hugiml-core 1.1.8

High-performance interpretable rule-based ML — HUG-IML classifier, adaptive binning, EBM-style plots, pattern pruning, and benchmark runner (IEEE Access 2024).

hugiml-core

High-performance interpretable rule-based ML infrastructure built on the HUG-IML algorithm published in IEEE Access (2024).

HUGIML learns human-readable High Utility Gain patterns and uses those patterns as the model representation itself. Instead of explaining a black-box after training, the learned model is already composed of inspectable intervals, categories, supports, utilities, and coefficients.

glucose=[157.1,177.3)                coef= +1.4077   support=0.067
bmi=[31.8,39.1)                      coef= +1.0839   support=0.200
duration=[24,48)                     coef= +0.84     support=0.28
checking_status=no_checking          coef= +1.12     support=0.39

---

Table of Contents

1. What Is HUG-IML?
2. Installation
3. Quick Start
4. Feature Modes
5. Hyperparameter Search
6. Governance Studio Dashboard
7. Augmented Pair Features
8. Adaptive Binning
9. Missing Value Handling
10. Model Explanation and Visualisations
11. Pattern Pruning
12. Interpretability Metrics
13. Multiclass, Imbalanced Data, High-Cardinality
14. Drift Detection & Monitoring
15. Calibration
16. Serialisation
17. Governance & Model Cards
18. Benchmark Suite
19. Validation Highlights
20. Inference Server
21. CI / CD
22. Repository Structure
23. License
24. Citation

---

What Is HUG-IML?

The High Utility Gain Interpretable Machine Learning (HUG-IML) framework extracts High Utility Gain patterns from labelled tabular data, transforms the input into a binary pattern-presence matrix, and fits an interpretable downstream classifier (logistic regression by default) on that matrix.

The resulting patterns are human-readable and serve as the primary source of model explanations, making the system suitable for regulated domains such as credit scoring, healthcare, and risk management.

Key reference:

Krishnamoorthy, S. (2024). Interpretable Classifier Models for Decision Support Using High Utility Gain Patterns. IEEE Access, 12, 126088–126107. DOI: 10.1109/ACCESS.2024.3455563

---

Installation

# Core
pip install hugiml-core

# With profile plots
pip install "hugiml-core[plots]"

# With Governance Studio dashboard
pip install "hugiml-core[dashboard]"

# With benchmark comparison suite
pip install "hugiml-core[benchmarks]"

# With imbalanced-data helpers
pip install "hugiml-core[imbalanced]"

# With SHAP interoperability
pip install "hugiml-core[explainability]"

# With MLflow integration
pip install "hugiml-core[mlflow]"

# Everything
pip install "hugiml-core[all]"

Build from source requires a C++17 compiler and pybind11:

git clone https://github.com/srikumar2050/hugiml-core.git
cd hugiml-core
pip install -e ".[dev]"
python setup.py build_ext --inplace

---

Quick Start

Note on prepareXy: prepareXy performs schema and type preparation only — it detects integer, float, and categorical columns and encodes the target. Discretisation, HUG pattern mining, and downstream classifier fitting occur inside fit() on the training data supplied to that call.

Path A — prepareXy

import pandas as pd
from sklearn.model_selection import train_test_split
from hugiml import HUGIMLClassifierNative

clf = HUGIMLClassifierNative(B=7, L=1, G=5e-3)

X_enc, y_enc = clf.prepareXy(X_df, y)   # schema/type prep — no model fitting

X_tr, X_te, y_tr, y_te = train_test_split(
    X_enc, y_enc, stratify=y_enc, random_state=42
)

clf.fit(X_tr, y_tr)                     # mining + downstream fit on train only
proba = clf.predict_proba(X_te)

print(clf.get_hug_features())
print(clf.feature_importances())
print(clf.model_summary())

Path B — explicit allCols for CV and production pipelines

from hugiml import HUGIMLClassifierNative

clf = HUGIMLClassifierNative(
    allCols=[int_col_names, float_col_names, cat_col_names],
    origColumns=X.columns.tolist(),
    B=15,
    L=1,
    G=1e-5,
    topK=150,
    adaptive_binning=True,
    b_candidates=[2, 3, 5, 7, 10, 15],
)

clf.fit(X_train, y_train)

pred = clf.predict(X_test)
proba = clf.predict_proba(X_test)

---

Feature Modes

HUGIML can use the mined binary pattern matrix in three downstream feature modes. The default remains the original pattern-only behavior, so existing code keeps the same semantics unless feature_mode is set explicitly.

feature_mode	Downstream estimator input	When to use
"patterns_only"	HUGIML binary pattern matrix only	Standard HUGIML; best when the mined pattern space itself captures the decision boundary.
"original_plus_patterns"	Original features plus all mined binary patterns	Useful when the original features contain strong marginal signal and HUGIML patterns add supervised nonlinear refinements.
"original_plus_interactions"	Original features plus only L > 1 mined patterns	Useful when original features should handle marginal effects and HUGIML should contribute interaction/compound-region features only.

from hugiml import HUGIMLClassifierNative

# Backward-compatible default: pattern matrix only
clf = HUGIMLClassifierNative(B=10, L=2, G=1e-2, topK=150,
                              adaptive_binning=True, feature_mode="patterns_only")

# Hybrid: original features + all binary HUGIML patterns
clf_hybrid = HUGIMLClassifierNative(B=10, L=2, G=1e-2, topK=150,
                                    adaptive_binning=True, feature_mode="original_plus_patterns")

# Hybrid: original features + higher-order/interaction patterns only
clf_interactions = HUGIMLClassifierNative(B=10, L=2, G=1e-2, topK=150,
                                          adaptive_binning=True,
                                          feature_mode="original_plus_interactions")

transform(X) always returns the HUGIML binary pattern matrix, regardless of feature_mode. The feature mode only changes the matrix passed to the downstream estimator inside fit(), predict(), predict_proba(), and score().

For hybrid modes, HUGIML standardizes numeric original features internally before concatenating them with the sparse binary pattern matrix and any active augmented-pair columns. feature_importances(), model_summary(), and get_model_composition() report the downstream feature representation, while get_hug_features() and get_pattern_info() remain pattern-only APIs.

---

Hyperparameter Search

HUGIML provides a fast cached tuning path for adaptive-binning grids. When adaptive_binning=True, the full binning and transaction construction phase runs once per unique (G, L, topK) combination; subsequent candidates that vary only feature_mode reuse the cached artefacts and skip re-mining.

tune() — cross-validated search with automatic fast path

result = HUGIMLClassifierNative.tune(
    X, y,
    cv=5,
    shuffle=True,
    random_state=42,
    scoring="roc_auc",
    refit=True,
)

print(result.best_params_)
print(f"CV score: {result.best_score_:.4f}")
print(f"Fast path used: {result.fast_path_used_}")

best_model = result.best_estimator_

A custom grid is supplied via param_grid. Only G, L, topK, and feature_mode may vary; B may appear but is ignored when adaptive_binning=True.

grid = {
    "G":            [1e-3, 1e-2],
    "L":            [1, 2],
    "topK":         [30, 50],
    "feature_mode": ["patterns_only", "original_plus_patterns"],
}

result = HUGIMLClassifierNative.tune(
    X, y,
    param_grid=grid,
    base_params={"adaptive_binning": True},
    cv=3,
    scoring="roc_auc",
    refit=True,
)

fast_grid_tune() — single-split cached path for custom CV loops

tune_result = HUGIMLClassifierNative.fast_grid_tune(
    X_train, y_train,
    X_val,   y_val,
    param_grid=grid,
    base_params={"adaptive_binning": True},
    scoring="roc_auc",
    refit_full=False,
)

print(tune_result["best_params"])
print(f"Validation score: {tune_result['best_score']:.4f}")

---

Governance Studio Dashboard

The HUGIML Governance Studio is a multi-view Streamlit application that provides audit-ready evidence for interpretable model review.

Launch

# Installed console script (recommended)
hugiml-dashboard

# Direct Streamlit invocation
streamlit run src/hugiml/dashboard/app.py

# With custom CV folds and random seed
hugiml-dashboard -- --cv 5 --random-state 42

Evidence views

View	What it shows
Overview	Dataset summary, best CV score, feature mode, top patterns
Validation	Per-fold performance metrics and calibration
Representation Audit	Complexity budget, feature-family provenance, original vs pattern vs augmented
Pattern Inventory	Full pattern table with coefficients, support, utility, and information gain
Case Review	Per-row predictions, probability, and active pattern explanations
Data Quality & Policy	Feature-level missingness rates and sensitive/proxy column review
Configuration Comparison	Side-by-side CV performance across feature_mode variants
Representation Pruning	Remove original features or downstream representation columns and re-evaluate
Monitoring	PSI and KL-divergence drift signals across features

Data sources

• Demo dataset — built-in credit-risk dataset; no upload required.
• Upload — CSV, TSV, Excel (.xlsx/.xls), or Parquet. Define target column, ID column, excluded columns, sensitive columns, and positive label from the sidebar.

Demo preview

• Open the HUGIML Governance Studio Demo

Installation

pip install "hugiml-core[dashboard]"

---

Augmented Pair Features

For interaction-oriented models, HUGIML can add native augmented-pair features to the downstream estimator. These are continuous product or absolute-difference transforms built from informative numeric features, for example:

glucose * bmi
abs(age - duration)

They are active when L > 1, adaptive_binning=True, and augmented_pair_transforms=True (the default). They are appended only to the downstream estimator; the mined HUG pattern matrix and transform(X) remain pattern-space APIs.

clf = HUGIMLClassifierNative(
    B=-1,
    adaptive_binning=True,
    L=2,
    topK=50,
    G=1e-2,
    feature_mode="original_plus_patterns",
    augmented_pair_transforms=True,
    topk_budget_strict=True,
)
clf.fit(X_train, y_train)

print(clf.get_model_composition())
print(clf.explain_augmented_pair_effects())

For selected pair features, HUGIML reports the raw formula, standardized formula, observed-row coverage, missing-pair policy, and raw-scale coefficient interpretation.

---

Adaptive Binning

The global B parameter controls how many quantile bins each numerical feature is discretised into. Adaptive binning selects the optimal bin count per feature via supervised information-gain search and elbow stopping.

from hugiml.adaptive import HUGIMLAdaptive

clf = HUGIMLAdaptive(b_candidates=[3, 5, 7, 10, 15], L=2, G=1e-2)

X_enc, y_enc = clf.prepareXy(X_df, y)
clf.fit(X_tr, y_tr)

print(clf.per_feature_b_)
clf.plot_bin_profiles()
clf.ig_heatmap()

Alternatively, enable adaptive binning directly on HUGIMLClassifierNative:

from hugiml import HUGIMLClassifierNative

clf = HUGIMLClassifierNative(
    adaptive_binning=True,
    b_candidates=[3, 5, 7, 10],
    min_marginal_gain_ratio=0.02,
)

How it works: for each numerical feature, HUGIML evaluates information gain at candidate B values and stops when the marginal gain falls below min_marginal_gain_ratio × current_IG. This prevents blindly selecting the maximum bin count.

---

Missing Value Handling

HUGIML treats NaN and Inf values as not observed — no imputation and no special parameter are required.

How it works: numerical columns are pre-binned at fit time. Non-finite cells become np.nan in the label array, and the C++ transaction builder skips them. The corresponding item is absent from the transaction. Patterns requiring that feature do not fire for that row.

import numpy as np
from hugiml import HUGIMLClassifierNative

X_train.iloc[5, 2] = np.nan

clf = HUGIMLClassifierNative(B=5, L=2, G=1e-4)
clf.fit(X_train, y_train)

X_test.iloc[0, 0] = np.nan
proba = clf.predict_proba(X_test)       # scored using available feature items

Mining Patterns About Missingness

To mine patterns that involve missingness (e.g., Glucose_MISSING=1 AND HeartRate=[110,140]), add binary missingness indicators as preprocessing features:

def add_missingness_indicators(X, threshold=0.05):
    X_aug = X.copy()
    for col in X.columns:
        if X[col].isna().mean() > threshold:
            X_aug[f"{col}__MISSING"] = X[col].isna().astype(int)
    return X_aug

X_with_indicators = add_missingness_indicators(X_raw)
clf = HUGIMLClassifierNative(B=7, L=2, G=1e-4)
clf.fit(X_with_indicators, y)

The Governance Studio Data Quality & Policy view shows feature-level missingness rates alongside sensitive column review.

---

Model Explanation and Visualisations

Interactive Plotly dashboard

from hugiml.plots import HUGPlotter

plotter = HUGPlotter(clf)

plotter.plot_dashboard(
    X_test,
    dataset_name="My Dataset",
    feature_names_for_profile=["age", "income", "glucose"],
    output_path="hugiml_dashboard.html",
)

plotter.plot_marginal_bin_profile("glucose", X=X_test).show()
plotter.plot_top_patterns(top_n=20).show()
plotter.plot_feature_importance(top_n=15).show()
plotter.plot_active_patterns(X_test, sample_idx=0).show()

Each profile panel shows the learned bin/pattern behavior for a feature: utility or coefficient-like contribution per bin, with support overlay where available.

Existing example dashboards:

Public tabular benchmark classification

Credit risk scoring

• Open the HUGIML Benchmark Analysis Dashboard

Profile visualisations

plotter.plot_marginal_bin_profile("age", X=X_test).show()  # EBM-style 1-D shape function
plotter.plot_feature_combinations("age").show()             # Feature-combination view
plotter.plot_top_patterns(top_n=20).show()                  # Top patterns by importance
plotter.plot_active_patterns(X_test, sample_idx=0).show()   # Local explanation for one sample

---

Pattern Pruning

In regulated domains, analysts often need to remove patterns that reference protected attributes, have high PSI, or are operationally invalid. HUGIML provides a controlled editing workflow with a JSON audit trail.

from hugiml.pruning import PatternEditor

editor = PatternEditor(clf, operator_name="risk-team")

print(editor.list_patterns().head(10))

editor.remove([3, 7], reason="references protected attribute 'gender'")
editor.remove_by_keyword("postcode", reason="high PSI — unstable feature")
editor.remove_low_support(min_support=0.01, reason="noise patterns")

editor.refit(X_tr, y_tr)
editor.calibrate(X_cal, y_cal, method="isotonic")

new_clf = editor.finalize()
print(editor.audit_report())

The Representation Pruning view in the Governance Studio provides an interactive version of this workflow without writing code.

---

Interpretability Metrics

from hugiml.metrics import compute_all_metrics

m = compute_all_metrics(clf, X_test)
print(m)

Example output:

InterpretabilityMetrics
==========================================
n_patterns              : 87
avg_pattern_length       : 1.34
coverage                 : 0.9812
mean_active_patterns     : 6.21
overlap_rate             : 0.0714
explanation_sparsity     : 0.0230

top-k cumulative |coef|:
top- 1 : 8.4%
top- 5 : 31.2%
top-10 : 54.7%

---

Multiclass, Imbalanced Data, High-Cardinality

Multiclass Classification

from hugiml.multiclass import MulticlassHUGReport

report = MulticlassHUGReport(clf)
print(report.importances_for_class(class_label=2, top_n=10))
print(report.summary())

Imbalanced Data Handling

from hugiml.multiclass import make_imbalanced_pipeline

clf_bal = make_imbalanced_pipeline(clf_proto, strategy="smote")
clf_bal.fit(X_tr, y_tr)

High-Cardinality Categorical Reduction

When categorical features have hundreds or thousands of unique values (ZIP codes, ICD-10 diagnoses, merchant IDs), grouping rare categories prevents combinatorial explosion in pattern mining:

def reduce_high_cardinality(X, y, threshold=50, min_frequency=0.01):
    """Group rare categories (<min_frequency) as '__OTHER__' for high-cardinality columns."""
    X_reduced = X.copy()
    for col in X.select_dtypes(include=["object", "category"]).columns:
        if X[col].nunique() <= threshold:
            continue
        value_counts = X[col].value_counts()
        min_count = len(X) * min_frequency
        rare_categories = value_counts[value_counts < min_count].index
        X_reduced[col] = X[col].apply(
            lambda x: "__OTHER__" if x in rare_categories else x
        )
    return X_reduced

X_reduced = reduce_high_cardinality(X_raw, y, threshold=50, min_frequency=0.01)
clf = HUGIMLClassifierNative(B=7, L=2, G=1e-4)
clf.fit(X_reduced, y)

# Or use built-in target encoding:
from hugiml.multiclass import encode_high_cardinality, apply_encoding
X_enc, enc_map = encode_high_cardinality(X_tr, y_tr, threshold=20, method="target_mean")
X_te_enc = apply_encoding(X_te, enc_map)

Note: Learn category groupings on training data only, then apply the same mapping to test/production data.

---

Drift Detection & Monitoring

clf.enable_monitoring(window_size=1000)

clf.predict_proba(X_new)

print(clf.monitor.report())

report = clf.detect_drift(X_new, current_labels=y_new)
print(report)

The Monitoring view in the Governance Studio shows PSI and KL-divergence drift signals per feature from the fitted model's training baseline.

---

Calibration

from hugiml.calibration import evaluate_calibration

result = evaluate_calibration(y_te.values, proba[:, 1])

print(f"ECE: {result.ece:.4f}")
print(f"Brier: {result.brier_score:.4f}")

---

Serialisation

from hugiml.serialization import save_model, load_model, generate_sbom

save_model(clf, "model.hugiml")
clf2 = load_model("model.hugiml")

sbom = generate_sbom(clf)

---

Governance & Model Cards

from hugiml.governance import generate_model_card

card = generate_model_card(
    clf,
    model_id="credit-scorer-v1.0.0",
    intended_use="Credit risk assessment for SME lending.",
    training_data_description="German Credit dataset, 1000 samples",
)

print(card.to_markdown())
card.save("model_card.json")

Model cards should include top positive/negative patterns, missing-value behavior, calibration metrics, drift-monitoring plan, and any pattern-pruning audit trail.

The Governance Studio dashboard provides interactive governance evidence views that complement programmatic model cards with visual audit artifacts.

---

Benchmark Suite

Reproduce paper claims or benchmark on your own datasets:

# Run full CV comparison
python -m hugiml.benchmarks.runner

# Specific datasets
python -m hugiml.benchmarks.runner --datasets german_credit pima adult

# Save results
python -m hugiml.benchmarks.runner --output benchmarks/results/

Or use the installed console script:

hugiml-bench --datasets german_credit --output results/

Scalability dashboard

For runtime and memory scaling evidence, see the static scalability dashboard:

• Open the HUGIML Scalability Dashboard

The dashboard summarizes measured fit time, prediction latency, memory delta, pattern counts, and test AUC against XGBoost and LightGBM. It covers sample-size scaling, feature-count scaling, and parameter sweeps over B, G, topK, L, and adaptive binning. HUGIML retains many training and test artifacts to support governance and audit requirements.

Worked notebooks in notebooks/ are organized as 12 self-contained folders:

Folder	Notebook	Brief description
00_quickstart	nb00_pattern_explanation_walkthrough.ipynb	Quick end-to-end walkthrough of fitting HUGIML, extracting patterns, and reading pattern-level explanations.
01_benchmark_baselines	nb01_benchmark_baselines.ipynb	Benchmark comparison across HUGIML and common tabular baselines such as XGBoost, LightGBM, Random Forest, and logistic regression.
02_hug_vs_ebm	nb02_hug_vs_ebm.ipynb	Side-by-side comparison of HUGIML pattern profiles and EBM-style additive shape functions.
03_modeling_special_cases	nb03_modeling_special_cases.ipynb	Practical modeling cases including multiclass targets, imbalance, high-cardinality categoricals, adaptive binning, and pruning workflows.
04_credit_risk	nb04_credit_risk.ipynb	Credit-risk governance example using German Credit-style data, scorecard-style features, and auditable risk patterns.
05_aml	nb05_aml.ipynb	Anti-money-laundering example focused on suspicious transaction pattern discovery and model review artifacts.
06_mobile_money	nb06_mobile_money_fraud.ipynb	Mobile-money fraud example showing compact transaction-risk patterns and operational fraud-review signals.
07_basel_ca	nb07_basel_ca.ipynb	Basel capital-adequacy oriented example for regulated risk analytics and explainable model validation.
08_clinical	nb08_healthcare_breast_cancer.ipynb	Clinical classification example using breast-cancer features to demonstrate interpretable healthcare pattern explanations.
09_insurance	nb09_insurance_underwriting.ipynb	Insurance underwriting example with risk-selection patterns and model-card-friendly feature narratives.
10_medicare	nb10_medicare_program_integrity.ipynb	Medicare program-integrity example for suspicious provider/claim behavior and audit-ready pattern summaries.
11_workforce_analytics	nb11_workforce_attrition.ipynb	Workforce attrition analytics example showing HR risk patterns, explanation tables, and governance-oriented summaries.

---

Validation Highlights

The finance panels use German Credit / HELOC-style risk features such as loan duration, credit amount, checking status, and repayment-risk signals. The healthcare panels use Pima diabetes-style features such as glucose, BMI, pregnancies, pedigree, and age.

HUGIML vs EBM shape profiles

EBM is excellent for smooth effect inspection; HUGIML is strong when the explanation needs to be reviewed as a set of readable thresholds and pattern contributions.

Real-world and synthetic benchmarks

Native missing-value handling

Model	Native missing-value behavior	What to monitor
HUGIML	Missing numerical values are absent from the transaction. Patterns requiring that feature item do not fire.	Missingness rate and activation frequency of top patterns.
XGBoost	Each split learns a default route for missing values.	Whether default-route behavior changes under deployment shift.
LightGBM	Histogram splits learn how missing values are routed.	Missing-value routing and feature missingness drift.
EBM	Missing values can be modeled as a separate bin/effect.	Size and sign of each missing-bin effect.

Adaptive binning

Adaptive binning is a safe default when you do not want to tune B; fixed B=5 is a useful fast baseline.

Pattern explanations

Model-card-ready artifacts

Observed benchmark results

Model	AUC (mean±std)	Fit time/fold	Complexity budget	Remarks
HUG B=3	0.9907 ± 0.0031	0.32 s	topK patterns	topK is an explicit cap; actual mined patterns can be lower.
HUG B=5	0.9909 ± 0.0028	0.34 s	topK patterns	More bins per feature.
HUG adaptive	0.9954 ± 0.0022	1.20 s	topK patterns	Per-feature B increases fit time.
EBM	0.9940 ± 0.0025	11.0 s	Additive terms + interactions	Reference interpretable baseline.
XGBoost	0.9882 ± 0.0040	0.12 s	Trees × leaves	High-performing ensemble; not directly pattern-interpretable.
LightGBM	0.9921 ± 0.0028	0.07 s	Leaves × trees	Fast histogram boosting.

Complexity budget

The number of downstream features passed to the logistic regression (D) depends on L, feature_mode, and augmented_pair_transforms. K = topK, k = patterns actually mined (≤ K), k_aug = augmented pair features (≤ K), p = number of original input features.

L	feature_mode	augmented_pair_transforms	D
L = 1	patterns_only	— (no effect at L=1)	k
L = 1	original_plus_patterns	— (no effect at L=1)	k + p
L > 1	patterns_only	False	k
L > 1	patterns_only	True	k + k_aug ≈ 2k
L > 1	original_plus_patterns	False	k + p
L > 1	original_plus_patterns	True	k + k_aug + p ≈ 2k + p

topk_budget_strict = True assembles all feature families first, then applies a single global information-gain filter retaining the best K features. D is hard-capped at K.

Missing value robustness

---

Capabilities Summary

Capability	Details
HUG pattern mining	C++ accelerated via pybind11; optional OpenMP parallelism
scikit-learn API	Full BaseEstimator / ClassifierMixin compliance
Mixed feature types	Integer, float, categorical — auto-detected or explicitly supplied
Feature modes	Pattern-only, original-plus-patterns, original-plus-interactions, augmented-pair downstream features
Fast hyperparameter search	Cached adaptive-binning grid; mining runs once per unique (G, L, topK) group
Governance Studio	Multi-view Streamlit dashboard with audit evidence views and upload support
Profile visualisations	EBM-style 1-D/2-D HUG profiles, active-pattern explanations, coefficient-support views (Plotly)
Interpretability metrics	Pattern count, coverage, overlap, sparsity, top-k cumulative contribution
Adaptive binning	Per-feature supervised B selection — addresses the B-sensitivity trap
Pattern pruning	Regulated remove/refit/calibrate workflow with full JSON audit trail
Multiclass & imbalance	Multiclass report, SMOTE/class-weight pipeline, high-cardinality encoding
Benchmark suite	Reproducible CV comparison vs EBM, XGBoost, RF, LR, RuleFit, GAM
Scalability dashboard	Static runtime, latency, memory, n-scaling, p-scaling, and parameter-sweep evidence vs XGBoost and LightGBM
Calibration	ECE, MCE, Brier score, reliability diagram data
Drift detection	PSI + symmetric KL divergence + label drift
Monitoring	Thread-safe PredictionMonitor, latency tracking
Governance	Model cards (JSON + Markdown), audit artifacts, SBOM
Observability	OpenTelemetry tracing, Prometheus metrics (both optional)
Secure serialisation	Allowlist-based _RestrictedUnpickler, versioned schema
Deployment	FastAPI inference server, Docker image, Kubernetes manifests
CI/CD	GitHub Actions: lint → coverage → native tests → wheels → PyPI

---

Inference Server

A FastAPI-based inference server is included for containerised deployments.

docker build -t hugiml-core:latest -f docker/Dockerfile .

docker run -p 8080:8080 -v /path/to/models:/models hugiml-core:latest

curl -s -X POST http://localhost:8080/predict \
  -H "Content-Type: application/json" \
  -d '{"instances": [{"age": 35, "savings": "moderate"}]}'

Kubernetes manifests are in kubernetes/deployment.yaml.

---

CI / CD

Workflow	Trigger	What it does
ci.yml	Every push / PR	Lint, type-check, coverage gate, native tests, sanitizer build, benchmark regression, wheel build
release.yml	Git tag v*.*.*	Build platform wheels, generate SBOM, publish to PyPI, create GitHub release

---

Repository Structure

hugiml-core/
├── src/
│   ├── _native/                 C++ extension sources
│   └── hugiml/
│       ├── classifier.py        HUGIMLClassifierNative
│       ├── calibration.py       ECE, Brier, reliability diagrams
│       ├── explainability.py    SHAP bridge, feature lineage, stability
│       ├── governance.py        Model cards, audit artifacts
│       ├── monitoring.py        PredictionMonitor, DriftDetector
│       ├── serialization.py     save/load, SBOM, restricted unpickler
│       ├── telemetry.py         OpenTelemetry, Prometheus
│       ├── exceptions.py        Exception hierarchy
│       ├── metrics.py           Interpretability-complexity metrics
│       ├── plots.py             EBM-style profile visualisations
│       ├── pruning.py           Pattern editor + audit trail
│       ├── adaptive.py          Per-feature adaptive binning
│       ├── multiclass.py        Multiclass / imbalanced / encoding
│       ├── dashboard/           Governance Studio Streamlit application
│       │   ├── app.py           Entry point (hugiml-dashboard console script)
│       │   ├── runner.py        Model training and scoring helpers
│       │   ├── components/      Individual evidence-view renderers
│       │   └── ...
│       └── benchmarks/          CV comparison suite
├── notebooks/                   Worked examples (12 domain folders)
├── tests/                       Pytest suite
├── benchmarks/                  Micro-benchmarks and regression gate
├── docker/                      Dockerfile + FastAPI inference server
├── kubernetes/                  Deployment manifests
├── scripts/                     Build and utility scripts
├── docs/                        Sphinx documentation and model-card templates
├── .github/workflows/           CI/CD pipelines
├── pyproject.toml
└── setup.py

---

License

Apache License 2.0 — see LICENSE.

---

Citation

If you use hugiml-core in research or commercial work, please cite:

@article{krishnamoorthy2026interpretability,
  title        = {Interpretability Myopia: Governance Fitness in Financial Risk Models},
  author       = {Krishnamoorthy, Srikumar},
  journal      = {SSRN Electronic Journal},
  year         = {2026},
  doi          = {10.2139/ssrn.6821418},
  url          = {https://dx.doi.org/10.2139/ssrn.6821418},
  keywords     = {Interpretable machine learning, analytics, financial risk governance, deployment evaluation, regulatory compliance, model risk management}
}

@article{krishnamoorthy2024hugIML,
  author  = {Krishnamoorthy, Srikumar},
  title   = {Interpretable Classifier Models for Decision Support Using High Utility Gain Patterns},
  journal = {IEEE Access},
  volume  = {12},
  pages   = {126088--126107},
  year    = {2024},
  doi     = {10.1109/ACCESS.2024.3455563}
}