**num_embeddings**
==================

A set of functions to compute and validate bin edges for numerical feature discretization, commonly used in embeddings like piecewise linear encoding.


Functions
~~~~~~~~~

.. code-block:: python

    def _check_bins(bins: List[Tensor]) -> None

Validates the structure of bin edges to ensure they meet requirements for discretization.

**Parameters:**

* **bins** *(List[Tensor])* - List of tensors, where each tensor contains bin edges for a feature.

**Raises:**

* **ValueError** - If bins are empty, not 1D tensors, have fewer than 2 edges, contain non-finite values, or are unsorted.
* **UserWarning** - If a feature has exactly 2 bin edges (only 1 bin, equivalent to MinMax scaling).


.. code-block:: python

    def compute_bins(
        X: torch.Tensor,
        n_bins: int = 48,
        *,
        tree_kwargs: Optional[Dict[str, Any]] = None,
        y: Optional[Tensor] = None,
        regression: Optional[bool] = None,
        verbose: bool = False,
    ) -> List[Tensor]

Computes bin edges for numerical features using either quantile-based or tree-based methods.

**Parameters:**

* **X** *(torch.Tensor)* - 2D tensor of shape `(n_samples, n_features)` containing numerical features.
* **n_bins** *(int, optional, Default is 48)* - Target number of bins (actual count may vary).
* **tree_kwargs** *(Optional[Dict[str, Any]])* - If provided, uses tree-based binning with these kwargs for the decision tree.
* **y** *(Optional[Tensor])* - 1D tensor of labels (required for tree-based binning).
* **regression** *(Optional[bool])* - Whether the task is regression (required for tree-based binning).
* **verbose** *(bool, optional, Default is False)* - If True, uses `tqdm` to show progress for tree-based binning.

**Returns:**

* **List[Tensor]** - List of tensors, where each tensor contains sorted, unique bin edges for a feature, with shape `(n_edges,)`.


.. code-block:: python

    class _PiecewiseLinearEncodingImpl(nn.Module)

Internal implementation of piecewise linear encoding (not recommended for direct use, as outputs contain infinite values).

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature (generated by `compute_bins`).


.. code-block:: python

    class PiecewiseLinearEncoding(nn.Module)

Wrapper for `_PiecewiseLinearEncodingImpl` that sanitizes outputs (removes infinite values).

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature.


.. code-block:: python

    class _UnaryEncodingImpl(nn.Module)

Internal implementation of unary encoding, converting feature values into binary indicators of bin membership.

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature.


.. code-block:: python

    class UnaryEncoding(nn.Module)

Wrapper for `_UnaryEncodingImpl` that sanitizes outputs.

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature.


.. code-block:: python

    class _JohnsonEncodingImpl(nn.Module)

Internal implementation of Johnson encoding, using Johnson codes for feature discretization.

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature.


.. code-block:: python

    class JohnsonEncoding(nn.Module)

Wrapper for `_JohnsonEncodingImpl` that sanitizes outputs.

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature.


.. code-block:: python

    class _BinsEncodingImpl(nn.Module)

Internal implementation of bins encoding, using simple binning for feature discretization.

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature.


.. code-block:: python

    class BinsEncoding(nn.Module)

Wrapper for `_BinsEncodingImpl` that sanitizes outputs.

**Parameters:**

* **bins** *(List[Tensor])* - List of 1D tensors, where each tensor represents bin edges for a feature.