FeatureEngineFeatureTransformer¶

This class is a subclass of BaseFeatureTransformer and is used for transforming input data using feature_engine transformers. It provides methods for fitting and transforming the data.

Configuration¶

Required Configuration¶

FeatureTransformerFeatureTransformer contains the following configuration:

transformers: A list of transformers to run. Each transformer is a dictionary with the following entries:
- transformer: The feature_engine class to use as the transformer. Required.
- transformer_args: A list of arguments to pass into the transformer. Optional.
- transformer_kwargs: A dictionary of keyword arguments to pass into the transformer. Optional.
- transformer_columns: A list of columns names to apply the transformer to.

Optional Configuration¶

FeatureTransformerFeatureTransformer contains the following optional configuration:

pipeline_kwargs: A dictionary of keyword arguments to pass into the sklearn.pipeline.Pipeline object that is used to pipeline together the feature_engine transformations.

Default Configuration¶

FeatureTransformerFeatureTransformer contains no default configuration.

Attributes¶

transformer : An instance of sklearn.pipeline.Pipeline, which is used to run all the transformers provided in the configuration.

Methods¶

fit¶

This method fits the feature transformer to the input data.

def fit(self, data, *args, **kwargs)

Arguments:

data (array-like): The input data to fit the transformer to.
**kwargs (optional): Keyword arguments to be passed to the fit method.

Returns:

self.transformer: The fitted Pipeline object.

transform¶

This method applies the fitted feature transformer to transform the input data.

def transform(self, data, *args, **kwargs)

Arguments:

data (array-like): The input data to be transformed.
**kwargs (optional): Keyword arguments to be passed to the transform method.

Returns:

A dataframe containing the transformed data.

fit_transform¶

This method fits the feature transformer to the data and transforms it.

def fit_transform(self, data, *args, **kwargs)

Arguments:

data (array-like): The input data to fit the transformer to and transform.
**kwargs (optional): Keyword arguments to be passed to the fit_transform method.

Returns:

A dataframe containing the transformed data.

_get_transformer¶

This method gets the feature_engine transformer object based on the given class name.

def _get_transformer(self, transformer_class, columns, *args, **kwargs)

Arguments:

transformer_class: The class name of the transformer.
columns: The columns to apply the transformer to
**kwargs (optional): Keyword arguments to be passed to the transformer constructor.

Returns:

object: The instantiated feature_engine transformer object.

_map_transformer¶

This method maps the feature_engine class name to the feature_engine object.

def _map_transformer(self, transformer_class)

Arguments:

transformer_class: The class name of the transformer.

Returns:

object: The feature_engine class.

Usage:¶

from lolpop.component import FeatureTransformerFeatureTransformer 

data = #generate data 

conf = {
    #insert component configuration here ... 

    "config" : {
        "transformers" : [
            {"transformer": "OneHotEncoder", "transformer_columns": ["Sex"]},
            {"transformer": "LogCpTransformer", "transformer_columns": ["Height", "Weight", "Diameter"]},
        ]
    }
}

transformer = FeatureTransformerFeatureTransformer(conf=conf)
transformed_data = transformer.fit_transform(data)

print(transformed_data)

Sample Configuration¶

conf = {
    "config": {
        "transformers": 
        [
            {"transformer": "OneHotEncoder", "transformer_columns": ["Sex"]},
            {"transformer": "LogCpTransformer", "transformer_columns": ["Height", "Weight", "Diameter"]},
        ]
    }
}