Multi-AutoML Interface v5.2.0

Multi-AutoML Interface 5.2.0

Fixed

• Most catalog rows pointed at engines the interpreter did not have. The bundled runtime
  installs requirements.txt, whose only AutoML engine is FLAML (plus LightGBM and XGBoost),
  yet the framework selector offered AutoGluon, PyCaret, Lale, TPOT, H2O and AutoKeras for most
  of the 23 (category, task) rows; the background thread then died on No module named 'autogluon', far from the widget that caused it. Both the training selector and the
  model-source selector now list only engines that can be imported and print the pip install
  line for the rest, and the orchestrator raises the same message before starting a thread.
• FLAML Forecast and Ranking could not train. ts_forecast asserts a forecast period
  before the search starts, so every Forecast run with FLAML failed on the first iteration;
  Forecast now passes the date column as time_col and the horizon as period (Sequential uses
  that native path, Tabular keeps the processor's lag features and trains as regression).
  Ranking handed LightGBM float relevance grades and rows in arbitrary order; it now sorts by a
  new Query / Group Column input and casts integer grades, and Ranking lists only the boosting
  learners because the sklearn forests reject the group argument the ranker forwards. Missing
  inputs raise a readable ValueError instead of failing inside the learner. Both were run end
  to end against the bundled interpreter, and tests/test_flaml_task_paths.py keeps them covered.
• Rows that no engine implemented. Semi-Supervised Classification was a task row while the
  real feature is the Classification checkbox that wraps the model in SelfTrainingClassifier;
  Text/Clustering had no text featurizer; four Sequential rows dispatched exactly like their
  Tabular twins. Hugging Face logged parameters and returned a successful run id without
  training anything, and its "models" could not be loaded back by the prediction service, so
  run_huggingface_experiment is gone - the Hub push/pull service stays.
• Forecast models were restored through the wrong PyCaret module. The catalog calls the task
  Forecast, but prediction_service and the generated code still compared the older
  "Time Series Forecasting", so a time-series artifact was loaded with
  pycaret.classification.load_model.
• The data lake offered Git LFS pointer files as datasets. Several data_lake/raw/*.csv are
  committed through LFS and were never pulled, so pandas read the 130-byte pointer as a
  one-column table and the Training page proposed version https://git-lfs.github.com/spec/v1 as a data column. Loading one now says to run
  git lfs pull.

Changed

• Text tasks train through AutoGluon's multimodal predictor with the columns you mark as text,
  the same path Multimodal already used, instead of a tabular predictor that treated the text as
  one categorical feature.
• Sequential is now one row (Forecast): the category exists to hand the raw time ordering to an
  engine's native time series task, which is also why AutoGluon is not offered there - its
  tabular predictor cannot forecast a future step from same-row features.

Added

• The documented support matrices are checked against the catalog. README.md and
  docs/DOCUMENTATION.md restate TASK_FRAMEWORK_MAP, and had drifted (rows for engines with no
  code path, the Forecast rename). tests/test_doc_matrix_sync.py parses both files and compares
  them pair by pair; it is dependency-free, so it runs in the PR gate.
• The dispatch contract is read from app.py, not transcribed. The engine-kwargs test kept a
  hand-written key list that had already drifted for PyCaret and Lale; the tests now parse the
  dispatch chain with ast.

What is inside

Each installer bundles a standalone CPython 3.12 with everything in
requirements.txt already installed, so no Python setup is needed on
the target machine. Runs, models and the data lake are written to the app's
per-user workspace (the Electron userData directory):

Operating system	Location
Windows	%APPDATA%\multi-automl-desktop\workspace\
macOS	~/Library/Application Support/multi-automl-desktop/workspace/
Linux	~/.config/multi-automl-desktop/workspace/

The heavy AutoML backends (AutoGluon, PyCaret, TPOT, Lale, H2O, AutoKeras)
stay optional and are lazy-imported; the bundled runtime
contains the core stack (Streamlit, MLflow, FLAML, scikit-learn, XGBoost,
LightGBM), so the desktop installers offer FLAML until you install whichever
engines you need into it. H2O additionally requires Java 11+.

Signing

These builds are not code-signed or notarized, so SmartScreen and Gatekeeper
will warn on first launch.