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da2cd7ca89
* 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
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
from flaml.automl.data import load_openml_dataset
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from flaml import AutoML
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from flaml.tune.spark.utils import check_spark
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import os
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import pytest
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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(
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skip_spark, reason="Spark is not installed. Skip all spark tests."
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)
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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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X_train, X_test, y_train, y_test = load_openml_dataset(
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dataset_id=537, data_dir="./"
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)
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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("Best accuracy on validation data: {0:.4g}".format(1 - automl.best_loss))
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print(
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"Training duration of best run: {0:.4g} s".format(automl.best_config_train_time)
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)
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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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