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.. "auto_examples/classification/plot_digits_classification.py"
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.. only:: html
.. note::
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.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_classification_plot_digits_classification.py:
================================
Recognizing hand-written digits
================================
This example shows how scikit-learn can be used to recognize images of
hand-written digits, from 0-9.
.. GENERATED FROM PYTHON SOURCE LINES 10-21
.. code-block:: default
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# License: BSD 3 clause
# Standard scientific Python imports
import matplotlib.pyplot as plt
# Import datasets, classifiers and performance metrics
from sklearn import datasets, metrics, svm
from sklearn.model_selection import train_test_split
.. GENERATED FROM PYTHON SOURCE LINES 22-34
Digits dataset
--------------
The digits dataset consists of 8x8
pixel images of digits. The ``images`` attribute of the dataset stores
8x8 arrays of grayscale values for each image. We will use these arrays to
visualize the first 4 images. The ``target`` attribute of the dataset stores
the digit each image represents and this is included in the title of the 4
plots below.
Note: if we were working from image files (e.g., 'png' files), we would load
them using :func:`matplotlib.pyplot.imread`.
.. GENERATED FROM PYTHON SOURCE LINES 34-43
.. code-block:: default
digits = datasets.load_digits()
_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))
for ax, image, label in zip(axes, digits.images, digits.target):
ax.set_axis_off()
ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")
ax.set_title("Training: %i" % label)
.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_001.png
:alt: Training: 0, Training: 1, Training: 2, Training: 3
:srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 44-57
Classification
--------------
To apply a classifier on this data, we need to flatten the images, turning
each 2-D array of grayscale values from shape ``(8, 8)`` into shape
``(64,)``. Subsequently, the entire dataset will be of shape
``(n_samples, n_features)``, where ``n_samples`` is the number of images and
``n_features`` is the total number of pixels in each image.
We can then split the data into train and test subsets and fit a support
vector classifier on the train samples. The fitted classifier can
subsequently be used to predict the value of the digit for the samples
in the test subset.
.. GENERATED FROM PYTHON SOURCE LINES 57-76
.. code-block:: default
# flatten the images
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
# Create a classifier: a support vector classifier
clf = svm.SVC(gamma=0.001)
# Split data into 50% train and 50% test subsets
X_train, X_test, y_train, y_test = train_test_split(
data, digits.target, test_size=0.5, shuffle=False
)
# Learn the digits on the train subset
clf.fit(X_train, y_train)
# Predict the value of the digit on the test subset
predicted = clf.predict(X_test)
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Below we visualize the first 4 test samples and show their predicted
digit value in the title.
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.. code-block:: default
_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))
for ax, image, prediction in zip(axes, X_test, predicted):
ax.set_axis_off()
image = image.reshape(8, 8)
ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")
ax.set_title(f"Prediction: {prediction}")
.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_002.png
:alt: Prediction: 8, Prediction: 8, Prediction: 4, Prediction: 9
:srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_002.png
:class: sphx-glr-single-img
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:func:`~sklearn.metrics.classification_report` builds a text report showing
the main classification metrics.
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.. code-block:: default
print(
f"Classification report for classifier {clf}:\n"
f"{metrics.classification_report(y_test, predicted)}\n"
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Classification report for classifier SVC(gamma=0.001):
precision recall f1-score support
0 1.00 0.99 0.99 88
1 0.99 0.97 0.98 91
2 0.99 0.99 0.99 86
3 0.98 0.87 0.92 91
4 0.99 0.96 0.97 92
5 0.95 0.97 0.96 91
6 0.99 0.99 0.99 91
7 0.96 0.99 0.97 89
8 0.94 1.00 0.97 88
9 0.93 0.98 0.95 92
accuracy 0.97 899
macro avg 0.97 0.97 0.97 899
weighted avg 0.97 0.97 0.97 899
.. GENERATED FROM PYTHON SOURCE LINES 97-99
We can also plot a :ref:`confusion matrix <confusion_matrix>` of the
true digit values and the predicted digit values.
.. GENERATED FROM PYTHON SOURCE LINES 99-106
.. code-block:: default
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)
disp.figure_.suptitle("Confusion Matrix")
print(f"Confusion matrix:\n{disp.confusion_matrix}")
plt.show()
.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_003.png
:alt: Confusion Matrix
:srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_003.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Confusion matrix:
[[87 0 0 0 1 0 0 0 0 0]
[ 0 88 1 0 0 0 0 0 1 1]
[ 0 0 85 1 0 0 0 0 0 0]
[ 0 0 0 79 0 3 0 4 5 0]
[ 0 0 0 0 88 0 0 0 0 4]
[ 0 0 0 0 0 88 1 0 0 2]
[ 0 1 0 0 0 0 90 0 0 0]
[ 0 0 0 0 0 1 0 88 0 0]
[ 0 0 0 0 0 0 0 0 88 0]
[ 0 0 0 1 0 1 0 0 0 90]]
.. GENERATED FROM PYTHON SOURCE LINES 107-111
If the results from evaluating a classifier are stored in the form of a
:ref:`confusion matrix <confusion_matrix>` and not in terms of `y_true` and
`y_pred`, one can still build a :func:`~sklearn.metrics.classification_report`
as follows:
.. GENERATED FROM PYTHON SOURCE LINES 111-129
.. code-block:: default
# The ground truth and predicted lists
y_true = []
y_pred = []
cm = disp.confusion_matrix
# For each cell in the confusion matrix, add the corresponding ground truths
# and predictions to the lists
for gt in range(len(cm)):
for pred in range(len(cm)):
y_true += [gt] * cm[gt][pred]
y_pred += [pred] * cm[gt][pred]
print(
"Classification report rebuilt from confusion matrix:\n"
f"{metrics.classification_report(y_true, y_pred)}\n"
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Classification report rebuilt from confusion matrix:
precision recall f1-score support
0 1.00 0.99 0.99 88
1 0.99 0.97 0.98 91
2 0.99 0.99 0.99 86
3 0.98 0.87 0.92 91
4 0.99 0.96 0.97 92
5 0.95 0.97 0.96 91
6 0.99 0.99 0.99 91
7 0.96 0.99 0.97 89
8 0.94 1.00 0.97 88
9 0.93 0.98 0.95 92
accuracy 0.97 899
macro avg 0.97 0.97 0.97 899
weighted avg 0.97 0.97 0.97 899
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 0.473 seconds)
.. _sphx_glr_download_auto_examples_classification_plot_digits_classification.py:
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