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"""
================================
Recognizing hand-written digits
================================
This example shows how scikit-learn can be used to recognize images of
hand-written digits, from 0-9.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: 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
###############################################################################
# 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`.
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)
###############################################################################
# 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.
# 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)
###############################################################################
# Below we visualize the first 4 test samples and show their predicted
# digit value in the title.
_, 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}")
###############################################################################
# :func:`~sklearn.metrics.classification_report` builds a text report showing
# the main classification metrics.
print(
f"Classification report for classifier {clf}:\n"
f"{metrics.classification_report(y_test, predicted)}\n"
)
###############################################################################
# We can also plot a :ref:`confusion matrix <confusion_matrix>` of the
# true digit values and the predicted digit values.
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)
disp.figure_.suptitle("Confusion Matrix")
print(f"Confusion matrix:\n{disp.confusion_matrix}")
plt.show()
###############################################################################
# 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:
# 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"
)
|
