kmds-featurization 0.1.1

KMDS Featurization Service and Orchestration Pipeline

KMDS Featurization

This repository provides a configurable, stage-based featurization engine for KMDS modeling workflows.

The design goal is simple:

• keep stage logic understandable and composable
• keep orchestration/configuration centralized
• keep modeling flow leakage-safe (fit on train only, reuse on val/active)

What This Produces

The pipeline writes two CSV outputs:

• featurized_data.csv: consolidated engineered dataset (modeled + active partitions)
• model_ready_numeric_data.csv: numeric model-ready export from the final stage output

Additional diagnostic artifact:

• feature_selection_knee_curve.png: ranked feature-importance knee plot saved in the featurization output directory

For the current SBA flow, the model-ready dataset is:

• numeric/bool only
• train-fitted feature-selected
• schema-aligned across train/val/active
• persisted with index=False (no index artifact column)

Core Concepts

• Anchor index: record_id
• Stage contract: method(context, stage_cfg) -> DataFrame
• Waterfall behavior: each stage can shrink survivor rows by index
• Horizontal feature assembly: stage outputs are concatenated by index
• Controlled index expansion: only stages marked allow_new_indices may re-introduce rows

Pipeline Layout (Current Hybrid Design)

Front section (feature assembly):

1. record_id_definition
2. borrower_geo_coding
3. prepare_categorical_data
4. prepare_numerical_data
5. merge_categorical_and_numerical
6. merge_with_borrower_geo

Merge stage design:

• package component: src/tabular/merge_ops.py
• user wrappers: featurization_scripts/featurization.py
• merge key: record_id index

Leakage-safe modeling section: 7. low_count_featurization_of_cat_vars 8. hierarchical_low_count_var_encoding 9. loan_status_recoding 10. filter_modeling_universe 11. stratified_train_val_split 12. target_encode_categorical_vars 13. harmonize_and_project_feature_space 14. merge_modeled_and_active_partitions

Current encoding rule:

• if both raw and rarity-corrected categorical variants exist (x and x_rcs), only x_rcs is target-encoded

Tree-Based Feature Selection

Feature selection runs in harmonize_and_project_feature_space using train rows only.

Supported selector modes:

• threshold
• tree_ensemble

Supported tree models:

• gbm
• random_forest
• xgboost (optional dependency)

All selector choices are config-driven via featurizer_config.yaml and surfaced through PathCoordinator (no stage-level hardcoded constants).

Feature-count tuning for kneedle mode:

• FEATURE_SELECTION_TOP_K_MODE: kneedle
• FEATURE_SELECTION_TOP_K_MIN_RATIO: conservative default floor, e.g. 0.5
• FEATURE_SELECTION_MIN_FEATURE_COUNT: hard floor for retained features
• FEATURE_SELECTION_TARGET_FEATURE_COUNT: explicit count override when the curve is too aggressive
• FEATURE_SELECTION_REQUIRE_KNEEDLE: fail loudly if the knee cannot be determined

Repository Organization

• src/featurization/core: orchestration, configuration bootstrap, path resolution
• src/featurization/transforms: reusable transformation primitives
• src/tabular: reusable tabular feature modules (encoding, splitting, feature space)
• src/tabular/merge_ops.py: reusable index-aligned tabular merge helper
• tests: package-level smoke and behavior checks
• documents: architecture and configuration references

Package Component Buckets

The tabular package modules are intentionally split into two modeling buckets:

• Row-selection components:

  • src/tabular/modeling_filter.py
  • src/tabular/train_val_split.py
  • Purpose: decide which records participate in training and how records are partitioned.

• Column-selection components:

  • src/tabular/feature_space.py
  • src/tabular/target_encoding.py
  • src/tabular/low_count_cat_var_encoding.py
  • src/tabular/hierarchical_low_count_var_encoding.py
  • Purpose: decide which feature columns are engineered, selected, encoded, and projected.

• Assembly components:

  • src/tabular/merge_ops.py
  • Purpose: index-aligned horizontal composition of prepared payloads.

CLI

Initialize config:

featurization-cli init \
  --working-dir /path/to/workspace \
  --metadata-file sba_loans_metadata_table.csv \
  --data-file sba_loans_user_cleaned.csv

Run pipeline:

featurization-cli run --working-dir /path/to/workspace

Run smoke test in this repo:

pytest -q tests/test_sba_pipeline.py

How To Extend Safely

1. Add reusable logic in src/tabular first whenever possible.
2. Keep stage wrappers in workspace featurization_scripts/featurization.py thin and explicit.
3. Add new tunables to:
   • featurizer_config.yaml
   • src/featurization/core/path_coordinator.py
   • src/featurization/core/featurization_init.py
4. Preserve leakage rules:
   • fit artifacts on train only
   • transform val/active using train-fitted artifacts
5. Validate with tests after each change.

Recommended Read Order

1. documents/sba_pipeline_featurization.md
2. documents/config_blueprint.md
3. documents/path_coordinator_function.md
4. src/featurization/core/sequential_pipeline_runner.py
5. src/tabular/feature_space.py