mlnext.pipeline.FeatureCreator

class mlnext.pipeline.FeatureCreator(features: List[Dict[str, Any] | NewFeatureModel])

Bases: BaseEstimator, TransformerMixin

Creates new features from existing or calculated features.

Added in version 0.6.0.

Parameters:

features (list(dict[str, Any] | NewFeatureModel)) – List of new features. Expects the dict to match NewFeatureModel.

Example

>>> import pandas as pd
>>> from mlnext import FeatureCreator
>>> df = pd.DataFrame(
...     {
...         "height": [1, 2, 3],
...         "width": [3, 2, 1],
...         "a": [True, False, True],
...         "b": [True, True, False],
...     }
... )

>>> t = FeatureCreator(
...     features=[
...         {
...             "name": "area",
...             "features": ["height", "width"],
...             "op": "mul",
...         },
...         {
...             "name": "AandB",
...             "features": ["a", "b"],
...             "op": "and",
...         },
...         {
...             "name": "sum",
...             "features": ["height", "width"],
...             "op": "add",
...             "keep": False,
...         },
...         {
...             "name": "area-sum",
...             "features": ["area", "sum"],
...             "op": "sub",
...         },
...     ]
... )
>>> t.transform(df)
    height      width   a           b    area   AandB   area-sum
        1           3   True    True    3       True    -1
        2           2   False   True    4       False   0
        3           1   True    False   3       False   -1

Methods

fit
fit_transform	Fit to data, then transform it.
get_metadata_routing	Get metadata routing of this object.
get_params	Get parameters for this estimator.
set_output	Set output container.
set_params	Set the parameters of this estimator.
transform	Calculates new featrues based on the given description.

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

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

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 – Parameter names mapped to their values.

Return type:

dict

set_output(*, transform=None)

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

• ”default”: Default output format of a transformer

• ”pandas”: DataFrame output

• ”polars”: Polars output

• None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

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.

Return type:

estimator instance

transform(X: DataFrame) → DataFrame

Calculates new featrues based on the given description.

Parameters:

X (pd.DataFrame) – Input data.

Raises:

ValueError – Raised if a feature is missing.

Returns:

Returns the updated dataframe.

Return type:

pd.DataFrame