def get_train_set_division(candidate_thresholds,num_mix,
detection_array,detection_lengths,
templates,fpr,tpr,first_pass_fnrs):
detection_clusters = rf.get_detect_clusters_threshold_array(candidate_thresholds,
detection_array,
detection_lengths,
C0,C1)
out = open('data/detection_clusters_%d.npy' % num_mix,
'wb')
cPickle.dump(detection_clusters,out)
out.close()
template_lengths = tuple(t.shape[0] for t in templates)
for fnr in first_pass_fnrs:
print "num_mix=%d,fnr=%d" % (num_mix,fnr)
thresh_id = int(len(detection_clusters)* fnr/100. + 5)
(pos_times,
false_pos_times,
false_neg_times) = rf.get_pos_false_pos_false_neg_detect_points(
detection_clusters[thresh_id],
detection_array,
detection_template_ids,
template_lengths,
window_start,
window_end,example_start_end_times,
utterances_path,
train_file_indices,
verbose=True)
out = open('data/false_pos_times_%d_%d.pkl' % (num_mix,fnr),'wb')
pickle.dump(false_pos_times,out)
out.close()
out = open('data/pos_times_%d_%d.pkl' % (num_mix,fnr),'wb')
pickle.dump(pos_times,out)
out.close()
out = open('data/false_neg_times_%d_%d.pkl' % (num_mix,fnr),'wb')
pickle.dump(false_pos_times,out)
out.close()
window_end)
np.save('data/max_detect_vals_aar_%d.npy' % num_mix,max_detect_vals)
C0 = 33
C1 = int( 33 * 1.5 + .5)
frame_rate = 1/.005
fpr, tpr = rf.get_roc_curve(max_detect_vals,
detection_array,
np.array(detection_lengths),
example_start_end_times,
C0,C1,frame_rate)
np.save('data/fpr_aar_%d.npy' % num_mix,
fpr)
np.save('data/tpr_aar_%d.npy' % num_mix,
tpr)
detection_clusters = rf.get_detect_clusters_threshold_array(max_detect_vals,
detection_array,
np.array(detection_lengths),
C0,C1)
out = open('data/detection_clusters_aar_%d.npy' % num_mix,
'wb')
cPickle.dump(detection_clusters,out)
out.close()
for i in xrange(1,11):
if not np.any(fpr*60 <= i): continue
thresh_idx = (np.arange(fpr.shape[0])[fpr*60 <= i]).min()
FOMS[num_mix].append(tpr[thresh_idx])
pos_cluster_responses,neg_cluster_responses = rf.get_pos_neg_detections(detection_clusters[thresh_idx],detection_array,C1,window_start,window_end,example_start_end_times)
pos_cluster_responses += np.random.randn(*pos_cluster_responses.shape)/1000
neg_cluster_responses += np.random.randn(*neg_cluster_responses.shape)/1000
if pos_cluster_responses.shape[0] > 1:
np.save("data/aar_pos_cluster_responses_%d_%d.npy"% (num_mix,i),pos_cluster_responses)
pos_response_grid, pos_response_points = rf.map_cluster_responses_to_grid(pos_cluster_responses)
def perform_phn_train_detection_SVM(phn, num_mix_params,
train_example_lengths,bgd,
train_path,file_indices,
first_pass_fnrs):
FOMS = collections.defaultdict(list)
for num_mix in num_mix_params:
if num_mix > 1:
outfile = np.load('data/%d_templates.npz' % num_mix)
templates = tuple( outfile['arr_%d'%i] for i in xrange(len(outfile.files)))
else:
templates = (np.load('data/1_templates.npy')[0],)
detection_array = np.zeros((train_example_lengths.shape[0],
train_example_lengths.max() + 2),dtype=np.float32)
linear_filters_cs = et.construct_linear_filters(templates,
bgd)
np.savez('data/linear_filter_%d.npy'% num_mix,*(tuple(lfc[0] for lfc in linear_filters_cs)))
np.savez('data/c_%d.npy'%num_mix,*(tuple(lfc[1] for lfc in linear_filters_cs)))
syllable = np.array((phn,))
(detection_array,
example_start_end_times,
detection_lengths,
detection_template_ids)=get_detection_scores_mixture_named_params(
utterances_path,
file_indices,
detection_array,
syllable,
linear_filters_cs,S_config=sp,
E_config=ep,
verbose = True,
num_examples =-1,
return_detection_template_ids=True)
np.save('data/detection_array_%d.npy' % num_mix,detection_array)
np.save('data/detection_template_ids_%d.npy' % num_mix,detection_template_ids)
if num_mix == num_mix[0]:
out = open('data/example_start_end_times.pkl','wb')
cPickle.dump(example_start_end_times,out)
out.close()
out = open('data/detection_lengths.pkl','wb')
cPickle.dump(detection_lengths,out)
out.close()
window_start = -int(np.mean(tuple( t.shape[0] for t in templates))/3.+.5)
window_end = -window_start
max_detect_vals = rf.get_max_detection_in_syllable_windows(detection_array,
example_start_end_times,
detection_lengths,
window_start,
window_end)
np.save('data/max_detect_vals_%d.npy' % num_mix,max_detect_vals)
C0 = int(np.mean(tuple( t.shape[0] for t in templates))/3.+.5)
C1 = int( 33 * 1.5 + .5)
frame_rate = 1/.005
fpr, tpr = rf.get_roc_curve(max_detect_vals,
detection_array,
np.array(detection_lengths),
example_start_end_times,
C0,C1,frame_rate)
np.save('data/fpr_%d.npy' % num_mix,
fpr)
np.save('data/tpr_%d.npy' % num_mix,
tpr)
detection_clusters = rf.get_detect_clusters_threshold_array(max_detect_vals,
detection_array,
np.array(detection_lengths),
C0,C1)
out = open('data/detection_clusters_%d.npy' % num_mix,
'wb')
cPickle.dump(detection_clusters,out)
out.close()
for i in xrange(1,11):
thresh_idx = np.arange(fpr.shape[0])[fpr*60 <= i].min()
FOMS[num_mix].append(tpr[thresh_idx])
template_lengths = tuple(t.shape[0] for t in templates)
for fnr in first_pass_fnrs:
print "num_mix=%d,fnr=%d" % (num_mix,fnr)
thresh_id = int(len(detection_clusters)* fnr/100. + 5)
(pos_times,
false_pos_times,
false_neg_times) = rf.get_pos_false_pos_false_neg_detect_points(
detection_clusters[thresh_id],
detection_array,
detection_template_ids,
template_lengths,
window_start,
window_end,example_start_end_times,
utterances_path,
train_file_indices,
verbose=True)
out = open('data/false_pos_times_%d_%d.pkl' % (num_mix,fnr),'wb')
pickle.dump(false_pos_times,out)
out.close()
out = open('data/pos_times_%d_%d.pkl' % (num_mix,fnr),'wb')
pickle.dump(pos_times,out)
out.close()
out = open('data/false_neg_times_%d_%d.pkl' % (num_mix,fnr),'wb')
pickle.dump(false_pos_times,out)
out.close()
return FOMS
