* 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
* WIP
* WIP - Notes below
Got to the point where the methods from AutoML are pulled to
GenericTask. Started removing private markers and removing the passing
of automl to these methods. Done with decide_split_type, started on
prepare_data. Need to do the others after
* Re-add generic_task
* Fix tests: add Task.__str__
* Fix tests: test for ray.ObjectRef
* Hotwire TS_Sklearn wrapper to fix test fail
* Remove unused data size field from Task
* Fix import for CLASSIFICATION in notebook
* Update flaml/automl/data.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Fix review comments
* Fix task -> str in custom learner constructor
* Remove unused CLASSIFICATION imports
* Hotwire TS_Sklearn wrapper to fix test fail by setting
optimizer_for_horizon == False
* Revert changes to the automl_classification and pin FLAML version
* Fix imports in reverted notebook
* Fix FLAML version in automl notebooks
* Fix ml.py line endings
* Fix CLASSIFICATION task import in automl_classification notebook
* Uncomment pip install in notebook and revert import
Not convinced this will work because of installing an older version of
the package into the environment in which we're running the tests, but
let's see.
* Revert c6a5dd1a0
* Revert "Revert c6a5dd1a0"
This reverts commit e55e35adea03993de87b23f092b14c6af623d487.
* Black format model.py
* Bump version to 1.1.2 in automl_xgboost
* Add docstrings to the Task ABC
* Fix import in custom_learner
* fix 'optimize_for_horizon' for ts_sklearn
* remove debugging print statements
* Check for is_forecast() before is_classification() in decide_split_type
* Attempt to fix formatting fail
* Another attempt to fix formatting fail
* And another attempt to fix formatting fail
* Add type annotations for task arg in signatures and docstrings
* Fix formatting
* Fix linting
---------
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
Co-authored-by: EgorKraevTransferwise <egor.kraev@transferwise.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Kevin Chen <chenkevin.8787@gmail.com>
* 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>
* FLAML_sample_size
* clean up
* starting_points as a list
* catch AssertionError
* per estimator sample size
* import
* per estimator min_sample_size
* Update flaml/automl.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update test/automl/test_warmstart.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* add warnings
* adding more tests
* fix a bug in validating starting points
* improve test
* revise test
* revise test
* documentation about custom_hp
* doc and efficiency
* update test
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
if save_best_model_per_estimator is False and retrain_final is True, unfit the model after evaluation in HPO.
retrain if using ray.
update ITER_HP in config after a trial is finished.
change prophet logging level.
example and notebook update.
allow settings to be passed to AutoML constructor. Are you planning to add multi-output-regression capability to FLAML #192 Is multi-tasking allowed? #277 can pass the auotml setting to the constructor instead of requiring a derived class.
remove model_history.
checkpoint bug fix.
* model_history meaning save_best_model_per_estimator
* ITER_HP
* example update
* prophet logging level
* comment update in forecast notebook
* print format improvement
* allow settings to be passed to AutoML constructor
* checkpoint bug fix
* time limit for autohf regression test
* skip slow test on macos
* cleanup before del
