def _parameter_estimation(self, objective, annotations,
estimate_gamma=True):
counts = compute_counts(annotations, self.nclasses)
params_start = self._random_initial_parameters(annotations,
estimate_gamma)
logger.info('Start parameters optimization...')
# TODO: use gradient, constrained optimization
params_best = scipy.optimize.fmin(objective,
params_start,
args=(counts,),
xtol=1e-4, ftol=1e-4,
disp=False, maxiter=10000)
logger.info('Parameters optimization finished')
# parse arguments and update
self.gamma, self.theta = self._vector_to_params(params_best)
def log_likelihood(self, annotations):
"""Compute the log likelihood of a set of annotations given the model.
Returns :math:`\log P(\mathbf{x} | \gamma, \\theta)`,
where :math:`\mathbf{x}` is the array of annotations.
Arguments
----------
annotations : ndarray, shape = (n_items, n_annotators)
annotations[i,j] is the annotation of annotator j for item i
Returns
-------
log_lhood : float
log likelihood of `annotations`
"""
self._raise_if_incompatible(annotations)
counts = compute_counts(annotations, self.nclasses)
return self._log_likelihood_counts(counts)
def log_likelihood(self, annotations):
"""Compute the log likelihood of a set of annotations given the model.
Returns :math:`\log P(\mathbf{x} | \omega, \\theta)`,
where :math:`\mathbf{x}` is the array of annotations.
Arguments
---------
annotations : ndarray, shape = (n_items, n_annotators)
annotations[i,j] is the annotation of annotator j for item i
Returns
-------
log_lhood : float
log likelihood of `annotations`
"""
self._raise_if_incompatible(annotations)
counts = compute_counts(annotations, self.nclasses)
return self._log_likelihood_counts(counts)
def _parameter_estimation(self, objective, annotations,
estimate_omega=True):
counts = compute_counts(annotations, self.nclasses)
params_start, omega = self._random_initial_parameters(annotations,
estimate_omega)
self.omega = omega
logger.info('Start parameters optimization...')
params_best = scipy.optimize.fmin(objective,
params_start,
args=(counts,),
xtol=1e-4, ftol=1e-4,
disp=False,
maxiter=10000)
logger.info('Parameters optimization finished')
self.theta = params_best
def _parameter_estimation(self,
objective,
annotations,
estimate_gamma=True):
counts = compute_counts(annotations, self.nclasses)
params_start = self._random_initial_parameters(annotations,
estimate_gamma)
logger.info('Start parameters optimization...')
# TODO: use gradient, constrained optimization
params_best = scipy.optimize.fmin(objective,
params_start,
args=(counts, ),
xtol=1e-4,
ftol=1e-4,
disp=False,
maxiter=10000)
logger.info('Parameters optimization finished')
# parse arguments and update
self.gamma, self.theta = self._vector_to_params(params_best)
def _parameter_estimation(self,
objective,
annotations,
estimate_omega=True):
counts = compute_counts(annotations, self.nclasses)
params_start, omega = self._random_initial_parameters(
annotations, estimate_omega)
self.omega = omega
logger.info('Start parameters optimization...')
params_best = scipy.optimize.fmin(objective,
params_start,
args=(counts, ),
xtol=1e-4,
ftol=1e-4,
disp=False,
maxiter=10000)
logger.info('Parameters optimization finished')
self.theta = params_best
def sample_posterior_over_accuracy(self,
annotations,
nsamples,
burn_in_samples=100,
thin_samples=5,
target_rejection_rate=0.3,
rejection_rate_tolerance=0.2,
step_optimization_nsamples=500,
adjust_step_every=100):
"""Return samples from posterior distribution over theta given data.
Samples are drawn using a variant of a Metropolis-Hasting Markov Chain
Monte Carlo (MCMC) algorithm. Sampling proceeds in two phases:
1) *step size estimation phase*: first, the step size in the
MCMC algorithm is adjusted to achieve a given rejection rate.
2) *sampling phase*: second, samples are collected using the
step size from phase 1.
Arguments
----------
annotations : ndarray, shape = (n_items, n_annotators)
annotations[i,j] is the annotation of annotator j for item i
nsamples : int
Number of samples to return (i.e., burn-in and thinning samples
are not included)
burn_in_samples : int
Discard the first `burn_in_samples` during the initial burn-in
phase, where the Monte Carlo chain converges to the posterior
thin_samples : int
Only return one every `thin_samples` samples in order to reduce
the auto-correlation in the sampling chain. This is called
"thinning" in MCMC parlance.
target_rejection_rate : float
target rejection rate for the step size estimation phase
rejection_rate_tolerance : float
the step size estimation phase is ended when the rejection rate for
all parameters is within `rejection_rate_tolerance` from
`target_rejection_rate`
step_optimization_nsamples : int
number of samples to draw in the step size estimation phase
adjust_step_every : int
number of samples after which the step size is adjusted during
the step size estimation pahse
Returns
-------
samples : ndarray, shape = (n_samples, n_annotators)
samples[i,:] is one sample from the posterior distribution over the
parameters `theta`
"""
self._raise_if_incompatible(annotations)
nsamples = self._compute_total_nsamples(nsamples, burn_in_samples,
thin_samples)
# optimize step size
counts = compute_counts(annotations, self.nclasses)
# wrap log likelihood function to give it to optimize_step_size and
# sample_distribution
_llhood_counts = self._log_likelihood_counts
_log_prior = self._log_prior
def _wrap_llhood(params, counts):
self.theta = params
return _llhood_counts(counts) + _log_prior()
# TODO this save-reset is rather ugly, refactor: create copy of
# model and sample over it
# save internal parameters to reset at the end of sampling
save_params = (self.gamma, self.theta)
try:
# compute optimal step size for given target rejection rate
params_start = self.theta.copy()
params_upper = np.ones((self.nannotators, ))
params_lower = np.zeros((self.nannotators, ))
step = optimize_step_size(_wrap_llhood, params_start, counts,
params_lower, params_upper,
step_optimization_nsamples,
adjust_step_every, target_rejection_rate,
rejection_rate_tolerance)
# draw samples from posterior distribution over theta
samples = sample_distribution(_wrap_llhood, params_start, counts,
step, nsamples, params_lower,
params_upper)
return self._post_process_samples(samples, burn_in_samples,
thin_samples)
finally:
# reset parameters
self.gamma, self.theta = save_params
def sample_posterior_over_accuracy(self, annotations, nsamples,
burn_in_samples = 100,
thin_samples = 5,
target_rejection_rate = 0.3,
rejection_rate_tolerance = 0.2,
step_optimization_nsamples = 500,
adjust_step_every = 100):
"""Return samples from posterior distribution over theta given data.
Samples are drawn using a variant of a Metropolis-Hasting Markov Chain
Monte Carlo (MCMC) algorithm. Sampling proceeds in two phases:
1) *step size estimation phase*: first, the step size in the
MCMC algorithm is adjusted to achieve a given rejection rate.
2) *sampling phase*: second, samples are collected using the
step size from phase 1.
Arguments
---------
annotations : ndarray, shape = (n_items, n_annotators)
annotations[i,j] is the annotation of annotator j for item i
nsamples : int
number of samples to draw from the posterior
burn_in_samples : int
Discard the first `burn_in_samples` during the initial burn-in
phase, where the Monte Carlo chain converges to the posterior
thin_samples : int
Only return one every `thin_samples` samples in order to reduce
the auto-correlation in the sampling chain. This is called
"thinning" in MCMC parlance.
target_rejection_rate : float
target rejection rate for the step size estimation phase
rejection_rate_tolerance : float
the step size estimation phase is ended when the rejection rate for
all parameters is within `rejection_rate_tolerance` from
`target_rejection_rate`
step_optimization_nsamples : int
number of samples to draw in the step size estimation phase
adjust_step_every : int
number of samples after which the step size is adjusted during
the step size estimation pahse
Returns
-------
samples : ndarray, shape = (n_samples, n_annotators)
samples[i,:] is one sample from the posterior distribution over the
parameters `theta`
"""
self._raise_if_incompatible(annotations)
nsamples = self._compute_total_nsamples(nsamples,
burn_in_samples,
thin_samples)
# optimize step size
counts = compute_counts(annotations, self.nclasses)
# wrap log likelihood function to give it to optimize_step_size and
# sample_distribution
_llhood_counts = self._log_likelihood_counts
_log_prior = self._log_prior
def _wrap_llhood(params, counts):
self.theta = params
return _llhood_counts(counts) + _log_prior()
# TODO this save-reset is rather ugly, refactor: create copy of
# model and sample over it
# save internal parameters to reset at the end of sampling
save_params = self.theta
try:
# compute optimal step size for given target rejection rate
params_start = self.theta.copy()
params_upper = np.ones((self.nannotators,))
params_lower = np.zeros((self.nannotators,))
step = optimize_step_size(_wrap_llhood, params_start, counts,
params_lower, params_upper,
step_optimization_nsamples,
adjust_step_every,
target_rejection_rate,
rejection_rate_tolerance)
# draw samples from posterior distribution over theta
samples = sample_distribution(_wrap_llhood, params_start, counts,
step, nsamples,
params_lower, params_upper)
return self._post_process_samples(samples, burn_in_samples,
thin_samples)
finally:
# reset parameters
self.theta = save_params
