SkorecardPipeline
A sklearn Pipeline with several attribute and methods added.
This Pipeline of bucketers behaves more like a bucketer and adds:
.summary(): See which columns are bucketed.plot_bucket(): Plot buckets of a column.bucket_table(): Table with buckets of a column.save_to_yaml(): Save information necessary for bucketing to a YAML file.features_bucket_mapping_: Access bucketing information.fit_interactive(): Edit fitted buckets interactively in a dash app
from skorecard.pipeline.pipeline import SkorecardPipeline
from skorecard.bucketers import DecisionTreeBucketer, OrdinalCategoricalBucketer
from skorecard import datasets
pipe = SkorecardPipeline([
('decisiontreebucketer', DecisionTreeBucketer(variables = ["LIMIT_BAL", "BILL_AMT1"],max_n_bins=5)),
('ordinalcategoricalbucketer', OrdinalCategoricalBucketer(variables = ["EDUCATION", "MARRIAGE"], tol =0.05)),
])
df = datasets.load_uci_credit_card(as_frame=True)
features = ["LIMIT_BAL", "BILL_AMT1", "EDUCATION", "MARRIAGE"]
X = df[features]
y = df["default"].values
pipe.fit(X, y)
pipe.bucket_table('LIMIT_BAL')
bucket_tables_
property
readonly
¶
Retrieve bucket tables.
Used by .bucket_table()
classes_
inherited
property
readonly
¶
The classes labels. Only exist if the last step is a classifier.
feature_names_in_
inherited
property
readonly
¶
Names of features seen during first step fit method.
features_bucket_mapping_
property
readonly
¶
Retrieve features bucket mapping.
n_features_in_
inherited
property
readonly
¶
Number of features seen during first step fit method.
named_steps
inherited
property
readonly
¶
Access the steps by name.
Read-only attribute to access any step by given name. Keys are steps names and values are the steps objects.
summary_dict_: Dict
property
readonly
¶
Retrieve summary_dicts and combine.
Used by .summary()
__class__ (type)
inherited
¶
Metaclass for defining Abstract Base Classes (ABCs).
Use this metaclass to create an ABC. An ABC can be subclassed directly, and then acts as a mix-in class. You can also register unrelated concrete classes (even built-in classes) and unrelated ABCs as 'virtual subclasses' -- these and their descendants will be considered subclasses of the registering ABC by the built-in issubclass() function, but the registering ABC won't show up in their MRO (Method Resolution Order) nor will method implementations defined by the registering ABC be callable (not even via super()).
__instancecheck__(cls, instance)
special
¶
Override for isinstance(instance, cls).
__new__(mcls, name, bases, namespace, **kwargs)
special
staticmethod
¶
Create and return a new object. See help(type) for accurate signature.
__subclasscheck__(cls, subclass)
special
¶
Override for issubclass(subclass, cls).
register(cls, subclass)
¶
Register a virtual subclass of an ABC.
Returns the subclass, to allow usage as a class decorator.
__init__(self, steps, *, memory=None, verbose=False)
special
¶
Wraps sklearn Pipeline.
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 |
decision_function(self, X)
inherited
¶
Transform the data, and apply decision_function with the final estimator.
Call transform of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
decision_function method. Only valid if the final estimator
implements decision_function.
Parameters¶
X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
Returns¶
y_score : ndarray of shape (n_samples, n_classes)
Result of calling decision_function on the final estimator.
fit(self, X, y=None, **fit_params)
inherited
¶
Fit the model.
Fit all the transformers one after the other and transform the data. Finally, fit the transformed data using the final estimator.
Parameters¶
X : iterable Training data. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_params : dict of string -> object
Parameters passed to the fit method of each step, where
each parameter name is prefixed such that parameter p for step
s has key s__p.
Returns¶
self : object Pipeline with fitted steps.
fit_interactive(self, X, y=None, mode='external')
¶
Fit a bucketer and then interactively 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_predict(self, X, y=None, **fit_params)
inherited
¶
Transform the data, and apply fit_predict with the final estimator.
Call fit_transform of each transformer in the pipeline. The
transformed data are finally passed to the final estimator that calls
fit_predict method. Only valid if the final estimator implements
fit_predict.
Parameters¶
X : iterable Training data. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_params : dict of string -> object
Parameters passed to the fit method of each step, where
each parameter name is prefixed such that parameter p for step
s has key s__p.
Returns¶
y_pred : ndarray
Result of calling fit_predict on the final estimator.
fit_transform(self, X, y=None, **fit_params)
inherited
¶
Fit the model and transform with the final estimator.
Fits all the transformers one after the other and transform the
data. Then uses fit_transform on transformed data with the final
estimator.
Parameters¶
X : iterable Training data. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_params : dict of string -> object
Parameters passed to the fit method of each step, where
each parameter name is prefixed such that parameter p for step
s has key s__p.
Returns¶
Xt : ndarray of shape (n_samples, n_transformed_features) Transformed samples.
get_feature_names_out(self, input_features=None)
inherited
¶
get_params(self, deep=True)
inherited
¶
Get parameters for this estimator.
Returns the parameters given in the constructor as well as the
estimators contained within the steps of the Pipeline.
Parameters¶
deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns¶
params : mapping of string to any Parameter names mapped to their values.
inverse_transform(self, Xt)
inherited
¶
Apply inverse_transform for each step in a reverse order.
All estimators in the pipeline must support inverse_transform.
Parameters¶
Xt : array-like of shape (n_samples, n_transformed_features)
Data samples, where n_samples is the number of samples and
n_features is the number of features. Must fulfill
input requirements of last step of pipeline's
inverse_transform method.
Returns¶
Xt : ndarray of shape (n_samples, n_features) Inverse transformed data, that is, data in the original feature space.
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, **predict_params)
inherited
¶
Transform the data, and apply predict with the final estimator.
Call transform of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls predict
method. Only valid if the final estimator implements predict.
Parameters¶
X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_params : dict of string -> object
Parameters to the predict called at the end of all
transformations in the pipeline. Note that while this may be
used to return uncertainties from some models with return_std
or return_cov, uncertainties that are generated by the
transformations in the pipeline are not propagated to the
final estimator.
.. versionadded:: 0.20
Returns¶
y_pred : ndarray
Result of calling predict on the final estimator.
predict_log_proba(self, X, **predict_log_proba_params)
inherited
¶
Transform the data, and apply predict_log_proba with the final estimator.
Call transform of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
predict_log_proba method. Only valid if the final estimator
implements predict_log_proba.
Parameters¶
X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_log_proba_params : dict of string -> object
Parameters to the predict_log_proba called at the end of all
transformations in the pipeline.
Returns¶
y_log_proba : ndarray of shape (n_samples, n_classes)
Result of calling predict_log_proba on the final estimator.
predict_proba(self, X, **predict_proba_params)
inherited
¶
Transform the data, and apply predict_proba with the final estimator.
Call transform of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
predict_proba method. Only valid if the final estimator implements
predict_proba.
Parameters¶
X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_proba_params : dict of string -> object
Parameters to the predict_proba called at the end of all
transformations in the pipeline.
Returns¶
y_proba : ndarray of shape (n_samples, n_classes)
Result of calling predict_proba on the final estimator.
save_yml(self, fout)
¶
Save the features bucket to a yaml file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fout |
file output |
required |
score(self, X, y=None, sample_weight=None)
inherited
¶
Transform the data, and apply score with the final estimator.
Call transform of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
score method. Only valid if the final estimator implements score.
Parameters¶
X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Targets used for scoring. Must fulfill label requirements for all steps of the pipeline.
sample_weight : array-like, default=None
If not None, this argument is passed as sample_weight keyword
argument to the score method of the final estimator.
Returns¶
score : float
Result of calling score on the final estimator.
score_samples(self, X)
inherited
¶
Transform the data, and apply score_samples with the final estimator.
Call transform of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
score_samples method. Only valid if the final estimator implements
score_samples.
Parameters¶
X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
Returns¶
y_score : ndarray of shape (n_samples,)
Result of calling score_samples on the final estimator.
set_params(self, **kwargs)
inherited
¶
Set the parameters of this estimator.
Valid parameter keys can be listed with get_params(). Note that
you can directly set the parameters of the estimators contained in
steps.
Parameters¶
**kwargs : dict
Parameters of this estimator or parameters of estimators contained
in steps. Parameters of the steps may be set using its name and
the parameter name separated by a '__'.
Returns¶
self : object Pipeline class 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)
inherited
¶
Transform the data, and apply transform with the final estimator.
Call transform of each transformer in the pipeline. The transformed
data are finally passed to the final estimator that calls
transform method. Only valid if the final estimator
implements transform.
This also works where final estimator is None in which case all prior
transformations are applied.
Parameters¶
X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline.
Returns¶
Xt : ndarray of shape (n_samples, n_transformed_features) Transformed data.