AUC_matrix.py

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels

# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)


def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    classes = classes[unique_labels(y_true, y_pred)]
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    cm = np.array([[1.0, 1.0, 1.0, 0.925, 0.793, 0.612, 0.5],
                   [1.0, 1.0, 0.999, 0.907, 0.772, 0.644, 0.5],
                   [1.0, 1.0, 1.0, 0.982, 0.906, 0.828, 0.5],
                   [1.0, 1.0, 1.0, 0.996, 0.983, 0.969, 0.5],
                   [1.0, 1.0, 1.0, 1.0, 0.984, 0.979, 0.5],
                   [1.0, 1.0, 0.996, 0.979, 0.991, 0.985, 0.5]])
    classes = ['1', '2', '3', '4', '5', '6', 'null']

    fig, ax = plt.subplots()
    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # ... and label them with the respective list entries
           xticklabels=classes, yticklabels=classes,
           title='',
           ylabel='Training Dataset',
           xlabel='Test Dataset')

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations.
    fmt = '.3f' if normalize else 'd'
    # thresh = cm.max() / 2.
    thresh = 0.9
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt),
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    return ax


np.set_printoptions(precision=2)

# # Plot non-normalized confusion matrix
# plot_confusion_matrix(y_test, y_pred, classes=class_names,
#                       title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

plt.savefig('test.png')