117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
"""
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================
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Confusion matrix
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================
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Example of confusion matrix usage to evaluate the quality
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of the output of a classifier on the iris data set. The
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diagonal elements represent the number of points for which
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the predicted label is equal to the true label, while
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off-diagonal elements are those that are mislabeled by the
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classifier. The higher the diagonal values of the confusion
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matrix the better, indicating many correct predictions.
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The figures show the confusion matrix with and without
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normalization by class support size (number of elements
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in each class). This kind of normalization can be
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interesting in case of class imbalance to have a more
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visual interpretation of which class is being misclassified.
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Here the results are not as good as they could be as our
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choice for the regularization parameter C was not the best.
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In real life applications this parameter is usually chosen
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using :ref:`grid_search`.
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"""
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import svm, datasets
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import confusion_matrix
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from sklearn.utils.multiclass import unique_labels
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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)
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y_pred = classifier.fit(X_train, y_train).predict(X_test)
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def plot_confusion_matrix(y_true, y_pred, classes,
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normalize=False,
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title=None,
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cmap=plt.cm.Blues):
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"""
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This function prints and plots the confusion matrix.
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Normalization can be applied by setting `normalize=True`.
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"""
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if not title:
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if normalize:
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title = 'Normalized confusion matrix'
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else:
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title = 'Confusion matrix, without normalization'
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# Compute confusion matrix
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cm = confusion_matrix(y_true, y_pred)
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# Only use the labels that appear in the data
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classes = classes[unique_labels(y_true, y_pred)]
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if normalize:
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cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
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print("Normalized confusion matrix")
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else:
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print('Confusion matrix, without normalization')
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print(cm)
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fig, ax = plt.subplots()
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im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
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ax.figure.colorbar(im, ax=ax)
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# We want to show all ticks...
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ax.set(xticks=np.arange(cm.shape[1]),
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yticks=np.arange(cm.shape[0]),
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# ... and label them with the respective list entries
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xticklabels=classes, yticklabels=classes,
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title=title,
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ylabel='True label',
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xlabel='Predicted label')
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# Rotate the tick labels and set their alignment.
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plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
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rotation_mode="anchor")
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# Loop over data dimensions and create text annotations.
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fmt = '.2f' if normalize else 'd'
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thresh = cm.max() / 2.
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for i in range(cm.shape[0]):
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for j in range(cm.shape[1]):
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ax.text(j, i, format(cm[i, j], fmt),
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ha="center", va="center",
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color="white" if cm[i, j] > thresh else "black")
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fig.tight_layout()
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return ax
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np.set_printoptions(precision=2)
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# Plot non-normalized confusion matrix
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plot_confusion_matrix(y_test, y_pred, classes=class_names,
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title='Confusion matrix, without normalization')
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# Plot normalized confusion matrix
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plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,
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title='Normalized confusion matrix')
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plt.show()
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