cursus.steps.configs.config_active_sample_selection_step

Active Sample Selection Step Configuration

This module implements the configuration class for the Active Sample Selection 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 ActiveSampleSelectionConfig(*, 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='active_sample_selection.py', processing_script_arguments=None, processing_framework_version='1.2-1', use_case, id_field, label_field, score_field, selection_strategy='confidence_threshold', output_format='csv', confidence_threshold=0.9, k_per_class=100, max_samples=0, uncertainty_mode='margin', batch_size=32, metric='euclidean', random_seed=42, score_field_prefix='prob_class_', job_type='ssl_selection', **extra_data)

Bases: ProcessingStepConfigBase

Configuration for Active Sample Selection step.

Supports both Semi-Supervised Learning (SSL) and Active Learning workflows with Pydantic validation to prevent strategy misuse.

Three-Tier Configuration: - Tier 1: Essential User Inputs (none - all have defaults) - Tier 2: System Fields with Defaults (all selection parameters) - Tier 3: Derived Fields (inherited from ProcessingStepConfigBase)

use_case: Literal['ssl', 'active_learning', 'auto']

id_field: str

label_field: str

score_field: str | None

selection_strategy: Literal['confidence_threshold', 'top_k_per_class', 'uncertainty', 'diversity', 'badge']

output_format: Literal['csv', 'parquet']

confidence_threshold: float

k_per_class: int

max_samples: int

uncertainty_mode: Literal['margin', 'entropy', 'least_confidence']

batch_size: int

metric: Literal['euclidean', 'cosine']

random_seed: int

score_field_prefix: str

processing_entry_point: str

processing_framework_version: str

job_type: str

use_large_processing_instance: 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].

classmethod validate_strategy(v)

Validate selection 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_use_case(v)

Validate use case 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_output_format(v)

Validate output format (case-insensitive).

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

validate_strategy_use_case_compatibility()

⚠️ CRITICAL: Validate strategy is compatible with use case.

This cross-field validation prevents: - Using uncertainty strategies for SSL (creates noisy pseudo-labels) - Using confidence strategies for Active Learning (wastes human effort)

Raises:

ValueError – If strategy is incompatible with use_case

initialize_derived_fields()

Initialize all derived fields once after validation.

get_environment_variables()

Get environment variables for the processing job.

Returns:

Dictionary of environment variables

Return type:

Dict[str, str]

get_public_init_fields()

Override get_public_init_fields to include selection-specific fields. Gets a dictionary of public fields suitable for initializing a child config.

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.

processing_instance_count: int

processing_volume_size: int

processing_instance_type_large: str

processing_instance_type_small: str

skip_volume_kms: bool | None

processing_source_dir: str | None

processing_script_arguments: List[str] | None

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