* notebook test
* add ipykernel, remove except
* only create dir if not empty
* Stop sequential tuning when result is None
* fix reproducibility of global search
* save gs seed
* use get to avoid KeyError
* test
* Do not persist entire AutoMLState in Searcher
Signed-off-by: Antoni Baum <antoni.baum@protonmail.com>
* Fix tests
Signed-off-by: Antoni Baum <antoni.baum@protonmail.com>
Signed-off-by: Antoni Baum <antoni.baum@protonmail.com>
* Added an info reminding user that if no time_budget and no max_iter is specified, then effectively zero-shot AutoML is used
* moved message to line 2818
* Update flaml/automl/automl.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update flaml/automl/automl.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
* Refactor into automl subpackage
Moved some of the packages into an automl subpackage to tidy before the
task-based refactor. This is in response to discussions with the group
and a comment on the first task-based PR.
Only changes here are moving subpackages and modules into the new
automl, fixing imports to work with this structure and fixing some
dependencies in setup.py.
* Fix doc building post automl subpackage refactor
* Fix broken links in website post automl subpackage refactor
* Fix broken links in website post automl subpackage refactor
* Remove vw from test deps as this is breaking the build
* Move default back to the top-level
I'd moved this to automl as that's where it's used internally, but had
missed that this is actually part of the public interface so makes sense
to live where it was.
* Re-add top level modules with deprecation warnings
flaml.data, flaml.ml and flaml.model are re-added to the top level,
being re-exported from flaml.automl for backwards compatability. Adding
a deprecation warning so that we can have a planned removal later.
* Fix model.py line-endings
* Pin pytorch-lightning to less than 1.8.0
We're seeing strange lightning related bugs from pytorch-forecasting
since the release of lightning 1.8.0. Going to try constraining this to
see if we have a fix.
* Fix the lightning version pin
Was optimistic with setting it in the 1.7.x range, but that isn't
compatible with python 3.6
* Remove lightning version pin
* Revert dependency version changes
* Minor change to retrigger the build
* Fix line endings in ml.py and model.py
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
Co-authored-by: EgorKraevTransferwise <egor.kraev@transferwise.com>
* make performance test reproducible
* fix test error
* Doc update and disable logging
* document random_state and version
* remove hardcoded budget
* fix test error and dependency; close#777
* iloc
* Pending changes exported from your codespace
* Update flaml/automl.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update flaml/automl.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update flaml/ml.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update flaml/ml.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update website/docs/Examples/Integrate - Scikit-learn Pipeline.md
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* added documentation for new metric
* Update flaml/ml.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* minor notebook changes
* Update Integrate - Scikit-learn Pipeline.md
* Update notebook/automl_classification.ipynb
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update integrate_azureml.ipynb
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* install editable package in codespace
* fix test error in test_forecast
* fix test error in test_space
* openml version
* break tests; pre-commit
* skip on py10+win32
* install mlflow in test
* install mlflow in [test]
* skip test in windows
* import
* handle PermissionError
* skip test in windows
* skip test in windows
* skip test in windows
* skip test in windows
* remove ts_forecast_panel from doc
