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)