def model_vote_help(train_map, q_labels, majority_vote, new_train):
new_model = ObjectClassifier(NZ = False)
indcs = np.where(q_labels>-1)
new_model.fit(train_map, (q_labels == majority_vote), indcs)
probs = new_model.predict_proba_segs(train_map, new_train)
probs[np.where(q_labels[new_train]==-2)] = 0
return probs
def rf_uncertainty(self):
"""
Selcts [self.batch_size] new segments to label based on their distance from [self.thresh]
First trains classifier on all labeled data, then predicts probability of all other segments
being damage and chooses segments closest to [self.thresh]
Returns
-------
ndarray
All indices of unlabeled data sorted in decreasing uncertainty
"""
model = ObjectClassifier(NZ = 0, verbose = 0)
#train and predict segs of classifier
training_sample = model.sample(self.training_labels, EVEN = 2)
model.fit(self.train_map, self.training_labels, training_sample)
proba_segs = model.predict_proba_segs(self.train_map)
#If show, save figures of heatmap from prediction
if self.show:
self.show_selected()
fig = plt.figure()
n_labeled = np.where(self.training_labels != -1)[0].shape[0]
img = self.train_map.seg_convert(self.seg, proba_segs)
plt.imshow(img, cmap = 'seismic', norm = plt.Normalize(0,1))
fig.savefig('{}test_{}{}.png'.format(self.path, n_labeled, self.postfix), format='png')
plt.close(fig)
#choose indices whose predictions minus thresh were closest to zero
unknown_indcs = np.where(self.training_labels == -1)[0]
uncertainties = 1-np.abs(proba_segs-self.thresh)
return self._uncertain_order(uncertainties.ravel(), unknown_indcs)
def model_start(self, train_map, indcs, truth = None):
predictions = np.zeros((self.labels.shape[0], train_map.unique_segs(20).shape[0]))
agreement = (self.labels == self.majority_vote())[:,train_map.unique_segs(20)]
if truth is not None:
for i in range(agreement.shape[0]):
print agreement[i,indcs].astype('int') - (self.labels[i,train_map.unique_segs(20)[indcs]] == truth).astype('int')
agreement[:,indcs] = self.labels[:,train_map.unique_segs(20)[indcs]] == truth
print "HERE"
for i in range(agreement.shape[0]):
print agreement[i,indcs].astype('int') - (self.labels[i,train_map.unique_segs(20)[indcs]] == truth).astype('int')
for i in range(self.labels.shape[0]):
new_model = ObjectClassifier(NZ = False)
new_model.fit(train_map, agreement[i], indcs)
probs = new_model.predict_proba_segs(train_map)
predictions[i] = probs
print predictions
best_labelers = np.argmax(predictions, axis = 0)
print best_labelers
np.save('predictions.npy', predictions)
np.save('best.npy',best_labelers)
assert(best_labelers.shape[0] == train_map.unique_segs(20).shape[0])
self.model_labels = self.labels[best_labelers,train_map.unique_segs(20)]
np.save('vote.npy', self.model_labels)
