p110_scaling_features.py

#!/usr/bin/python
''' scaling_features.py

    Investigate normalization versus standardization

    The features in the ocr_utils are already normalized.  That is, each image
    has been stretched to go from pure black to pure white.  The values in the
    .csv file are 0 to 255.  ocr_utils.py changes these value to be in the range
    0.0 to 1.0
    
    1) prints out a sampling of the normalized values. 
    2) standardize the values and print them out.
     
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)

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@author: richard lyman
'''
import numpy as np
import ocr_utils
from sklearn.neighbors import KNeighborsClassifier

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) 

# put the ASCII equivalent of the unique characters in y into the legend of the plot
legend=[]
for ys in np.unique(y_train):
    legend.append('{} \'{}\''.format(ys, chr(ys)))

X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))

knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train, y_train)


ocr_utils.plot_decision_regions(X=X_combined, 
                      y=y_combined, 
                      classifier=knn, 
                      labels=labels,                      
                      test_idx=range(len(X_test),len(X_combined)),
                      title='k_nearest_neighbors no scaling')

from sklearn.preprocessing import MinMaxScaler

mms = MinMaxScaler()
X_train_norm = mms.fit_transform(X_train)
X_test_norm = mms.transform(X_test)
X_combined_norm = np.vstack((X_train_norm, X_test_norm))

knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train_norm, y_train)


ocr_utils.plot_decision_regions(X=X_combined_norm, 
                      y=y_combined, 
                      classifier=knn, 
                      labels=labels,                      
                      test_idx=range(len(X_test_norm),len(X_combined_norm)),
                      title='k_nearest_neighbors MinMaxScaller')

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))

knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train_std, y_train)

ocr_utils.plot_decision_regions(X=X_combined_std, 
                      y=y_combined, 
                      classifier=knn, 
                      labels=labels,                      
                      test_idx=range(len(X_test_std),len(X_combined_std)),
                      title='k_nearest_neighbors Standard Normalized')

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