def test_runs_bayes_runs2():
np.random.seed(73)
bs = bayes.BayesianSearch()
r1 = Run('b', 'finished', {
'v1': {
'value': 7
},
'v2': {
'value': 6
}
}, {'zloss': 1.2}, [
{
'loss': 1.2
},
])
r2 = Run('b', 'finished', {
'v1': {
'value': 1
},
'v2': {
'value': 8
}
}, {'loss': 0.4}, [])
# need two (non running) runs before we get a new set of parameters
runs = [r1, r2]
sweep = {'config': sweep_config_2params, 'runs': runs}
params, info = bs.next_run(sweep)
assert params['v1']['value'] == 2 and params['v2']['value'] == 9
def test_runs_bayes():
np.random.seed(73)
bs = bayes.BayesianSearch()
runs = []
sweep = {'config': sweep_config_2params, 'runs': runs}
params, info = bs.next_run(sweep)
assert params['v1']['value'] == 7 and params['v2']['value'] == 6
def to_class(config):
method = config.get('method')
if method is None:
raise ValueError('config missing required "method" field.')
method = method.lower()
if method == 'grid':
return grid_search.GridSearch()
elif method == 'bayes':
return bayes_search.BayesianSearch()
elif method == 'random':
return random_search.RandomSearch()
raise ValueError('method "%s" is not supported' % config['method'])
def test_runs_bayes_runs2_missingmetric():
np.random.seed(73)
bs = bayes.BayesianSearch()
r1 = Run('b', 'finished', {
'v1': {
'value': 7
},
'v2': {
'value': 5
}
}, {'xloss': 0.2}, [])
runs = [r1, r1]
sweep = {'config': sweep_config_2params, 'runs': runs}
params, info = bs.next_run(sweep)
assert params['v1']['value'] == 1 and params['v2']['value'] == 1
def test_runs_bayes_nan():
np.random.seed(73)
bs = bayes.BayesianSearch()
r1 = Run('b', 'finished', {
'v1': {
'value': 7
},
'v2': {
'value': 6
}
}, {}, [
{
'loss': float('NaN')
},
])
r2 = Run('b', 'finished', {
'v1': {
'value': 1
},
'v2': {
'value': 8
}
}, {'loss': float('NaN')}, [])
r3 = Run('b', 'finished', {
'v1': {
'value': 2
},
'v2': {
'value': 3
}
}, {}, [
{
'loss': 'NaN'
},
])
r4 = Run('b', 'finished', {
'v1': {
'value': 4
},
'v2': {
'value': 5
}
}, {'loss': 'NaN'}, [])
# need two (non running) runs before we get a new set of parameters
runs = [r1, r2, r3, r4]
sweep = {'config': sweep_config_2params, 'runs': runs}
params, info = bs.next_run(sweep)
assert params['v1']['value'] == 10 and params['v2']['value'] == 2
def test_runs_bayes_categorical_list():
np.random.seed(73)
bs = bayes.BayesianSearch()
r1 = Run('b', 'finished', {
'v1': {
'value': [3, 4]
},
'v2': {
'value': 5
}
}, {'acc': 0.2}, [])
runs = [r1, r1]
sweep = {'config': sweep_config_2params_categorical, 'runs': runs}
params, info = bs.next_run(sweep)
assert params['v1']['value'] == [(7, 8), ['9', [10, 11]]
] and params['v2']['value'] == 1