def test_selects_all():
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.data import wine_data
X, y = wine_data()
knn = KNeighborsClassifier(n_neighbors=4)
sbs = SBS(knn, k_features=13, scoring='accuracy', cv=3, print_progress=False)
sbs.fit(X, y)
assert(len(sbs.indices_) == 13)
def test_selects_all():
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.data import wine_data
X, y = wine_data()
knn = KNeighborsClassifier(n_neighbors=4)
sbs = SBS(knn,
k_features=13,
scoring='accuracy',
cv=3,
print_progress=False)
sbs.fit(X, y)
assert (len(sbs.indices_) == 13)
def test_Iris():
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=4)
sbs = SBS(knn, k_features=2, scoring='accuracy', cv=5)
sbs.fit(X, y)
assert(sbs.indices_ == (0, 3))
assert(sbs.k_score_ == 0.96)
def test_hello():
print 'test'
iris = load_iris()
x, y = iris.data, iris.target
print x[0]
clf = DecisionTreeClassifier()
sbs = SBS(clf, k_features=2)
sbs = sbs.fit(x, y)
print sbs.transform(x)
def test_Iris():
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=4)
sbs = SBS(knn,
k_features=2,
scoring='accuracy',
cv=5,
print_progress=False)
sbs.fit(X, y)
assert (sbs.indices_ == (0, 3))
assert (round(sbs.k_score_, 2) == 0.96)