ner_plot_confusion_matrix.py

from sklearn.metrics import confusion_matrix
from itertools import product
import matplotlib.pyplot as plt

from matplotlib.pyplot import figure
figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')


def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = np.round(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], 3)
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

cm = confusion_matrix(
    list(flatten(y_true)),
    list(flatten(y_pred)),
    labels=labels)

plot_confusion_matrix(cm, classes=labels, normalize=True)

import numpy as np
from sklearn.metrics import confusion_matrix
from itertools import product
from more_itertools import flatten
import matplotlib.pyplot as plt

from matplotlib.pyplot import figure
figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')


def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = np.round(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], 3)
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

def ner_plot_confusion_matrix(y_true, y_pred, labels=None, normalize=False):
    if labels is None:
        labels = sorted(set([y for ys in y_true for y in ys]), key=lambda x: x.split('-')[-1]+x.split('-')[0])
    print(labels)
    cm = confusion_matrix(
        list(flatten(y_true)),
        list(flatten(y_pred)),
        labels=labels)

    plot_confusion_matrix(cm, classes=labels, normalize=normalize)