"""
Model Inference Step Configuration with Self-Contained Derivation Logic
This module implements the configuration class for the XGBoost model inference 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)
"""
from pydantic import Field, model_validator, field_validator
from typing import Any, Dict, List, Optional, TYPE_CHECKING
import logging
from .config_processing_step_base import ProcessingStepConfigBase
# Import for type hints only
if TYPE_CHECKING:
pass
logger = logging.getLogger(__name__)
[docs]
class XGBoostModelInferenceConfig(ProcessingStepConfigBase):
"""
Configuration for XGBoost model inference step with self-contained derivation logic.
This class defines the configuration parameters for the XGBoost model inference step,
which generates predictions from trained models without computing evaluation metrics.
This is designed for pure inference workflows where predictions are needed for
downstream processing (e.g., model calibration, batch scoring).
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)
"""
# ===== Essential User Inputs (Tier 1) =====
# These are fields that users must explicitly provide
id_name: str = Field(
...,
description="Name of the ID field in the dataset (required for inference).",
)
label_name: str = Field(
...,
description="Name of the label field in the dataset (required for inference).",
)
# ===== System Inputs with Defaults (Tier 2) =====
# These are fields with reasonable defaults that users can override
processing_entry_point: str = Field(
default="xgboost_model_inference.py",
description="Entry point script for model inference.",
)
job_type: str = Field(
default="calibration",
description="Which split to evaluate on (e.g., 'training', 'calibration', 'validation', 'test').",
)
output_format: str = Field(
default="csv",
description="Output format for inference results (csv, parquet, json).",
)
json_orient: str = Field(
default="records",
description="JSON orientation when output_format is 'json' (records, index, values, split, table).",
)
# XGBoost specific fields
xgboost_framework_version: str = Field(
default="1.7-1", description="XGBoost framework version for processing"
)
# For inference jobs, we typically use smaller instances than evaluation
use_large_processing_instance: bool = Field(
default=False,
description="Whether to use large instance type for processing (inference typically needs less resources)",
)
model_config = ProcessingStepConfigBase.model_config
# ===== Derived Fields (Tier 3) =====
# These are fields calculated from other fields, stored in private attributes
# with public read-only properties for access
# Currently no derived fields specific to model inference
# beyond what's inherited from the ProcessingStepConfigBase class
# Field validators
[docs]
@field_validator("json_orient")
@classmethod
def validate_json_orient(cls, v: str) -> str:
"""Validate JSON orientation is supported."""
valid_orients = {"records", "index", "values", "split", "table"}
if v.lower() not in valid_orients:
raise ValueError(f"json_orient must be one of {valid_orients}, got '{v}'")
return v.lower()
# Initialize derived fields at creation time to avoid potential validation loops
[docs]
@model_validator(mode="after")
def initialize_derived_fields(self) -> "XGBoostModelInferenceConfig":
"""Initialize all derived fields once after validation."""
# Call parent validator first
super().initialize_derived_fields()
# No additional derived fields to initialize for now
return self
[docs]
@model_validator(mode="after")
def validate_inference_config(self) -> "XGBoostModelInferenceConfig":
"""Additional validation specific to inference configuration"""
# Basic validation
if not self.processing_entry_point:
raise ValueError("inference step requires a processing_entry_point")
if self.job_type and (not self.job_type.replace("_", "").isalnum() or self.job_type != self.job_type.lower()):
raise ValueError(
f"job_type must be lowercase alphanumeric (with underscores), got '{self.job_type}'"
)
# Validate required fields from script contract
if not self.id_name:
raise ValueError(
"id_name must be provided (required by model inference contract)"
)
if not self.label_name:
raise ValueError(
"label_name must be provided (required by model inference contract)"
)
logger.debug(
f"ID field '{self.id_name}' and label field '{self.label_name}' will be used for inference"
)
return self
[docs]
def get_environment_variables(self) -> Dict[str, str]:
"""
Get environment variables for the model inference script.
Returns:
Dict[str, str]: Dictionary mapping environment variable names to values
"""
# Get base environment variables from parent class if available
env_vars = (
super().get_environment_variables()
if hasattr(super(), "get_environment_variables")
else {}
)
# Add model inference specific environment variables
env_vars.update(
{
"ID_FIELD": self.id_name,
"LABEL_FIELD": self.label_name,
"OUTPUT_FORMAT": self.output_format,
"JSON_ORIENT": self.json_orient,
}
)
return env_vars
[docs]
def get_public_init_fields(self) -> Dict[str, Any]:
"""
Override get_public_init_fields to include inference-specific fields.
Gets a dictionary of public fields suitable for initializing a child config.
Includes both base fields (from parent) and inference-specific fields.
Returns:
Dict[str, Any]: Dictionary of field names to values for child initialization
"""
# Get fields from parent class (ProcessingStepConfigBase)
base_fields = super().get_public_init_fields()
# Add model inference specific fields
inference_fields = {
# Tier 1 - Essential User Inputs
"id_name": self.id_name,
"label_name": self.label_name,
# Tier 2 - System Inputs with Defaults
"processing_entry_point": self.processing_entry_point,
"job_type": self.job_type,
"output_format": self.output_format,
"json_orient": self.json_orient,
"xgboost_framework_version": self.xgboost_framework_version,
"use_large_processing_instance": self.use_large_processing_instance,
}
# Combine base fields and inference fields (inference fields take precedence if overlap)
init_fields = {**base_fields, **inference_fields}
return init_fields
[docs]
def get_job_arguments(self) -> Optional[List[str]]:
"""CLI args — config is the single source (FZ 31e1d3h)."""
return self._job_type_arg()