47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
# Authors: The scikit-learn developers
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# SPDX-License-Identifier: BSD-3-Clause
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# https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets, svm
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from sklearn.metrics import ConfusionMatrixDisplay
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from sklearn.model_selection import train_test_split
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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class_names = iris.target_names
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# Split the data into a training set and a test set
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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# Run classifier, using a model that is too regularized (C too low) to see
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# the impact on the results
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classifier = svm.SVC(kernel="linear", C=0.01).fit(X_train, y_train)
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np.set_printoptions(precision=2)
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# Plot non-normalized confusion matrix
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titles_options = [
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("Confusion matrix, without normalization", None),
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("Normalized confusion matrix", "true"),
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]
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for title, normalize in titles_options:
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disp = ConfusionMatrixDisplay.from_estimator(
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classifier,
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X_test,
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y_test,
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display_labels=class_names,
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cmap=plt.cm.Blues,
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normalize=normalize,
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)
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disp.ax_.set_title(title)
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print(title)
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print(disp.confusion_matrix)
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plt.show() |