def test_SoftmaxRegressionPreprocessors(self):
table = Table('iris')
table.X[:,2] = table.X[:,2] * 0.001
table.X[:,3] = table.X[:,3] * 0.001
learners = [SoftmaxRegressionLearner(preprocessors=[]),
SoftmaxRegressionLearner()]
results = CrossValidation(table, learners, k=10)
ca = CA(results)
self.assertTrue(ca[0] < ca[1])
def test_SoftmaxRegressionPreprocessors(self):
table = self.iris.copy()
table.X[:, 2] = table.X[:, 2] * 0.001
table.X[:, 3] = table.X[:, 3] * 0.001
learners = [
SoftmaxRegressionLearner(preprocessors=[]),
SoftmaxRegressionLearner(),
]
results = CrossValidation(table, learners, k=10)
ca = CA(results)
self.assertLess(ca[0], ca[1])
def test_SoftmaxRegressionPreprocessors(self):
table = self.iris.copy()
with table.unlocked():
table.X[:, 2] = table.X[:, 2] * 0.001
table.X[:, 3] = table.X[:, 3] * 0.001
learners = [
SoftmaxRegressionLearner(preprocessors=[]),
SoftmaxRegressionLearner()
]
cv = CrossValidation(k=10)
results = cv(table, learners)
ca = CA(results)
self.assertLess(ca[0], ca[1])
def test_SoftmaxRegressionPreprocessors(self):
np.random.seed(42)
table = Table('iris')
new_attrs = (ContinuousVariable('c0'), ) + table.domain.attributes
new_domain = Domain(new_attrs, table.domain.class_vars,
table.domain.metas)
new_table = np.hstack((1000000 * np.random.random(
(table.X.shape[0], 1)), table))
table = table.from_numpy(new_domain, new_table)
learners = [
SoftmaxRegressionLearner(preprocessors=[]),
SoftmaxRegressionLearner()
]
results = CrossValidation(table, learners, k=3)
ca = CA(results)
self.assertTrue(ca[0] < ca[1])
def test_SoftmaxRegression(self):
learner = SoftmaxRegressionLearner()
cv = CrossValidation(k=3)
results = cv(self.iris, [learner])
ca = CA(results)
self.assertGreater(ca, 0.9)
self.assertLess(ca, 1.0)
def test_reprs(self):
lr = LogisticRegressionLearner(tol=0.0002)
m = MajorityLearner()
nb = NaiveBayesLearner()
rf = RandomForestLearner(bootstrap=False, n_jobs=3)
st = SimpleTreeLearner(seed=1, bootstrap=True)
sm = SoftmaxRegressionLearner()
svm = SVMLearner(shrinking=False)
lsvm = LinearSVMLearner(tol=0.022, dual=False)
nsvm = NuSVMLearner(tol=0.003, cache_size=190)
osvm = OneClassSVMLearner(degree=2)
tl = TreeLearner(max_depth=3, min_samples_split=1)
knn = KNNLearner(n_neighbors=4)
el = EllipticEnvelopeLearner(store_precision=False)
srf = SimpleRandomForestLearner(n_estimators=20)
learners = [lr, m, nb, rf, st, sm, svm,
lsvm, nsvm, osvm, tl, knn, el, srf]
for l in learners:
repr_str = repr(l)
new_l = eval(repr_str)
self.assertEqual(repr(new_l), repr_str)
def test_SoftmaxRegression(self):
table = Table('iris')
learner = SoftmaxRegressionLearner()
results = CrossValidation(table, [learner], k=3)
ca = CA(results)
self.assertTrue(0.9 < ca < 1.0)
def test_predict_numpy(self):
table = Table('iris')
learner = SoftmaxRegressionLearner()
c = learner(table)
c(table.X)
vals, probs = c(table.X, c.ValueProbs)
def test_probability(self):
table = Table('iris')
learn = SoftmaxRegressionLearner()
clf = learn(table)
p = clf(table, ret=Model.Probs)
self.assertTrue(all(abs(p.sum(axis=1) - 1) < 1e-6))
def test_predict_numpy(self):
learner = SoftmaxRegressionLearner()
c = learner(self.iris)
c(self.iris.X)
vals, probs = c(self.iris.X, c.ValueProbs)
def test_predict_table(self):
learner = SoftmaxRegressionLearner()
c = learner(self.iris)
c(self.iris)
vals, probs = c(self.iris, c.ValueProbs)
def test_probability(self):
learn = SoftmaxRegressionLearner()
clf = learn(self.iris)
p = clf(self.iris, ret=Model.Probs)
self.assertLess(abs(p.sum(axis=1) - 1).all(), 1e-6)
