Merge branch 'master' of https://github.com/microsoft/nni into dev-retiarii

nni/algorithms/hpo/bohb_advisor/__init__.py

+from .bohb_advisor import BOHB, BOHBClassArgsValidator

nni/algorithms/hpo/bohb_advisor/requirements.txt

-ConfigSpace==0.4.7
-statsmodels==0.10.0
\ No newline at end of file

nni/algorithms/hpo/curvefitting_assessor/__init__.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-from .curvefitting_assessor import CurvefittingAssessor
+from .curvefitting_assessor import CurvefittingAssessor, CurvefittingClassArgsValidator

nni/algorithms/hpo/gp_tuner/__init__.py

+from .gp_tuner import GPTuner, GPClassArgsValidator

nni/algorithms/hpo/gridsearch_tuner/__init__.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-from .gridsearch_tuner import GridSearchTuner

nni/algorithms/hpo/medianstop_assessor/__init__.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-from .medianstop_assessor import MedianstopAssessor

nni/algorithms/hpo/medianstop_assessor/test.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import argparse
-import logging
-import random
-
-from .medianstop_assessor import MedianstopAssessor
-from nni.assessor import AssessResult
-
-logger = logging.getLogger('nni.contrib.medianstop_assessor')
-logger.debug('START')
-
-
-def test():
-    '''
-    tests.
-    '''
-    parser = argparse.ArgumentParser(description='parse command line parameters.')
-    parser.add_argument('--start_from', type=int, default=10, dest='start_step',
-                        help='Assessing each trial from the step start_step.')
-    parser.add_argument('--optimize_mode', type=str, default='maximize',
-                        help='Select optimize mode for Tuner: minimize or maximize.')
-    FLAGS, _ = parser.parse_known_args()
-
-    lcs = [[1,1,1,1,1,1,1,1,1,1],
-           [2,2,2,2,2,2,2,2,2,2],
-           [3,3,3,3,3,3,3,3,3,3],
-           [4,4,4,4,4,4,4,4,4,4]]
-    #lcs = [[1,1,1,1,1,1,1,1,1,1],
-    #       [1,1,1,1,1,1,1,1,1,1],
-    #       [1,1,1,1,1,1,1,1,1,1]]
-
-    assessor = MedianstopAssessor(FLAGS.optimize_mode, FLAGS.start_step)
-    for i in range(len(lcs)):
-        #lc = []
-        to_complete = True
-        for k in range(len(lcs[0])):
-            #d = random.randint(i*100+0, i*100+100)
-            #lc.append(d)
-            ret = assessor.assess_trial(i, lcs[i][:k+1])
-            print('result: %d', ret)
-            if ret == AssessResult.Bad:
-                assessor.trial_end(i, False)
-                to_complete = False
-                break
-        if to_complete:
-            assessor.trial_end(i, True)
-
-try:
-    test()
-except Exception as exception:
-    logger.exception(exception)
-    raise

nni/algorithms/hpo/metis_tuner/__init__.py

+from .metis_tuner import MetisTuner, MetisClassArgsValidator

nni/algorithms/hpo/networkmorphism_tuner/__init__.py

+from .networkmorphism_tuner import NetworkMorphismTuner, NetworkMorphismClassArgsValidator