utils.py

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

# Seaborn options
sns.set(style="whitegrid", font_scale=1.4)


def plot_training_curves(history, figsize=(12, 5)):
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize)

    ax1.plot(history.history['accuracy'])
    ax1.plot(history.history['val_accuracy'])
    ax1.set_title('Model accuracy')
    ax1.set_ylabel('Accuracy')
    ax1.set_xlabel('Epoch')
    ax1.legend(['Train', 'Val'], loc='upper left')

    ax2.plot(history.history['loss'])
    ax2.plot(history.history['val_loss'])
    ax2.set_title('Model loss')
    ax2.set_ylabel('Loss')
    ax2.set_xlabel('Epoch')
    ax2.legend(['Train', 'Val'], loc='upper left')

    return fig


def print_confusion_matrix(confusion_matrix, class_names, figsize=(10, 7), fontsize=14):
    df_cm = pd.DataFrame(
        confusion_matrix, index=class_names, columns=class_names,
    )
    fig = plt.figure(figsize=figsize)

    heatmap = sns.heatmap(df_cm, annot=True, fmt="d", cmap="YlGnBu")
    heatmap.yaxis.set_ticklabels(
        heatmap.yaxis.get_ticklabels(), rotation=0,
        ha='right', fontsize=fontsize
    )
    heatmap.xaxis.set_ticklabels(
        heatmap.xaxis.get_ticklabels(), rotation=45,
        ha='right', fontsize=fontsize
    )

    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    return fig