mlxplain-xai 0.1.2

Translate ML model predictions into human-readable business explanations with visualizations.

mlxplain

Turn any ML model into a compliance-ready credit memo — in 4 lines of code.

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🔥 What You Get

One function call. Multi-pillar professional outputs. No complex configuration needed.

💻 Standing Out: Premium HTML Risk Dossier

In addition to standard text memos and charts, mlxplain generates a fully standalone, premium glassmorphic HTML Credit Risk Dossier with inlined high-resolution vector SVGs. This provides a portable, compliance-ready interactive risk dashboard out-of-the-box!

📊 Auto-Generated Diagnostic Charts

Every call to explain_risk() produces three publication-ready matplotlib figures:

Probability Gauge See the decision at a glance	Feature Drivers (Waterfall) What pushed the prediction?	Counterfactual Bars What needs to change?

📝 Auto-Generated Credit Underwriting Memo

For declined applicants, mlxplain generates a compliance-ready text summary with the decision, probability, ranked risk drivers, and actionable cure paths:

============================================================
CREDIT UNDERWRITING MEMO (XGBoost SHAP / RandomForest XAI)
============================================================
CREDIT DECISION: Declined
Default Probability: 65.7% (threshold: 45.0%)

RISK FACTORS:
  - Derogatory Public Records: 2 (impact: 0.1789)
  - Credit Score (FICO): 612.7 (impact: 0.1597)
  - Debt-to-Income (%): 42.54 (impact: 0.1022)
  - Loan-to-Value Ratio (%): 92.13 (impact: 0.08839)
  - Annual Income ($k): 68.41 (impact: 0.05231)
  - Credit Card Utilization (%): 25.97 (impact: 0.03185)
  - Employment Duration (yrs): 5.625 (impact: 0.005195)
  - Open Credit Lines: 6 (impact: 0.001935)
  - Requested Loan Amount ($k): 31.06 (impact: 0.0003766)

MITIGATING FACTORS:
  + Savings Balance ($k): 27.25 (impact: 0.06112)
  + On-Time Payment (%): 99.89 (impact: 0.01372)
  + Years of Credit History: 10.78 (impact: 8.391e-05)

CURE PATHS (changes needed for approval):
  → Derogatory Public Records: decrease from 2 to 0.92
  → Savings Balance ($k): increase from 27.25 to 30.52
  → Credit Card Utilization (%): decrease from 25.97 to 16.62
  → Debt-to-Income (%): decrease from 42.54 to 29.78
  → Loan-to-Value Ratio (%): decrease from 92.13 to 75.54
  → Credit Score (FICO): increase from 612.7 to 686.2
============================================================

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⚡ Quick Start (4 Lines)

from sklearn.ensemble import RandomForestClassifier
from mlxplain import explain_risk

# 1. Fit your standard ML model (e.g. 12-feature credit scoring model)
model = RandomForestClassifier().fit(X_train, y_train)

# 2. Define the 12 comprehensive attributes
feature_names = [
    "Annual Income ($k)", "Debt-to-Income (%)", "Credit Score (FICO)",
    "Employment Duration (yrs)", "Savings Balance ($k)", "Requested Loan Amount ($k)",
    "Loan-to-Value Ratio (%)", "Open Credit Lines", "On-Time Payment (%)",
    "Derogatory Public Records", "Credit Card Utilization (%)", "Years of Credit History"
]

# 3. Generate visual & compliance explanation report in 1 call!
report = explain_risk(model, X_train, idx=10, feature_names=feature_names, threshold=0.45)

# 4. Print the professional credit underwriting memo
print(report.summary)

# 5. Save vector SVG diagnostic charts
report.figures["gauge"].savefig("gauge.svg")
report.figures["drivers"].savefig("drivers.svg")
report.figures["counterfactuals"].savefig("counterfactuals.svg")

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💻 Supported Models & XAI Translators

mlxplain works across the most popular model families with zero configuration changes:

Model Family	XAI Extraction Method	Counterfactual Strategy
Logistic Regression	Coefficient weights × feature values	Analytical: Mathematical exact-solution inversion
Decision Trees & Random Forests	Split-level class probability differences along decision paths	Perturbation: Iterative split-boundary space search
Ensemble Boosting (XGBoost & LightGBM)	SHAP (Shapley Additive exPlanations) values	Perturbation: Sample-bounded boundary search
Anomaly Detection (Isolation Forest)	SHAP TreeExplainer on isolation trees	Perturbation: Iterative score boundary search
Clustering (K-Means)	Spatial distance differences vs target centroid	Analytical: Exact L2 half-space mathematical projection
Deep Learning (Neural Nets)	LIME / Integrated Gradients	(Planned / Deferred)

Charts Across All Model Types

	Logistic Regression	Decision Tree	XGBoost (SHAP)
Feature Drivers

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🎯 Core Capabilities

For any supported binary classification model, mlxplain delivers three pillars of explanation:

• The Decision: Classifies the prediction into a clear label (e.g., Approved/Declined) based on a configurable threshold.
• The Drivers: Identifies and ranks which features pushed the prediction in each direction, sorted independently by mathematical impact.
• The Counterfactuals: For unfavorable outcomes, calculates the exact minimum feature adjustments required to cross the threshold (e.g., "Reduce loan amount by $5,000").
• Smart Performance: Counterfactuals are only computed for unfavorable predictions (probability ≥ threshold) to save CPU cycles on favorable decisions.

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🧩 Pluggable Domain Architecture

The core of mlxplain is model-aware but completely business-agnostic. It separates what the model saw from what it means in business terms:

mlxplain/
├── core/              # Data structures, threshold logic, counterfactuals
├── translators/       # Model-specific feature extraction (domain-agnostic)
├── visualizations/    # Standard matplotlib figures
├── domains/           # Pluggable business interpreters
│   └── credit_risk/   # Credit-specific: Approved/Declined, Risk Factors, Cure Paths
└── engine.py          # Unified API entrypoint

To support a new domain (e.g., healthcare or fraud_detection), you simply create a new domain interpreter subclassing BaseDomain. The translators, math engines, and charting code remain 100% untouched.

🏦 Credit Risk Domain Mapping

The credit risk domain interpreter maps mathematical abstractions into commercial banking terminology:

Mathematical Concept	Credit Risk Business Term
Positive Prediction	Declined (High default probability)
Negative Prediction	Approved (Low default probability)
Positive Drivers	Risk Factors (Model weaknesses)
Negative Drivers	Mitigating Factors (Model strengths)
Counterfactuals	Cure Paths (Required adjustments for approval)

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🚀 Runnable Examples

The examples/ directory contains complete, runnable scripts showing mlxplain end-to-end. They generate synthetic credit data, train models, produce reports, and save diagnostic plots.

Run them directly using uv:

# 1. Run the Logistic Regression Credit Risk example
uv run python examples/01_logistic_credit_risk.py

# 2. Run the Decision Tree Credit Risk example
uv run python examples/02_decision_tree_credit_risk.py

# 3. Run the XGBoost SHAP-based Credit Risk example
uv run python examples/03_ensemble_credit_risk.py

# 4. Run the Advanced 12-Feature HTML Dossier Credit Risk example
uv run python examples/04_advanced_credit_risk.py

# 5. Run the Unsupervised Anomaly Detection example
uv run python examples/05_anomaly_detection.py

# 6. Run the Unsupervised KMeans Customer Segmentation example
uv run python examples/06_kmeans_clustering.py

All examples save their generated plots to examples/output/. The advanced example also creates dossier.html in that directory — open it in your browser to view the interactive glassmorphic dashboard!

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🌀 Unsupervised Model Explainability

mlxplain is the first general-purpose explainability engine to unify supervised classification with unsupervised learning XAI (Anomaly Detection & Clustering) under the exact same visual and structured output reporting standards!

1. Anomaly Detection (via IsolationForest)

• Standardized Anomaly Scoring: We normalize scikit-learn's Isolation Forest anomaly scores into $[0, 1]$, treating it exactly like a probability with a standard 0.5 decision boundary. Accessible via the unified explain() API.
• SHAP Drivers: Uses SHAP's TreeExplainer on isolation trees to extract exact features pushing the instance into anomalous vs normal states.
• Cure Counterfactuals: Employs sample-bounded perturbation search to identify the exact feature changes needed to restore the anomalous system to normal behavior.

2. Clustering (via KMeans)

• Dedicated Endpoint: Introduces explain_cluster() to explain K-Means cluster assignments vs runner-up or target clusters.
• Distance Drivers: Measures how much each feature contributes to keeping the instance closer to the assigned centroid $c$ rather than the competitor centroid $t$: $$\text{impact}_i = (x_i - t_i)^2 - (x_i - c_i)^2$$
• Closed-Form Upgrade Roadmap: Utilizes a highly robust, exact L2 mathematical projection to calculate the absolute minimum single/multi-feature changes needed to transition the instance to a target cluster instantly with zero loops!
• Cluster Gauge Score: Normalizes Euclidean distance vectors into a $[0, 1]$ proximity score ($p = d_t^2 / (d_c^2 + d_t^2)$) to reuse the standard gauge visualization.

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📦 Installation

This project is fully compatible with uv for lightning-fast package management.

For Users

Install mlxplain directly into your virtual environment:

uv pip install mlxplain
# or using standard pip
pip install mlxplain

Or install the locked dependencies using the provided requirements.txt:

pip install -r requirements.txt
pip install -e .

For Developers

Set up the project locally for development:

1. Clone the repository:

   git clone https://github.com/nguyen-thinh15/mlxplain.git
   cd mlxplain
   

2. Create and synchronize the environment:

   uv venv --python 3.10
   source .venv/bin/activate
   uv sync --all-extras
   

3. Run the test suite:

   uv run pytest tests/ -v
   

Core Dependencies: numpy, scikit-learn, shap, matplotlib Optional Dependencies (for ensembles): xgboost, lightgbm