Release: 0.2.0

All notable changes to this project will be documented in this file.

0.2.0

• Rebuilt amos as Automated Modeling for Open Scholarship: a
  package for reproducible data science in academic research, built on
  chrisjen 0.2.0. A study is described in a settings file (or a dict),
  which chrisjen turns into a workflow of workers, steps, and techniques.
• Added Dataset, the item that flows through an amos workflow. It keeps
  the data in one pandas.DataFrame with the label, the training and test
  rows, the fitted model, its predictions and probabilities, metrics, tables,
  figures, fitted tools, and a history of every technique applied.
• Added Operation, the base class of every amos technique, and 116
  techniques in nine genres, each stored in the chrisjen library under its
  snake case name: cleaners (10), describers (6), splitters (4), transformers
  (imputers, scalers, encoders, mixers, and reducers: 37), samplers (9),
  models (19), metrics (17), evaluators (6), and plots (8). They wrap
  scikit-learn, category_encoders, imbalanced-learn, xgboost, lightgbm,
  statsmodels, shap, matplotlib, and seaborn. Optional packages are imported
  only when they are used, and are installed with extras (such as
  amos[all]).
• Transformers and samplers learn from the training rows only, keep named
  columns, can be limited to some columns, and are given the label when
  they are fitted. Models do both tasks where they can, search for the best
  hyperparameters when asked, and (with ols and glm) report coefficients
  with standard errors, p-values, and confidence intervals. Every metric is
  also a chrisjen.Criteria, so it can be an experiment's criterion.
• Added the Experiment design, which tries every combination of
  techniques, keeps the best, and adds a table comparing every combination.
• Added amos.Project, which makes data into a Dataset, reads "label",
  "task", and "seed" from the "general" section of the settings, applies the
  workflow to a copy of the data, passes the seed to every tool that takes a
  random_state, and exports the report, settings, environment, history,
  predictions, tables, and figures.
• Added the Findings report.
• Added an ROC curve and an importance plot from the README's breast cancer
  study to the README, with docs/scripts/readme_figures.py to draw them
  again.
• Added tests that compare amos with each package that it wraps, using the
  same data, parameters, and seed.
• Removed the general-purpose containers, converters, factories, and
  registries of 0.1.x. They now live in bunches, camina, holden, and
  wonka.
• Moved to the snickerdoodle template, based on uv and hatchling, with
  ruff, mypy, pre-commit, mkdocs, and GitHub Actions. The README and
  documentation examples are run by the unit tests.

0.1.10

• The last release of the earlier, general-purpose amos, a toolkit of
  containers and converters for Python projects.