ApexLab

ApexLab is a lean standalone Python package for practical machine-learning utilities.

The current public package now spans a lean Leg 1 core plus an expanded Leg 2 analysis/model/reporting lane. ApexLab currently focuses on:

• simplex-constrained regression via ApexRegressor
• statistical comparison helpers (Mann-Whitney U, KS two-sample, Welch t-test, Cohen's d)
• lightweight OLS and binary logistic regression helpers
• deterministic anomaly detection via IsolationForest
• supervised binary classification via DecisionTreeClassifier and RandomForestClassifier
• deterministic dataset splitting with optional stratification
• regression and classification metrics
• threshold selection and anomaly-style score evaluation
• lightweight JSON/Markdown report emission
• lite CLI support for apexlab compare and apexlab report

The design goal is simple: ship a small, coherent toolkit first, then broaden it in later legs without dragging in a kitchen sink of dependencies. At the moment, numpy is the only non-stdlib runtime dependency.

Current shipped-version target: 1.2.0.

What ApexLab is for

ApexLab 1.2.0 is aimed at small, reproducible ML workflows where you want lightweight numerical tooling without depending on a full framework stack. The current release is especially suited to:

• constrained linear modeling experiments
• distribution-comparison and effect-size review
• lightweight explanatory regression analysis
• anomaly-oriented score review with a package-owned isolation forest
• deterministic supervised classification experiments using package-owned tree and forest models
• deterministic train/test split generation
• compact evaluation and reporting flows
• threshold-based score review for anomaly-style or binary decisions

Current status

The repository is past pure scaffolding and now contains working Leg 1 modules, runnable examples, focused tests, and validated source/wheel build artifacts.

Install

For development from projects/apexlab/:

• pip install -e .

For a release artifact install:

• pip install dist/apexlab-1.2.0-py3-none-any.whl

The wheel and source distribution for 1.2.0 should be treated as the active release artifact lane.

Quick start

After installation:

• python examples/simplex_regression_demo.py
• python examples/evaluation_demo.py

Or, after installing the package entry point:

• apexlab compare --sample-a 1,2,3 --sample-b 4,5,6 --out-dir out
• apexlab report --input out/compare_report.json --out-dir out/rerendered

The first demo trains a simplex-constrained regressor and prints learned weights plus a convergence summary. The second demo computes metrics, evaluates a thresholded score surface, and writes paired JSON/Markdown report output.

The evaluation demo now also acts as the canonical field test surface for the current Leg 1 + Leg 2 lite package lane.

Quick examples

From projects/apexlab/:

• python examples/simplex_regression_demo.py
• python examples/evaluation_demo.py

Representative demo output includes:

• simplex demo learning weights close to [0.6, 0.3, 0.1]
• evaluation demo producing regression metrics, classification accuracy, comparison artifacts, and paired JSON/Markdown report files
• successful apexlab compare and apexlab report execution during the field test

Package lanes

• src/apexlab/models/ — constrained model surfaces
• src/apexlab/datasets/ — deterministic data split helpers
• src/apexlab/evaluation/ — metrics, thresholds, and report generation
• src/apexlab/diagnostics/ — training-history summaries
• src/apexlab/utils/ — small reusable helpers
• tests/ — focused behavior tests
• examples/ — runnable demos using current package APIs

Dependency posture

ApexLab currently avoids heavyweight ML frameworks. There is no scikit-learn runtime dependency in 1.2.0.

Release posture

The 1.2.0 lane currently includes:

• passing focused test coverage
• comparison, regression, CLI-lite, and reporting expansion surfaces
• package-owned IsolationForest, DecisionTreeClassifier, and RandomForestClassifier surfaces
• CLI version-flag support via apexlab --version and apexlab -v
• reference validation against external baselines
• confirmed field-test execution via examples/evaluation_demo.py
• a release workflow that can skip already-published files during reruns

More detail

• docs/APEXLAB_TOOLKIT_AUTHORITATIVE_SCHEMATIC.md — authoritative package definitions
• docs/API_OVERVIEW.md — current public Leg 1 API overview
• docs/validation/REFERENCE_VALIDATION_20260324.md — public validation summary for the current Leg 2 analytical lane
• docs/INITIAL_RELEASE_SCOPE.md — release-shape summary
• docs/RELEASE_NOTES_DRAFT.md — current release notes draft for 1.2.0
• docs/PUBLISH_CHECKLIST.md — compact pre-publish checklist