* chat completion check

* add test

* doc

* timeout

* bump version to 1.2.4

* Mark experimental classes

* template

* multi model

* test

* multi-config doc

* doc

* doc

* test

---------

Co-authored-by: Li Jiang <bnujli@gmail.com>

* add mlflow and spark integration tests

* remove unused params

* remove mlflow tests

* update max_spark_parallelism to fit in auto-scale spark cluster

* update test

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* coding agent

* tsp

* tsp

* aoai

* logging

* compact

* Handle Import Error

* cost function

* reset counter; doc

* reset_counter

* home page update

* use case

* catboost in linux

* catboost

* catboost

* catboost

* doc

* intro

* catboost

* extract code from text

* solve_problem; request_timeout in config

* improve

* move import statement

* improve code

* generate assertions

* constant

* configs for implement; voting

* doc

* execute code in docker

* success indicator of code executation in docker

* success indicator

* execute code

* strip n

* add cost in generate_code

* add docstr

* filename

* bytes

* check docker version

* print log

* python test

* remove api key address

* rename exit code

* success exit code

* datasets

* exit code

* recover openai tests

* cache and pattern match

* wait

* wait

* cache and test

* timeout test

* python image name and skip macos

* windows image

* docker images

* volume path and yaml

* win path -> posix

* extensions

* path

* path

* path

* path

* path

* path

* path

* path

* path

* path

* path

* skip windows

* path

* timeout in windows

* use_docker

* use_docker

* hot fix from #1000

---------

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

* make context optional

* better error handling and doc

* skip instantiation if no context

* skip test

* news update

* doc update

* avoid KeyError

* bump version to 1.2.1

* handle empty responses

* typo

* eval function

* set black with 120 line length

* apply pre-commit

* apply black

* handling nlp divide by zero

* catching zerodivisionerror

* catching zerodivisionerror

* catching zerodivisionerror

* addressing comments

* addressing comments

* updating test case

* update

* add blank to last line

* update nlp notebook

* rerun

* rerun

* sync with main

* add model selection for nlg

* addressing keyerror

* add raise exception

* update

* fix bug

* revert

* updating automl_nlp

* Update flaml/automl/model.py

Co-authored-by: Zvi Baratz <z.baratz@gmail.com>

* address comments

* address comments

---------

Co-authored-by: Li Jiang <lijiang1@microsoft.com>
Co-authored-by: Zvi Baratz <z.baratz@gmail.com>

* math utils in autogen

* cleanup

* code utils

* remove check function from code response

* comment out test

* GPT-4

* increase request timeout

* name

* logging and error handling

* better doc

* doc

* codegen optimized

* GPT series

* text

* no demo example

* math

* import openai

* import openai

* azure model name

* azure model name

* openai version

* generate assertion if necessary

* condition to generate assertions

* init region key

* rename

* comments about budget

* prompt

---------

Co-authored-by: Susan Xueqing Liu <liususan091219@users.noreply.github.com>

* tentatively implement holt-winters-no covariates

* fix forecast method, clean class

* checking external regressors too

* update test forecast

* remove duplicated test file, re-add sarimax, search space cleanup

* Update flaml/automl/model.py

removed links. Most important one probably was: https://robjhyndman.com/hyndsight/ets-regressors/

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* prevent short series

* add docs

---------

Co-authored-by: Andrea W <a.ruggerini@ammagamma.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* improve max_valid_n and doc

* Update README.md

Co-authored-by: Li Jiang <lijiang1@microsoft.com>

* add support for chatgpt

* notebook

* newline at end of file

* chatgpt notebook

* ChatGPT in Azure

* doc

* math

* warning, timeout, log file name

* handle import error

* doc update; default value

* paper

* doc

* docstr

* eval_func

* add a test func in completion

* update notebook

* update math notebook

* improve notebok

* lint and handle exception

* flake8

* exception in test

* add agg_method

* NameError

* refactor

* Update flaml/integrations/oai/completion.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update flaml/integrations/oai/completion.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* add example

* merge files from oai_eval_test

* Revert "merge files from oai_eval_test"
This reverts commit 1e6a550f91.

* merge

* save results to notebook_output

* update version and cache

* update doc

* save nb cell results to file

* fix typo in model name

* code improvements

* improve docstr

* docstr

* docstr on the Returns of test

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Li Jiang <lijiang1@microsoft.com>
Co-authored-by: Susan Xueqing Liu <liususan091219@users.noreply.github.com>

* more tolerant time limit for test_overtime

* Cancel assertion becasue github VM sometimes is super slow

---------

Co-authored-by: Li Jiang <lijiang1@microsoft.com>

* workflow; model str; timeout

* voting

* notebook

* pull request

* recover workflow

* voted answer

* aoai

* ignore None answer

* default config

* note

* gpt-4

* n=5

* cleanup

* config name

* introduction

* readme

* avoid None

* add output/ to gitignore

* openai version

* invalid var

* comment long running cells

* add basic support to Spark dataframe

add support to SynapseML LightGBM model

update to pyspark>=3.2.0 to leverage pandas_on_Spark API

* clean code, add TODOs

* add sample_train_data for pyspark.pandas dataframe, fix bugs

* improve some functions, fix bugs

* fix dict change size during iteration

* update model predict

* update LightGBM model, update test

* update SynapseML LightGBM params

* update synapseML and tests

* update TODOs

* Added support to roc_auc for spark models

* Added support to score of spark estimator

* Added test for automl score of spark estimator

* Added cv support to pyspark.pandas dataframe

* Update test, fix bugs

* Added tests

* Updated docs, tests, added a notebook

* Fix bugs in non-spark env

* Fix bugs and improve tests

* Fix uninstall pyspark

* Fix tests error

* Fix java.lang.OutOfMemoryError: Java heap space

* Fix test_performance

* Update test_sparkml to test_0sparkml to use the expected spark conf

* Remove unnecessary widgets in notebook

* Fix iloc java.lang.StackOverflowError

* fix pre-commit

* Added params check for spark dataframes

* Refactor code for train_test_split to a function

* Update train_test_split_pyspark

* Refactor if-else, remove unnecessary code

* Remove y from predict, remove mem control from n_iter compute

* Update workflow

* Improve _split_pyspark

* Fix test failure of too short training time

* Fix typos, improve docstrings

* Fix index errors of pandas_on_spark, add spark loss metric

* Fix typo of ndcgAtK

* Update NDCG metrics and tests

* Remove unuseful logger

* Use cache and count to ensure consistent indexes

* refactor for merge maain

* fix errors of refactor

* Updated SparkLightGBMEstimator and cache

* Updated config2params

* Remove unused import

* Fix unknown parameters

* Update default_estimator_list

* Add unit tests for spark metrics

* 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>

* improve max_valid_n and doc

* Update README.md

Co-authored-by: Li Jiang <lijiang1@microsoft.com>

* add support for chatgpt

* notebook

* newline at end of file

* chatgpt notebook

* ChatGPT in Azure

* doc

* math

* warning, timeout, log file name

* handle import error

* doc update; default value

* paper

* doc

* docstr

* eval_func

* prompt and messages

* remove confusing words

* notebook name

---------

Co-authored-by: Li Jiang <lijiang1@microsoft.com>
Co-authored-by: Susan Xueqing Liu <liususan091219@users.noreply.github.com>

* precommit: end-of-file-fixer

* exclude .gitignore

* apply

---------

Co-authored-by: Shaokun <shaokunzhang529@gmail.com>

* 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>

* update config

* apply precommit

* azure oai

* price update in notebook

* text Davinci

* pytorch-lightning version

* trigger action in merge queue

* types

* doc check in mege group

* 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

* add cost budget; move loc of make_dir

* remove None in return

---------

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

* stratified group kfold splitter

* exclude catboost

---------

Co-authored-by: Shaokun <shaokunzhang529@gmail.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

* 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 spark support for parallel training.

* Added tests and fixed a bug

* Added more tests and updated docs

* Updated setup.py and docs

* Added customize_learner and tests

* Update spark tests and setup.py

* Update docs and verbose

* Update logging, fix issue in cloud notebook

* Update github workflow for spark tests

* Update github workflow

* Remove hack of handling _choice_

* Allow for failures

* Fix tests, update docs

* Update setup.py

* Update Dockerfile for Spark

* Update tests, remove some warnings

* Add test for notebooks, update utils

* Add performance test for Spark

* Fix lru_cache maxsize

* Fix test failures on some platforms

* Fix coverage report failure

* resovle PR comments

* resovle PR comments 2nd round

* resovle PR comments 3rd round

* fix lint and rename test class

* resovle PR comments 4th round

* refactor customize_learner to broadcast_code

* fix bug related to _choice_

* remove py 3.6

* sanitize config

* optimize 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>

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

* add test

* fix

* change test name

* 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

Co-authored-by: Chi Wang <wang.chi@microsoft.com>