def test_EvoMSA_predict_proba():
X, y = get_data()
evo = EvoMSA(stacked_method_args=dict(popsize=100,
early_stopping_rounds=100,
time_limit=5,
n_estimators=5),
n_jobs=2).fit(X, y)
hy = evo.predict_proba(X)
assert len(hy) == 1000
assert hy.min() >= 0 and hy.max() <= 1
def test_EvoMSA_predict_proba():
X, y = get_data()
evo = EvoMSA(
evodag_args=dict(popsize=100,
early_stopping_rounds=100,
time_limit=5,
n_estimators=5),
n_jobs=2).fit([X, [x for x, y0 in zip(X, y) if y0 in ['P', 'N']]],
[y, [x for x in y if x in ['P', 'N']]])
hy = evo.predict_proba(X)
assert len(hy) == 1000
assert hy.min() >= 0 and hy.max() <= 1
def test_EvoMSA_evodag_class():
from sklearn.neighbors import NearestCentroid
import numpy as np
X, y = get_data()
model = EvoMSA(models=[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernoulli']],
stacked_method="sklearn.neighbors.NearestCentroid",
TR=False,
n_jobs=2).fit(X, y)
assert isinstance(model._evodag_model, NearestCentroid)
cl = model.predict(X)
hy = model.predict_proba(X)
cl2 = model._le.inverse_transform(hy.argmax(axis=1))
print(cl, cl2)
assert np.all(cl == cl2)
def test_EvoMSA_predict():
import numpy as np
X, y = get_data()
evo = EvoMSA(stacked_method_args=dict(popsize=10,
early_stopping_rounds=10,
time_limit=15,
n_estimators=10),
models=[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernoulli']],
n_jobs=1).fit(X, y)
hy = evo.predict(X)
assert len(hy) == 1000
print((np.array(y) == hy).mean(), hy)
print(evo.predict_proba(X))
assert (np.array(y) == hy).mean() > 0.8
def test_EvoMSA_predict():
import numpy as np
X, y = get_data()
evo = EvoMSA(
evodag_args=dict(popsize=10,
early_stopping_rounds=10,
time_limit=15,
n_estimators=10),
models=[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernulli']],
n_jobs=1).fit([X, [x for x, y0 in zip(X, y) if y0 in ['P', 'N']]],
[y, [x for x in y if x in ['P', 'N']]])
hy = evo.predict(X)
assert len(hy) == 1000
print((np.array(y) == hy).mean(), hy)
print(evo.predict_proba(X))
assert (np.array(y) == hy).mean() > 0.8
def test_EvoMSA_identity():
from EvoMSA.model import Identity
import numpy as np
X, y = get_data()
model = EvoMSA(evodag_args=dict(popsize=10,
early_stopping_rounds=10,
n_estimators=3),
models=[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernulli']],
TR=False,
evodag_class="EvoMSA.model.Identity",
n_jobs=2).fit(X, y)
assert isinstance(model._evodag_model, Identity)
cl = model.predict(X)
hy = model.predict_proba(X)
cl2 = model._le.inverse_transform(hy.argmax(axis=1))
print(cl, cl2)
assert np.all(cl == cl2)
def test_EvoMSA_evodag_class():
from sklearn.neighbors import NearestCentroid
import numpy as np
X, y = get_data()
model = EvoMSA(evodag_args=dict(popsize=10,
early_stopping_rounds=10,
n_estimators=3),
models=[['EvoMSA.model.Corpus', 'EvoMSA.model.Bernulli']],
evodag_class="sklearn.neighbors.NearestCentroid",
TR=False,
n_jobs=2).fit(X, y)
assert isinstance(model._evodag_model, NearestCentroid)
cl = model.predict(X)
hy = model.predict_proba(X)
cl2 = model._le.inverse_transform(hy.argmax(axis=1))
print(cl, cl2)
assert np.all(cl == cl2)
