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e46573a01d
* increase test coverage * use define by run only when needed * warmstart bs * classification -> binary, multi * warm start with evaluated rewards * data transformer; resource attr for gs * BlendSearchTuner bug fix and unittest * bug fix * docstr and import * task type
61 lines
2.1 KiB
Python
61 lines
2.1 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 load_boston
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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'):
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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": 0.01,
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"verbose": 3,
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"ensemble": True,
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"keep_search_state": True,
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}
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X_train, y_train = load_boston(return_X_y=True)
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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automl._state._train_with_config(
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automl.best_estimator, automl.best_config)
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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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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)
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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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