WoeEncoderWorker

from AID.data.interface import WoeEncoderWorker

Step 7: Weight of Evidence (WOE) Encoding. Implements WOE transformation to convert variables into a continuous scale relative to the binary target probability.

Parameters

inputs : WoeEncoderInput

Custom WOE configuration. If None, resolves from ‘train.stable’, ‘features.encoded.num’ and ‘features.encoded.cat’.

**kwargs : dict

Additional parameters passed to the WoeEncoder worker.

Inputs full schema can be obtained by ctx.workers.woe_encoder.spec with main params:

`inputs == None`	→	pipeline data context
`train_set != None`	→	custom train data for woe_encoder training
`num_features != None`	→	custom subset of numerical encoded vars
`cat_features != None`	→	custom subset of categorical encoded vars

Returns

WoeEncoderWorker

The populated instance of WoeEncoderWorker.

Populates attributes into the worker context:

`model`	→	ctx.workers.woe_encoder
`inputs`	→	ctx.workers.woe_encoder.spec
`result`	→	ctx.workers.woe_encoder.result
`state`	→	ctx.workers.woe_encoder.state
`report`	→	ctx.workers.woe_encoder.report
`stats`	→	ctx.workers.woe_encoder.stats
`fitter`	→	ctx.workers.woe_encoder.fitter

Registers data into the datasets context:

`data`	→	ctx.datasets.train.encoded.num
`features`	→	ctx.datasets.features.woe.encoded
`features`	→	ctx.datasets.features.woe.selected
`mapping`	→	ctx.datasets.features.woe.mapping
`dropped`	→	ctx.datasets.features.woe.dropped.encoded
`encodings`	→	ctx.datasets.encodings.woe
`stats`	→	ctx.datasets.stats.encoded.woe.encoding

Examples

Defining the Project Context (Control Plane) & Project Scope:

import aidflow as aid

ctx = aid.ProjectContext()

ctx.genesis(
    project      = 'test',
    input_data   = 'test.csv',
    target       = 'target',
    yy_mm        = 'decision_date',
    yy_mm_format = '%Y-%m-%d'
)
ctx.params()

Worker → Model → Fitter

Accessing worker’s inputs schema → parameters & types (shown in the display output):

ctx.workers.woe_encoder.spec

Accessing model’s fitter options and schema → parameters & types (shown in the display output):

ctx.workers.woe_encoder.fitters

Executing workers with defaults → auto provided by the context:

ctx.workers.data_loader()    # 0. import data to the context scope
ctx.workers.fe_cleaner()     # 1. apply cleaning logic to the imported data (0)
ctx.workers.fe_stabilizer()  # 2. apply temporal stability logic to the cleaned data (1)
ctx.workers.obs_stabilizer() # 3. apply observation stability logic to the cleaned data (2)
ctx.workers.num_encoder()    # 4. apply numerical encoding logic to the stable data (3)
ctx.workers.cat_encoder()    # 5. apply categorical encoding logic to the stable data (3)
ctx.workers.ixn_encoder()    # 6. apply interactions encoding logic to the stable data (3)
ctx.workers.woe_encoder()    # 7. apply woe encoding logic using trained encoding in steps (4-5-6)

Note that steps 0-3 can be run by ExecutionContext one-liner:

ctx.flow.process('data', verbose = 0) # steps 0 - 3 have been trained within the context scope

Once worker has been executed, thus its main model has been built, we have access to all populated attributes.

worker   = ctx.workers.woe_encoder
inputs   = worker.spec.inputs
result   = worker.result
state    = worker.state
features = worker.features
params   = worker.params
stats    = worker.stats
fitter   = worker.fitter  # jump to chosen internal fitter
...

View to model report of executed worker:

ctx.workers.woe_encoder.report.show()

Model → Fitter

Once worker has been executed, we can obtain internal fitter state and its parametrization (shown in the display output):

ctx.workers.woe_encoder.fitter

The fitted fitter provides all populated attributes.

fitter    = ctx.workers.woe_encoder.fitter
result_   = fitter.result
features_ = fitter.features
params_   = fitter.params
stats_    = fitter.stats

Direct choice and parametrization of internal fitter:

fx  = ctx.workers.woe_encoder.fitters[0]
prm = dict(
        gini_cutoff = 0.05,
        alpha = 0.01
)
woe_encoder = ctx.workers.woe_encoder(fx_choice = fx, fx_params = prm)

In the experimenting phase when various targets might be under testing (note that ‘target2’ must be present in the data):

cfg = dict(
    target = 'target2',
)
woe_encoder = ctx.workers.woe_encoder(fx_config = cfg)

Attributes

All worker attributes return None prior its execution.

Attribute	Type	Description
`inputs`	`ModelInput`	Registered populated inputs containing execution timestamps and feature definitions.
`result`	`ModelResult`	Execution output payload containing model predictions and fit results.
`fitter`	`Fitter`	Underlying fitted estimator instance.
`state`	`ModelState`	Consolidated model state (fitter, choice, params, features, report, stats).
`report`	`DashboardInterface`	Interactive report interface. Call `report.show()` to render the visualization dashboard.
`stats`	`FitStats`	Performance metrics and training metadata gathered during fitting.
`features`	`TransResCols`	Feature schema dataclass required for downstream inference.
`params`	`tuple[FitParams, Parameters, ...]`	Tuple of fit settings, execution parameters, optionally transformer parameters.
`choice`	`RegGet.Choice`	Active configuration parameters including internal fitter choice (`fx_choice`).
`model_signature`	`str`	Unique hash identifier for the fitted model instance.

Execution Interface

Data Interface

Encoding Interface

Training Interface

Selection Interface

Inference Interface

Parameters

Returns

Examples

Worker → Model → Fitter

Model → Fitter

Attributes

​Parameters

​Returns

​Examples

​Worker → Model → Fitter

​Model → Fitter

​Attributes

Parameters

Returns

Examples

Worker → Model → Fitter

Model → Fitter

Attributes