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)