AsIsCategoricalBucketer

Bases: BaseBucketer

The AsIsCategoricalBucketer treats unique values as categories.

Support:

It will assign each a bucket number in the order of appearance. If new data contains new, unknown labels they will be replaced by 'Other'.

This is bucketer is useful when you have data that is already sufficiented bucketed, but you would like to be able to bucket new data in the same way.

Example:

from skorecard import datasets
from skorecard.bucketers import AsIsCategoricalBucketer

X, y = datasets.load_uci_credit_card(return_X_y=True)
bucketer = AsIsCategoricalBucketer(variables=['EDUCATION'])
bucketer.fit_transform(X)

Source code in skorecard/bucketers/bucketers.py

class AsIsCategoricalBucketer(BaseBucketer):
    """
    The `AsIsCategoricalBucketer` treats unique values as categories.

    Support: ![badge](https://img.shields.io/badge/numerical-false-red) ![badge](https://img.shields.io/badge/categorical-true-green) ![badge](https://img.shields.io/badge/supervised-false-blue)

    It will assign each a bucket number in the order of appearance.
    If new data contains new, unknown labels they will be replaced by 'Other'.

    This is bucketer is useful when you have data that is already sufficiented bucketed,
    but you would like to be able to bucket new data in the same way.

    Example:

    ```python
    from skorecard import datasets
    from skorecard.bucketers import AsIsCategoricalBucketer

    X, y = datasets.load_uci_credit_card(return_X_y=True)
    bucketer = AsIsCategoricalBucketer(variables=['EDUCATION'])
    bucketer.fit_transform(X)
    ```
    """  # noqa

    def __init__(
        self, variables=[], specials={}, missing_treatment="separate", remainder="passthrough", get_statistics=True
    ):
        """Init the class.

        Args:
            variables (list): The features to bucket. Uses all features if not defined.
            specials: (nested) dictionary of special values that require their own binning.
                The dictionary has the following format:
                 {"<column name>" : {"name of special bucket" : <list with 1 or more values>}}
                For every feature that needs a special value, a dictionary must be passed as value.
                This dictionary contains a name of a bucket (key) and an array of unique values that should be put
                in that bucket.
                When special values are defined, they are not considered in the fitting procedure.
            missing_treatment: Defines how we treat the missing values present in the data.
                If a string, it must be one of the following options:
                    separate: Missing values get put in a separate 'Other' bucket: `-1`
                    most_risky: Missing values are put into the bucket containing the largest percentage of Class 1.
                    least_risky: Missing values are put into the bucket containing the largest percentage of Class 0.
                    most_frequent: Missing values are put into the most common bucket.
                    neutral: Missing values are put into the bucket with WoE closest to 0.
                    similar: Missing values are put into the bucket with WoE closest to the bucket with only missing values.
                    passthrough: Leaves missing values untouched.
                If a dict, it must be of the following format:
                    {"<column name>": <bucket_number>}
                    This bucket number is where we will put the missing values.
            remainder: How we want the non-specified columns to be transformed. It must be in ["passthrough", "drop"].
                passthrough (Default): all columns that were not specified in "variables" will be passed through.
                drop: all remaining columns that were not specified in "variables" will be dropped.
        """  # noqa
        self.variables = variables
        self.specials = specials
        self.missing_treatment = missing_treatment
        self.remainder = remainder
        self.get_statistics = get_statistics

    @property
    def variables_type(self):
        """
        Signals variables type supported by this bucketer.
        """
        return "categorical"

    def _get_feature_splits(self, feature, X, y, X_unfiltered=None):
        """
        Finds the splits for a single feature.

        X and y have already been preprocessed, and have specials removed.

        Args:
            feature (str): Name of the feature.
            X (pd.Series): df with single column of feature to bucket
            y (np.ndarray): array with target
            X_unfiltered (pd.Series): df with single column of feature to bucket before any filtering was applied

        Returns:
            splits, right (tuple): The splits (dict or array), and whether right=True or False.
        """
        unq = X.unique().tolist()
        mapping = dict(zip(unq, range(0, len(unq))))

        # Note that right is set to True, but this is not used at all for categoricals
        return (mapping, True)

variables_type property

Signals variables type supported by this bucketer.

__init__(variables=[], specials={}, missing_treatment='separate', remainder='passthrough', get_statistics=True)

Init the class.

Parameters:

Name	Type	Description	Default
variables	list	The features to bucket. Uses all features if not defined.	[]
specials		(nested) dictionary of special values that require their own binning. The dictionary has the following format: {"" : {"name of special bucket" : }} For every feature that needs a special value, a dictionary must be passed as value. This dictionary contains a name of a bucket (key) and an array of unique values that should be put in that bucket. When special values are defined, they are not considered in the fitting procedure.	{}
missing_treatment		Defines how we treat the missing values present in the data. If a string, it must be one of the following options: separate: Missing values get put in a separate 'Other' bucket: -1 most_risky: Missing values are put into the bucket containing the largest percentage of Class 1. least_risky: Missing values are put into the bucket containing the largest percentage of Class 0. most_frequent: Missing values are put into the most common bucket. neutral: Missing values are put into the bucket with WoE closest to 0. similar: Missing values are put into the bucket with WoE closest to the bucket with only missing values. passthrough: Leaves missing values untouched. If a dict, it must be of the following format: {"": } This bucket number is where we will put the missing values.	'separate'
remainder		How we want the non-specified columns to be transformed. It must be in ["passthrough", "drop"]. passthrough (Default): all columns that were not specified in "variables" will be passed through. drop: all remaining columns that were not specified in "variables" will be dropped.	'passthrough'

Source code in skorecard/bucketers/bucketers.py

def __init__(
    self, variables=[], specials={}, missing_treatment="separate", remainder="passthrough", get_statistics=True
):
    """Init the class.

    Args:
        variables (list): The features to bucket. Uses all features if not defined.
        specials: (nested) dictionary of special values that require their own binning.
            The dictionary has the following format:
             {"<column name>" : {"name of special bucket" : <list with 1 or more values>}}
            For every feature that needs a special value, a dictionary must be passed as value.
            This dictionary contains a name of a bucket (key) and an array of unique values that should be put
            in that bucket.
            When special values are defined, they are not considered in the fitting procedure.
        missing_treatment: Defines how we treat the missing values present in the data.
            If a string, it must be one of the following options:
                separate: Missing values get put in a separate 'Other' bucket: `-1`
                most_risky: Missing values are put into the bucket containing the largest percentage of Class 1.
                least_risky: Missing values are put into the bucket containing the largest percentage of Class 0.
                most_frequent: Missing values are put into the most common bucket.
                neutral: Missing values are put into the bucket with WoE closest to 0.
                similar: Missing values are put into the bucket with WoE closest to the bucket with only missing values.
                passthrough: Leaves missing values untouched.
            If a dict, it must be of the following format:
                {"<column name>": <bucket_number>}
                This bucket number is where we will put the missing values.
        remainder: How we want the non-specified columns to be transformed. It must be in ["passthrough", "drop"].
            passthrough (Default): all columns that were not specified in "variables" will be passed through.
            drop: all remaining columns that were not specified in "variables" will be dropped.
    """  # noqa
    self.variables = variables
    self.specials = specials
    self.missing_treatment = missing_treatment
    self.remainder = remainder
    self.get_statistics = get_statistics