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old-bearandRichard Liaw
f3c1194be3
Add new search algorithm (genetic) along with the base framework of the searcher (which performs some basic jobs such as logging, recording and organizing in our project). Note that this is the initial commit. In the following days, we will add example, UT, and other refinements.
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import random
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import unittest
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from ray.tune import register_trainable
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from ray.tune.automl import SearchSpace, DiscreteSpace, GridSearch
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class AutoMLSearcherTest(unittest.TestCase):
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def setUp(self):
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def dummy_train(config, reporter):
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reporter(timesteps_total=100, done=True)
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register_trainable("f1", dummy_train)
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def testExpandSearchSpace(self):
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exp = {"test-exp": {"run": "f1", "config": {"a": {'d': 'dummy'}}}}
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space = SearchSpace([
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DiscreteSpace('a.b.c', [1, 2]),
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DiscreteSpace('a.d', ['a', 'b']),
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])
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searcher = GridSearch(space, 'reward')
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searcher.add_configurations(exp)
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trials = searcher.next_trials()
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self.assertEqual(len(trials), 4)
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self.assertTrue(trials[0].config['a']['b']['c'] in [1, 2])
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self.assertTrue(trials[1].config['a']['d'] in ['a', 'b'])
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def testSearchRound(self):
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exp = {"test-exp": {"run": "f1", "config": {"a": {'d': 'dummy'}}}}
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space = SearchSpace([
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DiscreteSpace('a.b.c', [1, 2]),
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DiscreteSpace('a.d', ['a', 'b']),
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])
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searcher = GridSearch(space, 'reward')
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searcher.add_configurations(exp)
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trials = searcher.next_trials()
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self.assertEqual(len(searcher.next_trials()), 0)
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for trial in trials[1:]:
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searcher.on_trial_complete(trial.trial_id)
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searcher.on_trial_complete(trials[0].trial_id, error=True)
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self.assertTrue(searcher.is_finished())
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def testBestTrial(self):
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exp = {"test-exp": {"run": "f1", "config": {"a": {'d': 'dummy'}}}}
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space = SearchSpace([
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DiscreteSpace('a.b.c', [1, 2]),
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DiscreteSpace('a.d', ['a', 'b']),
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])
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searcher = GridSearch(space, 'reward')
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searcher.add_configurations(exp)
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trials = searcher.next_trials()
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self.assertEqual(len(searcher.next_trials()), 0)
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for i, trial in enumerate(trials):
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rewards = [x for x in range(i, i + 10)]
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random.shuffle(rewards)
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for reward in rewards:
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searcher.on_trial_result(trial.trial_id, {"reward": reward})
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best_trial = searcher.get_best_trial()
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self.assertEqual(best_trial, trials[-1])
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self.assertEqual(best_trial.best_result['reward'], 3 + 10 - 1)
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