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# -*- coding: utf-8 -*-
###################################################
#
# Script to
# - Calculate prediction of the test dataset
# - Calculate the parameters to evaluate the prediction
#
##################################################
#Python
import numpy as np
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score,f1_score,jaccard_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_curve
from lib.extract_patches2 import pred_only_FOV
def evalue(preImg, gtruth_masks, test_border_masks):
#predictions only inside the FOV
y_scores, y_true = pred_only_FOV(preImg,gtruth_masks, test_border_masks) #returns data only inside the FOV
#Area under the ROC curve
fpr, tpr, thresholds = roc_curve((y_true), y_scores)
AUC_ROC = roc_auc_score(y_true, y_scores)
# test_integral = np.trapz(tpr,fpr) #trapz is numpy integration
#Precision-recall curve
precision, recall, thresholds = precision_recall_curve(y_true, y_scores)
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)
#Confusion matrix
threshold_confusion = 0.5
y_pred = np.empty((y_scores.shape[0]))
for i in range(y_scores.shape[0]):
if y_scores[i]>=threshold_confusion:
y_pred[i]=1
else:
y_pred[i]=0
confusion = confusion_matrix(y_true, y_pred)
accuracy = 0
if float(np.sum(confusion))!=0:
accuracy = float(confusion[0,0]+confusion[1,1])/float(np.sum(confusion))
specificity = 0
if float(confusion[0,0]+confusion[0,1])!=0:
specificity = float(confusion[0,0])/float(confusion[0,0]+confusion[0,1])
sensitivity = 0
if float(confusion[1,1]+confusion[1,0])!=0:
sensitivity = float(confusion[1,1])/float(confusion[1,1]+confusion[1,0])
precision = 0
if float(confusion[1,1]+confusion[0,1])!=0:
precision = float(confusion[1,1])/float(confusion[1,1]+confusion[0,1])
#Jaccard similarity index
#jaccard_index = jaccard_similarity_score(y_true, y_pred, normalize=True)
#F1 score
F1_score = f1_score(y_true, y_pred, labels=None, average='binary', sample_weight=None)
iou_score = jaccard_score(y_true, y_pred)
dice_score = 2*iou_score/(1+iou_score)
return AUC_ROC,accuracy,specificity,sensitivity,F1_score,dice_score,iou_score
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