Release: 0.2.2

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

0.2.2

• Added eleven plots from shap to the artist: shap_bar, shap_beeswarm,
  shap_violin, shap_heatmap, shap_decision, shap_embedding,
  shap_waterfall, shap_force, shap_scatter (a dependence plot),
  shap_partial_dependence, and shap_group_difference. They draw the SHAP
  values that shap_importance found (or find them), for the class chosen
  with "category" (by default, the last class). shap draws with
  matplotlib.pyplot, so these plots lend it their figure while it draws,
  without opening a window, and leave pyplot as it was. The random numbers
  that shap uses to jitter dots and to bootstrap are seeded with the
  dataset's seed, so the figures are reproducible.
• Added plots from the other packages:
  • scikit-learn: calibration_curve, det_curve, partial_dependence,
    learning_curve, validation_curve, and tree_plot.
  • seaborn: box_plots, count_plots, pair_plot, label_plot, and
    missing_heatmap, split or colored by the label's classes (or by a
    group, or by "by").
  • statsmodels: qq_plot and influence_plot.
  • InterpretML: shape_functions, the shape functions of an
    explainable_boosting model.
  • Plots of the tables made with other packages: coefficient_plot (of
    the coefficients of ols, glm, fixest, or cox, or of an effect from
    DoubleML), fairness_plot (fairlearn), prediction_intervals (MAPIE),
    and search_plot (of any search, including Optuna's).
• survival_curves can add a table of the number of rows at risk
  (at_risk).
• A plot can have its own usual size (Plot.size), which "width" and
  "height" still change, and a plot whose tool makes its own figure (as
  shap's force plot does) can return that figure from draw.
• Moved the technique catalog out of the advanced user guide into its own
  page of the user guide, "Technique Catalog". Its tables are written from
  the techniques' docstrings by docs/scripts/technique_catalog.py, and a
  test checks that they are up to date.

0.2.1

• Replaced the scorecard technique of the critic stage with Scorecard,
  which compares every branch of an analysis: one row for each combination of
  techniques that the most recent experiment tried, ranked by its criterion,
  with the technique used at each step and every standard metric for the
  task, computed from each branch's own predictions. It still stores the final
  model's scores in the dataset's metrics. Its table now has one row for
  each branch (instead of one row for each metric).
• A scorecard can be saved as a csv file (to_csv), a Markdown table
  (to_markdown), a Word document (to_word), or an image (to_image), or
  in all four formats at once (export). Scorecard.create makes one from a
  dataset or an applied project, and Project.scorecard returns the
  project's scorecard. Project.export saves it in every format.
• An experiment now keeps a Branch (the technique used at each step, the
  score, and the predictions) for every combination in the winning dataset's
  branches.
• Added the word extra, which installs python-docx for Word documents.
• Added the scorecard of the README's breast cancer study to the README.
• Added groups to Dataset (and a "groups" setting in the "general"
  section): columns that identify groups of people or places, which stay in
  the data but are not features. Cleaners and samplers keep them (synthetic
  rows have none), and group_split uses the first group by default.
• Added support for 13 more packages, for 139 techniques in ten genres:
  • fairlearn: six fairness metrics (demographic_parity, equalized_odds,
    and equal_opportunity, with a "_ratio" version of each) in the new
    GroupMetric genre, and the fairness evaluator, which compares the
    model across groups. The scorecard adds demographic_parity and
    equalized_odds for every branch when a dataset has groups and a label
    with two classes.
  • MAPIE: the conformal evaluator, which makes prediction intervals (or
    sets of classes) with a known rate of coverage.
  • eli5: the explain_weights evaluator.
  • InterpretML: the explainable_boosting model, whose term importances
    feature_importance reports.
  • CatBoost: the catboost model, which uses categorical columns directly.
  • TabPFN: the tabpfn model (in its own tabpfn extra, which is not part
    of all because it needs PyTorch).
  • pyfixest: the fixest model, a regression (or logit) with fixed effects
    and clustered standard errors, whose coefficients are stored in a table.
  • lifelines: the cox model, the concordance metric, the kaplan_meier
    describer, and the survival_curves plot.
  • DoubleML: the new Effect genre, with partially_linear and
    interactive_regression, which estimate the causal effect of a
    treatment with double machine learning.
  • skrub: the date_parts, gap, min_hash, and tfidf encoders.
  • Optuna: search = "optuna" for any model.
  • Polars: Dataset.create accepts Polars data frames (lazy or not).
  • great_tables: scorecards as HTML tables (to_html). Scorecards can also
    be saved as LaTeX tables (to_latex), and export saves both.
• Added the causal, fairness, polars, survival, tables, tuning,
  uncertainty, and tabpfn extras, and added packages to the boosting,
  encoders, explain, and statistics extras.
• Added Dataset.dates, the names of the date and time columns.
• Code that calls pyfixest no longer changes the level or handlers of the root
  logger.
• Added recipes for fairness and for fixed effects, and sections of the
  advanced user guide on groups and fairness, uncertainty, causal effects,
  and survival analysis.
• Left private classes, methods, and functions out of the API documentation.

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.