aix360

# AIX360 Use this repository skill for AI Explainability 360 (`aix360`) workflows over tabular, text, image, and time-series data. It covers package version 0.3.0, including modern CPU-usable routes and explicit boundaries around historical TensorFlow/Keras and other optional dependency stacks. AIX360 is a toolkit, not one universal explainer. First identify: 1. whether the artifact to explain is **data**, an already-trained **black-box model**, or a **directly interpretable model** to fit; 2. whether the requested result is **local**, **global**, **counterfactual**, **certified**, or a **quality metric**; 3. the input domain and model-callable output shape; 4. constraints on actions, downloads, hardware, latency, and optional packages. ## Installation and inspection Use an isolated environment because AIX360 extras pin mutually incompatible versions for some algorithm families. ```bash python -m pip install aix360 python -c "import aix360; from importlib.metadata import version; print(version('aix360'))" ``` Install only the extra for the chosen workflow, for example: ```bash python -m pip install 'aix360[lime]' python -m pip install 'aix360[tslime]' python -m pip install 'aix360[rbm]' ``` Do not install every extra into one environment. In particular, CEM/ProfWeight and historical SHAP paths pin TensorFlow 1.14 with Keras 2.3.1, whereas nearest-neighbor contrastive pins TensorFlow 2.9.3; keep incompatible families in separate environments. Read [installation and runtime troubleshooting](references/troubleshooting.md) before changing versions. Run the bundled [environment diagnostic](scripts/check_environment.py) to inspect the base package and selected optional modules without downloading data or models. ## Route by task ### Local black-box attribution and examples Read [local-black-box](sub-skills/local-black-box/SKILL.md) for LIME, SHAP, Grouped Conditional Expectation, nearest-neighbor contrastive examples, faithfulness, monotonicity, prediction-callable contracts, feature names, and local explanation output validation. Typical signals: `explain_instance`, `predict_proba`, local feature weights, SHAP values, tabular/text/image attribution, exemplar/nearest-neighbor explanation, or local metric debugging. ### Counterfactuals, recourse, certification, and matching Read [counterfactual-and-certification](sub-skills/counterfactual-and-certification/SKILL.md) for CEM/CEM-MAF pertinent positives and negatives, Ecertify trust regions, GLANCE recourse/action costs, and order-constrained optimal-transport matching. Typical signals: target class, actionable or immutable features, feature bounds, recourse, robustness certificate, perturbation budget, `OTMatchingExplainer`, or legacy CEM model setup. ### Directly interpretable models, rules, and prototypes Read [interpretable-models](sub-skills/interpretable-models/SKILL.md) for ProtoDash, Boolean/linear rule models, RIPPER/TRXF, interpretable model differencing, teaching explanations, and optional CoFrNet, DIPVAE, and ProfWeight workflows. Typical signals: prototype selection, `FeatureBinarizer`, BRCG/GLRM, rule induction, model comparison, explanation labels, directly interpretable training, solver errors, graph export, or rule serialization. ### Time-series explanations Read [time-series](sub-skills/time-series/SKILL.md) for TSICE, TSLime, and TSSaliency over univariate or multivariate histories, including forecast lookahead, relevant history, exogenous variables, perturbation windows, data shapes, and numeric-versus-plot output. Typical signals: temporal attribution, integrated gradients, local surrogate, forecast window, time axis, feature axis, perturbation count, or exogenous series alignment. ### Datasets, preprocessing, and explanation metrics Read [datasets-and-metrics](sub-skills/datasets-and-metrics/SKILL.md) for AIX360 dataset constructors, local data layout, offline checks, preprocessing, and Faithfulness/Monotonicity metrics. Typical signals: HELOC, COMPAS, CDC, MEPS, Ford, Sunspots, CIFAR, MNIST, CelebA, e-SNLI, missing dataset paths, downloads, coefficient alignment, or explanation-quality evaluation. ## Cross-route decisions - Use dataset and metric guidance as support for any algorithm route, but keep explainer construction with the algorithm-owning sub-skill. - Prefer a callable whose batch input and output shape are explicit; many local explainers fail because a classifier returns labels instead of probabilities or a time-series forecaster drops its batch axis. - Treat notebook-scale image training, remote datasets, pretrained weights, and graph rendering as opt-in operations. The bundled skill defaults to tiny, local, deterministic checks. - Verify whether an explanation is local/global and post-hoc/direct before comparing methods. Their outputs are not interchangeable. - A successful `import aix360` proves only the base package. Import the selected algorithm module and run a tiny fixture before trusting an optional route. Read [API and method overview](references/api-overview.md) when the user names an algorithm but not its route. Read [repository provenance](references/repo-provenance.md) before deciding whether this skill is stale for another checkout.