p124_random_forest.py

#!/usr/bin/python
''' random_forest.py 
    The random forest uses weak learners to build a strong learner
    
    A random subset of samples is drawn and then at each node a decision
    tree is grown from a smaller subset of those bootstrap samples
    
    This is repeated a number of times and then the decision trees are 
    combined via majority vote.
    
    1) run the random forest on the e13b data
    2) plot the decision regions
    
    
Created on Jun 23, 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
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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
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SOFTWARE.

@author: richard lyman
'''
import numpy as np
import ocr_utils
from sklearn.preprocessing import StandardScaler

if __name__ == '__main__':
    
    y_train, X_train, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,49,50) , columns=(9,17), test_size=0.3, nChars=300, random_state=0) 


    sc = StandardScaler()
    sc.fit(X_train)
    X_train_std = sc.transform(X_train)
    X_test_std = sc.transform(X_test)
    X_combined_std = np.vstack((X_train_std, X_test_std))
    y_combined = np.hstack((y_train, y_test))
    X_combined = np.vstack((X_train, X_test))
    y_combined = np.hstack((y_train, y_test))

    from sklearn.ensemble import RandomForestClassifier

    forest = RandomForestClassifier(criterion='entropy',
                                    n_estimators=10, 
                                    random_state=1,
                                    n_jobs=2)
    forest.fit(X_train, y_train)

    ocr_utils.plot_decision_regions(X=X_combined, 
                                             y=y_combined, 
                                             classifier=forest, 
                                             labels=labels,                                         
                                             test_idx=range(len(X_test_std),len(X_combined_std)),
                                             title='random_forest')

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