72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
"""
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================================
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Recognizing hand-written digits
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================================
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An example showing how the scikit-learn can be used to recognize images of
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hand-written digits.
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This example is commented in the
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:ref:`tutorial section of the user manual <introduction>`.
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"""
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print(__doc__)
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# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
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# License: BSD 3 clause
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# Standard scientific Python imports
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import matplotlib.pyplot as plt
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# Import datasets, classifiers and performance metrics
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from sklearn import datasets, svm, metrics
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from sklearn.model_selection import train_test_split
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# The digits dataset
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digits = datasets.load_digits()
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# The data that we are interested in is made of 8x8 images of digits, let's
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# have a look at the first 4 images, stored in the `images` attribute of the
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# dataset. If we were working from image files, we could load them using
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# matplotlib.pyplot.imread. Note that each image must have the same size. For these
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# images, we know which digit they represent: it is given in the 'target' of
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# the dataset.
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_, axes = plt.subplots(2, 4)
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images_and_labels = list(zip(digits.images, digits.target))
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for ax, (image, label) in zip(axes[0, :], images_and_labels[:4]):
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ax.set_axis_off()
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ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
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ax.set_title('Training: %i' % label)
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# To apply a classifier on this data, we need to flatten the image, to
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# turn the data in a (samples, feature) matrix:
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n_samples = len(digits.images)
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data = digits.images.reshape((n_samples, -1))
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# Create a classifier: a support vector classifier
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classifier = svm.SVC(gamma=0.001)
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# Split data into train and test subsets
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X_train, X_test, y_train, y_test = train_test_split(
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data, digits.target, test_size=0.5, shuffle=False)
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# We learn the digits on the first half of the digits
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classifier.fit(X_train, y_train)
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# Now predict the value of the digit on the second half:
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predicted = classifier.predict(X_test)
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images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
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for ax, (image, prediction) in zip(axes[1, :], images_and_predictions[:4]):
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ax.set_axis_off()
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ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
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ax.set_title('Prediction: %i' % prediction)
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print("Classification report for classifier %s:\n%s\n"
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% (classifier, metrics.classification_report(y_test, predicted)))
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disp = metrics.plot_confusion_matrix(classifier, X_test, y_test)
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disp.figure_.suptitle("Confusion Matrix")
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print("Confusion matrix:\n%s" % disp.confusion_matrix)
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plt.show()
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