def test_process_params():
from EvoDAG.utils import RandomParameterSearch
from EvoDAG import EvoDAG
rs = RandomParameterSearch(npoints=1)
args = [x for x in rs][0]
evo = EvoDAG(**rs.process_params(args))
params = evo.get_params()
for k, v in args.items():
if k in params:
print(v, params[k])
if hasattr(params[k], '__name__'):
assert v == params[k].__name__
else:
assert v == params[k]
def test_params():
from EvoDAG.utils import RandomParameterSearch
rs = RandomParameterSearch(npoints=734)
l = np.unique([x['random_generations'] for x in rs])
assert len(l) == 2
l = np.unique([x['population_class'] for x in rs])
assert len(l) == 2
def test_constraints2():
from EvoDAG.utils import RandomParameterSearch
params = dict(popsize=[100, 1000],
population_class=['Generational', 'SteadyState'],
early_stopping_rounds=[100, 1000])
rs = RandomParameterSearch(params=params, npoints=32, training_size=70)
args = [x for x in rs if x['population_class'] == 'Generational']
assert len(args) == 1
def test_iter_params():
from EvoDAG.utils import RandomParameterSearch
params = dict(popsize=[100, 1000],
population_class=['SteadyState'],
early_stopping_rounds=[50, 1000])
rs = RandomParameterSearch(params=params, npoints=5)
args = [x for x in rs]
assert len(args) == 4
def test_random_parameter_search_len():
from itertools import product
from EvoDAG.utils import RandomParameterSearch
params = {'a': [1, 2, 3], 'b': [-1, -2], 'c': ['a', 'b', 'c', 'd', 'e']}
rs = RandomParameterSearch(params=params)
params = sorted(params.items())
a = product(*[x[1] for x in params])
assert len([x for x in a]) == len(rs)
def test_popsize_constraint():
from EvoDAG.utils import RandomParameterSearch
params = dict(popsize=[100, 1000],
population_class=['Generational', 'SteadyState'],
early_stopping_rounds=[100, 1000])
rs = RandomParameterSearch(params=params, npoints=32, training_size=70)
popsize = [x['popsize'] for x in rs]
r = [x for x in popsize if x > 70]
assert len(r) == 0
def test_process_params():
from EvoDAG.utils import RandomParameterSearch
from EvoDAG import EvoDAG
rs = RandomParameterSearch(npoints=1)
args = [x for x in rs][0]
evo = EvoDAG(**rs.process_params(args))
params = evo.get_params()
for k, v in args.items():
if k in params:
if k == 'generations':
v = np.inf
print(k, v, params[k])
if isinstance(v, list):
for a, b in zip(v, params[k]):
assert a == b.__name__
elif hasattr(params[k], '__name__'):
assert v == params[k].__name__
else:
assert v == params[k]
def test_random_parameter_search_getitem():
from itertools import product
from EvoDAG.utils import RandomParameterSearch
params = {'a': [1, 2, 3], 'b': [-1, -2], 'c': ['a', 'b', 'c', 'd', 'e']}
rs = RandomParameterSearch(params=params)
params = sorted(params.items())
a = product(*[x[1] for x in params])
for k, v in enumerate(a):
v1 = [x[1] for x in sorted(rs[k].items())]
print(v, v1)
assert np.all([x == y for x, y in zip(v, v1)])
def test_HGeneration_pr_variable():
import json
from EvoDAG.utils import RandomParameterSearch
from EvoDAG import EvoDAG
get_remote_data()
params = json.loads(open('evodag.params').read())
params['population_class'] = 'HGenerational'
params['pr_variable'] = 1.0
kw = RandomParameterSearch.process_params(params)
y = cl.copy()
try:
EvoDAG(**kw).fit(X, y)
except AssertionError:
return
assert False
def test_HGeneration():
import json
import gzip
import pickle
from EvoDAG.utils import RandomParameterSearch
from EvoDAG import EvoDAG
get_remote_data()
params = json.loads(open('evodag.params').read())
try:
with gzip.open('train.sp') as fpt:
X = pickle.load(fpt)
y = pickle.load(fpt)
except ValueError:
return
params['population_class'] = 'HGenerational'
kw = RandomParameterSearch.process_params(params)
gp = EvoDAG(**kw).fit(X, y)
assert gp
def test_parameter_grid_results():
from EvoDAG.utils import RandomParameterSearch
a = RandomParameterSearch()
assert len([x for x in a]) == 1468