p119_squential_backward_selection.py

#!/usr/bin/python
''' sequential backward selection

In order to reduce the complexity of the model, the number of features
    can be reduced by Sequential Backward Selection
    
Th e13b dataset has 20 column sums, one for each column in the original
    images.  Only a few of these would be needed to produce a good
    fit.
    
The SBS algorithm removes features by repeatedly running a fit of the data,
    selecting the feature for removal that makes the least difference to the 
    accuracy of the fit.
    
    
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
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in the Software without restriction, including without limitation the rights
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copies of the Software, and to permit persons to whom the Software is
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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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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
import matplotlib.pyplot as plt


y, X, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,49,50) , columns=range(0,20), nChars=1000, random_state=0) 


from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
         X, y, test_size=0.3, random_state=0)

from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)

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))

from sklearn.base import clone
from itertools import combinations

from sklearn.metrics import accuracy_score

class SBS():
    def __init__(self, estimator, k_features,
        scoring=accuracy_score,
        test_size=0.25, random_state=1):
        self.scoring = scoring
        self.estimator = clone(estimator)
        self.k_features = k_features
        self.test_size = test_size
        self.random_state = random_state
    def fit(self, X, y):
        X_train, X_test, y_train, y_test = \
        train_test_split(X, y, test_size=self.test_size,
        random_state=self.random_state)
        dim = X_train.shape[1]
        self.indices_ = tuple(range(dim))
        self.subsets_ = [self.indices_]
        score = self._calc_score(X_train, y_train,
        X_test, y_test, self.indices_)
        self.scores_ = [score]
        while dim > self.k_features:
            scores = []
            subsets = []
            for p in combinations(self.indices_, r=dim-1):
                score = self._calc_score(X_train, y_train,
                X_test, y_test, p)
                scores.append(score)
                subsets.append(p)
            best = np.argmax(scores)
            self.indices_ = subsets[best]
            self.subsets_.append(self.indices_)
            dim -= 1
            self.scores_.append(scores[best])
        self.k_score_ = self.scores_[-1]
        return self
    def transform(self, X):
        return X[:, self.indices_]

    def _calc_score(self, X_train, y_train,
        X_test, y_test, indices):
        self.estimator.fit(X_train[:, indices], y_train)
        y_pred = self.estimator.predict(X_test[:, indices])
        score = self.scoring(y_test, y_pred)
        return score
 
from sklearn.neighbors import KNeighborsClassifier


knn = KNeighborsClassifier(n_neighbors=2)

# selecting features
sbs = SBS(knn, k_features=1)
sbs.fit(X_train_std, y_train)

# plotting performance of feature subsets
k_feat = [len(k) for k in sbs.subsets_]

title='Sequential Backward Selection'
plt.plot(k_feat, sbs.scores_, marker='o')
plt.ylim([0.7, 1.1])
plt.ylabel('Accuracy')
plt.xlabel('Number of features')
plt.grid()
plt.title(title)
plt.tight_layout()
ocr_utils.show_figures(plt,title)

best=10
k5 = list(sbs.subsets_[best])
print('The best {} column_sums'.format(best))
for s in k5:
    print(labels[s])
print() 

    
knn.fit(X_train_std, y_train)
print('Training accuracy using all features:', knn.score(X_train_std, y_train))
print('Test accuracy using all features:', knn.score(X_test_std, y_test))


knn.fit(X_train_std[:, k5], y_train)
print('Training accuracy using {} features:'.format(best), knn.score(X_train_std[:, k5], y_train))
print('Test accuracy using {} features:'.format(best), knn.score(X_test_std[:, k5], y_test))

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