confusion matrix

conf_matrix.py

import matplotlib.pyplot as plt
import itertools
import numpy as np
import sklearn.metrics as metrics

def get_confusion_matrix(target, preds):
        return metrics.confusion_matrix(target, preds)

def plot_confusion_matrix(cm, classes,
                          normalize=True,
                          figsize=(8,5),
                          title='Confusion Matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """

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

    fig, ax = plt.subplots(figsize=figsize)
    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)

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

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.close(fig)
    return fig
  
      
 # Example - 
cnf_matrix = metrics.confusion_matrix(target, preds)
plot_confusion_matrix(cnf_matrix, classes=['Negatives','Positives'])