cursus.steps.configs.config_xgboost_model_eval_step

Model Evaluation Step Configuration with Self-Contained Derivation Logic

This module implements the configuration class for the XGBoost model evaluation 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 XGBoostModelEvalConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='2.1.0', py_version='py310', 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=True, skip_volume_kms=None, processing_source_dir=None, processing_entry_point='xgboost_model_eval.py', processing_script_arguments=None, processing_framework_version='1.2-1', id_name, label_name, job_type='calibration', eval_metric_choices=<factory>, xgboost_framework_version='1.5-1', comparison_mode=False, previous_score_field='', comparison_metrics='all', statistical_tests=True, comparison_plots=True, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for XGBoost model evaluation step with self-contained derivation logic.

This class defines the configuration parameters for the XGBoost model evaluation step, which calculates evaluation metrics for trained models. This is crucial for measuring model performance and comparing different models or configurations.

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)

id_name: str

label_name: str

processing_entry_point: str

job_type: str

eval_metric_choices: List[str]

xgboost_framework_version: str

use_large_processing_instance: bool

comparison_mode: bool

previous_score_field: str

comparison_metrics: str

statistical_tests: bool

comparison_plots: bool

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_eval_config()

Additional validation specific to evaluation configuration

get_environment_variables()

Get environment variables for the model evaluation script.

Returns:

Dictionary mapping environment variable names to values

Return type:

Dict[str, str]

get_public_init_fields()

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