helpers.py

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

def plt_rectangle(plt,x1,y1,x2,y2,label="", color="yellow", linewidth= 2):
    '''
    == Input ==
    
    plt   : matplotlib.pyplot object
    label : string containing the object class name
    x1    : top left corner x coordinate
    y1    : top left corner y coordinate
    x2    : bottom right corner x coordinate
    y2    : bottom right corner y coordinate
    ''' 
    if(label != ""):
        plt.text(x1,y1,label,fontsize=20,backgroundcolor="magenta")

    plt.plot([x1,x1],[y1,y2], linewidth=linewidth,color=color)
    plt.plot([x2,x2],[y1,y2], linewidth=linewidth,color=color)
    plt.plot([x1,x2],[y1,y1], linewidth=linewidth,color=color)
    plt.plot([x1,x2],[y2,y2], linewidth=linewidth,color=color)

def plot_loss_acc(history):
    """Plot training and (optionally) validation loss and accuracy"""

    loss = history.history['loss']
    epochs = range(1, len(loss) + 1)

    plt.figure(figsize=(10, 10))

    plt.subplot(2, 1, 1)
    plt.plot(epochs, loss, '.--', label='Training loss')
    final_loss = loss[-1]
    title = 'Training loss: {:.4f}'.format(final_loss)
    plt.ylabel('Loss')
    if 'val_loss' in history.history:
        val_loss = history.history['val_loss']
        plt.plot(epochs, val_loss, 'o-', label='Validation loss')
        final_val_loss = val_loss[-1]
        title += ', Validation loss: {:.4f}'.format(final_val_loss)
    plt.title(title)
    plt.legend()

    acc = history.history['accuracy']

    plt.subplot(2, 1, 2)
    plt.plot(epochs, acc, '.--', label='Training acc')
    final_acc = acc[-1]
    title = 'Training accuracy: {:.2f}%'.format(final_acc * 100)
    plt.xlabel('Epochs')
    plt.ylabel('Accuracy')
    if 'val_accuracy' in history.history:
        val_acc = history.history['val_accuracy']
        plt.plot(epochs, val_acc, 'o-', label='Validation acc')
        final_val_acc = val_acc[-1]
        title += ', Validation accuracy: {:.2f}%'.format(final_val_acc * 100)
    plt.title(title)
    plt.legend()

def plot_multiclass_heatmap(y_test, y_predict, labels):

    y_pred = np.argmax(y_predict, axis=-1)
    y_true=np.argmax(y_test, axis=-1)

    cm = confusion_matrix(y_true, y_pred, normalize='true')

    # cm_normalized = []
    # for ligne in cm : 
    #     sum = np.sum(ligne)
    #     new_ligne = []
    #     for value in ligne : 
    #         new_ligne.append(value/sum)
    #     cm_normalized.append(new_ligne)

    class_names = list(labels)

    # Plot confusion matrix in a beautiful manner
    plt.figure(figsize=(16, 14))
    ax= plt.subplot()
    cm = cm*100
    sns.heatmap(cm, annot=True, ax = ax, fmt = '.1f'); #annot=True to annotate cells
    # for t in ax.texts: t.set_text(t.get_text()+ " %")
    
    # labels, title and ticks
    ax.set_xlabel('Predicted', fontsize=20)
    ax.xaxis.set_label_position('bottom')
    plt.xticks(rotation=90)
    ax.xaxis.set_ticklabels(class_names, fontsize = 10)
    ax.xaxis.tick_bottom()

    ax.set_ylabel('True', fontsize=20)
    ax.yaxis.set_ticklabels(class_names, fontsize = 10)
    plt.yticks(rotation=0)

    plt.title('Refined Confusion Matrix', fontsize=20)

    plt.show()