def test_EvoMSA_transform():
from sklearn.preprocessing import LabelEncoder
X, y = get_data()
Xn = [X, [x for x, y0 in zip(X, y) if y0 in ['P', 'N']]]
Y = [y, [x for x in y if x in ['P', 'N']]]
Yn = []
for y0 in Y:
_ = LabelEncoder().fit(y0)
Yn.append(_.transform(y0).tolist())
X = Xn
y = Yn
for m, shape, TR in zip(
[[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernulli']],
[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernulli']]], [11, 6],
[True, False]):
evo = EvoMSA(evodag_args=dict(popsize=10,
early_stopping_rounds=10,
time_limit=15,
n_estimators=10),
TR=TR,
models=m,
n_jobs=1)
evo.fit_svm(X, y)
D = evo.transform(X[0], y[0])
D.shape[1] == shape
def test_EvoMSA_fit_svm():
from sklearn.preprocessing import LabelEncoder
X, y = get_data()
from sklearn.svm import LinearSVC
from EvoMSA.model import Bernulli
model = EvoMSA(evodag_args=dict(popsize=10,
early_stopping_rounds=10,
n_estimators=3),
models=[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernulli']],
n_jobs=2)
le = LabelEncoder().fit(y)
y = le.transform(y)
model.fit_svm(X, y)
print(model._svc_models)
assert len(model._svc_models) == 2
for ins, klass in zip(model._svc_models, [LinearSVC, Bernulli]):
assert isinstance(ins, klass)