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776aa55189
* v0.2.2 separate the HPO part into the module flaml.tune enhanced implementation of FLOW^2, CFO and BlendSearch support parallel tuning using ray tune add support for sample_weight and generic fit arguments enable mlflow logging Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com> Co-authored-by: qingyun-wu <qw2ky@virginia.edu>
42 lines
1.3 KiB
Python
42 lines
1.3 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):
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with TemporaryDirectory() as d:
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filename = os.path.join(d, 'test_training_log.log')
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# Run a simple job.
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 2,
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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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}
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X_train, y_train = load_boston(return_X_y=True)
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automl_experiment.fit(X_train=X_train, y_train=y_train,
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**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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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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