def test_estimator_entry_point(): config = Config.fromdict( {'estimator': { 'entry_point': 'sklearn.cluster.KMeans', }}, check_fields=False) assert isinstance(config.estimator(), KMeans)
def test_search_engine_moe_2(): config = Config.fromdict({ 'strategy': {'name': 'moe', 'params': {'url': 'abc'}} }, check_fields=False) strat = config.strategy() assert isinstance(strat, MOE) assert strat.url == 'abc'
def test_search_space(): config = Config.fromdict({ 'search_space': { 'intvar': {'type': 'int', 'min': 1, 'max': 2}, 'logivar': {'type': 'int', 'min': 1, 'max': 2, 'warp': 'log'}, 'fvar': {'type': 'float', 'min': 1, 'max': 3.5}, 'logfvar': {'type': 'float', 'min': 1, 'max': 2.5, 'warp': 'log'}, 'enumvar': {'type': 'enum', 'choices': [1, False]}, 'jumpivar': {'type': 'jump', 'min': 1, 'max': 3, 'num': 3, 'var_type': int}, 'jumpfvar': {'type': 'jump', 'min': 1, 'max': 3, 'num': 3, 'var_type': float}, 'logjumpivar': {'type': 'jump', 'min': 10, 'max': 1000, 'num': 3, 'warp': 'log', 'var_type': int}, 'logjumpfvar': {'type': 'jump', 'min': 10, 'max': 1000, 'num': 3, 'warp': 'log', 'var_type': float} }}, check_fields=False) searchspace = config.search_space() assert searchspace['intvar'] == IntVariable('intvar', 1, 2, warp=None) assert searchspace['logivar'] == IntVariable('logivar', 1, 2, warp='log') assert searchspace['fvar'] == FloatVariable('fvar', 1, 3.5, warp=None) assert searchspace['logfvar'] == FloatVariable('logfvar', 1, 2.5, warp='log') assert searchspace['enumvar'] == EnumVariable('enumvar', [1, False]) assert searchspace['jumpivar'] == EnumVariable('jumpivar', [1, 2, 3]) assert searchspace['jumpfvar'] == EnumVariable('jumpfvar', [1.0, 2.0, 3.0]) assert searchspace['logjumpivar'] == EnumVariable('logjumpivar', [10, 100, 1000]) assert searchspace['logjumpfvar'] == EnumVariable('logjumpfvar', [10.0, 100.0, 1000.0])
def test_estimator_entry_point(): config = Config.fromdict({ 'estimator': { 'entry_point': 'sklearn.cluster.KMeans', } }, check_fields=False) assert isinstance(config.estimator(), KMeans)
def test_estimator_eval_2(): config = Config.fromdict( {'estimator': { 'eval': 'KMeans()', 'eval_scope': ['sklearn'], }}, check_fields=False) assert isinstance(config.estimator(), KMeans)
def test_stratified_cv(): from sklearn.cross_validation import StratifiedShuffleSplit config = Config.fromdict({ 'cv': {'name': 'stratifiedshufflesplit', 'params': {'n_iter': 10}} }, check_fields=False) cv = config.cv(range(100), np.random.randint(2, size=100)) assert isinstance(cv, StratifiedShuffleSplit) assert cv.n_iter == 10
def test_estimator_eval_2(): config = Config.fromdict({ 'estimator': { 'eval': 'KMeans()', 'eval_scope': ['sklearn'], } }, check_fields=False) assert isinstance(config.estimator(), KMeans)
def test_cv_1(): from sklearn.cross_validation import ShuffleSplit for name in ['shufflesplit', 'ShuffleSplit']: config = Config.fromdict({ 'cv': {'name': name, 'params': {'n_iter': 10}} }, check_fields=False) cv = config.cv(range(100)) assert isinstance(cv, ShuffleSplit) assert cv.n_iter == 10
def test_estimator_pickle(): with tempfile.NamedTemporaryFile('w+b', 0) as f: cPickle.dump(KMeans(), f) config = Config.fromdict({ 'estimator': {'pickle': f.name} }, check_fields=False) assert isinstance(config.estimator(), KMeans)
def test_estimator_pickle(): with tempfile.NamedTemporaryFile('w+b', 0) as f: cPickle.dump(KMeans(), f) config = Config.fromdict({'estimator': { 'pickle': f.name }}, check_fields=False) assert isinstance(config.estimator(), KMeans)
def test_estimator_entry_point_params(): config = Config.fromdict({ 'estimator': { 'entry_point': 'sklearn.cluster.KMeans', 'params': { 'n_clusters': 15 } } }, check_fields=False) assert isinstance(config.estimator(), KMeans) assert config.estimator().n_clusters == 15
def test_estimator_entry_point_params(): config = Config.fromdict( { 'estimator': { 'entry_point': 'sklearn.cluster.KMeans', 'params': { 'n_clusters': 15 } } }, check_fields=False) assert isinstance(config.estimator(), KMeans) assert config.estimator().n_clusters == 15
def test_stratified_cv(): from sklearn.model_selection import StratifiedShuffleSplit config = Config.fromdict( {'cv': { 'name': 'stratifiedshufflesplit', 'params': { 'n_splits': 10 } }}, check_fields=False) cv = config.cv(range(100), np.random.randint(2, size=100)) assert isinstance(cv, StratifiedShuffleSplit) assert cv.n_splits == 10
def test_search_space(): config = Config.fromdict({ 'search_space': { 'intvar': {'type': 'int', 'min': 1, 'max': 2}, 'fvar': {'type': 'float', 'min': 1, 'max': 3.5}, 'logvar': {'type': 'float', 'min': 1, 'max': 2.5, 'warp': 'log'}, 'enumvar': {'type': 'enum', 'choices': [1, False]}, }}, check_fields=False) searchspace = config.search_space() assert searchspace['intvar'] == IntVariable('intvar', 1, 2) assert searchspace['fvar'] == FloatVariable('fvar', 1, 3.5, warp=None) assert searchspace['logvar'] == FloatVariable('logvar', 1, 2.5, warp='log') assert searchspace['enumvar'] == EnumVariable('enumvar', [1, False])
def test_cv_1(): from sklearn.model_selection import ShuffleSplit for name in ['shufflesplit', 'ShuffleSplit']: config = Config.fromdict( {'cv': { 'name': name, 'params': { 'n_splits': 10 } }}, check_fields=False) cv = config.cv(range(100)) assert isinstance(cv, ShuffleSplit) assert cv.n_splits == 10
def test_search_engine_bayes(): config = Config.fromdict({'strategy': { 'name': 'bayes' }}, check_fields=False) assert isinstance(config.strategy(), Bayes)
def test_scoring(): config = Config.fromdict({'scoring': 'sdfsfsdf'}, check_fields=False) assert config.scoring() is 'sdfsfsdf'
def test_scoring(): config = Config.fromdict({ 'scoring': 'sdfsfsdf' }, check_fields=False) assert config.scoring() is 'sdfsfsdf'
def test_random_seed(): config = Config.fromdict({'random_seed': 42}, check_fields=False) assert config.random_seed() == 42
def test_search_engine_hyperopt_tpe(): config = Config.fromdict({'strategy': { 'name': 'hyperopt_tpe' }}, check_fields=False) assert isinstance(config.strategy(), HyperoptTPE)
def test_search_engine_hyperopt_tpe(): config = Config.fromdict({ 'strategy': {'name': 'hyperopt_tpe'} }, check_fields=False) assert isinstance(config.strategy(), HyperoptTPE)
def test_search_space(): config = Config.fromdict( { 'search_space': { 'intvar': { 'type': 'int', 'min': 1, 'max': 2 }, 'logivar': { 'type': 'int', 'min': 1, 'max': 2, 'warp': 'log' }, 'fvar': { 'type': 'float', 'min': 1, 'max': 3.5 }, 'logfvar': { 'type': 'float', 'min': 1, 'max': 2.5, 'warp': 'log' }, 'enumvar': { 'type': 'enum', 'choices': [1, False] }, 'jumpivar': { 'type': 'jump', 'min': 1, 'max': 3, 'num': 3, 'var_type': int }, 'jumpfvar': { 'type': 'jump', 'min': 1, 'max': 3, 'num': 3, 'var_type': float }, 'logjumpivar': { 'type': 'jump', 'min': 10, 'max': 1000, 'num': 3, 'warp': 'log', 'var_type': int }, 'logjumpfvar': { 'type': 'jump', 'min': 10, 'max': 1000, 'num': 3, 'warp': 'log', 'var_type': float } } }, check_fields=False) searchspace = config.search_space() assert searchspace['intvar'] == IntVariable('intvar', 1, 2, warp=None) assert searchspace['logivar'] == IntVariable('logivar', 1, 2, warp='log') assert searchspace['fvar'] == FloatVariable('fvar', 1, 3.5, warp=None) assert searchspace['logfvar'] == FloatVariable('logfvar', 1, 2.5, warp='log') assert searchspace['enumvar'] == EnumVariable('enumvar', [1, False]) assert searchspace['jumpivar'] == EnumVariable('jumpivar', [1, 2, 3]) assert searchspace['jumpfvar'] == EnumVariable('jumpfvar', [1.0, 2.0, 3.0]) assert searchspace['logjumpivar'] == EnumVariable('logjumpivar', [10, 100, 1000]) assert searchspace['logjumpfvar'] == EnumVariable('logjumpfvar', [10.0, 100.0, 1000.0])
def test_search_engine_bayes(): config = Config.fromdict({ 'strategy': {'name': 'bayes'} }, check_fields=False) assert isinstance(config.strategy(), Bayes)
def test_strategy_random(): config = Config.fromdict({ 'strategy': {'name': 'random'} }, check_fields=False) assert isinstance(config.strategy(), RandomSearch)
def test_random_seed(): config = Config.fromdict({ 'random_seed': 42 }, check_fields=False) assert config.random_seed() == 42
def test_search_engine_moe_1(): config = Config.fromdict({ 'strategy': {'name': 'moe', 'params': {'url': 'sdfsdf'}} }, check_fields=False) assert isinstance(config.strategy(), MOE)
def test_strategy_random(): config = Config.fromdict({'strategy': { 'name': 'random' }}, check_fields=False) assert isinstance(config.strategy(), RandomSearch)