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Explain & debug any blackbox machine learning model with a single line of code.

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# explainX: LLM-native Explainable AI
<img src="explainx_logo.png" align="right" width="150"/>

[![Downloads](https://pepy.tech/badge/explainx)](https://pepy.tech/project/explainx)

**explainX 3.0** is a modern, **LLM-native** rewrite of the explainability
engine. Train any machine-learning model, then let a human *or an LLM agent*
inspect it: understand *why* a prediction was made, surface bias, find the
minimal change that flips a decision, and feed those insights back into training.

Where the original explainX rendered a human-only Plotly dashboard on a 2020
dependency stack, this rewrite returns **structured, machine-readable results**
(typed objects that serialize to JSON) plus a natural-language summary — usable
from plain Python *and* over the **Model Context Protocol (MCP)** so agents like
Claude can call it as tools while they build models.

> The goal: bring state-of-the-art explainability research into one place, with
> an interface designed for the era where LLMs train and debug models.

---

## Why

1. **Explain predictions** — global feature importance + per-prediction reasoning.
2. **Debug models** — counterfactuals and partial-dependence curves show what the
   model actually learned.
3. **Detect bias** — group-fairness metrics (disparate impact, demographic parity,
   equal opportunity) answer "is my model rejecting one group regardless of profile?"
4. **Build trust** — a plain-language summary for stakeholders and agents.
5. **Close the loop** — results are structured so an LLM can read them and decide
   how to fix the training data or model.

## What's inside

A unified API over the methods the XAI literature identifies as the most
deployed — SHAP, LIME, surrogate trees and counterfactuals — plus modern
additions (ALE, anchors) and a 2024–2025 research frontier most tools skip:
**quantifying whether an explanation can be trusted**.

| Capability | Method | Notes |
|---|---|---|
| Global importance | **SHAP** (auto) → permutation → intrinsic | SHAP used automatically when installed |
| Local explanation | **SHAP** (auto) → model-agnostic ablation | per-prediction signed contributions |
| Local surrogate | **LIME** (from scratch) | local weighted linear approximation |
| Sufficient rules | **Anchors** | high-precision IF-THEN rule per prediction |
| Counterfactuals | greedy model-agnostic search | smallest change that flips a decision |
| Global surrogate | **decision tree** + fidelity | inspectable glassbox rules + how faithful they are |
| Feature effects | **PDP** and **ALE** | ALE stays unbiased under feature correlation |
| Explanation quality | **faithfulness** + **stability** | does the explanation reflect the model, and is it robust? |
| Counterfactuals & **recourse** | greedy search with immutable / monotonic constraints | actionable "what to change" |
| **Uncertainty** | **conformal prediction** | distribution-free prediction sets / intervals with coverage guarantee |
| Fairness / bias | demographic parity, disparate impact (4/5 rule), equal opportunity | per sensitive attribute |
| Bias **mitigation** | post-processing per-group thresholds | detect → *fix* |
| **Interactions** | Friedman's H-statistic | which features matter *together* |
| Example-based | **prototypes & criticisms** (MMD) | representative vs. atypical cases |
| Metrics | classification + regression | accuracy/precision/recall/f1/auc, r²/mae/rmse |
| Monitoring | **data drift** (PSI + KS) | reference vs. current dataset |
| **LLM narration** | Claude (`claude-opus-4-8`) | plain-language briefings / Q&A grounded in the report |
| Reporting | **HTML export** + **CLI** + **dashboard** | shareable artifact; no-code usage |

### Improve accuracy (data-centric diagnostics)

Beyond explaining a model, explainX helps you make it **more accurate** — the
data-centric-AI playbook, returned as actionable reports:

| Diagnostic | What it finds | Why it improves accuracy |
|---|---|---|
| **Error analysis** | data slices with the highest error (slice discovery) | tells you where to add data / features or split the model |
| **Label issues** | likely-mislabeled rows (confident learning) | cleaning labels is often the highest-ROI accuracy lever |
| **Target leakage** | features that alone predict the target | catches inflated offline accuracy that collapses in production |
| **Calibration** | ECE / Brier + reliability | flags untrustworthy probabilities and recommends a fix |

```python
ex.error_analysis()   # ErrorAnalysis: worst slices + recommendation
ex.label_issues()     # LabelIssues: rows to relabel (cross-validated)
ex.leakage()          # LeakageReport: suspected leaky features
ex.calibration()      # CalibrationReport: ECE, Brier, fix recommendation
```

## Works with any ML framework

explainX speaks the scikit-learn `predict` / `predict_proba` convention, so many
frameworks work **with no wrapping**: scikit-learn, **XGBoost**, **LightGBM**,
**CatBoost** (their sklearn-API estimators). For anything else, `wrap_model()`
adapts it — native XGBoost/LightGBM `Booster`s, **Keras/TensorFlow**, **PyTorch**,
**statsmodels**, or any custom prediction function:

```python
from explainx import explain_model, wrap_model

explain_model(sklearn_or_xgb_or_lgbm_or_catboost_model, X, y)      # direct
explain_model(wrap_model(keras_or_torch_model, task="classification"), X, y)
explain_model(wrap_model(predict_proba_fn=my_api_call, classes=[0, 1]), X, y)
```

Runnable, studyable examples for **every** framework live in
[`examples/`](examples/) (one file per framework).

## Install

> ⚠️ **`pip install explainx` does not give you 3.0 yet.** This LLM-native rewrite
> (v3.0.0) has not been published to PyPI, so `pip install explainx` currently
> still installs the **legacy 2.x** package. Until the 3.0 release is on PyPI,
> use one of the methods below.

**From PyPI** (works once 3.0.0 is published):

```sh
pip install "explainx[all]"   # core + SHAP + MCP + drift + LLM narration + dashboard
# or minimal:
pip install explainx          # core only (extras optional)
```

**From GitHub** (installs the current 3.0 code on `master`):

```sh
pip install "git+https://github.com/explainX/explainx.git"
# with all optional extras:
pip install "explainx[all] @ git+https://github.com/explainX/explainx.git"
```

**From source** (for development / running the examples & tests):

```sh
git clone https://github.com/explainX/explainx.git
cd explainx
pip install -e ".[all]"       # editable install with every extra
pytest                        # run the test suite
```

Extras can be combined or used individually: `shap`, `mcp`, `drift`, `llm`,
`dashboard`, or `all` (e.g. `pip install "explainx[shap,dashboard]"`).

## Python API

```python
from explainx import explain_model

report = explain_model(
    model, X_test, y_test,
    sensitive_features=["gender"],   # run bias analysis on these columns
    n_local=3,                       # explain a few individual predictions
)

print(report.summary)      # natural-language briefing for a human/LLM
report.to_dict()           # full structured result (JSON-ready)
report.to_json()
```

Need finer control? Use the stateful explainer:

```python
from explainx import ModelExplainer

ex = ModelExplainer(model, X_test, y_test)
ex.metrics()                       # ModelMetrics
ex.importance()                    # GlobalImportance (SHAP when available)
ex.explain(index=0, top_k=5)       # LocalExplanation (SHAP/ablation)
ex.lime(index=0)                   # LocalExplanation (LIME)
ex.anchor(index=0)                 # Anchor: high-precision sufficient rule
ex.fairness("gender")              # FairnessReport
ex.counterfactual(index=0)         # Counterfactual: minimal flip
ex.recourse(index=0, immutable_features=["age", "gender"])  # actionable recourse
ex.surrogate()                     # SurrogateExplanation: glassbox tree + fidelity
ex.partial_dependence("income")    # PartialDependence curve
ex.ale("income")                   # ALEResult: correlation-robust effect
ex.explanation_quality(index=0)    # ExplanationQuality: faithfulness + stability
ex.conformal(X_cal, y_cal, X_test) # ConformalResult: guaranteed-coverage sets/intervals
ex.mitigate_bias("gender")         # MitigationResult: per-group thresholds that fix parity
ex.interactions(top_k=5)           # InteractionResult: Friedman H-statistic
ex.prototypes()                    # PrototypesResult: representative + atypical rows
```

### LLM narration (optional)

```python
from explainx.narrate import narrate_report   # needs: pip install "explainx[llm]"

report = explain_model(model, X_test, y_test, sensitive_features=["gender"])
print(narrate_report(report, question="Why was applicant 5 rejected, and what would change it?"))
```

The engine computes the evidence (SHAP, fairness, counterfactuals, conformal sets);
Claude narrates it. Numbers stay in the engine, prose comes from the LLM — so the
explanation is grounded, not hallucinated.

### Monitoring & reporting

```python
from explainx import detect_drift, save_html

detect_drift(reference_df, current_df)   # DriftReport (PSI + KS per feature)
save_html(report, "report.html")         # shareable page; embeds the full JSON
```

### Interactive dashboard

```sh
pip install "explainx[dashboard]"
explainx-dashboard
```

Opens a Streamlit app: upload a fitted model + dataset, then run any module
(importance, local/LIME/anchor, counterfactual & recourse, PDP/ALE, interactions,
fairness, mitigation, conformal, prototypes, quality, drift) or the full report,
see live tables and charts, and download the HTML/JSON.

| Global importance | Local explanation |
|---|---|
| ![Global importance view](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/dashboard_importance.png) | ![Local explanation view](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/dashboard_local.png) |

| Fairness (bias detected) | Full report |
|---|---|
| ![Fairness view](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/dashboard_fairness.png) | ![Full report view](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/dashboard_full_report.png) |

### No-code CLI

```sh
explainx report --model m.joblib --data d.csv --target y --sensitive gender --html out.html
explainx bias   --model m.joblib --data d.csv --target y --sensitive gender
explainx drift  --reference train.csv --current prod.csv
```

### Try the demo

```sh
python -m explainx.examples.demo
```

It trains a deliberately gender-biased loan model and shows the fairness check
firing, plus a counterfactual that flips a rejection to an approval.

## Use it from an LLM agent (MCP)

Start the server (stdio transport):

```sh
explainx-mcp              # installed console script
# or:  python -m explainx.mcp_server
```

```json
{
  "mcpServers": {
    "explainx": { "command": "explainx-mcp" }
  }
}
```

The agent saves a fitted model and dataset to disk, then calls tools by path:

| Tool | Purpose |
|---|---|
| `explain_model` | Full report (metrics, importance, local, fairness, surrogate, quality) |
| `feature_importance` | Global importance ranking |
| `explain_prediction` | Why one row was predicted as it was (SHAP/ablation) |
| `lime_explain_prediction` | Local LIME explanation for one row |
| `anchor_rule` | High-precision sufficient rule for one row |
| `counterfactual` | Minimal change that flips a row's class |
| `surrogate_rules` | Glassbox decision-tree rules + fidelity |
| `check_bias` | Group-fairness analysis on a sensitive feature |
| `model_metrics` | Performance metrics |
| `partial_dependence` | Marginal effect curve for a feature |
| `accumulated_local_effects` | Correlation-robust effect curve (ALE) |
| `explanation_quality` | Faithfulness + stability of an explanation |
| `conformal_prediction` | Guaranteed-coverage prediction sets / intervals |
| `actionable_recourse` | Minimal flip respecting immutable features |
| `mitigate_bias` | Per-group thresholds that equalize selection rate |
| `feature_interactions_tool` | Strongest pairwise interactions (H-statistic) |
| `prototypes_and_criticisms_tool` | Representative + atypical rows |
| `detect_data_drift` | Distribution drift between two datasets |
| `error_analysis` | Worst-performing data slices (slice discovery) |
| `label_issues` | Likely-mislabeled rows (confident learning) |
| `detect_target_leakage` | Features that leak the target |
| `assess_calibration` | Probability calibration (ECE / Brier) |
| `html_report` | Write a shareable HTML report |

Each returns a JSON-ready dict the agent can reason over — e.g. read a
`disparate_impact_ratio` below 0.8, conclude the model is biased, and rebalance
the training data.

```python
# what the agent does first:
import joblib
joblib.dump(model, "model.joblib")
df.to_csv("data.csv", index=False)   # features + target column
# then it calls:  check_bias(model_path="model.joblib", data_path="data.csv",
#                            sensitive_feature="gender", target_column="approved")
```

## Example outputs

All outputs below come from the bundled demo — a deliberately **gender-biased**
loan-approval model. Reproduce them with `python docs/generate_examples.py`.

**Natural-language summary** (`explain_model(...).summary`):

```
Model `RandomForestClassifier` is a classification model evaluated on 800 samples across 4 features.
Performance: accuracy=1.000, precision=1.000, recall=1.000, f1=1.000, roc_auc=1.000.
The most influential features (via shap_mean_abs) are: credit_score (0.257), gender (0.168), debt_ratio (0.128), income (0.062).
A depth-4 decision-tree surrogate reproduces the model with accuracy=0.896 fidelity, giving an inspectable rule set.
Explanation quality (shap): faithfulness=1.00, stability=0.97 (higher is more trustworthy; ~1.0 is excellent).
Fairness on `gender`: BIAS DETECTED. Disparate impact ratio 0.37 is below the 0.8 four-fifths threshold:
group '0' receives the positive outcome (1) at 22.6% vs '1' at 61.3%. Demographic parity gap of 38.7%.
Recommended next steps: rebalance/reweight the training data across the sensitive groups, consider
removing or decorrelating proxy features, or apply a fairness constraint, then re-evaluate.
```

**Global importance** — `ex.importance()` &nbsp;|&nbsp; **Local explanation** — `ex.explain(0)`

| ![global importance](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/importance.png) | ![local explanation](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/local.png) |
|---|---|

**Feature effects** — `ex.partial_dependence(...)` / `ex.ale(...)` &nbsp;|&nbsp; **Fairness** — `ex.fairness("gender")`

| ![pdp and ale](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/effects.png) | ![fairness](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/fairness.png) |
|---|---|

**Interactions** — `ex.interactions()` &nbsp;|&nbsp; **Conformal coverage** — `ex.conformal(...)` &nbsp;|&nbsp; **Drift** — `detect_drift(...)`

| ![interactions](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/interactions.png) | ![conformal](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/conformal.png) | ![drift](https://raw.githubusercontent.com/explainX/explainx/master/docs/images/drift.png) |
|---|---|---|

**Counterfactual / recourse** (`gender` held immutable):

```
credit_score: 530.3 -> 739.3   =>  prediction flips 0 (rejected) -> 1 (approved)
```

**Anchor** (sufficient rule): `IF 410 <= credit_score <= 584 THEN rejected` (precision 0.96, coverage 0.21)

**Glassbox surrogate** (`accuracy=0.859` fidelity to the model):

**Bias mitigation** — `ex.mitigate_bias("gender")`: demographic-parity gap **38.7% → 0.2%** via per-group thresholds.

## Tests

```sh
pytest          # or: python -m pytest explainx/tests
```

## Migrating from legacy explainX

The 2020 Dash dashboard (`explain.py`, `main.py`, `lib/`) and its pinned,
no-longer-installable stack have been removed in favour of this engine. The new
import is `explainx`; explanations are returned as data rather than rendered
as a web app, which is what makes them consumable by both humans and LLMs.

## License

[MIT](https://choosealicense.com/licenses/mit/)

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