def test_samme_proba():
# Test the `_samme_proba` helper function.
# Define some example (bad) `predict_proba` output.
probs = np.array([[1, 1e-6, 0],
[0.19, 0.6, 0.2],
[-999, 0.51, 0.5],
[1e-6, 1, 1e-9]])
probs /= np.abs(probs.sum(axis=1))[:, np.newaxis]
# _samme_proba calls estimator.predict_proba.
# Make a mock object so I can control what gets returned.
class MockEstimator(object):
def predict_proba(self, X):
assert_array_equal(X.shape, probs.shape)
return probs
mock = MockEstimator()
samme_proba = weight_boosting._samme_proba(mock, 3, np.ones_like(probs))
assert_array_equal(samme_proba.shape, probs.shape)
assert_true(np.isfinite(samme_proba).all())
# Make sure that the correct elements come out as smallest --
# `_samme_proba` should preserve the ordering in each example.
assert_array_equal(np.argmin(samme_proba, axis=1), [2, 0, 0, 2])
assert_array_equal(np.argmax(samme_proba, axis=1), [0, 1, 1, 1])
def test_samme_proba():
# Test the `_samme_proba` helper function.
# Define some example (bad) `predict_proba` output.
probs = np.array([[1, 1e-6, 0], [0.19, 0.6, 0.2], [-999, 0.51, 0.5],
[1e-6, 1, 1e-9]])
probs /= np.abs(probs.sum(axis=1))[:, np.newaxis]
# _samme_proba calls estimator.predict_proba.
# Make a mock object so I can control what gets returned.
class MockEstimator(object):
def predict_proba(self, X):
assert_array_equal(X.shape, probs.shape)
return probs
mock = MockEstimator()
samme_proba = weight_boosting._samme_proba(mock, 3, np.ones_like(probs))
assert_array_equal(samme_proba.shape, probs.shape)
assert_true(np.isfinite(samme_proba).all())
# Make sure that the correct elements come out as smallest --
# `_samme_proba` should preserve the ordering in each example.
assert_array_equal(np.argmin(samme_proba, axis=1), [2, 0, 0, 2])
assert_array_equal(np.argmax(samme_proba, axis=1), [0, 1, 1, 1])
def cascade():
"""
:return:
Beginning of cascade classification. Still being developed.
"""
pred = 0
bdt = joblib.load('models/adaboost256.pkl')
frame = cv2.imread('images/gradient.jpg')
# X = cf.compute_chans(frame).ravel().reshape(1, -1)
# X = cf.compute_chans(cv2.resize(frame, (64, 128))).ravel().reshape(1, -1)
x = cf.compute_chans(cv2.resize(frame, (64, 128))).ravel()
# np.random.shuffle(x)
N = 100
pred = [None] * N
X = np.array([x for i in range(N)])
start = time.time()
for i in range(N):
for t, estimator in enumerate(bdt.estimators_):
pred += _samme_proba(estimator, 2, X)
p_t = pred[0, 1] / (t + 1)
print('p_t is: ', p_t)
if p_t < -.2 and t > 8:
return False
return False
out = casc.cascade(X, bdt)
print((time.time() - start) / N)
start = time.time()
for i in range(N):
out = bdt.predict(X)
print((time.time() - start) / N)
print(type(bdt))
def predict_proba2(self, X):
proba = sum(_samme_proba(est , 2 ,X) for est in self.models )
proba = np.array(proba)
proba = proba / sum(self.alphas)
proba = np.exp((1. / (2 - 1)) * proba)
normalizer = proba.sum(axis=1)[:, np.newaxis]
normalizer[normalizer == 0.0] = 1.0
proba = proba / normalizer
return proba.astype(float)
def predict_proba2(self, X):
# if self.alphas == 'SAMME.R'
proba = sum(_samme_proba(est , 2 ,X) for est in self.models )
proba = np.array(proba)
proba = proba / sum(self.alphas)
proba = np.exp((1. / (2 - 1)) * proba)
normalizer = proba.sum(axis=1)[:, np.newaxis]
normalizer[normalizer == 0.0] = 1.0
# proba = np.linspace(proba)
# proba = np.array(proba).astype(float)
proba = proba / normalizer
# print('proba = ',proba)
return proba.astype(float)
