p189_nested_cross_validation.py

''' nested_cross_validation.py
Nested Cross Validation is a method for tuning model parameters minimizing bias.

There is an outer k-fold cross validation loop and an inner k-fold cross 
validation loop.

The outer fold selects a number, such as 10 different training and
test sets without replacement so each sample ends up being used as a
test sample exactly once.

The inner fold uses the training portion of the outer fold, and does a 
Grid Search to select a classification model, such as 'linear' SVM version 'rbf'
or Decision Tree versus SVM.

If the model is stable, then the inner loops should all chose the same 
classifier type.

After selecting the classifier then the outer folds are used for tuning, via
k-fold classification.

This program uses the sklearn GridSearch Cross Validation that internally uses
a 5 outer fold, 2 inner folder algorithm to tune parameters.


Created on Jul 8, 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
'''

import numpy as np
import ocr_utils
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import cross_val_score
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
if __name__ == '__main__':
        y_train, X_train, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,51) , test_size=0.3, columns=(9,17), random_state=0) 


        pipe_svc = Pipeline([('scl', StandardScaler()),
                    ('clf', SVC(random_state=1))])

        c_gamma_range = [0.01, 0.1, 1.0, 10.0]
         
        param_grid = [{'clf__C': c_gamma_range, 
                       'clf__kernel': ['linear']},
                         {'clf__C': c_gamma_range, 
                          'clf__gamma': c_gamma_range, 
                          'clf__kernel': ['rbf'],}]

        gs = GridSearchCV(estimator=pipe_svc, 
                                    param_grid=param_grid, 
                                    scoring='accuracy', 
                                    cv=5,
                                    n_jobs=-1)


        scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)
        print('\nSupport Vector Cross Validation accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))

        gs = gs.fit(X_train, y_train)
        print('Support Vector Machine Grid Search best score: {}'.format(gs.best_score_))
        print('Support Vector Machine Grid Search best params: {}'.format(sorted(gs.best_params_.items())))
        from sklearn.tree import DecisionTreeClassifier
        gs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=0), 
                                    param_grid=[{'max_depth': [1, 2, 3, 4, 5, 6, 7, None]}], 
                                    scoring='accuracy', 
                                    cv=5)


        scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)
        print('Decision Tree Cross Validation accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))

        gs = gs.fit(X_train, y_train)
        print('Decision Tree Grid Search best score: {}'.format(gs.best_score_))
        print('Decision Tree Grid Search best params: {}'.format(gs.best_params_))

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