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549a0dfb53
* limit time and memory * separate tests * lrl1 can't be limited by limit_resource * free memory when possible * passthrough=False when ensemble fails; retrain when trained_estimator is None * use callback to for resource limit * handle lower version of xgb with no callback * free mem ratio * reduce verbosity * retrain_final when max_iter==1 * remove trained_estimator from result * model_history * wheel * retrain time as best_config_train_time * ci: libomp version for xgboost on macos * limit_resource not working in windows * test pickle load * mute forecaster * notebook update * check hard * preventive callback * add use_ray
98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
import os
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import unittest
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from tempfile import TemporaryDirectory
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from sklearn.datasets import fetch_california_housing
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from flaml import AutoML
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from flaml.training_log import training_log_reader
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class TestTrainingLog(unittest.TestCase):
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def test_training_log(self, path="test_training_log.log", estimator_list="auto"):
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with TemporaryDirectory() as d:
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filename = os.path.join(d, path)
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# Run a simple job.
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automl = AutoML()
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automl_settings = {
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"time_budget": 1,
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"metric": "mse",
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"task": "regression",
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"log_file_name": filename,
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"log_training_metric": True,
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"mem_thres": 1024 * 1024,
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"n_jobs": 1,
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"model_history": True,
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"train_time_limit": 0.1,
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"verbose": 3,
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# "ensemble": True,
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"keep_search_state": True,
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"estimator_list": estimator_list,
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"model_history": True,
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}
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X_train, y_train = fetch_california_housing(return_X_y=True)
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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# Check if the training log file is populated.
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self.assertTrue(os.path.exists(filename))
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if automl.best_estimator:
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estimator, config = automl.best_estimator, automl.best_config
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model0 = automl.best_model_for_estimator(estimator)
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print(model0.params["n_estimators"], config)
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# train on full data with no time limit
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automl._state.time_budget = None
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model, _ = automl._state._train_with_config(estimator, config)
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# assuming estimator & config are saved and loaded as follows
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automl = AutoML()
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automl.fit(
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X_train=X_train,
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y_train=y_train,
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max_iter=1,
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task="regression",
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estimator_list=[estimator],
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n_jobs=1,
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starting_points={estimator: config},
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)
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print(automl.best_config)
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# then the fitted model should be equivalent to model
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assert (
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str(model.estimator) == str(automl.model.estimator)
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or estimator == "xgboost"
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and str(model.estimator.get_dump())
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== str(automl.model.estimator.get_dump())
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or estimator == "catboost"
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and str(model.estimator.get_all_params())
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== str(automl.model.estimator.get_all_params())
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)
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with training_log_reader(filename) as reader:
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count = 0
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for record in reader.records():
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print(record)
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count += 1
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self.assertGreater(count, 0)
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automl_settings["log_file_name"] = None
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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automl._selected.update(None, 0)
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automl = AutoML()
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automl.fit(X_train=X_train, y_train=y_train, max_iter=0, task="regression")
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def test_illfilename(self):
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try:
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self.test_training_log("/")
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except IsADirectoryError:
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print("IsADirectoryError happens as expected in linux.")
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except PermissionError:
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print("PermissionError happens as expected in windows.")
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def test_each_estimator(self):
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self.test_training_log(estimator_list=["xgboost"])
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self.test_training_log(estimator_list=["catboost"])
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self.test_training_log(estimator_list=["extra_tree"])
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self.test_training_log(estimator_list=["rf"])
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self.test_training_log(estimator_list=["lgbm"])
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