cursus.steps.configs.config_model_metrics_computation_step

Model Metrics Computation Step Configuration with Self-Contained Derivation Logic

This module implements the configuration class for the model metrics computation 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 ModelMetricsComputationConfig(*, 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='model_metrics_computation.py', processing_script_arguments=None, processing_framework_version='1.2-1', id_name, label_name, score_field='prob_class_1', score_fields=None, task_label_names=None, job_type='calibration', amount_field='order_amount', input_format='auto', compute_dollar_recall=True, compute_count_recall=True, generate_plots=True, dollar_recall_fpr=0.1, count_recall_cutoff=0.1, comparison_mode=False, previous_score_field='', previous_score_fields=None, comparison_metrics='all', statistical_tests=True, comparison_plots=True, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for model metrics computation step with self-contained derivation logic.

This class defines the configuration parameters for the model metrics computation step, which loads prediction data, computes comprehensive performance metrics, generates visualizations, and creates detailed reports. Supports both binary and multiclass classification with domain-specific metrics like dollar and count recall.

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

score_field: str | None

score_fields: List[str] | None

task_label_names: List[str] | None

processing_entry_point: str

job_type: str

amount_field: str | None

input_format: str

compute_dollar_recall: bool

compute_count_recall: bool

generate_plots: bool

dollar_recall_fpr: float

count_recall_cutoff: float

processing_framework_version: str

use_large_processing_instance: bool

comparison_mode: bool

previous_score_field: str

previous_score_fields: List[str] | None

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].

classmethod validate_input_format(v)

Validate input format is supported.

classmethod validate_probability_range(v)

Validate probability values are in valid range.

initialize_derived_fields()

Initialize all derived fields once after validation.

validate_metrics_computation_config()

Additional validation specific to metrics computation configuration

get_environment_variables()

Get environment variables for the model metrics computation 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 metrics computation specific fields. Gets a dictionary of public fields suitable for initializing a child config. Includes both base fields (from parent) and metrics computation 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.

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