Explain spectral ML models with zone-level predicates
SMX is a global, model-agnostic explainer for spectral classifiers.
It turns spectra into expert zones, generates logical predicates, and
ranks them with a graph-based centrality score.
```
## What SMX provides
- End-to-end pipeline via the `SMX` class
- Zone extraction and PCA aggregation tailored to spectral inputs
- Quantile predicates with bagging and perturbation-based ranking
- Directed predicate graph with Local Reaching Centrality (LRC)
- Natural-scale threshold reconstruction and Plotly visuals
- Faithfulness evaluation with progressive masking
::::{grid} 3
:::{grid-item-card} Quickstart
:link: quickstart
:link-type: doc
Run SMX end to end on a synthetic dataset in minutes.
:::
:::{grid-item-card} Pipeline
:link: pipeline
:link-type: doc
Understand how zones, predicates, and graphs fit together.
:::
:::{grid-item-card} Plotting Gallery
:link: plotting
:link-type: doc
Explore the full suite of SMX visualization helpers.
:::
:::{grid-item-card} API Reference
:link: api_reference
:link-type: doc
Auto-generated reference for every public module.
:::
:::{grid-item-card} Examples
:link: examples
:link-type: doc
Scripts and notebooks shipped with the repository.
:::
:::{grid-item-card} Repository Map
:link: repository
:link-type: doc
A guided tour of the SMX source tree.
:::
::::
## Contents
```{toctree}
:maxdepth: 2
:caption: User Guide
installation
quickstart
pipeline
zones
predicates
graph
faithfulness
plotting
datasets
examples
repository
```
```{toctree}
:maxdepth: 2
:caption: API Reference
api_reference
```