from adaboost import AdaBoost
def create_data():
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['label'] = iris.target
df.columns = [
'sepal length', 'sepal width', 'petal length', 'petal width', 'label'
]
data = np.array(df.iloc[:100, [0, 1, -1]])
for i in range(len(data)):
if data[i, -1] == 0:
data[i, -1] = -1
return data[:, :2], data[:, -1]
X, y = create_data()
# print(X)
# print(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# plt.scatter(X[:50, 0], X[:50, 1], label='0')
# plt.scatter(X[50:, 0], X[50:, 1], label='1')
# plt.legend()
# plt.show()
clf = AdaBoost(n_estimators=50, learning_rate=0.2)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))
