p221_bagging_bootstrap_samples.py

'''bagging_bootstrap_samples.py

Bagging draws samples with replacement in order to train classifiers that are 
then combined my majority voting.


Created on Jul 10, 2016

from Python Machine Learning by Sebastian Raschka under the following license

The MIT License (MIT)

Copyright (c) 2015, 2016 SEBASTIAN RASCHKA (mail@sebastianraschka.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

@author: richard lyman
'''

from sklearn.preprocessing import LabelEncoder
import ocr_utils
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
    
    charsToTrain=(48,51)
    nChars = 1000
    y, X, y_test, X_test, labels  = ocr_utils.load_E13B(chars_to_train = charsToTrain , columns=(9,17), nChars=nChars)

    le = LabelEncoder()
    y = le.fit_transform(y)

    X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.40,random_state=1)

    from sklearn.ensemble import BaggingClassifier
    from sklearn.tree import DecisionTreeClassifier

    tree = DecisionTreeClassifier(criterion='entropy', 
                                  max_depth=None)

    bag = BaggingClassifier(base_estimator=tree,
                            n_estimators=500, 
                            max_samples=1.0, 
                            max_features=1.0, 
                            bootstrap=True, 
                            bootstrap_features=False, 
                            n_jobs=-1, 
                            random_state=1)

    from sklearn.metrics import accuracy_score

    tree = tree.fit(X_train, y_train)
    y_train_pred = tree.predict(X_train)
    y_test_pred = tree.predict(X_test)

    tree_train = accuracy_score(y_train, y_train_pred)
    tree_test = accuracy_score(y_test, y_test_pred)
    print('Decision tree train/test accuracies %.3f/%.3f'
          % (tree_train, tree_test))

    bag = bag.fit(X_train, y_train)
    y_train_pred = bag.predict(X_train)
    y_test_pred = bag.predict(X_test)

    bag_train = accuracy_score(y_train, y_train_pred) 
    bag_test = accuracy_score(y_test, y_test_pred) 
    print('Bagging train/test accuracies %.3f/%.3f'
          % (bag_train, bag_test))

    x_min = X_train[:, 0].min() - 1
    x_max = X_train[:, 0].max() + 1
    y_min = X_train[:, 1].min() - 1
    y_max = X_train[:, 1].max() + 1

    xx, yy = np.meshgrid(np.arange(x_min, x_max, (x_max-x_min)/100),
                         np.arange(y_min, y_max, (y_max-y_min)/100))

    f, axarr = plt.subplots(nrows=1, ncols=2, 
                            sharex='col', 
                            sharey='row', 
                            figsize=(8, 3))


    for idx, clf, tt in zip([0, 1],
                            [tree, bag],
                            ['Decision Tree', 'Bagging']):
        clf.fit(X_train, y_train)
        
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)

        axarr[idx].contourf(xx, yy, Z, alpha=0.3)
        axarr[idx].scatter(X_train[y_train==0, 0], 
                           X_train[y_train==0, 1], 
                           c='blue', marker='^')
        
        axarr[idx].scatter(X_train[y_train==1, 0], 
                           X_train[y_train==1, 1], 
                           c='red', marker='o')
        
        axarr[idx].set_title(tt)
        axarr[idx].set_ylabel(labels[0], fontsize=12)
        axarr[idx].set_xlabel(labels[1], fontsize=12)
    # plt.text(10.2, -1.2, 
    #          s='Hue', 
    #          ha='center', va='center', fontsize=12)
        
    plt.tight_layout()
    title='Bagging'
    #plt.savefig('./figures/bagging_region.png', 
    #            dpi=300, 
    #            bbox_inches='tight')
    ocr_utils.show_figures(plt, title)

    print ('\n########################### No Errors ####################################')