def fit_probability_sigmoid(self, neg):
print('Fitting probablity sigmoid to {} detector'.format(self.cls))
#apply calibration
pos = self.pos * self.calib_params[0] + self.calib_params[1]
neg = neg * self.calib_params[0] + self.calib_params[1]
num_pos = len(pos)
num_neg = len(neg)
X = np.hstack((pos, neg))
y = np.hstack((np.ones(num_pos), -np.ones(num_neg)))
#enforce max ratio between neg and pos example num
if (num_neg / num_pos) > self.cfg.MAX_RATIO_NEG_POS:
curr_pos_w = self.pos_weight * (num_neg / num_pos) * (
1 / self.cfg.MAX_RATIO_NEG_POS)
curr_neg_w = 1.
else:
curr_pos_w = self.pos_weight
curr_neg_w = 1.
weights = ([curr_pos_w] * num_pos) + ([curr_neg_w] * num_neg)
A, B = _sigmoid_calibration(X, y, sample_weight=np.asarray(weights))
#A,B = _sigmoid_calibration(X, y, sample_weight=None)
#TODO-maybe these should be saved seperatly?
self.sigmoid_params = [
A, B, self.calib_params[0], self.calib_params[1]
]
#self.evaluate_sigmoid_match(X_test,y_test,A,B)
print('Sigmoid parameters : {},{}'.format(self.sigmoid_params[0],
self.sigmoid_params[1]))
def fit_probability_sigmoid(self,neg):
print('Fitting probablity sigmoid to {} detector'.format(self.cls))
pos = self.pos
num_pos = pos.shape[0]
num_neg = neg.shape[0]
X = np.vstack((pos, neg)) * self.feature_scale
y = np.hstack((np.ones(num_pos),-np.ones(num_neg)))
A,B = _sigmoid_calibration(self.svm.decision_function(X), y, sample_weight=None)
self.sigmoid_params = [A,B]
#self.evaluate_sigmoid_match(X_test,y_test,A,B)
print('Sigmoid parameters : {},{}'.format(self.sigmoid_params[0],self.sigmoid_params[1]))
def fit_probability_sigmoid(self, neg):
print('Fitting probablity sigmoid to {} detector'.format(self.cls))
pos = self.pos
num_pos = pos.shape[0]
num_neg = neg.shape[0]
X = np.vstack((pos, neg)) * self.feature_scale
y = np.hstack((np.ones(num_pos), -np.ones(num_neg)))
A, B = _sigmoid_calibration(self.svm.decision_function(X),
y,
sample_weight=None)
self.sigmoid_params = [A, B]
#self.evaluate_sigmoid_match(X_test,y_test,A,B)
print('Sigmoid parameters : {},{}'.format(self.sigmoid_params[0],
self.sigmoid_params[1]))
def test_sigmoid_calibration():
"""Test calibration values with Platt sigmoid model"""
exF = np.array([5, -4, 1.0])
exY = np.array([1, -1, -1])
# computed from my python port of the C++ code in LibSVM
AB_lin_libsvm = np.array([-0.20261354391187855, 0.65236314980010512])
assert_array_almost_equal(AB_lin_libsvm, _sigmoid_calibration(exF, exY), 3)
lin_prob = 1. / (1. + np.exp(AB_lin_libsvm[0] * exF + AB_lin_libsvm[1]))
sk_prob = _SigmoidCalibration().fit(exF, exY).predict(exF)
assert_array_almost_equal(lin_prob, sk_prob, 6)
# check that _SigmoidCalibration().fit only accepts 1d array or 2d column
# arrays
assert_raises(ValueError,
_SigmoidCalibration().fit, np.vstack((exF, exF)), exY)
def test_sigmoid_calibration():
"""Test calibration values with Platt sigmoid model"""
exF = np.array([5, -4, 1.0])
exY = np.array([1, -1, -1])
# computed from my python port of the C++ code in LibSVM
AB_lin_libsvm = np.array([-0.20261354391187855, 0.65236314980010512])
assert_array_almost_equal(AB_lin_libsvm,
_sigmoid_calibration(exF, exY), 3)
lin_prob = 1. / (1. + np.exp(AB_lin_libsvm[0] * exF + AB_lin_libsvm[1]))
sk_prob = _SigmoidCalibration().fit(exF, exY).predict(exF)
assert_array_almost_equal(lin_prob, sk_prob, 6)
# check that _SigmoidCalibration().fit only accepts 1d array or 2d column
# arrays
assert_raises(ValueError, _SigmoidCalibration().fit,
np.vstack((exF, exF)), exY)
def fit_probability_sigmoid(self,neg):
print('Fitting probablity sigmoid to {} detector'.format(self.cls))
#apply calibration
pos = self.pos * self.calib_params[0] + self.calib_params[1]
neg = neg * self.calib_params[0] + self.calib_params[1]
num_pos = len(pos)
num_neg = len(neg)
X = np.hstack((pos,neg))
y = np.hstack((np.ones(num_pos),-np.ones(num_neg)))
#enforce max ratio between neg and pos example num
if (num_neg/num_pos)>self.cfg.MAX_RATIO_NEG_POS:
curr_pos_w = self.pos_weight * (num_neg / num_pos) * (1 / self.cfg.MAX_RATIO_NEG_POS)
curr_neg_w = 1.
else:
curr_pos_w = self.pos_weight
curr_neg_w = 1.
weights = ([curr_pos_w] * num_pos) + ([curr_neg_w] * num_neg)
A,B = _sigmoid_calibration(X, y, sample_weight=np.asarray(weights))
#A,B = _sigmoid_calibration(X, y, sample_weight=None)
#TODO-maybe these should be saved seperatly?
self.sigmoid_params = [A,B,self.calib_params[0],self.calib_params[1]]
#self.evaluate_sigmoid_match(X_test,y_test,A,B)
print('Sigmoid parameters : {},{}'.format(self.sigmoid_params[0],self.sigmoid_params[1]))
def fit(self, x, y):
self.a, self.b = _sigmoid_calibration(x, y)
return self
