p51_standard_scalar.py

#!/usr/bin/python
'''standard_scalar illustrates the use of the scaling from
    the sklearn tools.
    1) column sums from the E13B dataset are read in as features.
    2) Features are scaled with the sklearn StandardScaler
    3) The features are then fitted to a Perceptron and the decision regions
        are plotted.

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
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.linear_model import Perceptron


#############################################################################
# read images and scatter plot

# retrieve 500 sets of target numbers and column sums
#    y: the ascii characters 48 and 49 ('0', '1')
#    X: the sum of the vertical pixels in the rows in horizontal columns 9 and 17

y, X, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,51) , columns=(9,17),nChars=500, random_state=0) 

print('Class labels:', np.unique(y))

from sklearn.cross_validation import train_test_split
#############################################################################
# standardize the features

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

sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)
ppn.fit(X_train_std, y_train)

y_pred = ppn.predict(X_test_std)
print('Misclassified samples: %d' % (y_test != y_pred).sum())

from sklearn.metrics import accuracy_score

print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
       
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

ocr_utils.plot_decision_regions(X_combined_std, y_combined, ppn, 
                           test_idx=range(len(X_test_std),len(X_combined_std)),
                           labels=labels, 
                           title='perceptron_scikit')



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