def test_selects_all():
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.data import wine_data
X, y = wine_data()
knn = KNeighborsClassifier(n_neighbors=4)
sfs = SFS(knn, k_features=13, scoring='accuracy', cv=3, print_progress=False)
sfs.fit(X, y)
assert(len(sfs.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)
sfs = SFS(knn,
k_features=13,
scoring='accuracy',
cv=3,
print_progress=False)
sfs.fit(X, y)
assert (len(sfs.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)
sfs = SFS(knn, k_features=2, scoring='accuracy', cv=5, print_progress=False)
sfs.fit(X, y)
assert(sfs.indices_ == (2, 3))
assert(round(sfs.k_score_, 2) == 0.97 )
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)
sfs = SFS(knn,
k_features=2,
scoring='accuracy',
cv=5,
print_progress=False)
sfs.fit(X, y)
assert (sfs.indices_ == (2, 3))
assert (round(sfs.k_score_, 2) == 0.97)
