FeatureCleanerWorker

from AID.data.interface import FeatureCleanerWorker

Step 1: Feature Quality Screening. Enforces data standards by cleaning raw datasets and identifying valid numerical and categorical features. This step creates the ‘clean’ lineage.

Parameters

inputs : FeCleanerInput

Custom cleaning inputs configuration. If None, automatically resolves ‘train.raw’, ‘test.raw’, and ‘dtypes.raw’ from preceding step.

**kwargs : dict

Additional parameters passed to the FeatureCleaner worker.

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

`inputs == None`	→	pipeline data context
`train_set != None`	→	custom train data for fe_cleaner training and feature cleaning
`test_set != None`	→	custom test data might be provided to apply dtypes by trained model
`dtypes != None`	→	custom dtypes of all variables in train_set to apply on train and test

Returns

FeatureCleanerWorker

The populated instance of FeatureCleanerWorker.

Populates attributes into the worker context:

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

Registers data into the datasets context:

`data`	→	ctx.datasets.train.clean
`data`	→	ctx.datasets.test.clean
`dtypes`	→	ctx.datasets.dtypes.clean
`dropped`	→	ctx.datasets.dtypes.dropped
`features`	→	ctx.datasets.features.all.clean
`features`	→	ctx.datasets.features.num.clean
`features`	→	ctx.datasets.features.cat.clean
`stats`	→	ctx.datasets.stats.clean

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.fe_cleaner.spec

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

ctx.workers.fe_cleaner.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)

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

worker   = ctx.workers.fe_cleaner
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.fe_cleaner.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.fe_cleaner.fitter

The fitted fitter provides all populated attributes.

fitter  = ctx.workers.fe_cleaner.fitter
params_ = fitter.params
stats_  = fitter.stats

Direct choice and parametrization of internal fitter:

fx  = ctx.workers.fe_cleaner.fitters[0]
prm = dict(
    max_miss = 0.5,
    max_1val = 0.6,
    max_corr = 0.8,
)
fe_cleaner = ctx.workers.fe_cleaner(fx_choice = fx, fx_params = prm)

In the experimenting phase, when various time variables might be under testing (note that ‘time_var2’ must be present in the data):

cfg = dict(
    time_var     = 'time_var2',
    time_format  = '%Y%m%d',
)
fe_cleaner = ctx.workers.fe_cleaner(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