classes = len(dataset.train.classes)
+
+ # Get values for min-max normalization and add the transformation
+ min_val = torch.min(dataset.train.data, 0).values.to(device)
+ max_val = torch.max(dataset.train.data, 0).values.to(device)
+ transform = create_min_max_normalize(min_val, max_val)
+ dataset.test.transform = transform
+
+ # Set up data loaders
+ test_loader = data.DataLoader(dataset.test, batch_size=batch_size)
+
+ # Run for the requested number of simulations
+ for r in range(repeats):
+ # Creates a model to be evaluated. The model should specify both transformation of input data as weel as the algortihm for forming the classifier.
+ model = IntRVFLGLVQ(
+ getattr(torchhd.datasets, dataset.name), num_feat, device=device
+ ).to(device)
+
+ # Replace raw data with hypervector encodings
+ train_ds = copy.deepcopy(dataset.train)
+ train_ds.data = torch.tensor(model.encode(transform(train_ds.data)))
+
+ # Set up train loader
+ train_loader = torch.utils.data.DataLoader(
+ train_ds, batch_size=batch_size, shuffle=True
+ )
+
+ # Obtain the classifier for the model
+ model.fit(train_loader)
+ accuracy = torchmetrics.Accuracy(
+ task="multiclass", top_k=1, num_classes=num_classes
+ )
+
+ with torch.no_grad():
+ for samples, targets in tqdm(test_loader, desc="Testing"):
+ samples = samples.to(device)
+ # Make prediction
+ predictions = model(samples)
+ accuracy.update(predictions.cpu(), targets)
+
+ print(f"Accuracy: {(accuracy.compute().item() * 100):.3f}%")
+ benchmark.report(dataset, accuracy.compute().item())
+
+# Returns a dictionary with names of the datasets and their respective accuracy that is averaged over folds (if applicable) and repeats
+benchmark_accuracy = benchmark.score()
+print(benchmark_accuracy)
diff --git a/examples/emg_hand_gestures.py b/examples/emg_hand_gestures.py
index 74b9b1e0..85cb0792 100644
--- a/examples/emg_hand_gestures.py
+++ b/examples/emg_hand_gestures.py
@@ -74,7 +74,9 @@ def experiment(subjects=[0]):
sample_hv = encode(samples)
model.add(sample_hv, targets)
- accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)
+ accuracy = torchmetrics.Accuracy(
+ task="multiclass", top_k=1, num_classes=num_classes
+ )
with torch.no_grad():
model.normalize()
diff --git a/examples/graphhd.py b/examples/graphhd.py
index fa86bd08..60a5d01a 100644
--- a/examples/graphhd.py
+++ b/examples/graphhd.py
@@ -115,7 +115,9 @@ def forward(self, x):
samples_hv = encode(samples).unsqueeze(0)
model.add(samples_hv, samples.y)
-accuracy = torchmetrics.Accuracy("multiclass", num_classes=graphs.num_classes)
+accuracy = torchmetrics.Accuracy(
+ task="multiclass", top_k=1, num_classes=graphs.num_classes
+)
with torch.no_grad():
model.normalize()
diff --git a/examples/language_recognition.py b/examples/language_recognition.py
index fd5da321..352575a0 100644
--- a/examples/language_recognition.py
+++ b/examples/language_recognition.py
@@ -81,7 +81,7 @@ def forward(self, x):
samples_hv = encode(samples)
model.add(samples_hv, labels)
-accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)
+accuracy = torchmetrics.Accuracy(task="multiclass", top_k=1, num_classes=num_classes)
with torch.no_grad():
model.normalize()
diff --git a/examples/mnist.py b/examples/mnist.py
index 79bac5a9..2b3ddb3f 100644
--- a/examples/mnist.py
+++ b/examples/mnist.py
@@ -59,7 +59,7 @@ def forward(self, x):
samples_hv = encode(samples)
model.add(samples_hv, labels)
-accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)
+accuracy = torchmetrics.Accuracy(task="multiclass", top_k=1, num_classes=num_classes)
with torch.no_grad():
model.normalize()
diff --git a/examples/mnist_hugging_face.py b/examples/mnist_hugging_face.py
index 9a6a6b8d..f0a9f459 100644
--- a/examples/mnist_hugging_face.py
+++ b/examples/mnist_hugging_face.py
@@ -72,7 +72,7 @@ def forward(self, x, dot=False):
samples_hv = model.encode(samples)
model.classify.add(samples_hv, labels)
-accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)
+accuracy = torchmetrics.Accuracy(task="multiclass", top_k=1, num_classes=num_classes)
with torch.no_grad():
model.classify.normalize()
diff --git a/examples/mnist_nonlinear.py b/examples/mnist_nonlinear.py
index 493cc6c2..ce9a4c9c 100644
--- a/examples/mnist_nonlinear.py
+++ b/examples/mnist_nonlinear.py
@@ -57,7 +57,7 @@ def forward(self, x):
samples_hv = encode(samples)
model.add(samples_hv, labels)
-accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)
+accuracy = torchmetrics.Accuracy(task="multiclass", top_k=1, num_classes=num_classes)
with torch.no_grad():
model.normalize()
diff --git a/examples/voicehd.py b/examples/voicehd.py
index 39a6e075..77ed94ee 100644
--- a/examples/voicehd.py
+++ b/examples/voicehd.py
@@ -52,7 +52,7 @@ def forward(self, x):
samples_hv = encode(samples)
model.add(samples_hv, labels)
-accuracy = torchmetrics.Accuracy("multiclass", num_classes=num_classes)
+accuracy = torchmetrics.Accuracy(task="multiclass", top_k=1, num_classes=num_classes)
with torch.no_grad():
model.normalize()