* merging
* clean commit
* Delete mylearner.py
This file is not needed.
* fix py4j import error
* more tolerant cancelling time
* fix problems following suggestions
* Update flaml/tune/spark/utils.py
Co-authored-by: Li Jiang <bnujli@gmail.com>
* remove redundant model
* Update test/spark/custom_mylearner.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* add docstr
* reverse change in gitignore
* Update test/spark/custom_mylearner.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
---------
Co-authored-by: Li Jiang <bnujli@gmail.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* add cost budget; move loc of make_dir
* support openai completion
* install pytest in workflow
* skip openai test
* test openai
* path for docs rebuild
* install datasets
* signal
* notebook
* notebook in workflow
* optional arguments and special params
* key -> k
* improve readability
* assumption
* optimize for model selection
* larger range of max_tokens
* notebook
* python package workflow
* skip on win
* 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
* 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
* skip in-search-space check for small max iter
* resolve Pickle Transformer #730
* resolve default config unrecognized #784
* Change definition of init_config
* copy points_to_evaluate
* make test pass
* check learner selector
* rm classification head in nlp
* rm classification head in nlp
* rm classification head in nlp
* adding test cases for switch classification head
* adding test cases for switch classification head
* Update test/nlp/test_autohf_classificationhead.py
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* adding test cases for switch classification head
* run each test separately
* skip classification head test on windows
* disabling wandb reporting
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* fix test nlp custom metric
* Update website/docs/Examples/AutoML-NLP.md
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* Update website/docs/Examples/AutoML-NLP.md
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* fix test nlp custom metric
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* add vw version requirement
* vw version
* version range
* add documentation
* vw version range
* skip test on py3.10
* vw version
* rephrase
* don't install vw on py 3.10
* move import location
* remove inherit
* 3.10 in version
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
* categorical choice can be ordered or unordered
* ordered -> order
* move choice into utils
* version comparison
* packaging -> setuptools
* import version
* version_parse
* test order for choice
* Skip transform
* Fix logic and docstring, add test
* Add period ending to skip_transform doc
* Add skip_transform to retrain_from_log method
* Update test/automl/test_classification.py
Co-authored-by: Xueqing Liu <liususan091219@users.noreply.github.com>
Co-authored-by: Xueqing Liu <liususan091219@users.noreply.github.com>
* add pipeline tuner component and dependencies.
* clean code.
* do not need force rerun.
* replace the resources.
* update metrics retrieving.
* Update test/pipeline_tuning_example/requirements.txt
* Update test/pipeline_tuning_example/train/env.yaml
* Update test/pipeline_tuning_example/tuner/env.yaml
* Update test/pipeline_tuning_example/tuner/tuner_func.py
* Update test/pipeline_tuning_example/data_prep/env.yaml
* fix issues found by lint with flake8.
* add documentation
* add data.
* do not need AML resource for local run.
* AML -> AzureML
* clean code.
* Update website/docs/Examples/Tune-AzureML pipeline.md
* rename and add pip install.
* update figure name.
* align docs with code.
* remove extra line.
