def test_exp_m_step(self):
"""_exp_m_step is symmetric."""
a = numpy.random.random(size=(self.n_labellers, 1))
b = numpy.random.random(size=(self.n_labellers, 1))
w = numpy.random.random(size=(self.n_features + 1))
x = numpy.hstack([self.x, numpy.ones((self.n_examples, 1))])
y_mask = self.y.mask
m = self.rc._exp_m_step(a, b, w, x, self.y.filled(0), y_mask)
w_flip = scipy.optimize.fmin_bfgs(
lambda k: ((logistic_regression(w, x) - 1 +
logistic_regression(k, x)) ** 2 +
0.000001 * numpy.linalg.norm(k)).sum(), w, disp=False)
m_ = self.rc._exp_m_step(b, a, w_flip, x, 1 - self.y.filled(1), y_mask)
self.assertTrue(numpy.allclose(m, 1 - m_, atol=1e-3))
def _em_step(self, n_samples, n_annotators, n_dim, a, w, x, y):
# Expectation step.
# Posterior for each i. p(z_i = 1 | x_i, y_i).
lr = logistic_regression(a, x)
posteriors = lr.copy()
posteriors *= self._annotator_model(w, x, y, 1).prod(axis=0)
# Repeat for p(z_i = 0 | x_i, y_i).
posteriors_0 = 1 - lr
posteriors_0 *= self._annotator_model(w, x, y, 0).prod(axis=0)
# We want to normalise. We want p(z = 1) + p(z = 0) == 1.
# Currently, p(z = 1) + p(z = 0) == q.
# :. Divide p(z = 1) and p(z = 0) by q.
total = posteriors + posteriors_0
# It's apparently possible for both of these to be 0. That's really
# strange, but if that happens we'll set them both to 0.5.
posteriors[total == 0] = 0.5
posteriors_0[total == 0] = 0.5
total[total == 0] = 1
posteriors /= total
posteriors_0 /= total
assert numpy.allclose(posteriors, 1 - posteriors_0), (posteriors, posteriors_0)
# Maximisation step.
theta = self._pack(a, w)
theta_, fv, inf = scipy.optimize.fmin_l_bfgs_b(
self._Q, x0=theta, approx_grad=False, args=(n_dim, n_annotators, n_samples, posteriors, posteriors_0, x, y)
)
logging.debug("Terminated with Q = %4f", fv)
logging.debug(inf["task"].decode("ascii"))
a_, w_, = self._unpack(theta_, n_dim, n_annotators)
return a_, w_
def predict_proba(self, x):
"""Predict probabilities of data points using logistic regression.
x: Data points. (n_samples, n_dim) NumPy array.
"""
x = numpy.hstack([x, numpy.ones((x.shape[0], 1))])
return logistic_regression(self.a_, x)
def predict_proba(self, x):
"""Predict probabilities of data points using logistic regression.
x: Data points. (n_samples, n_dim) NumPy array.
"""
x = numpy.hstack([x, numpy.ones((x.shape[0], 1))])
return logistic_regression(self.a_, x)
def _Q(self, params, n_dim, n_annotators, n_samples, posteriors, posteriors_0, x, y):
"""Maximisation step minimisation target."""
a, w = self._unpack(params, n_dim, n_annotators)
expectation = (
posteriors.dot(
(numpy.log(self._annotator_model(w, x, y, 1) + EPS) + numpy.log(logistic_regression(a, x) + EPS)).T
)
+ posteriors_0.dot(
(numpy.log(self._annotator_model(w, x, y, 0) + EPS) + numpy.log(1 - logistic_regression(a, x) + EPS)).T
)
).sum()
# Also need the gradients.
dQ_da = n_annotators * (
numpy.dot(posteriors * logistic_regression(-a, x) + posteriors_0 * (logistic_regression(-a, x) - 1), x)
)
dQ_dw = numpy.zeros(w.shape)
# Inefficient, but unrolled for clarity.
# TODO(MatthewJA): Speed this up. (Numba?)
for t in range(n_annotators):
dQ_dw[t] += sum(
x[i] * posteriors[i] * (logistic_regression(-w[t], x[i]) - abs(y[t, i] - 1))
+ x[i] * posteriors_0[i] * (logistic_regression(-w[t], x[i]) - abs(y[t, i] - 0))
for i in range(n_samples)
)
grad = self._pack(dQ_da, dQ_dw)
return -expectation, -grad
def _Q(self, params, n_dim, n_annotators, n_samples, posteriors,
posteriors_0, x, y):
"""Maximisation step minimisation target."""
a, w = self._unpack(params, n_dim, n_annotators)
expectation = (posteriors.dot(
(numpy.log(self._annotator_model(w, x, y, 1) + EPS) +
numpy.log(logistic_regression(a, x) + EPS)).T) + posteriors_0.dot(
(numpy.log(self._annotator_model(w, x, y, 0) + EPS) +
numpy.log(1 - logistic_regression(a, x) + EPS)).T)).sum()
# Also need the gradients.
dQ_da = n_annotators * (numpy.dot(
posteriors * logistic_regression(-a, x) + posteriors_0 *
(logistic_regression(-a, x) - 1), x))
dQ_dw = numpy.zeros(w.shape)
# Inefficient, but unrolled for clarity.
# TODO(MatthewJA): Speed this up. (Numba?)
for t in range(n_annotators):
dQ_dw[t] += sum(
x[i] * posteriors[i] *
(logistic_regression(-w[t], x[i]) - abs(y[t, i] - 1)) +
x[i] * posteriors_0[i] *
(logistic_regression(-w[t], x[i]) - abs(y[t, i] - 0))
for i in range(n_samples))
grad = self._pack(dQ_da, dQ_dw)
return -expectation, -grad
def _annotator_model(self, w, x, y, z):
"""Yan et al. (2010) Bernoulli annotator model.
w: Annotator weights w_t. (n_dim,) NumPy array
x: Data point x_i. (n_dim,) NumPy array
y: Label y_i^(t). int
z: "True" label z_i. int
-> float in [0, 1]
"""
eta = logistic_regression(w, x)
label_difference = numpy.abs(y - z)
anno = (numpy.power(1 - eta, label_difference.T) * numpy.power(eta, 1 - label_difference.T)).T
assert (anno >= 0).all()
return anno
def _annotator_model(self, w, x, y, z):
"""Yan et al. (2010) Bernoulli annotator model.
w: Annotator weights w_t. (n_dim,) NumPy array
x: Data point x_i. (n_dim,) NumPy array
y: Label y_i^(t). int
z: "True" label z_i. int
-> float in [0, 1]
"""
eta = logistic_regression(w, x)
label_difference = numpy.abs(y - z)
anno = (numpy.power(1 - eta, label_difference.T) *
numpy.power(eta, 1 - label_difference.T)).T
assert (anno >= 0).all()
return anno
def _likelihood(self, w, a, b, X, Y_0, Y_1):
"""Computes the likelihood of labels and data under a model.
X: (n_samples, n_features) NumPy array of data.
Y: (n_labellers, n_samples) NumPy masked array of crowd labels.
"""
n_examples = X.shape[0]
exp_p = logistic_regression(w, X)
exp_a = numpy.ones((n_examples,))
exp_b = numpy.ones((n_examples,))
exp_a = numpy.power(a, Y_0).prod(axis=0)
exp_a *= numpy.power(1 - a, 1 - Y_1).prod(axis=0)
exp_b *= numpy.power(b, 1 - Y_1).prod(axis=0)
exp_b *= numpy.power(1 - b, Y_0).prod(axis=0)
return (exp_a * exp_p.T + exp_b * (1 - exp_p).T).prod()
def _likelihood(self, w, a, b, X, Y_0, Y_1):
"""Computes the likelihood of labels and data under a model.
X: (n_samples, n_features) NumPy array of data.
Y: (n_labellers, n_samples) NumPy masked array of crowd labels.
"""
n_examples = X.shape[0]
exp_p = logistic_regression(w, X)
exp_a = numpy.ones((n_examples, ))
exp_b = numpy.ones((n_examples, ))
exp_a = numpy.power(a, Y_0).prod(axis=0)
exp_a *= numpy.power(1 - a, 1 - Y_1).prod(axis=0)
exp_b *= numpy.power(b, 1 - Y_1).prod(axis=0)
exp_b *= numpy.power(1 - b, Y_0).prod(axis=0)
return (exp_a * exp_p.T + exp_b * (1 - exp_p).T).prod()
def _likelihood(self, a, w, X, Y):
"""Computes the likelihood of labels and data under a model.
X: (n_samples, n_features) NumPy array of data.
Y: (n_labellers, n_samples) NumPy masked array of crowd labels.
"""
lh = 1
for i in range(X.shape[0]):
for t in range(Y.shape[0]):
if Y.mask[t, i]:
continue
lr = logistic_regression(a, X[i])
p1 = self._annotator_model(w[t], X[i], Y[t, i], 1) * lr
p0 = self._annotator_model(w[t], X[i], Y[t, i], 0) * (1 - lr)
lh *= p1 + p0
return lh
def _likelihood(self, a, w, X, Y):
"""Computes the likelihood of labels and data under a model.
X: (n_samples, n_features) NumPy array of data.
Y: (n_labellers, n_samples) NumPy masked array of crowd labels.
"""
lh = 1
for i in range(X.shape[0]):
for t in range(Y.shape[0]):
if Y.mask[t, i]:
continue
lr = logistic_regression(a, X[i])
p1 = self._annotator_model(w[t], X[i], Y[t, i], 1) * lr
p0 = self._annotator_model(w[t], X[i], Y[t, i], 0) * (1 - lr)
lh *= p1 + p0
return lh
def _exp_m_step(self, a, b, w, x, y, y_mask):
"""Computes expectation value of μ."""
lr = logistic_regression(w, x)
exp_a = numpy.ones((x.shape[0], ))
exp_b = numpy.ones((x.shape[0], ))
for t in range(a.shape[0]):
for i in range(x.shape[0]):
if y_mask[t, i]:
continue
exp_a[i] *= a[t]**y[t, i] * (1 - a[t])**(1 - y[t, i])
exp_b[i] *= b[t]**(1 - y[t, i]) * (1 - b[t])**y[t, i]
logging.debug('Average a_i: {:.02}'.format(exp_a.mean()))
logging.debug('Average alpha_t: {:.02}'.format(a.mean()))
logging.debug('Max alpha_t: {}'.format(a.max()))
logging.debug('Min alpha_t: {}'.format(a.min()))
return exp_a * lr / (exp_a * lr + exp_b * (1 - lr) + EPS)
def _exp_m_step(self, a, b, w, x, y, y_mask):
"""Computes expectation value of μ."""
lr = logistic_regression(w, x)
exp_a = numpy.ones((x.shape[0],))
exp_b = numpy.ones((x.shape[0],))
for t in range(a.shape[0]):
for i in range(x.shape[0]):
if y_mask[t, i]:
continue
exp_a[i] *= a[t] ** y[t, i] * (1 - a[t]) ** (1 - y[t, i])
exp_b[i] *= b[t] ** (1 - y[t, i]) * (1 - b[t]) ** y[t, i]
logging.debug('Average a_i: {:.02}'.format(exp_a.mean()))
logging.debug('Average alpha_t: {:.02}'.format(a.mean()))
logging.debug('Max alpha_t: {}'.format(a.max()))
logging.debug('Min alpha_t: {}'.format(a.min()))
return exp_a * lr / (exp_a * lr + exp_b * (1 - lr) + EPS)
def _em_step(self, n_samples, n_annotators, n_dim, a, w, x, y):
# Expectation step.
# Posterior for each i. p(z_i = 1 | x_i, y_i).
lr = logistic_regression(a, x)
posteriors = lr.copy()
posteriors *= self._annotator_model(w, x, y, 1).prod(axis=0)
# Repeat for p(z_i = 0 | x_i, y_i).
posteriors_0 = 1 - lr
posteriors_0 *= self._annotator_model(w, x, y, 0).prod(axis=0)
# We want to normalise. We want p(z = 1) + p(z = 0) == 1.
# Currently, p(z = 1) + p(z = 0) == q.
# :. Divide p(z = 1) and p(z = 0) by q.
total = posteriors + posteriors_0
# It's apparently possible for both of these to be 0. That's really
# strange, but if that happens we'll set them both to 0.5.
posteriors[total == 0] = 0.5
posteriors_0[total == 0] = 0.5
total[total == 0] = 1
posteriors /= total
posteriors_0 /= total
assert numpy.allclose(posteriors, 1 - posteriors_0), \
(posteriors, posteriors_0)
# Maximisation step.
theta = self._pack(a, w)
theta_, fv, inf = scipy.optimize.fmin_l_bfgs_b(
self._Q,
x0=theta,
approx_grad=False,
args=(n_dim, n_annotators, n_samples, posteriors, posteriors_0, x,
y))
logging.debug('Terminated with Q = %4f', fv)
logging.debug(inf['task'].decode('ascii'))
a_, w_, = self._unpack(theta_, n_dim, n_annotators)
return a_, w_
def predict_proba(self, X):
X = numpy.hstack([X, numpy.ones((X.shape[0], 1))])
return logistic_regression(self.w_, X)
def predict_proba(self, X):
X = numpy.hstack([X, numpy.ones((X.shape[0], 1))])
return logistic_regression(self.w_, X)
