cursus.steps.configs.config_xgboost_mt_model_eval_step

Multi-Task Model Evaluation Step Configuration with Self-Contained Derivation Logic

This module implements the configuration class for the XgboostMt multi-task 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 XgboostMtModelEvalConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='2.1.2', 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_mt_model_eval.py', processing_script_arguments=None, processing_framework_version='1.2-1', id_name, task_label_names, job_type='calibration', eval_metric_choices=<factory>, generate_plots=True, comparison_mode=False, previous_score_fields='', comparison_metrics='all', statistical_tests=True, comparison_plots=True, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for XgboostMt multi-task model evaluation step with self-contained derivation logic.

This class defines the configuration parameters for the XgboostMt multi-task model evaluation step, which calculates per-task and aggregate evaluation metrics for trained multi-task models. This is crucial for measuring model performance across multiple tasks 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

task_label_names: List[str]

processing_entry_point: str

job_type: str

eval_metric_choices: List[str]

framework_version: str

py_version: str

use_large_processing_instance: bool

generate_plots: bool

comparison_mode: bool

previous_score_fields: 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 multi-task evaluation configuration

get_environment_variables()

Get environment variables for the multi-task 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 multi-task 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]

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.