3.6.10.13. Simple visualization and classification of the digits dataset

Plot the first few samples of the digits dataset and a 2D representation built using PCA, then do a simple classification

from sklearn.datasets import load_digits
digits = load_digits()

Plot the data: images of digits

Each data in a 8x8 image

from matplotlib import pyplot as plt
fig = plt.figure(figsize=(6, 6)) # figure size in inches
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(64):
ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')
# label the image with the target value
ax.text(0, 7, str(digits.target[i]))
../../../_images/sphx_glr_plot_digits_simple_classif_001.png

Plot a projection on the 2 first principal axis

plt.figure()
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
proj = pca.fit_transform(digits.data)
plt.scatter(proj[:, 0], proj[:, 1], c=digits.target, cmap="Paired")
plt.colorbar()
../../../_images/sphx_glr_plot_digits_simple_classif_002.png

Classify with Gaussian naive Bayes

from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
# split the data into training and validation sets
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target)
# train the model
clf = GaussianNB()
clf.fit(X_train, y_train)
# use the model to predict the labels of the test data
predicted = clf.predict(X_test)
expected = y_test
# Plot the prediction
fig = plt.figure(figsize=(6, 6)) # figure size in inches
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
# plot the digits: each image is 8x8 pixels
for i in range(64):
ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
ax.imshow(X_test.reshape(-1, 8, 8)[i], cmap=plt.cm.binary,
interpolation='nearest')
# label the image with the target value
if predicted[i] == expected[i]:
ax.text(0, 7, str(predicted[i]), color='green')
else:
ax.text(0, 7, str(predicted[i]), color='red')
../../../_images/sphx_glr_plot_digits_simple_classif_003.png

Quantify the performance

First print the number of correct matches

matches = (predicted == expected)
print(matches.sum())

Out:

402

The total number of data points

print(len(matches))

Out:

450

And now, the ration of correct predictions

matches.sum() / float(len(matches))

Print the classification report

from sklearn import metrics
print(metrics.classification_report(expected, predicted))

Out:

precision    recall  f1-score   support
0 0.98 1.00 0.99 53
1 0.82 0.88 0.85 42
2 0.90 0.91 0.90 57
3 0.94 0.76 0.84 38
4 0.97 0.85 0.91 40
5 0.93 0.96 0.95 45
6 0.95 0.97 0.96 40
7 0.78 0.98 0.87 51
8 0.78 0.87 0.82 46
9 1.00 0.66 0.79 38
avg / total 0.90 0.89 0.89 450

Print the confusion matrix

print(metrics.confusion_matrix(expected, predicted))
plt.show()

Out:

[[53  0  0  0  0  0  0  0  0  0]
[ 0 37 1 0 0 0 2 1 1 0]
[ 0 2 52 0 1 0 0 0 2 0]
[ 0 0 3 29 0 1 0 1 4 0]
[ 0 1 0 0 34 0 0 4 1 0]
[ 0 0 0 1 0 43 0 0 1 0]
[ 0 0 0 0 0 1 39 0 0 0]
[ 0 0 0 0 0 1 0 50 0 0]
[ 0 4 0 0 0 0 0 2 40 0]
[ 1 1 2 1 0 0 0 6 2 25]]

Total running time of the script: ( 0 minutes 4.225 seconds)

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