def testing_suite(self, map_test, prediction_prob):
v_print('generating visuals', self.verbose)
heat_fig = analyze_results.probability_heat_map(map_test, prediction_prob, self.test_name, save = True)
analyze_results.ROC(map_test.getLabels('damage'), prediction_prob, self.test_name, save = True)
map_test.newPxMask(prediction_prob.ravel()>.4, 'damage_pred')
sbs_fig = analyze_results.side_by_side(map_test, 'damage', 'damage_pred', self.test_name, True)
v_print('done generating visuals', self.verbose)
def main_haiti():
from Xie import EM
from labeler import Labelers
model = ObjectClassifier(0,1)
labelers = Labelers()
y = labelers.majority_vote()
train = np.ix_(np.arange(4096/3, 4096), np.arange(4096/2))
test = np.ix_( np.arange(4096/3, 4096), np.arange(4096/2, 4096))
haiti_map = map_overlay.haiti_setup()
train_map = haiti_map.sub_map(train)
test_map = haiti_map.sub_map(test)
#em = EM(train_map, labelers)
#em.run()
#y2 = em.G[:,1]>0.5
g_truth = y[test_map.segmentations[20]]
FPRs = []
TPRs = []
for email in labelers.emails:
print email
a = time.time()
labels = labelers.labeler(email)[test_map.segmentations[20]]
b = time.time()
FPR, TPR = analyze_results.confusion_analytics(g_truth.ravel(), labels.ravel())
c = time.time()
FPRs.append(FPR)
TPRs.append(TPR)
probs = model.fit_and_predict(train_map, test_map, y[train_map.unique_segs(20)])
print analyze_results.FPR_from_FNR(g_truth.ravel(), probs.ravel(), TPR = .95)
analyze_results.probability_heat_map(test_map, probs.ravel(), '')
fig, _, _, _, _, _ = analyze_results.ROC(g_truth.ravel(), probs.ravel(), 'Classifier')
plt.scatter(FPRs, TPRs)
names = labelers.emails
for i in range(len(FPRs)):
plt.annotate(names[i], (FPRs[i], TPRs[i]))
fig.savefig('All_ROCs/{}_ROC.png'.format('Classifier'), format='png')
plt.show()
