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1c9835dc0a
* 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
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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"`.
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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)
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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.
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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
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#### 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.
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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'
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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
47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
import os
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import shutil
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import sys
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import pytest
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from utils import get_automl_settings, get_toy_data_seqclassification
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try:
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import transformers
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_transformers_installed = True
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except ImportError:
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_transformers_installed = False
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pytestmark = pytest.mark.spark # set to spark as parallel testing raised MlflowException of changing parameter
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@pytest.mark.skipif(
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sys.platform in ["darwin", "win32"] or not _transformers_installed,
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reason="do not run on mac os or windows or transformers not installed",
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)
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def test_cv():
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import requests
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from flaml import AutoML
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X_train, y_train, X_val, y_val, X_test = get_toy_data_seqclassification()
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automl = AutoML()
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automl_settings = get_automl_settings()
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automl_settings["n_splits"] = 3
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try:
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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except requests.exceptions.HTTPError:
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return
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if os.path.exists("test/data/output/"):
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try:
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shutil.rmtree("test/data/output/")
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except PermissionError:
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print("PermissionError when deleting test/data/output/")
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if __name__ == "__main__":
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test_cv()
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