Note
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3.4.8.13. 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
import matplotlib.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="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()
<matplotlib.colorbar.Colorbar object at 0x7f22b60004d0>
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="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
395
The total number of data points
print(len(matches))
450
And now, the ration of correct predictions
np.float64(0.8777777777777778)
Print the classification report
from sklearn import metrics
print(metrics.classification_report(expected, predicted))
precision recall f1-score support
0 0.97 0.95 0.96 37
1 0.83 0.85 0.84 41
2 0.89 0.84 0.86 49
3 0.93 0.83 0.88 47
4 0.93 0.90 0.92 42
5 0.89 0.95 0.92 42
6 0.98 0.97 0.97 60
7 0.81 0.98 0.88 47
8 0.65 0.87 0.75 39
9 0.97 0.63 0.76 46
accuracy 0.88 450
macro avg 0.89 0.88 0.87 450
weighted avg 0.89 0.88 0.88 450
Print the confusion matrix
print(metrics.confusion_matrix(expected, predicted))
plt.show()
[[35 0 0 0 1 0 0 1 0 0]
[ 0 35 0 0 0 0 1 1 4 0]
[ 0 1 41 0 0 0 0 0 7 0]
[ 0 0 2 39 0 1 0 2 2 1]
[ 0 1 0 0 38 0 0 2 1 0]
[ 0 0 0 0 1 40 0 1 0 0]
[ 0 0 1 0 1 0 58 0 0 0]
[ 0 0 0 0 0 1 0 46 0 0]
[ 0 2 0 1 0 1 0 1 34 0]
[ 1 3 2 2 0 2 0 3 4 29]]
Total running time of the script: (0 minutes 1.736 seconds)