def choose_thresh(infr):
#prob_annots /= prob_annots.sum(axis=1)[:, None]
# Find connected components
#thresh = .25
#thresh = 1 / (1.2 * np.sqrt(prob_names.shape[1]))
unique_nids, prob_names = infr.make_prob_names()
if len(unique_nids) <= 2:
return .5
nscores = np.sort(prob_names.flatten())
# x = np.gradient(nscores).argmax()
# x = (np.gradient(np.gradient(nscores)) ** 2).argmax()
# thresh = nscores[x]
curve = nscores
idx1 = vt.find_elbow_point(curve)
idx2 = vt.find_elbow_point(curve[idx1:]) + idx1
if False:
import plottool as pt
idx3 = vt.find_elbow_point(curve[idx1:idx2 + 1]) + idx1
pt.plot(curve)
pt.plot(idx1, curve[idx1], 'bo')
pt.plot(idx2, curve[idx2], 'ro')
pt.plot(idx3, curve[idx3], 'go')
thresh = nscores[idx2]
#print('thresh = %r' % (thresh,))
#thresh = .999
#thresh = .1
return thresh
def choose_thresh(infr):
#prob_annots /= prob_annots.sum(axis=1)[:, None]
# Find connected components
#thresh = .25
#thresh = 1 / (1.2 * np.sqrt(prob_names.shape[1]))
unique_nids, prob_names = infr.make_prob_names()
if len(unique_nids) <= 2:
return .5
nscores = np.sort(prob_names.flatten())
# x = np.gradient(nscores).argmax()
# x = (np.gradient(np.gradient(nscores)) ** 2).argmax()
# thresh = nscores[x]
curve = nscores
idx1 = vt.find_elbow_point(curve)
idx2 = vt.find_elbow_point(curve[idx1:]) + idx1
if False:
import plottool as pt
idx3 = vt.find_elbow_point(curve[idx1:idx2 + 1]) + idx1
pt.plot(curve)
pt.plot(idx1, curve[idx1], 'bo')
pt.plot(idx2, curve[idx2], 'ro')
pt.plot(idx3, curve[idx3], 'go')
thresh = nscores[idx2]
#print('thresh = %r' % (thresh,))
#thresh = .999
#thresh = .1
return thresh
def estimate_threshold(model, method=None):
"""
import plottool as pt
idx3 = vt.find_elbow_point(curve[idx1:idx2 + 1]) + idx1
pt.plot(curve)
pt.plot(idx1, curve[idx1], 'bo')
pt.plot(idx2, curve[idx2], 'ro')
pt.plot(idx3, curve[idx3], 'go')
"""
if method is None:
method = 'mean'
curve = sorted(model.edge_weights)
if method == 'mean':
thresh = np.mean(curve)
elif method == 'elbow':
idx1 = vt.find_elbow_point(curve)
idx2 = vt.find_elbow_point(curve[idx1:]) + idx1
thresh = curve[idx2]
else:
raise ValueError('method = %r' % (method, ))
return thresh
def estimate_threshold(model, method=None):
"""
import plottool as pt
idx3 = vt.find_elbow_point(curve[idx1:idx2 + 1]) + idx1
pt.plot(curve)
pt.plot(idx1, curve[idx1], 'bo')
pt.plot(idx2, curve[idx2], 'ro')
pt.plot(idx3, curve[idx3], 'go')
"""
if method is None:
method = 'mean'
curve = sorted(model.edge_weights)
if method == 'mean':
thresh = np.mean(curve)
elif method == 'elbow':
idx1 = vt.find_elbow_point(curve)
idx2 = vt.find_elbow_point(curve[idx1:]) + idx1
thresh = curve[idx2]
else:
raise ValueError('method = %r' % (method,))
return thresh
