cursus.steps.configs.config_risk_table_mapping_step

Configuration for Risk Table Mapping Processing Step.

This module defines the configuration class for the risk table mapping processing step, which is responsible for creating and applying risk tables for categorical features.

After Phase 6 refactor: Hyperparameters are now embedded in the source directory, eliminating the need for S3 upload logic and hyperparameters_s3_uri field.

class RiskTableMappingConfig(*, 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=False, skip_volume_kms=None, processing_source_dir=None, processing_entry_point='risk_table_mapping.py', processing_script_arguments=None, processing_framework_version='1.2-1', job_type='training', cat_field_list=[], label_name='target', smooth_factor=0.01, count_threshold=5, max_unique_threshold=100, enable_true_streaming=False, max_workers=0, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for Risk Table Mapping Processing Step.

This class extends ProcessingStepConfigBase to include specific fields for risk table mapping, including categorical fields and job type.

Source Directory Integration: After Phase 6 refactor, hyperparameters are embedded in the source directory structure and no longer require separate S3 upload. The expected source directory structure is:

source_dir/ ├── risk_table_mapping.py # Main script (entry point) └── hyperparams/ # Hyperparameters directory

└── hyperparameters.json # Generated hyperparameters file

The hyperparameters.json file is automatically generated from the configuration fields (cat_field_list, label_name, smooth_factor, count_threshold) and embedded in the source directory for runtime access.

processing_entry_point: str

job_type: str

cat_field_list: List[str]

label_name: str

smooth_factor: float

count_threshold: int

max_unique_threshold: int

enable_true_streaming: bool

max_workers: int

property environment_variables: Dict[str, str]

Get environment variables for the risk table mapping script.

Returns:

Dictionary of environment variables

classmethod validate_max_workers(v)

Ensure max_workers is 0 (auto-detect) or a positive integer.

classmethod validate_job_type(v)

Validate job type is one of the allowed values.

validate_risk_table_config()

Validate risk table mapping configuration.

After Phase 6 refactor: Simplified validation focusing on core configuration without S3 upload logic or external hyperparameters handling.

get_job_arguments()

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

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].

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.

processing_instance_count: int

processing_volume_size: int

processing_instance_type_large: str

processing_instance_type_small: str

use_large_processing_instance: bool

skip_volume_kms: bool | None

processing_source_dir: str | None

processing_script_arguments: List[str] | None

processing_framework_version: str

author: str

bucket: str

role: str

region: str

service_name: str

pipeline_version: str

model_class: str

current_date: str

framework_version: str

py_version: str

source_dir: str | None

enable_caching: bool

use_secure_pypi: bool

max_runtime_seconds: int

project_root_folder: str