Unsupervised functionalities

Warning

This functionality is currently only supported using the Local TabPFN Version but not the API.

Data Generation

import numpy as np
import torch
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from tabpfn_extensions import TabPFNClassifier, TabPFNRegressor
from tabpfn_extensions import unsupervised

# Load the breast cancer dataset
df = load_breast_cancer(return_X_y=False)
X, y = df["data"], df["target"]
attribute_names = df["feature_names"]

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.5, random_state=42
)

# Initialize TabPFN models
clf = TabPFNClassifier(n_estimators=3)
reg = TabPFNClassifier(n_estimators=3)

# Initialize unsupervised model
model_unsupervised = unsupervised.TabPFNUnsupervisedModel(
    tabpfn_clf=clf, tabpfn_reg=reg
)

# Select features for analysis (e.g., first two features)
feature_indices = [0, 1]

# Create and run synthetic experiment
exp_synthetic = unsupervised.experiments.GenerateSyntheticDataExperiment(
    task_type="unsupervised"
)

# Convert data to torch tensors
X_tensor = torch.tensor(X_train, dtype=torch.float32)
y_tensor = torch.tensor(y_train, dtype=torch.float32)

# Run the experiment
results = exp_synthetic.run(
    tabpfn=model_unsupervised,
    X=X_tensor,
    y=y_tensor,
    attribute_names=attribute_names,
    temp=1.0,
    n_samples=X_train.shape[0] * 3,  # Generate 3x original samples
    indices=feature_indices,
)

Outlier Detection

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

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

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

# Run outlier detection
exp_outlier = unsupervised.experiments.OutlierDetectionUnsupervisedExperiment(
    task_type="unsupervised"
)
results = exp_outlier.run(
    tabpfn=model_unsupervised,
    X=torch.tensor(X),
    y=torch.tensor(y),
    attribute_names=attribute_names,
    indices=[4, 12],  # Analyze features 4 and 12
)