Agent4PredictorWorker

from AID.learner.interface import Agent4PredictorWorker

Step 13: Agent 4 Training Worker. Orchestrates the fitting of Learning Agent 4. By default, it consumes ‘train.ml’ and utilizes numerical/categorical features from the global context.

Parameters

inputs : Agent4Input

Custom training configuration. If None, resolves defaults from pipeline sets.

**kwargs : dict

Additional parameters for the Agent 4 worker.

Inputs full schema can be obtained by ctx.workers.agent4_predictor.spec with main params:

`inputs == None`	→	pipeline data context
`train_set != None`	→	custom train data for agent4_predictor training
`woe_features != None`	→	optional subset of woe features

Returns

Agent4PredictorWorker

The populated instance of Agent4PredictorWorker.

Populates attributes into the worker context:

`model`	→	ctx.workers.agent4_predictor
`inputs`	→	ctx.workers.agent4_predictor.spec
`result`	→	ctx.workers.agent4_predictor.result
`state`	→	ctx.workers.agent4_predictor.state
`report`	→	ctx.workers.agent4_predictor.report
`stats`	→	ctx.workers.agent4_predictor.stats
`fitter`	→	ctx.workers.agent4_predictor.fitter

Registers data into the datasets context:

`predictions`	→	ctx.datasets.predictions.train.agent4
`features`	→	ctx.datasets.features.agent4.predicted
`features`	→	ctx.datasets.features.agent4.selected
`dropped`	→	ctx.datasets.features.agent4.dropped
`stats`	→	ctx.datasets.stats.agents.agent4

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.agent4_predictor.spec

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

ctx.workers.agent4_predictor.fitters

Executing workers with defaults → auto provided by the context:

ctx.flow.process('data')        # 1. process workers 0-3 → data load - data stability
ctx.flow.process('encoding')    # 2. process workers 4-9 → train and apply encoding to train and test samples
ctx.workers.agent4_predictor()  # 3. train agent4 on stabilized and encoded woe data (2)

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

worker   = ctx.workers.agent4_predictor
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.agent4_predictor.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.agent4_predictor.fitter

The fitted fitter provides all populated attributes.

fitter    = ctx.workers.agent4_predictor.fitter
result_   = fitter.result
features_ = fitter.features
config_   = fitter.config
params_   = fitter.params
stats_    = fitter.stats
model_    = fitter.model

Direct choice and parametrization of internal fitter:

fx  = ctx.workers.agent4_predictor.fitters[0]
prm = dict(
    alpha = 0.05,
    eps = 1e-3,
    tol = 1e-4,
    cv = 3,
    shadow_quantile = 0.9
)
agent4 = ctx.workers.agent4_predictor(fx_choice = fx, fx_params = prm)

In the experimenting phase, when various parametrization settings might be under testing (note that internal fitter API (fx_config) can vary with respect to chosen fitter):

cfg = dict(
    target = 'target2',
    val_size = 0.33,
    n_permutations = 3
)
agent4 = ctx.workers.agent4_predictor(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