agi-app-sklearn-pipeline

PyPI version Python versions License: BSD 3-Clause

agi-app-sklearn-pipeline publishes the sklearn_pipeline_project AGILAB app as a self-contained PyPI payload. It keeps a normal scikit-learn pipeline workflow intact while making the executable app boundary, arguments, and evidence artifacts explicit.

Purpose

Use this package to run a compact classic ML proof: generate a deterministic classification dataset, train a StandardScaler + LogisticRegression pipeline, and persist the model, metrics, predictions, report, and run manifest together.

Installed Project

The distribution name is agi-app-sklearn-pipeline; the AGILAB project name is sklearn_pipeline_project. The package exposes both sklearn_pipeline and sklearn_pipeline_project through the agilab.apps entry point group, so AgiEnv(app="sklearn_pipeline_project") resolves the project without a monorepo checkout.

Install

pip install agi-app-sklearn-pipeline

The app project installs scikit-learn when AGILAB prepares its project environment. The payload package stays lightweight and only exposes the project root.

Run In AGILAB

Select sklearn_pipeline_project, then open ORCHESTRATE. Keep the defaults, run Deploy scheduler & workers, then run RUN. The worker exports model quality evidence and a manifest under sklearn_pipeline/evidence.

Expected Inputs

The default run generates a synthetic dataset with sklearn.datasets.make_classification. No external dataset, API key, notebook, cloud service, or private model is required.

Expected Outputs

The worker writes metrics.json, predictions.csv, model.joblib, sklearn_report.md, run_manifest.json, and sklearn_pipeline_summary.json.

Change One Thing

Change regularization_c from 1.0 to 0.5, then rerun the app. The manifest and metrics artifacts should make the changed behavior auditable.

Scope

This is a reproducible scikit-learn app example. It is not a generic apps-page, production model registry, or serving stack. Sklearn-specific logic stays inside the app project; shared pages should only consume app-agnostic artifact contracts such as metrics, predictions, and manifests.

Project links

Data verified by PyPI on Jul 31, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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1 maintainer

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Credits

Author/Maintainer: Jean-Pierre Morard

GitHub Statistics

Data verified by PyPI on Jul 31, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.12

Tags

agilab apps scikit-learn sklearn reproducibility workflow-orchestration

Classifiers

Report project as malware

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