MatchCategories

class feature_engine.preprocessing.MatchCategories(variables=None, ignore_format=False, missing_values='raise')

MatchCategories() ensures that categorical variables are encoded as pandas 'categorical' dtype, instead of generic python 'object' or other dtypes.

Under the hood, 'categorical' dtype is a representation that maps each category to an integer, thus providing a more memory-efficient object structure than, e.g., ‘str’, and allowing faster grouping, mapping, and similar operations on the resulting object.

MatchCategories() remembers the encodings or levels that represent each category, and can thus can be used to ensure that the correct encoding gets applied when passing categorical data to modeling packages that support this dtype, or to prevent unseen categories from reaching a further transformer or estimator in a pipeline, for example.

More details in the User Guide.

Parameters

variables: list, default=None

The list of categorical variables that will be encoded. If None, the encoder will find and transform all variables of type object or categorical by default. You can also make the transformer accept numerical variables, see the parameter ignore_format.

ignore_format: bool, default=False

This transformer operates only on variables of type object or categorical. To override this behaviour and allow the transformer to transform numerical variables as well, set to True.

If ignore_format is False, the encoder will automatically select variables of type object or categorical, or check that the variables entered by the user are of type object or categorical. If True, the encoder will select all variables or accept all variables entered by the user, including those cast as numeric.

In short, set to True when you want to encode numerical variables.

missing_values: string, default=’raise’

Indicates if missing values should be ignored or raised. If 'raise' the transformer will return an error if the the datasets to fit or transform contain missing values. If 'ignore', missing data will be ignored when learning parameters or performing the transformation.

Attributes

category_dict_:

Dictionary with the category encodings assigned to each variable.

variables_:

The group of variables that will be transformed.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

Examples

>>> import pandas as pd
>>> from feature_engine.preprocessing import MatchCategories
>>> X_train = pd.DataFrame(dict(x1 = ["a","b","c"], x2 = [4,5,6]))
>>> X_test = pd.DataFrame(dict(x1 = ["c","b","a","d"], x2 = [5,6,4,7]))
>>> mc = MatchCategories(missing_values="ignore")
>>> mc.fit(X_train)
>>> mc.transform(X_train)
  x1  x2
0  a   4
1  b   5
2  c   6
>>> mc.transform(X_test)
    x1  x2
0    c   5
1    b   6
2    a   4
3  NaN   7

Methods

fit:	Learn the encodings or levels to use for each variable.
fit_transform:	Fit to the data. Then transform it.
get_feature_names_out:	Get output feature names for transformation.
get_params:	Get parameters for this estimator.
set_params:	Set the parameters of this estimator.
transform:	Enforce the type of categorical variables as dtype categorical.

fit(X, y=None)

Learn the encodings or levels to use for representing categorical variables.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]

The training dataset. Can be the entire dataframe, not just the variables to be transformed.

y: pandas Series, default = None

y is not needed in this encoder. You can pass y or None.

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

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

Parameters

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

• If None, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

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.

inverse_transform(X)

Convert the encoded variable back to the original values.

Parameters

X: pandas dataframe of shape = [n_samples, n_features].

The transformed dataframe.

Returns

X_tr: pandas dataframe of shape = [n_samples, n_features].

The un-transformed dataframe, with the categorical variables containing the original values.

rtype

DataFrame ..

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.

transform(X)

Encode categorical variables as pandas categorical dtype.

Parameters

X: pandas dataframe of shape = [n_samples, n_features].

The dataset to encode.

Returns

X_new: pandas dataframe of shape = [n_samples, n_features].

The dataframe with the variables encoded as pandas categorical dtype.

rtype

DataFrame ..