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]))

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()

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')

Quantify the performance

First print the number of correct matches

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

Out:

388

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       1.00      1.00      1.00        51
           1       0.62      0.93      0.75        41
           2       0.94      0.70      0.80        46
           3       0.93      0.87      0.90        47
           4       1.00      0.84      0.91        43
           5       0.86      0.93      0.89        40
           6       0.98      0.98      0.98        45
           7       0.86      0.96      0.91        52
           8       0.65      0.69      0.67        49
           9       0.96      0.69      0.81        36

    accuracy                           0.86       450
   macro avg       0.88      0.86      0.86       450
weighted avg       0.88      0.86      0.86       450

Print the confusion matrix

print(metrics.confusion_matrix(expected, predicted))

plt.show()

Out:

[[51  0  0  0  0  0  0  0  0  0]
 [ 0 38  0  0  0  0  0  0  3  0]
 [ 0  5 32  0  0  0  0  0  9  0]
 [ 0  1  0 41  0  2  0  0  2  1]
 [ 0  2  1  0 36  0  1  2  1  0]
 [ 0  1  0  0  0 37  0  1  1  0]
 [ 0  0  1  0  0  0 44  0  0  0]
 [ 0  0  0  0  0  1  0 50  1  0]
 [ 0 12  0  0  0  1  0  2 34  0]
 [ 0  2  0  3  0  2  0  3  1 25]]

Total running time of the script: ( 0 minutes 1.639 seconds)