def test_progress(self):
"""Evaluates progress of active learning run by looking at results tested on testing map"""
model = ObjectClassifier(NZ = 0, verbose = 0)
#Pulls all training data thats been labeled and samples evenly between the classes
training_sample = model.sample(self.training_labels, EVEN = 2)
#Trains on training data and tests on test map
model.fit(self.train_map, self.training_labels, training_sample)
proba = model.predict_proba(self.test_map)
#Uses majority vote as ground truth
g_truth = self.labelers.majority_vote()[self.test_map.segmentations[self.seg]]
n_labeled = np.where(self.training_labels > -1)[0].shape[0]
#If show is true, saves the ROC curve
if self.show:
fig, AUC = analyze_results.ROC(g_truth.ravel(), proba.ravel(), 'Haiti Test')[:2]
fig.savefig('{}ROC_{}{}.png'.format(self.path, n_labeled, self.postfix), format='png')
plt.close(fig)
#Evaluates progress by finding FPR at self.FNR and adding it to the fprs list
FPR, thresh = analyze_results.FPR_from_FNR(g_truth.ravel(), proba.ravel(), TPR = self.TPR)
self.fprs.append(FPR)
#saves all fprs to document every iteration so results are not loss and progress can be seen mid-run
np.save('{}fprs{}.npy'.format(self.path, self.postfix), self.fprs)
