Confusion Matrix: cm(test_g, model**)

def cm(df, model):
    
    import sklearn.metrics as metrics
    
    num_label = {'ok': 1, 'nok' : 0}
    Y_test = df_test['class'].copy().map(num_label).astype('int')
    
    df.reset()
    predictions = model.predict(df, steps=len(df), verbose=0)
    pred_labels= np.where(predictions>0.5, 1, 0)
    
    confusion_matrix = metrics.confusion_matrix(Y_test, pred_labels)

    group_names = ['TN','FP','FN','TP']

    group_counts = ["{0:0.0f}".format(value) for value in
                    confusion_matrix.flatten()]

    group_percentages = ["{0:.2%}".format(value) for value in
                         confusion_matrix.flatten()/np.sum(confusion_matrix)]

    labels = [f"{v1}\n{v2}\n{v3}" for v1, v2, v3 in
              zip(group_names,group_counts,group_percentages)]

    labels = np.asarray(labels).reshape(2,2)
    ax = sns.heatmap(confusion_matrix, annot=labels, fmt='', cmap='Blues')

    ax.set_title('Confusion Matrix \n');
    ax.set_xlabel('\nPredicted Values')
    ax.set_ylabel('Actual Values ');

    ## Ticket labels - List must be in alphabetical order
    ax.xaxis.set_ticklabels(['NOK','OK'])
    ax.yaxis.set_ticklabels(['NOK','OK'])
    
    fig1 = plt.gcf()
    plt.show()
    plt.draw()
    fig1.savefig('cm.png', dpi=50)