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* 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
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
import os
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import pytest
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from flaml import AutoML
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from flaml.automl.data import load_openml_dataset
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from flaml.tune.spark.utils import check_spark
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spark_available, _ = check_spark()
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skip_spark = not spark_available
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pytestmark = [pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests."), pytest.mark.spark]
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os.environ["FLAML_MAX_CONCURRENT"] = "2"
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def base_automl(n_concurrent_trials=1, use_ray=False, use_spark=False, verbose=0):
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from minio.error import ServerError
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try:
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X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir="./")
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except (ServerError, Exception):
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from sklearn.datasets import fetch_california_housing
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X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
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automl = AutoML()
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settings = {
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"time_budget": 3, # total running time in seconds
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"metric": "r2", # primary metrics for regression can be chosen from: ['mae','mse','r2','rmse','mape']
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"estimator_list": ["lgbm", "rf", "xgboost"], # list of ML learners
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"task": "regression", # task type
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"log_file_name": "houses_experiment.log", # flaml log file
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"seed": 7654321, # random seed
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"n_concurrent_trials": n_concurrent_trials, # the maximum number of concurrent learners
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"use_ray": use_ray, # whether to use Ray for distributed training
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"use_spark": use_spark, # whether to use Spark for distributed training
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"verbose": verbose,
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}
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automl.fit(X_train=X_train, y_train=y_train, **settings)
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print("Best ML leaner:", automl.best_estimator)
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print("Best hyperparmeter config:", automl.best_config)
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print(f"Best accuracy on validation data: {1 - automl.best_loss:.4g}")
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print(f"Training duration of best run: {automl.best_config_train_time:.4g} s")
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def test_both_ray_spark():
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with pytest.raises(ValueError):
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base_automl(n_concurrent_trials=2, use_ray=True, use_spark=True)
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def test_verboses():
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for verbose in [1, 3, 5]:
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base_automl(verbose=verbose)
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def test_import_error():
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from importlib import reload
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import flaml.tune.spark.utils as utils
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reload(utils)
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utils._have_spark = False
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spark_available, spark_error_msg = utils.check_spark()
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assert not spark_available
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assert isinstance(spark_error_msg, ImportError)
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reload(utils)
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utils._spark_major_minor_version = (1, 1)
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spark_available, spark_error_msg = utils.check_spark()
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assert not spark_available
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assert isinstance(spark_error_msg, ImportError)
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reload(utils)
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if __name__ == "__main__":
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base_automl()
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test_import_error()
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