Anomaly Detection

pip install "tabpfn-extensions[unsupervised]"

import torch
from sklearn.datasets import load_breast_cancer
from tabpfn import TabPFNClassifier, TabPFNRegressor
from tabpfn_extensions.unsupervised import TabPFNUnsupervisedModel

# Load dataset
df = load_breast_cancer(return_X_y=False)
X = df["data"]
attribute_names = df["feature_names"]

# Initialize models
clf = TabPFNClassifier(n_estimators=4)
reg = TabPFNRegressor(n_estimators=4)
model_unsupervised = TabPFNUnsupervisedModel(
    tabpfn_clf=clf, tabpfn_reg=reg
)

# Compute anomaly scores
model_unsupervised.fit(X)

scores = model_unsupervised.outliers(
    torch.tensor(X, dtype=torch.float32),
    n_permutations=10,
)

print("Outlier scores:", scores[:10])