# skfolio.descriptor.BookLeverage
### *class* skfolio.descriptor.BookLeverage
Book leverage descriptor.
Computes the proportion of total book capital financed by debt:
$$
\text{book\_leverage}(t) =
\frac{\text{total\_debt}(t)}
{\text{total\_debt}(t) + \text{book\_equity}(t)}
$$
Book leverage measures financial risk through the lens of the capital structure: the
fraction of a firm’s total invested capital (debt plus common equity) that comes
from creditors rather than common shareholders [[1]](#r302fa65a63b9-1).
NaNs are allowed as missing observations and propagate to the output.
Non-missing `total_debt` and `book_equity` values must be finite.
This form is preferred over the debt-to-equity ratio ($D / E$) because the two
are monotonically related ($D / E = \text{book\_leverage} / (1 - \text{book\_leverage})$)
but book leverage is bounded in $[0, 1]$ for healthy firms, producing
well-behaved cross-sectional distributions that do not require aggressive
winsorization.
`book_equity` is common shareholders’ equity (excluding preferred stock and
minority interest). When it is negative (e.g., firms with accumulated losses
exceeding paid-in capital), the denominator `total_debt + book_equity` may remain
positive, become zero or turn negative:
- **Denominator > 0 and book_equity < 0**: the ratio exceeds 1. The firm is extremely
leveraged, with debt exceeding total book capital. The value is a valid distress
signal and is preserved in the output.
- **Denominator <= 0**: the ratio is undefined or negative, and no longer has its
intended interpretation as a measure of book leverage. These observations are
masked to NaN.
This differs from [`ReturnOnEquity`](https://skfolio.org/generated/skfolio.descriptor.ReturnOnEquity.html.md#skfolio.descriptor.ReturnOnEquity), where any negative equity makes the
concept meaningless. Here, a ratio above 1 carries real information about financial
risk.
* **Parameters:**
**None**
* **Attributes:**
**n_assets_** *int*
: Number of assets seen during fitting.
**asset_names_** *ndarray of shape (n_assets,)*
: Asset names seen during fitting.
### Methods
| [`fit_transform`](#skfolio.descriptor.BookLeverage.fit_transform)(X[, y]) | Compute book leverage ratios. |
|--------------------------------------------------------------------------------|------------------------------------------------|
| [`get_metadata_routing`](#skfolio.descriptor.BookLeverage.get_metadata_routing)() | Get metadata routing of this object. |
| [`get_params`](#skfolio.descriptor.BookLeverage.get_params)([deep]) | Get parameters for this estimator. |
| [`partial_fit_transform`](#skfolio.descriptor.BookLeverage.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. |
| [`set_params`](#skfolio.descriptor.BookLeverage.set_params)(\*\*params) | Set the parameters of this estimator. |
#### SEE ALSO
[`DebtToAssets`](https://skfolio.org/generated/skfolio.descriptor.DebtToAssets.html.md#skfolio.descriptor.DebtToAssets)
: Leverage relative to total assets.
[`MarketLeverage`](https://skfolio.org/generated/skfolio.descriptor.MarketLeverage.html.md#skfolio.descriptor.MarketLeverage)
: Leverage as a fraction of total market capital.
### References
* **[1]** “Capital structure decisions: which factors are reliably important?” Financial Management. Frank, M. Z., & Goyal, V. K. (2009).
### Examples
```pycon
>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import BookLeverage
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = BookLeverage()
>>> book_leverage = descriptor.fit_transform(X)
```
#### fit_transform(X, y=None, \*\*fit_params)
Compute book leverage ratios.
* **Parameters:**
**X** *AssetPanel*
: Input panel containing `total_debt` and `book_equity`.
**y** *None*
: Ignored. Present for compatibility with scikit-learn’s API.
**\*\*fit_params** *dict*
: Additional fit parameters. Ignored.
* **Returns:**
**book_leverage** *ndarray of shape (n_observations, n_assets)*
: Book leverage ratio for each observation and asset.
#### get_metadata_routing()
Get metadata routing of this object.
Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing
mechanism works.
* **Returns:**
**routing** *MetadataRequest*
: A `MetadataRequest` encapsulating
routing information.
#### get_params(deep=True)
Get parameters for this estimator.
* **Parameters:**
**deep** *bool, default=True*
: If True, will return the parameters for this estimator and
contained subobjects that are estimators.
* **Returns:**
**params** *dict*
: Parameter names mapped to their values.
#### partial_fit_transform(X, y=None, \*\*fit_params)
Stateless class delegation to `fit_transform`.
#### set_params(\*\*params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as `Pipeline`). The latter have
parameters of the form `__` so that it’s
possible to update each component of a nested object.
* **Parameters:**
**\*\*params** *dict*
: Estimator parameters.
* **Returns:**
**self** *estimator instance*
: Estimator instance.