MELITE v0.2.1. Hardening strict tabular workflows for v0.2.1

.github/workflows/ci.yml

@@ -62,30 +62,8 @@ jobs:
       - name: Check distributions
         run: python -m twine check dist/*
 
-      - name: Smoke install built wheel
-        shell: bash
-        run: |
-          set -euo pipefail
-          WHEEL="$(ls dist/*.whl | head -n 1)"
-          python -m venv .smoke_venv
-          .smoke_venv/bin/python -m pip install --upgrade pip
-          .smoke_venv/bin/python -m pip install "$WHEEL"
-          mkdir -p .smoke_outside_checkout
-          cd .smoke_outside_checkout
-          ../.smoke_venv/bin/melite --version
-          ../.smoke_venv/bin/python -c "
-          import melite
-          expected = ['Config', 'load_datasets', 'plot_cv_distributions', 'predict', '__version__']
-          assert melite.__all__ == expected, melite.__all__
-          for name in expected:
-              assert hasattr(melite, name), f'{name} missing'
-          assert 'load_dataset' not in melite.__all__,     'load_dataset must not be top-level public API'
-          assert 'ResultManager' not in melite.__all__,    'ResultManager must not be top-level public API'
-          assert not hasattr(melite, 'Pipeline'),          'Pipeline must not be public'
-          from melite.result_manager import ResultManager
-          assert ResultManager is not None,                'ResultManager internal import missing'
-          print(melite.__version__, 'wheel OK')
-          "
+      - name: Smoke installed wheel toy workflow
+        run: python scripts/smoke_install_wheel.py
 
       - name: Smoke install sdist
         shell: bash

CHANGELOG.md

@@ -7,6 +7,26 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ---
 
+## [v0.2.1] - 2026-05-27
+
+### Changed
+- `[models].active` / `ACTIVE_MODELS` now controls which model families are
+  trained during benchmarking.
+- `melite export` now uses strict dataset loading for registry-based datasets
+  and no longer falls back to `arr.files[0]` when an `.npz` file lacks `X`.
+- Added an installed-wheel smoke test that builds the wheel, installs it
+  outside the repository checkout, runs a toy `[datasets.toy]` smoke benchmark,
+  exports row 0 non-interactively, and verifies generated artifacts.
+
+### Compatibility
+- The top-level public API remains unchanged:
+  `Config`, `load_datasets`, `plot_cv_distributions`, `predict`, and
+  `__version__`.
+- Legacy `reduction_type` + `level` export rows remain supported, but
+  individual legacy `.npz` files must now contain an explicit `X` array.
+
+---
+
 ## [v0.2.0] - 2026-05-26
 
 ### Added

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: "If you use this software, please cite it as below."
 type: software
 title: "MELITE: Multi-model Evaluation and Learning for Inference-ready Tabular Experiments"
-version: "0.2.0"
-date-released: "2026-05-26"
+version: "0.2.1"
+date-released: "2026-05-27"
 authors:
   - family-names: "Contreras-Torres"
     given-names: "Flavio F."

README.md

@@ -2,7 +2,7 @@
 
 [![CI](https://github.com/NanoBiostructuresRG/melite/actions/workflows/ci.yml/badge.svg)](https://github.com/NanoBiostructuresRG/melite/actions/workflows/ci.yml)
 [![License: LGPL v3](https://img.shields.io/badge/License-LGPL_v3-blue.svg)](LICENSE)
-[![Version](https://img.shields.io/badge/version-v0.1.11-blue.svg)]()
+[![Version](https://img.shields.io/badge/version-v0.2.1-blue.svg)]()
 [![Python](https://img.shields.io/badge/python-3.11%20%7C%203.12-blue)]()
 
 **MELITE** is a pre-stable Python toolkit for tabular classification
@@ -21,7 +21,7 @@ Project: MELITE
 PyPI distribution: melite
 Import package: melite
 CLI: melite
-Version: 0.2.0
+Version: 0.2.1
 License: LGPL-3.0-or-later
 Status: alpha / pre-stable
 ```
@@ -133,6 +133,16 @@ Registered datasets are loaded strictly: missing files, missing `X`, non-2D or
 non-numeric `X`, length mismatches, and embedded `y` mismatches fail the run.
 Legacy `[benchmark].reduction_types` and `levels` configs are still accepted
 and are normalized into equivalent dataset entries such as `PCA70` and `UMAP90`.
+
+Model families are controlled by `[models].active`:
+
+```toml
+[models]
+active = ["svc", "rf", "xgb"]
+```
+
+Remove a key to skip that family during training. Valid keys are `svc`, `rf`,
+and `xgb`.
 
 ## CLI
 
@@ -165,7 +175,7 @@ from melite import __version__
 ```
 
 Modules not listed above are importable directly but are not part of the public
-contract and may change before 0.2.0.
+contract and may change before 1.0.
 
 ## Input Format
 
@@ -196,13 +206,14 @@ Local inputs and generated artifacts such as `raw/`, `data/`, `output/`,
 
 ## Validation
 
-The current `dev/v0.2.0` branch targets:
+The current `dev/v0.2.1` branch targets:
 
 ```bash
 python -m pytest tests/ -v --basetemp=.review_pytest_tmp -o cache_dir=.review_pytest_cache
 mkdocs build --strict
-python -m build
+python -m build --no-isolation
 python -m twine check dist/*
+python scripts/smoke_install_wheel.py
 melite --help
 melite run --help
 melite export --help
@@ -216,7 +227,7 @@ If you use MELITE in your research, please cite it using the metadata in
 
 ```text
 Contreras-Torres, F. F., & Murrieta, A. C. (2026). MELITE: Multi-model
-Evaluation and Learning for Inference-ready Tabular Experiments (0.1.11).
+Evaluation and Learning for Inference-ready Tabular Experiments (0.2.1).
 Tecnologico de Monterrey. https://github.com/NanoBiostructuresRG/melite
 ```
 

docs/api.md

@@ -1,7 +1,7 @@
 # API Reference
 
 MELITE exposes an intended public API through five symbols. The project is
-pre-stable, so this API may change before 0.2.0. Internal modules are importable
+pre-stable, so this API may change before 1.0. Internal modules are importable
 directly but are not part of the public contract.
 
 ```python

docs/index.md

@@ -21,17 +21,17 @@
     </div>
     <div class="ms-badges" aria-label="Project badges">
       <img alt="CI" src="https://github.com/NanoBiostructuresRG/melite/actions/workflows/ci.yml/badge.svg">
-      <img alt="Version" src="https://img.shields.io/badge/version-v0.1.11-blue.svg">
+      <img alt="Version" src="https://img.shields.io/badge/version-v0.2.1-blue.svg">
       <img alt="Python versions" src="https://img.shields.io/badge/python-3.11%20%7C%203.12-blue">
       <img alt="License: LGPL v3+" src="https://img.shields.io/badge/License-LGPL_v3%2B-blue.svg">
     </div>
   </div>
 </section>
 
 !!! note "Pre-stable"
-    MELITE is currently in alpha-stage development (`v0.1.x`). Publication on
+    MELITE is currently in alpha-stage development (`v0.2.x`). Publication on
     PyPI is prepared under the package name `melite`. Public APIs may
-    change before 0.2.0.
+    change before 1.0.
 
 ## Workflow
 
@@ -118,32 +118,72 @@ industrial features, or manually selected numeric features.
 MELITE uses a dataset registry under `[datasets.<dataset_id>]`. Each
 `dataset_id` names one concrete numeric `X` matrix candidate.
 
-```toml
-[datasets.morgan_r2_2048]
-path = "data/morgan_r2_2048.npz"
-label_path = "raw/labels.npy"
-family = "fingerprints"
-method = "Morgan"
-
-[datasets.rdkit_descriptors]
-path = "data/rdkit_descriptors.npz"
-label_path = "raw/labels.npy"
-family = "descriptors"
-method = "RDKit"
-
-[datasets.pca85]
-path = "data/PCA85.npz"
-label_path = "raw/labels.npy"
-family = "dimensionality"
-method = "PCA"
-level = 85
-```
+<section class="ms-dataset-panel" aria-label="Dataset registry examples">
+  <div class="ms-dataset-panel__intro">
+    <span class="ms-dataset-panel__kicker">Registry pattern</span>
+    <strong>One dataset id, one numeric matrix.</strong>
+    <p>Use metadata for reporting and traceability; execution follows the
+    registered files, not hardcoded dataset families.</p>
+  </div>
+</section>
+
+=== "Fingerprints"
+
+    ```toml
+    [datasets.morgan_r2_2048]
+    path = "data/morgan_r2_2048.npz"
+    label_path = "raw/labels.npy"
+    family = "fingerprints"
+    method = "Morgan"
+    variant = "radius2_2048"
+    ```
+
+    `morgan_r2_2048` is just a user-defined id. MELITE treats it as a concrete
+    feature matrix candidate and reports the metadata with its results.
+
+=== "Descriptors"
+
+    ```toml
+    [datasets.rdkit_descriptors]
+    path = "data/rdkit_descriptors.npz"
+    label_path = "raw/labels.npy"
+    family = "descriptors"
+    method = "RDKit"
+    description = "Curated numeric descriptor table"
+    ```
+
+    Descriptor tables follow the same strict contract: numeric, two-dimensional
+    `X`, plus a label vector loaded from `label_path`.
+
+=== "Dimensionality"
+
+    ```toml
+    [datasets.pca85]
+    path = "data/PCA85.npz"
+    label_path = "raw/labels.npy"
+    family = "dimensionality"
+    method = "PCA"
+    level = 85
+
+    [datasets.umap90]
+    path = "data/UMAP90.npz"
+    label_path = "raw/labels.npy"
+    family = "dimensionality"
+    method = "UMAP"
+    level = 90
+    ```
+
+    PCA and UMAP are ordinary dataset entries. `method` and `level` preserve
+    legacy reporting context without driving special execution logic.
 
 Required fields are `path` and `label_path`; optional metadata fields are
 `family`, `method`, `variant`, `level`, and `description`. Legacy
 `[benchmark].reduction_types` and `levels` configs are still normalized into
 dataset entries when `[datasets]` is absent.
 
+Each `.npz` dataset must contain an explicit `X` array; missing `X` fails
+strict dataset loading.
+
 ## Quick Example
 
 ```bash

docs/installation.md

@@ -49,5 +49,5 @@ melite --version
 Expected version for this release:
 
 ```text
-MELITE 0.1.11
+MELITE 0.2.1
 ```

docs/release.md

@@ -1,29 +1,19 @@
 # Release Notes
 
-MELITE `0.2.0` introduces the generalized tabular dataset registry and keeps
-legacy PCA/UMAP configuration compatibility.
+MELITE `0.2.1` hardens the generalized tabular dataset workflow while
+preserving the top-level public API.
 
-## 0.2.0 Highlights
+## 0.2.1 Highlights
 
-- Registers concrete tabular matrices under `[datasets.<dataset_id>]`.
-- Requires `path` and `label_path`; preserves optional metadata fields
-  `family`, `method`, `variant`, `level`, and `description`.
-- Runs benchmarks through strict `cfg.DATASETS` loading.
-- Exports dataset-based artifacts such as `Model_SVC_morgan_r2_2048.pkl`.
-- Falls back to legacy `reduction_type` + `level` export rows for older CSVs.
-
-## 0.1.11 Highlights
-
-MELITE `0.1.11` prepared the project documentation and package metadata for
-the first PyPI publication as `melite`.
-
-- Uses final release metadata version `0.1.11`.
-- Clarifies that MELITE is tabular at the modeling level and consumes numeric
-  `X` and `y` arrays.
-- Documented generalized `[datasets.*]` definitions as a future direction at
-  that time.
-- Does not change functional training, selection, export, prediction, or CLI
-  behavior.
+- `[models].active` controls which model families are trained.
+- Export uses strict dataset loading and requires explicit `X` in individual
+  `.npz` files.
+- Installed-wheel smoke validation runs and exports a toy `[datasets.toy]`
+  workflow outside the repository checkout.
+- The public API remains `Config`, `load_datasets`, `plot_cv_distributions`,
+  `predict`, and `__version__`.
+- Legacy `reduction_type` + `level` export rows remain supported, but
+  individual legacy `.npz` files must contain an explicit `X` array.
 
 ## Validation Targets
 
@@ -32,8 +22,9 @@ Before release, validate:
 ```bash
 mkdocs build --strict
 python -m pytest tests/ -v --basetemp=.review_pytest_tmp -o cache_dir=.review_pytest_cache
-python -m build
+python -m build --no-isolation
 python -m twine check dist/*
+python scripts/smoke_install_wheel.py
 melite --help
 melite run --help
 melite export --help
@@ -42,6 +33,6 @@ melite --version
 
 ## Full Changelog
 
-The complete version history is maintained in the repository changelog:
+The complete release history is maintained in the repository changelog:
 
 --8<-- "CHANGELOG.md"