plotter.py

from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np


def plot_confusion_matrix(y_true, y_pred, 
                          classes=[1, 2, 3, 4, 5],
                          normalize=False,
                          cmap=plt.cm.YlOrBr):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    (Adapted from scikit-learn docs).
    """
    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    fig, ax = plt.subplots()
    im = ax.imshow(cm, interpolation='nearest', origin='lower', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # Show all ticks
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # Label with respective list entries
           xticklabels=classes, yticklabels=classes,
           ylabel='True label',
           xlabel='Predicted label')

    # Set alignment of tick labels
    plt.setp(ax.get_xticklabels(), rotation=0, ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    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")
    return fig, ax