CS545 fall 2016

import numpy as np
import matplotlib.pyplot as plt
 
from sklearn import datasets
from sklearn.svm import SVC
from sklearn import cross_validation
from sklearn.decomposition import PCA
from sklearn import preprocessing
 
digits = datasets.load_digits()
X = digits.data
y = digits.target
 
# if you want to standardize the data, uncomment the following lines
#scaler = preprocessing.StandardScaler().fit(X)
#X = scaler.transform(X)
 
pca = PCA(n_components=10)
X_reduced = pca.fit_transform(X)
 
print (pca.explained_variance_ratio_)
 
# a scatter-plot in the space of the principal components:
 
plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, cmap=plt.cm.Paired)
 
# let's see if this feature representation is useful:
X /= X.max()
 
from sklearn.grid_search import GridSearchCV
 
param_grid = [
  {'C': [1, 10, 100], 'kernel': ['linear']},
  {'C': [1, 10, 100], 'gamma': [0.01, 0.001, 0.0001], 'kernel': ['rbf']},
 ]
classifier = GridSearchCV(estimator=SVC(), param_grid=param_grid)
 
cv = cross_validation.StratifiedKFold(y, 5, shuffle=True, random_state=0)
# accuracy with all the features:
print (np.mean(cross_validation.cross_val_score(classifier, X, y, cv=cv)))
# accuracy with the PCA features:
print (np.mean(cross_validation.cross_val_score(classifier, X_reduced, y, cv=cv)))