skfolio.descriptor.CapexToAssetsChangeInIntensity#

class skfolio.descriptor.CapexToAssetsChangeInIntensity(lag=252)[source]#

Lagged change in capex-to-assets intensity.

Computes the change in the capex-to-assets ratio over a fixed lag:

\[\text{capex\_to\_assets\_change\_in\_intensity}(t) = \frac{\text{capex\_ttm}(t)}{\text{total\_assets}(t)} - \frac{\text{capex\_ttm}(t - \text{lag})} {\text{total\_assets}(t - \text{lag})}\]

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

NaNs are allowed as missing observations and propagate when the current, lagged or scale value is missing. Non-missing capex_ttm values must be finite. Non-missing total_assets values must be finite and strictly positive.

A positive value indicates that capex intensity increased relative to total assets and a negative value indicates that it decreased.

This is a convenience subclass of ChangeInIntensity with field="capex_ttm" and scale_field="total_assets".

Parameters:

lagint, default=252: 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 capex-to-assets intensity change for each asset.

Methods

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

See also

ChangeInIntensity: Generic field-to-scale intensity change descriptor.
GrowthRate: Period-over-period growth rate for non-negative fields.

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import CapexToAssetsChangeInIntensity
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = CapexToAssetsChangeInIntensity(lag=252)
>>> capex_intensity_change = descriptor.fit_transform(X)

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

Compute changes in the intensity ratio over the configured lag.

Parameters:

XAssetPanel: Input panel containing the field and scale_field characteristics configured at construction.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

change_in_intensityndarray of shape (n_observations, n_assets): Change in field / scale_field over the lag window for each observation and asset.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

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:

XAssetPanel: Input panel containing the field and scale_field fields configured at construction.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

change_in_intensityndarray of shape (n_observations, n_assets): Change in field / scale_field over the lag window for each observation and asset.

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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.