bagging_model_size.py

import matplotlib.pyplot as plt

def running_predictions(prediction_dataset, targets):
    n_trees = prediction_dataset.shape[1]
    running_percent_1s = np.cumsum(prediction_dataset, axis=1)/np.arange(1,n_trees+1)
    running_conclusions = running_percent_1s > 0.5
    running_correctnesss = running_conclusions == targets.reshape(-1,1)
    return np.mean(running_correctnesss, axis=0)

def plot_bagging_predictions(bagging_size, bagging_train, y_train, bagging_test, y_test, 
                             sm_best_tree_accuracy_test, sm_overfit_accuracy_test, title):
    bagging_accuracy_train = running_predictions(bagging_train, y_train)
    bagging_accuracy_test = running_predictions(bagging_test, y_test)
    fig, ax = plt.subplots(1,1, figsize=(15,5))
    bagging_sizes = range(1, bagging_size+1)
    ax.plot(bagging_sizes, bagging_accuracy_train, '-o', label='Bagging accuracy on training set', alpha=0.9)
    ax.plot(bagging_sizes, bagging_accuracy_test, '-x', label='Bagging accuracy on test set', alpha=0.9)
    ax.axhline(y=sm_best_tree_accuracy_test, label='Best single tree accuracy', alpha=0.9)
    ax.axhline(y=sm_overfit_accuracy_test, label='Overfit single tree accuracy', c='r', alpha=0.9)
    ax.set_title(title, fontsize=16)
    ax.set_xlabel('Number of bootstraps', fontsize=14)
    ax.set_ylabel('Accuracy', fontsize=14)
    ax.set_xticks(bagging_sizes)
    ax.legend()
    
 plot_bagging_predictions(bagging_size, bagging_train, y_train, bagging_test, y_test, 
                          sm_best_tree_accuracy_test, sm_overfit_accuracy_test, 
                          'Bagging ensemble performance')