dc1/visualise_performance_metrics.py

from pathlib import Path
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay,classification_report
from train_test import test_model
import torch
from net import Net
from batch_sampler import BatchSampler
from image_dataset import ImageDataset
from sklearn.metrics import roc_curve, auc, RocCurveDisplay
from sklearn.preprocessing import label_binarize
from itertools import cycle
# from scipy import interp
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import LabelBinarizer

def create_confusion_matrix(true_labels, predicted_labels):
    cm = confusion_matrix(true_labels, predicted_labels)
    # Display it as a heatmap
    disp = ConfusionMatrixDisplay(confusion_matrix=cm)
    disp.plot(cmap=plt.cm.Blues)
    plt.title('Confusion Matrix')
    plt.show()