plot.py

"""Helper scripts to make plots
"""

import numpy as np
from predict import load_test_data
from gan import preprocess
from scipy import interp
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import f1_score


path = "./data/file/"
predicted_masks = np.load(path + "test_predict.npy")
imgs_test, imgs_test_mask = load_test_data()
imgs_test_gt = preprocess(imgs_test_mask)
predicted_masks_flat = predicted_masks.flatten()
test_gt_masks_flat = imgs_test_gt.flatten()


# Area under the ROC curve
fpr, tpr, thresholds = roc_curve(
    test_gt_masks_flat, predicted_masks_flat, pos_label=255
)
auc = metrics.auc(fpr, tpr)
print("auc:", auc)
import matplotlib.pyplot as plt

# plt.plot(list(fpr),list(tpr))
plt.plot([0, 1], [0, 1], "k--")
(line1,) = plt.plot(fpr, tpr, "b", label="RDAUnet ROC (AUC = %0.4f)" % auc)

plt.legend(handles=[line1], loc=4, prop={"size": 12})
# plt.plot(list(fpr),list(tpr))
plt.xlim(0, 1.0)
plt.ylim(0, 1.0)
plt.xlabel("False Positive Rate (1-Specificity)")
plt.ylabel("True Positive Rate (Sensitivity)")
plt.grid()
plt.savefig(path + "roc")
# plt.show()


# Precision-recall curve
precision, recall, thresholds = precision_recall_curve(
    test_gt_masks_flat, predicted_masks_flat, pos_label=255
)
precision = np.fliplr([precision])[
    0
]  # so the array is increasing (you won't get negative AUC)
recall = np.fliplr([recall])[
    0
]  # so the array is increasing (you won't get negative AUC)
AUC_prec_rec = np.trapz(precision, recall)
print("\nArea under Precision-Recall curve: " + str(AUC_prec_rec))
prec_rec_curve = plt.figure()
plt.plot(
    recall, precision, "-", label="Area Under the Curve (AUC = %0.4f)" % AUC_prec_rec
)
plt.title("Precision - Recall curve")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.legend(loc="lower right")
plt.savefig(path + "Precision_recall.png")