See log_plus for an explanation why None acts as a special case "float" number.
-TODO: Separate optimizations specific to PLRsearch
- and distribute as a standalone package so other projects may reuse.
+TODO: Separate optimizations specific to PLRsearch and distribute the rest
+ as a standalone package so other projects may reuse.
"""
-import math
+import copy
import traceback
import dill
-import numpy
+from numpy import random
-# TODO: The preferred way to consume this code is via a pip package.
-# If your project copies code instead, make sure your pylint check does not
-# require these imports to be absolute and descending from your superpackage.
-from log_plus import log_plus
+# TODO: Teach FD.io CSIT to use multiple dirs in PYTHONPATH,
+# then switch to absolute imports within PLRsearch package.
+# Current usage of relative imports is just a short term workaround.
+import stat_trackers # pylint: disable=relative-import
-def try_estimate_nd(communication_pipe, scale_coeff=10.0, trace_enabled=False):
+def try_estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
"""Call estimate_nd but catch any exception and send traceback."""
try:
return estimate_nd(communication_pipe, scale_coeff, trace_enabled)
raise
-# FIXME: Pylint reports multiple complexity violations.
+# TODO: Pylint reports multiple complexity violations.
# Refactor the code, using less (but structured) variables
# and function calls for (relatively) loosly coupled code blocks.
-def estimate_nd(communication_pipe, scale_coeff=10.0, trace_enabled=False):
+def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False):
"""Use Bayesian inference from control queue, put result to result queue.
TODO: Use a logging framework that works in a user friendly way.
Anyway, the current implementation with trace_enabled looks ugly.)
The result is average and standard deviation for posterior distribution
- of a single dependent (positive scalar) value.
+ of a single dependent (scalar, float) value.
The prior is assumed to be uniform on (-1, 1) for every parameter.
Number of parameters and the function for computing
the dependent value and likelihood both come from input.
thus integration algorithm returns also the distribution data,
to be used as initial focus in next iteration.
- After some number of samples (depends on dimension),
- the algorithm starts tracking few most likely samples to base the Gaussian
- distribution around, mixing with estimates from observed samples.
- The idea is that even when (usually) one of the samples dominates,
- first few are treated as if equally likely, to get reasonable focus.
- During the "find the maximum" phase, this set of best samples
- frequently takes a wrong shape (compared to observed samples
- in equilibrium). Therefore scale_coeff argument is left for humans to tweak,
+ There are workarounds in place that allow old or default focus tracker
+ to be updated reasonably, even when initial samples
+ of new iteration have way smaller (or larger) weights.
+
+ During the "find the maximum" phase, the focus tracker frequently takes
+ a wrong shape (compared to observed samples in equilibrium).
+ Therefore scale_coeff argument is left for humans to tweak,
so the convergence is reliable and quick.
- Any data (other than correctly weighted samples) used to keep
- distribution shape reasonable is called "bias", regardles of
- whether it comes from input hint, or from tracking top samples.
Until the distribution locates itself roughly around
the maximum likeligood point, the integration results are probably wrong.
That means some minimal time is needed for the result to become reliable.
+
+ TODO: The folowing is not currently implemented.
The reported standard distribution attempts to signal inconsistence
(when one sample has dominating weight compared to the rest of samples),
but some human supervision is strongly encouraged.
To facilitate running in worker processes, arguments and results
- are communicated via pipe. The computation does not start
+ are communicated via a pipe. The computation does not start
until arguments appear in the pipe, the computation stops
when another item (stop object) is detected in the pipe
(and result is put to pipe).
- dilled_function: Function (serialized using dill), which:
- - Takes the dimension number of float parameters from (-1, 1).
- - Returns float 2-tuple of dependent value and parameter log-likelihood.
- - param_hint_avg: Dimension-tuple of floats to start searching around.
- - param_hint_cov: Covariance matrix defining initial focus shape.
+ - param_focus_tracker: VectorStatTracker to use for initial focus.
+ - max_samples: None or a limit for samples to use.
Output/result object (sent to pipe queue)
- is a 6-tuple of the following fields:
- - value_avg: Float estimate of posterior average dependent value.
- - value_stdev: Float estimate of posterior standard deviation of the value.
- - param_importance_avg: Float tuple, center of Gaussian to use next.
- - param_importance_cov: Float covariance matrix of the Gaussian to use next.
+ is a 5-tuple of the following fields:
+ - value_tracker: ScalarDualStatTracker estimate of value posterior.
+ - param_focus_tracker: VectorStatTracker to use for initial focus next.
- debug_list: List of debug strings to log at main process.
- trace_list: List of trace strings to pass to main process if enabled.
+ - samples: Number of samples used in computation (to make it reproducible).
Trace strings are very verbose, it is not recommended to enable them.
In they are not enabled, trace_list will be empty.
It is recommended to edit some lines manually to debug_list if needed.
(due to rounding errors). Try changing scale_coeff.
"""
- # Block until input object appears.
- dimension, dilled_function, param_hint_avg, param_hint_cov = (
- communication_pipe.recv())
debug_list = list()
trace_list = list()
+ # Block until input object appears.
+ dimension, dilled_function, param_focus_tracker, max_samples = (
+ communication_pipe.recv())
+ debug_list.append("Called with param_focus_tracker {tracker!r}"
+ .format(tracker=param_focus_tracker))
def trace(name, value):
"""
Add a variable (name and value) to trace list (if enabled).
if trace_enabled:
trace_list.append(name + " " + repr(value))
value_logweight_function = dill.loads(dilled_function)
- len_top = (dimension + 2) * (dimension + 1) / 2
- top_weight_param = list()
samples = 0
- log_sum_weight = None
# Importance sampling produces samples of higher weight (important)
# more frequently, and corrects that by adding weight bonus
# for the less frequently (unimportant) samples.
# But "corrected_weight" is too close to "weight" to be readable,
# so "importance" is used instead, even if it runs contrary to what
# important region is.
- log_sum_importance = None
- log_importance_best = None
- value_avg = 0.0
- # 1x1 dimensional covariance matrix is just variance.
- # As variance is never negative, we can track logarithm.
- value_log_variance = None
- # Here "secondary" means "excluding the weightest sample".
- log_secondary_sum_importance = None
- value_secondary_avg = 0.0
- value_log_secondary_variance = None
- param_sampled_avg = [0.0 for first in range(dimension)]
- # TODO: Examine whether we can gain speed by tracking triangle only.
- # Covariance matrix can contain negative element (off-diagonal),
- # so no logarithm here. This can lead to zeroes on diagonal,
- # but we have biasing to make sure it does not hurt much.
- param_sampled_cov = [[0.0 for first in range(dimension)]
- for second in range(dimension)]
- # The next two variables do NOT need to be initialized here,
- # but pylint is not a mathematician enough to understand that.
- param_top_avg = [0.0 for first in range(dimension)]
- param_top_cov = [[0.0 for first in range(dimension)]
- for second in range(dimension)]
- if not (param_hint_avg and param_hint_cov):
- # First call has Nones instead of useful hints.
- param_hint_avg = [0.0 for first in range(dimension)]
- param_hint_cov = [
- [1.0 if first == second else 0.0 for first in range(dimension)]
- for second in range(dimension)]
+ value_tracker = stat_trackers.ScalarDualStatTracker()
+ param_sampled_tracker = stat_trackers.VectorStatTracker(dimension).reset()
+ if not param_focus_tracker:
+ # First call has None instead of a real (even empty) tracker.
+ param_focus_tracker = stat_trackers.VectorStatTracker(dimension)
+ param_focus_tracker.unit_reset()
+ else:
+ # Focus tracker has probably too high weight.
+ param_focus_tracker.log_sum_weight = None
+ # TODO: Teach pylint the used version of numpy.random does have this member.
+ random.seed(0)
while not communication_pipe.poll():
- # Compute focus data.
- if len(top_weight_param) < len_top:
- # Not enough samples for reasonable top, use hint bias.
- param_focus_avg = param_hint_avg
- param_focus_cov = param_hint_cov
- else:
- # We have both top samples and overall samples.
- # Mix them according to how much the weightest sample dominates.
- log_top_weight = top_weight_param[0][0]
- log_weight_norm = log_plus(log_sum_weight, log_top_weight)
- top_ratio = math.exp(log_top_weight - log_weight_norm)
- sampled_ratio = math.exp(log_sum_weight - log_weight_norm)
- trace("log_top_weight", log_top_weight)
- trace("log_sum_weight", log_sum_weight)
- trace("top_ratio", top_ratio)
- trace("sampled_ratio", sampled_ratio)
- param_focus_avg = [
- sampled_ratio * param_sampled_avg[first]
- + top_ratio * param_top_avg[first]
- for first in range(dimension)]
- param_focus_cov = [[
- scale_coeff * (
- sampled_ratio * param_sampled_cov[first][second]
- + top_ratio * param_top_cov[first][second])
- for first in range(dimension)] for second in range(dimension)]
- trace("param_focus_avg", param_focus_avg)
- trace("param_focus_cov", param_focus_cov)
+ if max_samples and samples >= max_samples:
+ break
# Generate next sample.
+ averages = param_focus_tracker.averages
+ covariance_matrix = copy.deepcopy(param_focus_tracker.covariance_matrix)
+ for first in range(dimension):
+ for second in range(dimension):
+ covariance_matrix[first][second] *= scale_coeff
while 1:
- # TODO: Inform pylint that correct version of numpy is available.
- sample_point = numpy.random.multivariate_normal(
- param_focus_avg, param_focus_cov, 1)[0]
+ # TODO: Teach pylint that numpy.random does also have this member.
+ sample_point = random.multivariate_normal(
+ averages, covariance_matrix, 1)[0].tolist()
# Multivariate Gauss can fall outside (-1, 1) interval
for first in range(dimension):
sample_coordinate = sample_point[first]
break
trace("sample_point", sample_point)
samples += 1
- value, log_weight = value_logweight_function(*sample_point)
+ trace("samples", samples)
+ value, log_weight = value_logweight_function(trace, *sample_point)
trace("value", value)
trace("log_weight", log_weight)
- # Update bias related statistics.
- log_sum_weight = log_plus(log_sum_weight, log_weight)
- if len(top_weight_param) < len_top:
- top_weight_param.append((log_weight, sample_point))
- # Hack: top_weight_param[-1] is either the smallest,
- # or the just appended to len_top-1 item list.
- if (len(top_weight_param) >= len_top
- and log_weight >= top_weight_param[-1][0]):
- top_weight_param = top_weight_param[:-1]
- top_weight_param.append((log_weight, sample_point))
- top_weight_param.sort(key=lambda item: -item[0])
- trace("top_weight_param", top_weight_param)
- # top_weight_param has changed, recompute biases.
- param_top_avg = top_weight_param[0][1]
- param_top_cov = [[0.0 for first in range(dimension)]
- for second in range(dimension)]
- top_item_count = 1
- for _, near_top_param in top_weight_param[1:]:
- top_item_count += 1
- next_item_ratio = 1.0 / top_item_count
- previous_items_ratio = 1.0 - next_item_ratio
- param_shift = [
- near_top_param[first] - param_top_avg[first]
- for first in range(dimension)]
- # Do not move center from the weightest sample.
- for second in range(dimension):
- for first in range(dimension):
- param_top_cov[first][second] += (
- param_shift[first] * param_shift[second]
- * next_item_ratio)
- param_top_cov[first][second] *= previous_items_ratio
- trace("param_top_avg", param_top_avg)
- trace("param_top_cov", param_top_cov)
+ trace("focus tracker before adding", param_focus_tracker)
+ # Update focus related statistics.
+ param_distance = param_focus_tracker.add_without_dominance_get_distance(
+ sample_point, log_weight)
# The code above looked at weight (not importance).
# The code below looks at importance (not weight).
- param_shift = [sample_point[first] - param_focus_avg[first]
- for first in range(dimension)]
- rarity_gradient = numpy.linalg.solve(param_focus_cov, param_shift)
- rarity_step = numpy.vdot(param_shift, rarity_gradient)
- log_rarity = rarity_step / 2.0
+ log_rarity = param_distance / 2.0
trace("log_rarity", log_rarity)
- trace("samples", samples)
log_importance = log_weight + log_rarity
trace("log_importance", log_importance)
+ value_tracker.add(value, log_importance)
# Update sampled statistics.
- old_log_sum_importance = log_sum_importance
- log_sum_importance = log_plus(old_log_sum_importance, log_importance)
- trace("new log_sum_weight", log_sum_weight)
- trace("log_sum_importance", log_sum_importance)
- if old_log_sum_importance is None:
- param_sampled_avg = list(sample_point)
- value_avg = value
- # Other value related quantities stay None.
- continue
- previous_samples_ratio = math.exp(
- old_log_sum_importance - log_sum_importance)
- new_sample_ratio = math.exp(log_importance - log_sum_importance)
- param_shift = [sample_point[first] - param_sampled_avg[first]
- for first in range(dimension)]
- value_shift = value - value_avg
- for first in range(dimension):
- param_sampled_avg[first] += param_shift[first] * new_sample_ratio
- old_value_avg = value_avg
- value_avg += value_shift * new_sample_ratio
- value_absolute_shift = abs(value_shift)
- for second in range(dimension):
- for first in range(dimension):
- param_sampled_cov[first][second] += (
- param_shift[first] * param_shift[second] * new_sample_ratio)
- param_sampled_cov[first][second] *= previous_samples_ratio
- trace("param_sampled_avg", param_sampled_avg)
- trace("param_sampled_cov", param_sampled_cov)
- update_secondary_stats = True
- if log_importance_best is None or log_importance > log_importance_best:
- log_importance_best = log_importance
- log_secondary_sum_importance = old_log_sum_importance
- value_secondary_avg = old_value_avg
- value_log_secondary_variance = value_log_variance
- update_secondary_stats = False
- # TODO: Update all primary quantities before secondary ones.
- # (As opposed to current hybrid code.)
- if value_absolute_shift > 0.0:
- value_log_variance = log_plus(
- value_log_variance, 2 * math.log(value_absolute_shift)
- + log_importance - log_sum_importance)
- if value_log_variance is not None:
- value_log_variance -= log_sum_importance - old_log_sum_importance
- if not update_secondary_stats:
- continue
- # TODO: Pylint says the following variable name is bad.
- # Make sure the refactor uses shorter names.
- old_log_secondary_sum_importance = log_secondary_sum_importance
- log_secondary_sum_importance = log_plus(
- old_log_secondary_sum_importance, log_importance)
- if old_log_secondary_sum_importance is None:
- value_secondary_avg = value
- continue
- new_sample_secondary_ratio = math.exp(
- log_importance - log_secondary_sum_importance)
- # TODO: No would-be variable named old_value_secondary_avg
- # appears in subsequent computations. Probably means there is a bug.
- value_secondary_shift = value - value_secondary_avg
- value_secondary_absolute_shift = abs(value_secondary_shift)
- value_secondary_avg += (
- value_secondary_shift * new_sample_secondary_ratio)
- if value_secondary_absolute_shift > 0.0:
- value_log_secondary_variance = log_plus(
- value_log_secondary_variance, (
- 2 * math.log(value_secondary_absolute_shift)
- + log_importance - log_secondary_sum_importance))
- if value_log_secondary_variance is not None:
- value_log_secondary_variance -= (
- log_secondary_sum_importance - old_log_secondary_sum_importance)
+ param_sampled_tracker.add_get_shift(sample_point, log_importance)
debug_list.append("integrator used " + str(samples) + " samples")
- debug_list.append(
- "value_avg " + str(value_avg)
- + " param_sampled_avg " + repr(param_sampled_avg)
- + " param_sampled_cov " + repr(param_sampled_cov)
- + " value_log_variance " + str(value_log_variance)
- + " value_log_secondary_variance " + str(value_log_secondary_variance))
- value_stdev = math.exp(
- (2 * value_log_variance - value_log_secondary_variance) / 2.0)
- debug_list.append("top_weight_param[0] " + repr(top_weight_param[0]))
- # Intentionally returning param_focus_avg and param_focus_cov,
- # instead of possibly hyper-focused bias or sampled.
+ debug_list.append(" ".join([
+ "value_avg", str(value_tracker.average),
+ "param_sampled_avg", repr(param_sampled_tracker.averages),
+ "param_sampled_cov", repr(param_sampled_tracker.covariance_matrix),
+ "value_log_variance", str(value_tracker.log_variance),
+ "value_log_secondary_variance",
+ str(value_tracker.secondary.log_variance)]))
communication_pipe.send(
- (value_avg, value_stdev, param_focus_avg, param_focus_cov, debug_list,
- trace_list))
+ (value_tracker, param_focus_tracker, debug_list, trace_list, samples))
Code Review / csit.git / blobdiff