<a id="skfolio-descriptor-changeinintensity"></a>

# skfolio.descriptor.ChangeInIntensity

<a id="skfolio.descriptor.ChangeInIntensity"></a>

### *class* skfolio.descriptor.ChangeInIntensity(field, scale_field, lag)

Lagged change in a field-to-scale ratio.

Computes the change in the ratio $A/S$ over a fixed lag:

$$
\text{ChangeInIntensity}_\ell(t)
= \frac{A(t)}{S(t)} - \frac{A(t - \ell)}{S(t - \ell)}
$$

where $A$ is the `field` value and $S$ is the `scale_field` value.

The first `lag` observations are NaN because no lagged history is available.

This descriptor is appropriate when the economic concept of interest is the ratio
itself, such as capex/assets, R&D/sales or a margin, and whether that ratio improved
or deteriorated over the lag window. NaNs are allowed as missing observations and
propagate when the current, lagged or scale value is missing.

Non-missing numerator values must be finite. Non-missing scale values must be finite
and strictly positive. A `ValueError` is raised otherwise.

* **Parameters:**
  **field** *str*
  : Field name in the [`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.AssetPanel) used as numerator
    $A$. Non-missing values must be finite.

  **scale_field** *str*
  : Field name in the [`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.AssetPanel) used as denominator
    $S$. Non-missing values must be finite and strictly positive.

  **lag** *int*
  : Number of observations to look back. The interpretation depends on the data
    frequency: `lag=12` means 1 year for monthly data, `lag=252` for daily data,
    `lag=4` for quarterly data.
* **Attributes:**
  **n_assets_** *int*
  : Number of assets seen during fitting.

  **asset_names_** *ndarray of shape (n_assets,)*
  : Asset names seen during fitting.

  **change_in_intensity_** *ndarray of shape (n_assets,)*
  : Last change-in-intensity value for each asset.

### Methods

| [`fit_transform`](#skfolio.descriptor.ChangeInIntensity.fit_transform)(X[, y])         | Compute changes in the intensity ratio over the configured lag.   |
|--------------------------------------------------------------------------------|-------------------------------------------------------------------|
| [`get_metadata_routing`](#skfolio.descriptor.ChangeInIntensity.get_metadata_routing)()        | Get metadata routing of this object.                              |
| [`get_params`](#skfolio.descriptor.ChangeInIntensity.get_params)([deep])            | Get parameters for this estimator.                                |
| [`partial_fit_transform`](#skfolio.descriptor.ChangeInIntensity.partial_fit_transform)(X[, y]) | Compute changes in the intensity ratio over the configured lag.   |
| [`set_params`](#skfolio.descriptor.ChangeInIntensity.set_params)(\*\*params)        | Set the parameters of this estimator.                             |

#### SEE ALSO
[`ChangeToScale`](https://skfolio.org/generated/skfolio.descriptor.ChangeToScale.html.md#skfolio.descriptor.ChangeToScale)
: Change in $A$ normalized by current $S$.

[`GrowthRate`](https://skfolio.org/generated/skfolio.descriptor.GrowthRate.html.md#skfolio.descriptor.GrowthRate)
: Simple growth rate for positive-definite characteristics.

### Examples

```pycon
>>> from skfolio.descriptor import ChangeInIntensity
>>>
>>> # Capex intensity change (capex / total_assets)
>>> capex_int = ChangeInIntensity("capex_ttm", "total_assets", lag=12)
>>>
>>> # R&D intensity change (R&D / sales)
>>> rd_int = ChangeInIntensity("rd_ttm", "sales_ttm", lag=12)
```

<a id="skfolio.descriptor.ChangeInIntensity.fit_transform"></a>

#### fit_transform(X, y=None, \*\*fit_params)

Compute changes in the intensity ratio over the configured lag.

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing the `field` and `scale_field` characteristics
    configured at construction.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **change_in_intensity** *ndarray of shape (n_observations, n_assets)*
  : Change in `field` / `scale_field` over the lag window for each observation
    and asset.

<a id="skfolio.descriptor.ChangeInIntensity.get_metadata_routing"></a>

#### 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.

<a id="skfolio.descriptor.ChangeInIntensity.get_params"></a>

#### 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.

<a id="skfolio.descriptor.ChangeInIntensity.partial_fit_transform"></a>

#### partial_fit_transform(X, y=None, \*\*fit_params)

Compute changes in the intensity ratio over the configured lag.

This method supports online updates by continuing from the current fitted state.
Use `fit_transform` to start from a clean state.

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing the `field` and `scale_field` fields configured at
    construction.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **change_in_intensity** *ndarray of shape (n_observations, n_assets)*
  : Change in `field` / `scale_field` over the lag window for each observation
    and asset.

<a id="skfolio.descriptor.ChangeInIntensity.set_params"></a>

#### 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 `<component>__<parameter>` so that it’s
possible to update each component of a nested object.

* **Parameters:**
  **\*\*params** *dict*
  : Estimator parameters.
* **Returns:**
  **self** *estimator instance*
  : Estimator instance.