cursus.steps.configs.config_stratified_sampling_step

Stratified Sampling Configuration with Self-Contained Derivation Logic

This module implements the configuration class for SageMaker Processing steps for stratified sampling, using a self-contained design where each field is properly categorized according to the three-tier design: 1. Essential User Inputs (Tier 1) - Required fields that must be provided by users 2. System Fields (Tier 2) - Fields with reasonable defaults that can be overridden 3. Derived Fields (Tier 3) - Fields calculated from other fields, private with read-only properties

class StratifiedSamplingConfig(*, 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='stratified_sampling.py', processing_script_arguments=None, processing_framework_version='1.2-1', strata_column, job_type='training', sampling_strategy='balanced', target_sample_size=1000, min_samples_per_stratum=10, variance_column=None, sampling_multiplier=1.0, allow_replacement=False, reference_counts_json=None, sampling_filter_column=None, sampling_filter_value=None, random_state=42, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for the Stratified Sampling step with three-tier field categorization. Inherits from ProcessingStepConfigBase.

Fields are categorized into: - Tier 1: Essential User Inputs - Required from users - Tier 2: System Fields - Default values that can be overridden - Tier 3: Derived Fields - Private with read-only property access

strata_column: str

processing_entry_point: str

job_type: str

sampling_strategy: str

target_sample_size: int

min_samples_per_stratum: int

variance_column: str | None

sampling_multiplier: float

allow_replacement: bool

reference_counts_json: str | None

sampling_filter_column: str | None

sampling_filter_value: str | None

random_state: int

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].

classmethod validate_strata_column(v)

Ensure strata_column is a non-empty string.

classmethod validate_entry_point_relative(v)

Ensure processing_entry_point is a non‐empty relative path.

classmethod validate_job_type(v)

Ensure job_type is one of the allowed values.

classmethod validate_sampling_strategy(v)

Ensure sampling_strategy is one of the allowed values (case-insensitive).

Matching is case-insensitive and the stored value is normalized to the canonical-cased allowed value.

classmethod validate_variance_column(v)

Ensure variance_column is a non-empty string if provided.

validate_strategy_requirements()

Validate that required fields are provided for specific strategies.

initialize_derived_fields()

Initialize all derived fields once after validation.

get_public_init_fields()

Override get_public_init_fields to include stratified sampling 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.