cursus.steps.configs.config_percentile_model_calibration_step

Percentile Model Calibration Step Configuration with Self-Contained Derivation Logic

This module implements the configuration class for the PercentileModelCalibration step using a self-contained design where derived fields are private with read-only properties. Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private with properties)

class PercentileModelCalibrationConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='2.1.0', py_version='py310', image_uri=None, subnets=None, security_group_ids=None, enable_network_isolation=None, source_dir=None, enable_caching=False, use_secure_pypi=False, max_runtime_seconds=172800, project_root_folder, processing_instance_count=1, processing_volume_size=500, processing_instance_type_large='ml.m5.4xlarge', processing_instance_type_small='ml.m5.2xlarge', use_large_processing_instance=False, skip_volume_kms=None, processing_source_dir=None, processing_entry_point='percentile_model_calibration.py', processing_script_arguments=None, processing_framework_version='1.2-1', job_type, score_field=None, score_fields=None, n_bins=1000, accuracy=0.001, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for PercentileModelCalibration step with self-contained derivation logic.

This class defines the configuration parameters for the PercentileModelCalibration step, which creates percentile mapping from model scores using ROC curve analysis for consistent risk interpretation. The step converts raw model scores to percentile values that represent the relative risk ranking of predictions.

Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private with properties)

job_type: str

score_field: str | None

score_fields: List[str] | None

n_bins: int

accuracy: float

processing_entry_point: str

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

initialize_derived_fields()

Initialize all derived fields once after validation.

validate_config()

Validate configuration and ensure defaults are set.

Returns:

The validated configuration object

Return type:

Self

Raises:

ValueError – If any validation fails

get_environment_variables()

Get environment variables for the processing script.

Returns:

Dictionary of environment variables to be passed to the processing script.

Return type:

dict

get_public_init_fields()

Override get_public_init_fields to include percentile calibration-specific fields. Gets a dictionary of public fields suitable for initializing a child config. Includes both base fields (from parent) and percentile calibration-specific fields.

Returns:

Dictionary of field names to values for child initialization

Return type:

Dict[str, Any]

get_job_arguments()

CLI args — config is the single source (FZ 31e1d3h).

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• context (Any) – The context.