Adaboost.py 文件源码

def perform_adaboost(self,X_train_std,y_train,X_test_std, y_test): ##perform adaboost

      ada = AdaBoostClassifier(n_estimators=10)
      ada.fit(X_train_std, y_train)
      train_score=cross_val_score(ada,X_train_std, y_train)
      print('The training accuracy is {:.2f}%'.format(train_score.mean()*100))
      test_score=cross_val_score(ada,X_test_std, y_test)
      print('The test accuracy is {:.2f}%'.format(test_score.mean()*100))
      X=X_test_std
      y=y_test
      resolution=0.01
      #Z = svm.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
      markers = ('s', 'x', 'o', '^', 'v')
      colors = ('red', 'blue', 'green', 'gray', 'cyan')
      cmap = ListedColormap(colors[:len(np.unique(y_test))])
      X=X_test_std
      y=y_test    
    # plot the decision surface
      x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
      x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
      xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))

      Z = ada.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
      Z = Z.reshape(xx1.shape)
      plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
      plt.xlim(xx1.min(), xx1.max())
      plt.ylim(xx2.min(), xx2.max())

      for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                    alpha=0.5, c=cmap(idx),
                    marker=markers[idx], label=cl)
      plt.show()