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eeaf5b5963
* subspace in flow2 * search space and trainable from AutoML * experimental features: multivariate TPE, grouping, add_evaluated_points * test experimental features * readme * define by run * set time_budget_s for bs Co-authored-by: liususan091219 <Xqq630517> * version * acl * test define_by_run_func * size * constraints Co-authored-by: Chi Wang <wang.chi@microsoft.com>
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
from flaml import AutoML
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from sklearn.datasets import load_boston
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import os
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import unittest
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import logging
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import tempfile
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import io
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class TestLogging(unittest.TestCase):
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def test_logging_level(self):
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from flaml import logger, logger_formatter
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with tempfile.TemporaryDirectory() as d:
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training_log = os.path.join(d, "training.log")
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# Configure logging for the FLAML logger
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# and add a handler that outputs to a buffer.
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logger.setLevel(logging.INFO)
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buf = io.StringIO()
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ch = logging.StreamHandler(buf)
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ch.setFormatter(logger_formatter)
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logger.addHandler(ch)
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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": 'rmse',
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"task": 'regression',
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"log_file_name": training_log,
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"log_training_metric": True,
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"n_jobs": 1,
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"model_history": True,
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"learner_selector": "roundrobin",
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}
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X_train, y_train = load_boston(return_X_y=True)
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n = len(y_train) >> 1
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automl.fit(X_train=X_train[:n], y_train=y_train[:n],
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X_val=X_train[n:], y_val=y_train[n:],
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**automl_settings)
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logger.info(automl.search_space)
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logger.info(automl.low_cost_partial_config)
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logger.info(automl.points_to_evalaute)
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import optuna as ot
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study = ot.create_study()
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from flaml.tune.space import define_by_run_func
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logger.info(define_by_run_func(study.ask(), automl.search_space))
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config = automl.best_config.copy()
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config['learner'] = automl.best_estimator
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automl.trainable({"ml": config})
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# Check if the log buffer is populated.
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self.assertTrue(len(buf.getvalue()) > 0)
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import pickle
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with open('automl.pkl', 'wb') as f:
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pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)
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print(automl.__version__)
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