AsIsCategoricalBucketer

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.

Examples:

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)

variables_type property readonly

Signals variables type supported by this bucketer.

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

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'

bucket_table(self, column) inherited

Generates the statistics for the buckets of a particular column.

The pre-buckets are matched to the post-buckets, so that the user has a much clearer understanding of how the BucketingProcess ends up with the final buckets. An example:

bucket	label	Count	Count (%)	Non-event	Event	% Event	% Non-event	Event Rate	WoE	IV
0	(-inf, 25.0)	61.0	1.36	57.0	4.0	0.41	1.62	0.066	1.380	0.017
1	[25.0, 45.0)	2024.0	44.98	1536.0	488.0	49.64	43.67	0.241	-0.128	0.008

Parameters:

Name	Type	Description	Default
column		The column we wish to analyse	required

Returns:

Type	Description
df (pd.DataFrame)	A pandas dataframe of the format above

fit(self, X, y=None) inherited

Fit X, y.

fit_interactive(self, X, y=None, mode='external', **server_kwargs) inherited

Fit a bucketer and then interactive edit the fit using a dash app.

Note we are using a jupyterdash app, which supports 3 different modes:

• 'external' (default): Start dash server and print URL
• 'inline': Start dash app inside an Iframe in the jupyter notebook
• 'jupyterlab': Start dash app as a new tab inside jupyterlab

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

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 : ndarray array of shape (n_samples, n_features_new) Transformed array.

get_params(self, deep=True) inherited

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 : dict Parameter names mapped to their values.

plot_bucket(self, column, line='event_rate', format=None, scale=None, width=None, height=None) inherited

Plot the buckets.

Parameters:

Name	Type	Description	Default
column		The column we want to visualise	required
line		The line to plot on the secondary axis. Default is Event Rate.	'event_rate'
format		The format of the image, such as 'png'. The default None returns a plotly image.	None
scale		If format is specified, the scale of the image	None
width		If format is specified, the width of the image	None
height		If format is specified, the image of the image	None

Returns:

Type	Description
plot	plotly fig

predict(self, X) inherited

Applies the transform method. To be used for the grid searches.

Parameters:

Name	Type	Description	Default
X	pd.DataFrame	The numerical data which will be transformed into the corresponding buckets	required

Returns:

Type	Description
y (np.array)	Transformed X, such that the values of X are replaced by the corresponding bucket numbers

predict_proba(self, X) inherited

Applies the transform method. To be used for the grid searches.

Parameters:

Name	Type	Description	Default
X	pd.DataFrame	The numerical data which will be transformed into the corresponding buckets	required

Returns:

Type	Description
yhat (np.array)	transformed X, such that the values of X are replaced by the corresponding bucket numbers

save_yml(self, fout) inherited

Save the features bucket to a yaml file.

Parameters:

Name	Type	Description	Default
fout	~PathLike	file output	required

set_params(self, **params) inherited

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as :class:~sklearn.pipeline.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 Estimator instance.

summary(self) inherited

Display a summary table for columns passed to .fit().

The format is the following:

column	num_prebuckets	num_buckets	dtype
LIMIT_BAL	15	10	float64
BILL_AMT1	15	6	float64

transform(self, X, y=None) inherited

Transforms an array into the corresponding buckets fitted by the Transformer.

Parameters:

Name	Type	Description	Default
X	pd.DataFrame	dataframe which will be transformed into the corresponding buckets	required
y	array	target	None

Returns:

Type	Description
df (pd.DataFrame)	dataset with transformed features

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Last update: 2021-11-24