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e031c2eb7d
* pickle the AutoML object * get best model per estimator * test deberta * stateless API * pickle the AutoML object * get best model per estimator * test deberta * stateless API * prevent divide by zero * test roberta * BlendSearchTuner * sync * version number * update gitignore * delta time * reindex columns when dropping int-indexed columns * add seed * add seed in Args * merge * stabilize SearchThread speed * add seed * fix import * use except * add restore test for CFO * remove test_restore * remove inspect * remove print * change to SearchThread._esp * add _eps lower bound * _eps in SearchThread * add test_restore * 1<<32 Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com> Co-authored-by: Chi Wang <wang.chi@microsoft.com> Co-authored-by: Qingyun Wu <qiw@microsoft.com>
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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from flaml.searcher.suggestion import ConcurrencyLimiter
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from flaml import tune
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from flaml import CFO
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from flaml import BlendSearch
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class AbstractWarmStartTest:
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def setUp(self):
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# ray.init(num_cpus=1, local_mode=True)
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self.tmpdir = tempfile.mkdtemp()
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self.experiment_name = "searcher-state-Test.pkl"
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def tearDown(self):
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shutil.rmtree(self.tmpdir)
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# ray.shutdown()
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def set_basic_conf(self):
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raise NotImplementedError()
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def run_part_from_scratch(self):
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np.random.seed(162)
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search_alg, cost = self.set_basic_conf()
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search_alg = ConcurrencyLimiter(search_alg, 1)
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results_exp_1 = tune.run(
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cost,
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num_samples=5,
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search_alg=search_alg,
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verbose=0,
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local_dir=self.tmpdir)
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checkpoint_path = os.path.join(self.tmpdir, self.experiment_name)
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search_alg.save(checkpoint_path)
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return results_exp_1, np.random.get_state(), checkpoint_path
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def run_explicit_restore(self, random_state, checkpoint_path):
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np.random.set_state(random_state)
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search_alg2, cost = self.set_basic_conf()
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search_alg2 = ConcurrencyLimiter(search_alg2, 1)
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search_alg2.restore(checkpoint_path)
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return tune.run(cost, num_samples=5, search_alg=search_alg2, verbose=0)
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def run_full(self):
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np.random.seed(162)
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search_alg3, cost = self.set_basic_conf()
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search_alg3 = ConcurrencyLimiter(search_alg3, 1)
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return tune.run(
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cost, num_samples=10, search_alg=search_alg3, verbose=0)
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def testReproduce(self):
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results_exp_1, _, _ = self.run_part_from_scratch()
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results_exp_2, _, _ = self.run_part_from_scratch()
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trials_1_config = [trial.config for trial in results_exp_1.trials]
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trials_2_config = [trial.config for trial in results_exp_2.trials]
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self.assertEqual(trials_1_config, trials_2_config)
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def testWarmStart(self):
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results_exp_1, r_state, checkpoint_path = self.run_part_from_scratch()
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results_exp_2 = self.run_explicit_restore(r_state, checkpoint_path)
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results_exp_3 = self.run_full()
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trials_1_config = [trial.config for trial in results_exp_1.trials]
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trials_2_config = [trial.config for trial in results_exp_2.trials]
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trials_3_config = [trial.config for trial in results_exp_3.trials]
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self.assertEqual(trials_1_config + trials_2_config, trials_3_config)
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class CFOWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
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def set_basic_conf(self):
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space = {
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"height": tune.uniform(-100, 100),
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"width": tune.randint(0, 100),
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}
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def cost(param):
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tune.report(loss=(param["height"] - 14)**2 - abs(param["width"] - 3))
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search_alg = CFO(
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space=space,
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metric="loss",
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mode="min",
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seed=20,
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)
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return search_alg, cost
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# # # Not doing test for BS because of problems with random seed in OptunaSearch
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# class BlendsearchWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
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# def set_basic_conf(self):
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# space = {
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# "height": tune.uniform(-100, 100),
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# "width": tune.randint(0, 100),
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# }
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# def cost(param):
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# tune.report(loss=(param["height"] - 14)**2 - abs(param["width"] - 3))
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# search_alg = BlendSearch(
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# space=space,
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# metric="loss",
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# mode="min",
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# seed=20,
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# )
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# return search_alg, cost
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