* Initial plan
* Fix training/test set overlap in holdout classification by only adding missing labels when needed
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Address code review feedback: add bounds checking and fix edge cases
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Fix bounds checking: use correct comparison operator for array indexing
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Fix potential ValueError with max() on empty lists and simplify test assertions
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Add extra bounds checking for label_matches indices
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Fix pandas_on_spark compatibility by using iloc_pandas_on_spark util method
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Run pre-commit to fix formatting issues
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Improve missing label handling to avoid overlap when possible
For classes with multiple instances that end up in one set, properly
re-split them between train and val instead of duplicating. Only add
to both sets when the class has exactly 1 instance (unavoidable overlap).
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Fix: ensure at least 1 instance remains in original set when re-splitting
Addresses comments on lines 580 and 724 - prevents moving ALL instances
from validation to training or vice versa by using min(num_instances - 1, ...).
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Refactor: Extract missing label handling into two methods (fast/slow)
- Created _handle_missing_labels_fast(): Fast version that adds first instance to both sets (may overlap)
- Created _handle_missing_labels_no_overlap(): Precise version that avoids overlap when possible
- Added allow_label_overlap parameter to AutoML.fit() (default=True for fast version)
- Updated documentation with new parameter
- Both versions maintain label completeness while offering speed/precision tradeoff
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Add usage example for allow_label_overlap to Best-Practices.md
- Added comprehensive documentation in Classification section
- Included code examples showing both fast and precise versions
- Explained tradeoffs between speed and precision
- Noted that parameter only affects holdout evaluation
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Address code review feedback: update documentation and tests
- Updated docstrings to clarify fast version only adds instances to missing sets
- Fixed documentation to reflect actual behavior (not "both sets" but "set with missing label")
- Completely rewrote test_no_overlap.py to test both allow_label_overlap modes
- Added tests with sample_weights for better code coverage
- Added test for single-instance class handling
- All 5 tests passing
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Fix AttributeError: initialize _allow_label_overlap in settings and retrain_from_log
- Added allow_label_overlap to settings initialization with default=True
- Added parameter defaulting in fit() method to use settings value if not provided
- Added _allow_label_overlap initialization in retrain_from_log method
- Fixes test failures in test_multiclass, test_regression, and spark tests
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Add docstring to fit()
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
Co-authored-by: Li Jiang <bnujli@gmail.com>
Co-authored-by: Li Jiang <lijiang1@microsoft.com>
* Initial plan
* Fix: Preserve FLAML_sample_size in best_config_per_estimator
Modified best_config_per_estimator property to keep FLAML_sample_size when returning best configurations. Previously, AutoMLState.sanitize() was removing this key, which caused the sample size information to be lost when using starting_points from a previous run.
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Add a test to verify the improvement of starting_points
* Update documentation to reflect FLAML_sample_size preservation
Updated Task-Oriented-AutoML.md to document that best_config_per_estimator now preserves FLAML_sample_size:
- Added note in "Warm start" section explaining that FLAML_sample_size is preserved for effective warm-starting
- Added note in "Get best configuration" section with example showing FLAML_sample_size in output
- Explains importance of sample size preservation for continuing optimization with correct sample sizes
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
* Fix unintended code change
* Improve docstrings and docs
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>
Co-authored-by: Li Jiang <bnujli@gmail.com>
Co-authored-by: Li Jiang <lijiang1@microsoft.com>
* Merged PR 1686010: Bump version to 2.3.5.post2, Distribute source and wheel, Fix license-file, Only log better models
- Fix license-file
- Bump version to 2.3.5.post2
- Distribute source and wheel
- Log better models only
- Add artifact_path to register_automl_pipeline
- Improve logging of _automl_user_configurations
----
This pull request fixes the project’s configuration by updating the license metadata for compliance with FLAML OSS 2.3.5.
The changes in `/pyproject.toml` update the project’s license and readme metadata by replacing deprecated keys with the new structured fields.
- `/pyproject.toml`: Replaced `license_file` with `license = { text = "MIT" }`.
- `/pyproject.toml`: Replaced `description-file` with `readme = "README.md"`.
<!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot -->
Related work items: #4252053
* Merged PR 1688479: Handle feature_importances_ is None, Catch RuntimeError and wait for spark cluster to recover
- Add warning message when feature_importances_ is None (#3982120)
- Catch RuntimeError and wait for spark cluster to recover (#3982133)
----
Bug fix.
This pull request prevents an AttributeError in the feature importance plotting function by adding a check for a `None` value with an informative warning message.
- `flaml/fabric/visualization.py`: Checks if `result.feature_importances_` is `None`, logs a warning with possible reasons, and returns early.
- `flaml/fabric/visualization.py`: Imports `logger` from `flaml.automl.logger` to support the warning message.
<!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot -->
Related work items: #3982120, #3982133
* Removed deprecated metadata section
* Fix log_params, log_artifact doesn't support run_id in mlflow 2.6.0
* Remove autogen
* Remove autogen
* Remove autogen
* Merged PR 1776547: Fix flaky test test_automl
Don't throw error when time budget is not enough
----
#### AI description (iteration 1)
#### PR Classification
Bug fix addressing a failing test in the AutoML notebook example.
#### PR Summary
This PR fixes a flaky test by adding a conditional check in the AutoML test that prints a message and exits early if no best estimator is set, thereby preventing unpredictable test failures.
- `test/automl/test_notebook_example.py`: Introduced a check to print "Training budget is not sufficient" and return if `automl.best_estimator` is not found.
<!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot -->
Related work items: #4573514
* Merged PR 1777952: Fix unrecognized or malformed field 'license-file' when uploading wheel to feed
Try to fix InvalidDistribution: Invalid distribution metadata: unrecognized or malformed field 'license-file'
----
Bug fix addressing package metadata configuration.
This pull request fixes the error with unrecognized or malformed license file fields during wheel uploads by updating the setup configuration.
- In `setup.py`, added `license="MIT"` and `license_files=["LICENSE"]` to provide proper license metadata.
<!-- GitOpsUserAgent=GitOps.Apps.Server.pullrequestcopilot -->
Related work items: #4560034
* Cherry-pick Merged PR 1879296: Add support to python 3.12 and spark 4.0
* Cherry-pick Merged PR 1890869: Improve time_budget estimation for mlflow logging
* Cherry-pick Merged PR 1879296: Add support to python 3.12 and spark 4.0
* Disable openai workflow
* Add python 3.12 to test envs
* Manually trigger openai
* Support markdown files with underscore-prefixed file names
* Improve save dependencies
* SynapseML is not installed
* Fix syntax error:Module !flaml/autogen was never imported
* macos 3.12 also hangs
* fix syntax error
* Update python version in actions
* Install setuptools for using pkg_resources
* Fix test_automl_performance in Github actions
* Fix test_nested_run
* Update gitignore
* Bump version to 2.4.0
* Update readme
* Pre-download california housing data
* Use pre-downloaded california housing data
* Pin lightning<=2.5.6
* Fix typo in find and replace
* Fix estimators has no attribute __sklearn_tags__
* Pin torch to 2.2.2 in tests
* Fix conflict
* Update pytorch-forecasting
* Update pytorch-forecasting
* Update pytorch-forecasting
* Use numpy<2 for testing
* Update scikit-learn
* Run Build and UT every other day
* Pin pip<24.1
* Pin pip<24.1 in pipeline
* Loosen pip, install pytorch_forecasting only in py311
* Add support to new versions of nlp dependecies
* Fix formats
* Remove redefinition
* Update mlflow versions
* Fix mlflow version syntax
* Update gitignore
* Clean up cache to free space
* Remove clean up action cache
* Fix blendsearch
* Update test workflow
* Update setup.py
* Fix catboost version
* Update workflow
* Prepare for python 3.14
* Support no catboost
* Fix tests
* Fix python_requires
* Update test workflow
* Fix vw tests
* Remove python 3.9
* Fix nlp tests
* Fix prophet
* Print pip freeze for better debugging
* Fix Optuna search does not support parameters of type Float with samplers of type Quantized
* Save dependencies for later inspection
* Fix coverage.xml not exists
* Fix github action permission
* Handle python 3.13
* Address openml is not installed
* Check dependencies before run tests
* Update dependencies
* Fix syntax error
* Use bash
* Update dependencies
* Fix git error
* Loose mlflow constraints
* Add rerun, use mlflow-skinny
* Fix git error
* Remove ray tests
* Update xgboost versions
* Fix automl pickle error
* Don't test python 3.10 on macos as it's stuck
* Rebase before push
* Reduce number of branches
* Sync Fabric till 2cd1c3da
* Remove synapseml from tag names
* Fix 'NoneType' object has no attribute 'DataFrame'
* Deprecated 3.8 support
* Fix 'NoneType' object has no attribute 'DataFrame'
* Still use python 3.8 for pydoc
* Don't run tests in parallel
* Remove autofe and lowcode
* Add try except to resource.setrlimit
* Set time limit only in main thread
* Check only test model
* Pytest debug
* Test separately
* Move test_model.py to automl folder
* fix: Now resetting indexes for regression datasets when using group folds
* refactor: Simplified if statement to include all fold types
* docs: Updated docs to make it clear that group folds can be used for regression tasks
---------
Co-authored-by: Daniel Grindrod <daniel.grindrod@evotec.com>
Co-authored-by: Li Jiang <bnujli@gmail.com>
* fix: Fixed bug where every underlying LGBMRegressor or LGBMClassifier had n_estimators = 1
* test: Added test showing case where FLAMLised CatBoostModel result isn't reproducible
* fix: Fixing issue where callbacks cause LGBM results to not be reproducible
* Update test/automl/test_regression.py
Co-authored-by: Li Jiang <bnujli@gmail.com>
* fix: Adding back the LGBM EarlyStopping
* refactor: Fix tweaked to ensure other models aren't likely to be affected
* test: Fixed test to allow reproduced results to be better than the FLAML results, when LGBM earlystopping is involved
---------
Co-authored-by: Daniel Grindrod <Daniel.Grindrod@evotec.com>
Co-authored-by: Li Jiang <bnujli@gmail.com>
* Merged PR 1444697: Fix json dumps TypeError
Fix json dumps TypeError
----
Bug fix to address a `TypeError` in `json.dumps`.
This pull request fixes a `TypeError` encountered when using `json.dumps` on `automl._automl_user_configurations` by introducing a safe JSON serialization function.
- Added `safe_json_dumps` function in `flaml/fabric/mlflow.py` to handle non-serializable objects.
- Updated `MLflowIntegration` class in `flaml/fabric/mlflow.py` to use `safe_json_dumps` for JSON serialization.
- Modified `test/automl/test_multiclass.py` to test the new `safe_json_dumps` function.
Related work items: #3439408
* Fix data transform issue and spark log_loss metric compute error
* fix: CatBoostRegressors metrics are now reproducible
* test: Made tests live, which ensure the reproducibility of catboost models
* fix: Added defunct line of code as a comment
* fix: Re-adding removed if statement, and test to show one issue that if statement can cause
* fix: Stopped ending CatBoost training early when time budget is running out
---------
Co-authored-by: Daniel Grindrod <Daniel.Grindrod@evotec.com>
* Remove temporary pickle files
* Update version to 2.3.1
* Use TemporaryDirectory for pickle and log_artifact
* Fix 'CatBoostClassifier' object has no attribute '_get_param_names'
* Add more spark models and improved mlflow integration
* Update test_extra_models, setup and gitignore
* Remove autofe
* Remove autofe
* Remove autofe
* Sync changes in internal
* Fix test for env without pyspark
* Fix import errors
* Fix tests
* Fix typos
* Fix pytorch-forecasting version
* Remove internal funcs, rename _mlflow.py
* Fix import error
* Fix dependency
* Fix experiment name setting
* Fix dependency
* Update pandas version
* Update pytorch-forecasting version
* Add warning message for not has_automl
* Fix test errors with nltk 3.8.2
* Don't enable mlflow logging w/o an active run
* Fix pytorch-forecasting can't be pickled issue
* Update pyspark tests condition
* Update synapseml
* Update synapseml
* No parent run, no logging for OSS
* Log when autolog is enabled
* upgrade code
* Enable autolog for tune
* Increase time budget for test
* End run before start a new run
* Update parent run
* Fix import error
* clean up
* skip macos and win
* Update notes
* Update default value of model_history
* Fix typos, upgrade yarn packages, add some improvements
* Fix joblib 1.4.0 breaks joblib-spark
* Fix xgboost test error
* Pin xgboost<2.0.0
* Try update prophet to 1.5.1
* Update github workflow
* Revert prophet version
* Update github workflow
* Update install libomp
* Fix test errors
* Fix test errors
* Add retry to test and coverage
* Revert "Add retry to test and coverage"
This reverts commit ce13097cd5.
* Increase test budget
* Add more data to test_models, try fixing ValueError: Found array with 0 sample(s) (shape=(0, 252)) while a minimum of 1 is required.
* support xgboost 2.0
* try classes_
* test version
* quote
* use_label_encoder
* Fix xgboost test error
* remove deprecated files
* remove deprecated files
* remove deprecated import
* replace deprecated import in integrate_spark.ipynb
* replace deprecated import in automl_lightgbm.ipynb
* formatted integrate_spark.ipynb
* replace deprecated import
* try fix driver python path
* Update python-package.yml
* replace deprecated reference
* move spark python env var to other section
* Update setup.py, install xgb<2 for MacOS
* Fix typo
* assert
* Try assert xgboost version
* Fail fast
* Keep all test/spark to try fail fast
* No need to skip spark test in Mac or Win
* Remove assert xgb version
* Remove fail fast
* Found root cause, fix test_sparse_matrix_xgboost
* Revert "No need to skip spark test in Mac or Win"
This reverts commit a09034817f.
* remove assertion
---------
Co-authored-by: Li Jiang <bnujli@gmail.com>
Co-authored-by: levscaut <57213911+levscaut@users.noreply.github.com>
Co-authored-by: levscaut <lwd2010530@qq.com>
Co-authored-by: Li Jiang <lijiang1@microsoft.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
* 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
* Most of the merge done, test_forecast_automl fit succeeds, fails at predict()
* Remaining fixes - test_forecast.py passes
* Comment out holidays-related code as it's not currently used
* Further holidays cleanup
* Fix imports in a test
* tidy up validate_data in time series task
* Test fixes
* Fix tests: add Task.__str__
* Fix tests: test for ray.ObjectRef
* Hotwire TS_Sklearn wrapper to fix test fail
* Attempt at test fix
* Fix test where val_pred_y is a list
* Attempt to fix remaining tests
* Push to retrigger tests
* Push to retrigger tests
* Push to retrigger tests
* Push to retrigger tests
* Remove plots from automl/test_forecast
* Remove unused data size field from Task
* Fix import for CLASSIFICATION in notebook
* Monkey patch TFT to avoid plotting, to fix tests on MacOS
* Monkey patch TFT to avoid plotting v2, to fix tests on MacOS
* Monkey patch TFT to avoid plotting v2, to fix tests on MacOS
* Fix circular import
* remove redundant code in task.py post-merge
* Fix test: set svd_solver="full" in PCA
* 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
* Fix get_classification_objective import in suggest.py
* Remove hcrystallball docs reference in TS_Sklearn
* Merge markharley:extract-task-class-from-automl into this
* Fix import, remove smooth.py
* Fix dependencies to fix TFT fail on Windows Python 3.8 and 3.9
* Add tensorboardX dependency to fix TFT fail on Windows Python 3.8 and 3.9
* Set pytorch-lightning==1.9.0 to fix TFT fail on Windows Python 3.8 and 3.9
* Set pytorch-lightning==1.9.0 to fix TFT fail on Windows Python 3.8 and 3.9
* Disable PCA reduction of lagged features for now, to fix svd convervence fail
* Merge flaml/main into time_series_task
* Attempt to fix formatting
* Attempt to fix formatting
* 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
* First attempt at merging Holt-Winters
* Linter fix
* Add holt-winters to TimeSeriesTask.estimators
* Fix spark test fail
* Attempt to fix another spark test fail
* Attempt to fix another spark test fail
* Change Black max line length to 127
* Change Black max line length to 120
* Add logging for ARIMA params, clean up time series models inheritance
* Add more logging for missing ARIMA params
* Remove a meaningless test causing a fail, add stricter check on ARIMA params
* Fix a bug in HoltWinters
* A pointless change to hopefully trigger the on and off KeyError in ARIMA.fit()
* Fix formatting
* Attempt to fix formatting
* Attempt to fix formatting
* Attempt to fix formatting
* Attempt to fix formatting
* Add type annotations to _train_with_config() in state.py
* Add type annotations to prepare_sample_train_data() in state.py
* Add docstring for time_col argument of AutoML.fit()
* Address @sonichi's comments on PR
* Fix formatting
* Fix formatting
* Reduce test time budget
* Reduce test time budget
* Increase time budget for the test to pass
* Remove redundant imports
* Remove more redundant imports
* Minor fixes of points raised by Qingyun
* Try to fix pandas import fail
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Try to fix pandas import fail, again
* Formatting fixes
* More formatting fixes
* Added test that loops over TS models to ensure coverage
* Fix formatting issues
* Fix more formatting issues
* Fix random fail in check
* Put back in tests for ARIMA predict without fit
* Put back in tests for lgbm
* Update test/test_model.py
cover dedup
* Match target length to X length in missing test
---------
Co-authored-by: Mark Harley <mark.harley@transferwise.com>
Co-authored-by: Mark Harley <mharley.code@gmail.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Andrea W <a.ruggerini@ammagamma.com>
Co-authored-by: Andrea Ruggerini <nescio.adv@gmail.com>
Co-authored-by: Egor Kraev <Egor.Kraev@tw.com>
Co-authored-by: Li Jiang <bnujli@gmail.com>
* version update post release v1.2.2
* automl option
* import pandas
* remove automl.utils
* default
* test
* type hint and version update
* dependency update
* link to open in colab
* use packging.version to close#725
---------
Co-authored-by: Li Jiang <lijiang1@microsoft.com>
Co-authored-by: Li Jiang <bnujli@gmail.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>
* 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 e55e35adea.
* 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>
* 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>
