UserInputBucketer

The UserInputBucketer transformer creates buckets by implementing user-defined boundaries.

Support:

This is a special bucketer that is not fitted but rather relies on pre-defined user input. The most common use-case is loading bucket mapping information previously fitted by other bucketers.

Examples:

from skorecard import datasets
from skorecard.bucketers import AgglomerativeClusteringBucketer, UserInputBucketer

X, y = datasets.load_uci_credit_card(return_X_y=True)

ac_bucketer = AgglomerativeClusteringBucketer(n_bins=3, variables=['LIMIT_BAL'])
ac_bucketer.fit(X)
mapping = ac_bucketer.features_bucket_mapping_

ui_bucketer = UserInputBucketer(mapping)
new_X = ui_bucketer.fit_transform(X)
assert len(new_X['LIMIT_BAL'].unique()) == 3

#Map some values to the special buckets
specials = {
    "LIMIT_BAL":{
        "=50000":[50000],
        "in [20001,30000]":[20000,30000],
        }
}

ac_bucketer = AgglomerativeClusteringBucketer(n_bins=3, variables=['LIMIT_BAL'], specials = specials)
ac_bucketer.fit(X)
mapping = ac_bucketer.features_bucket_mapping_

ui_bucketer = UserInputBucketer(mapping)
new_X = ui_bucketer.fit_transform(X)
assert len(new_X['LIMIT_BAL'].unique()) == 5

__init__(self, features_bucket_mapping=None, variables=[], remainder='passthrough') special

Initialise the user-defined boundaries with a dictionary.

Notes: - features_bucket_mapping is stored without the trailing underscore (_) because it is not fitted.

Parameters:

Name	Type	Description	Default
features_bucket_mapping	None, Dict, FeaturesBucketMapping, str or Path	Contains the feature name and boundaries defined for this feature. If a dict, it will be converted to an internal FeaturesBucketMapping object. If a string or path, which will attempt to load the file as a yaml and convert to FeaturesBucketMapping object.	None
variables	list	The features to bucket. Uses all features in features_bucket_mapping if not defined.	[]
remainder	str	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)

Init the class.

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