utilsds-models 0.0.4

Solution for specific models

utilsds-models

A library of classes and functions used in DS Team modeling projects. It extends the utilsds package with components specific to selected models (data processing, NGR metrics, EVIP metrics, and a custom LightGBM objective).

Requires Python >= 3.12.

Installation

uv sync
source .venv/bin/activate

Or from PyPI (after publication):

pip install utilsds-models

Modules

data_processing

Scikit-learn-compatible classes and helper functions for combining test results.

• ColumnCopyImputer: fills missing values by copying from other columns.
• NullImputerWithFlags: imputes nulls with optional flags (_isnull_flag) and strategies mean, median, mode.
• MaxMultiplierImputer: imputes using max * multiplier from training data, with optional flags.
• SportsHybridEncoder: encodes sports as binary features for the top N disciplines plus aggregations for the rest.
• LabelEncoderTransformer: label encoding for categorical columns (e.g. for LightGBM).
• DerivedFeatureCreator: creates derived features (e.g. division by 7 or 30).
• combine_test_data: combines test features, target, predictions, and metadata into a single DataFrame.

custom_metrics

Evaluation metrics with time-based weights (days_since_ftd) and a custom LightGBM objective.

• EvalMetric: cohort-level (aggregated by days_since_ftd) and sample-level metrics with time-decay weighting:
  • cohort_weighted_mae, cohort_weighted_mse, cohort_weighted_mape
  • sample_weighted_mae, sample_weighted_mse, sample_weighted_mape
  • create_lgb_metric: factory for LightGBM metrics with time weights
• DaysWeightedObjective: custom LightGBM objective with time weights (modes: mae, mse, mape).

metrics

• calculate_ngr_metrics: computes NGR error metrics (MAE, MAPE, ME, MPE) in both standard and business-optimal variants with weights based on days_since_ftd. Optionally applies late-stage prediction correction over the customer lifecycle.

visualization

• calculate_ngr_metrics: NGR evaluation function (equivalent to the metrics module).

evip_dynamic

Classification metrics with a false-positive (FP) budget constraint.

• recall_with_fp_cap: recall with a penalty for exceeding the FP budget in binary classification.
• weighted_premium_recall_with_fp_cap: weighted recall for premium classes (1 and 2) with a penalty for exceeding the FPR budget among class 0 samples.

Dependencies

• pandas>=2.2.2
• numpy>=1.26.0
• scikit-learn>=1.5.0
• matplotlib>=3.9.0

Publishing to PyPI

uv pip install build twine
uv run python -m build
twine upload --skip-existing dist/*

Before publishing, bump the version in pyproject.toml (section [project]).