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_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_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)
import SequentialFeatureSelector as SBS from sklearn.linear_model import LogisticRegression import matplotlib.pyplot as plt #%% load sample data iris = load_iris() x = pd.DataFrame(iris.data, \ columns=iris.feature_names) #%% create a logistic regression object lr = LogisticRegression() #%% create an SBS object sbs = SBS(estimator=lr, k_features=(1, 3), forward=False, scoring='accuracy', cv=5) #%% fit the model sbs = sbs.fit(x, iris.target) #%% show the selected features sbs.k_feature_names_ # console output: # ('sepal length (cm)', 'petal length (cm)', # 'petal width (cm)') #%% show a full report on the feature selection sbs_results = pd.DataFrame(sbs.get_metric_dict()).\ T. \