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Calculate outlier scores as the log of the arithmetic mean (AM) of the densities across the permutations used to approximate the chain rule.
TabPFNUnsupervisedModel.density_TabPFNUnsupervisedModel.density_(
X_predict: torch.Tensor,
X_fit: torch.Tensor,
conditional_idx: list[int],
column_idx: int,
) -> tuple[Any, torch.Tensor, torch.Tensor]
| Parameter | Type | Default | Description |
|---|---|---|---|
X_ | torch.Tensor | Required | Input data for which to make predictions |
X_ | torch.Tensor | Required | Training data to fit the model |
conditional_ | list[int] | Required | Indices of features to condition on |
column_ | int | Required | Index of the feature to predict |
| Type | Description |
|---|---|
tuple[Any, torch.Tensor, torch.Tensor] | tuple containing: - The fitted model (classifier or regressor) - The filtered features used for prediction - The target feature values to predict |
TabPFNUnsupervisedModel.outliersTabPFNUnsupervisedModel.outliers(
X: torch.Tensor | np.ndarray | pd.DataFrame,
n_permutations: int = 10,
) -> torch.Tensor
| Parameter | Type | Default | Description |
|---|---|---|---|
X | torch.Tensor | np.ndarray | pd.Data | Required | Union[torch.Tensor, np.ndarray, pd.DataFrame] Samples to calculate outlier scores for, shape (n_samples, n_features) |
n_ | int | 10 | int, default=10 Number of permutations to use for more robust probability estimates. Higher values may produce more stable results but increase computation time. |
| Type | Description |
|---|---|
torch.Tensor | torch.Tensor: Tensor of outlier scores as log(AM(densities)), (lower values indicate more likely outliers), shape (n_samples,). |
RuntimeError
If the model initialization fails
ValueError
If the input data has incompatible dimensions
TabPFNUnsupervisedModel.outliers_pdfTabPFNUnsupervisedModel.outliers_pdf(
X: torch.Tensor,
n_permutations: int = 10,
) -> torch.Tensor
| Parameter | Type | Default | Description |
|---|---|---|---|
X | torch.Tensor | Required | Input data tensor |
n_ | int | 10 | Number of permutations to use for the outlier calculation |
| Type | Description |
|---|---|
torch.Tensor | log_pdf (lower values indicate more likely outliers). |
TabPFNUnsupervisedModel.outliers_pmfTabPFNUnsupervisedModel.outliers_pmf(
X: torch.Tensor,
n_permutations: int = 10,
) -> torch.Tensor
| Parameter | Type | Default | Description |
|---|---|---|---|
X | torch.Tensor | Required | Input data tensor |
n_ | int | 10 | Number of permutations to use for the outlier calculation |
| Type | Description |
|---|---|
torch.Tensor | Tensor of outlier scores (lower values indicate more likely outliers) |
TabPFNUnsupervisedModel.outliers_single_permutation_TabPFNUnsupervisedModel.outliers_single_permutation_(
X: torch.tensor,
feature_permutation: list[int] | tuple[int],
) -> torch.tensor
| Parameter | Type | Default | Description |
|---|---|---|---|
X | torch.tensor | Required | — |
feature_ | list[int] | tuple[int] | Required | — |
| Type | Description |
|---|---|
torch.tensor | — |
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