Training numbers.py

import matplotlib. pyplot as plt
# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, metrics
from sklearn.model_selection import train_test_split
# The digits dataset
digits = datasets.load_digits()

# The data that we are interested in is made of 8x8 images of digits, let's
# have a look at the first 4 images, stored in the "images" attribute of the
# dataset. If we were working from image files, we could load them using
# matplotlib. pyplot. imread. Note that each image must have the same size. For these
# images, we know which digit they represent: it is given in the 'target' of

# the dataset.
_, axes = plt.subplots(2,4)
images_and_labels = list(zip(digits.images, digits.target))
for ax, (image, label) in zip(axes[0, :], images_and_labels[:4]):
    ax.set_axis_off( )
    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    ax.set_title('Training: %i' % label)

# To apply a classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
#Create a classifier: a support vector classifier

classifier = svm.SVC(gamma=0.001)
# Split data into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.5, shuffle=False)

# We learn the digits on the first half of the digits
classifier. fit(X_train, y_train)
# Now predict the value of the digit on the second half:
predicted = classifier.predict(X_test)
images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
for ax, (image, prediction) in zip(axes[1, :], images_and_predictions[:4]):
    ax.set_axis_off( )
    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    ax. set_title( 'Prediction: %i' % prediction)

print("Classification report for classifier %s:\n%s\n"%(classifier, metrics. classification_report(y_test, predicted)))
disp = metrics.plot_confusion_matrix(classifier, X_test, y_test)
disp.figure_.suptitle("Confusion Matrix")
print("Confusion matrix:\n%s" %disp.confusion_matrix)
plt.show()