【高階程式設計技術】第15周作業
阿新 • • 發佈:2019-01-27
from sklearn import datasets, cross_validation from sklearn.naive_bayes import GaussianNB from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from sklearn import metrics def evaluate(y_test, pred, method): acc = metrics.accuracy_score(y_test, pred) f1 = metrics.f1_score(y_test, pred) auc = metrics.roc_auc_score(y_test, pred) print(method + ":") print("\nacc: ") print(acc) print("\nf1: ") print(f1) print("\nauc: ") print(auc) print("\n") dataset = datasets.make_classification(n_samples=1000, n_features=10,n_informative=2, n_redundant=2, n_repeated=0, n_classes=2) kf = cross_validation.KFold(1000, n_folds=10, shuffle=True) for train_index, test_index in kf: X_train, y_train = dataset[0][train_index], dataset[1][train_index] X_test, y_test = dataset[0][test_index], dataset[1][test_index] clf = GaussianNB() clf.fit(X_train, y_train) pred = clf.predict(X_test) evaluate(y_test,pred,"naive_bayes") C_values = [1e-02, 1e-01, 1e00, 1e01, 1e02] for C_value in C_values: clf = SVC(C=C_value, kernel='rbf', gamma=0.1) clf.fit(X_train, y_train) pred = clf.predict(X_test) evaluate(y_test,pred,"SVC, C_value= %s" % str(C_value)) clf = RandomForestClassifier(n_estimators = 6) clf.fit(X_train, y_train) pred = clf.predict(X_test) evaluate(y_test,pred,"RandomForestClassifier")