diff options
Diffstat (limited to 'resources/libraries/python/PLRsearch')
| -rw-r--r-- | resources/libraries/python/PLRsearch/Integrator.py | 59 | ||||
| -rw-r--r-- | resources/libraries/python/PLRsearch/PLRsearch.py | 186 | ||||
| -rw-r--r-- | resources/libraries/python/PLRsearch/log_plus.py | 8 | ||||
| -rw-r--r-- | resources/libraries/python/PLRsearch/stat_trackers.py | 58 |
4 files changed, 192 insertions, 119 deletions
diff --git a/resources/libraries/python/PLRsearch/Integrator.py b/resources/libraries/python/PLRsearch/Integrator.py index a7a59391ed..cc8f838fe6 100644 --- a/resources/libraries/python/PLRsearch/Integrator.py +++ b/resources/libraries/python/PLRsearch/Integrator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021 Cisco and/or its affiliates. +# Copyright (c) 2024 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -189,12 +189,15 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False): :raises numpy.linalg.LinAlgError: If the focus shape gets singular (due to rounding errors). Try changing scale_coeff. """ - debug_list = list() - trace_list = list() + debug_list = [] + trace_list = [] # Block until input object appears. - dimension, dilled_function, param_focus_tracker, max_samples = ( - communication_pipe.recv() - ) + ( + dimension, + dilled_function, + param_focus_tracker, + max_samples, + ) = communication_pipe.recv() debug_list.append( f"Called with param_focus_tracker {param_focus_tracker!r}" ) @@ -237,39 +240,47 @@ def estimate_nd(communication_pipe, scale_coeff=8.0, trace_enabled=False): if max_samples and samples >= max_samples: break sample_point = generate_sample( - param_focus_tracker.averages, param_focus_tracker.covariance_matrix, - dimension, scale_coeff + param_focus_tracker.averages, + param_focus_tracker.covariance_matrix, + dimension, + scale_coeff, ) - trace(u"sample_point", sample_point) + trace("sample_point", sample_point) samples += 1 - trace(u"samples", samples) + trace("samples", samples) value, log_weight = value_logweight_function(trace, *sample_point) - trace(u"value", value) - trace(u"log_weight", log_weight) - trace(u"focus tracker before adding", param_focus_tracker) + trace("value", value) + trace("log_weight", log_weight) + 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). - log_rarity = param_distance / 2.0 - trace(u"log_rarity", log_rarity) + log_rarity = param_distance / 2.0 / scale_coeff + trace("log_rarity", log_rarity) log_importance = log_weight + log_rarity - trace(u"log_importance", log_importance) + trace("log_importance", log_importance) value_tracker.add(value, log_importance) # Update sampled statistics. param_sampled_tracker.add_get_shift(sample_point, log_importance) debug_list.append(f"integrator used {samples!s} samples") debug_list.append( - u" ".join([ - u"value_avg", str(value_tracker.average), - u"param_sampled_avg", repr(param_sampled_tracker.averages), - u"param_sampled_cov", repr(param_sampled_tracker.covariance_matrix), - u"value_log_variance", str(value_tracker.log_variance), - u"value_log_secondary_variance", - str(value_tracker.secondary.log_variance) - ]) + " ".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_tracker, param_focus_tracker, debug_list, trace_list, samples) diff --git a/resources/libraries/python/PLRsearch/PLRsearch.py b/resources/libraries/python/PLRsearch/PLRsearch.py index 0e78cc936d..326aa2e2d2 100644 --- a/resources/libraries/python/PLRsearch/PLRsearch.py +++ b/resources/libraries/python/PLRsearch/PLRsearch.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022 Cisco and/or its affiliates. +# Copyright (c) 2024 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -53,8 +53,14 @@ class PLRsearch: log_xerfcx_10 = math.log(xerfcx_limit - math.exp(10) * erfcx(math.exp(10))) def __init__( - self, measurer, trial_duration_per_trial, packet_loss_ratio_target, - trial_number_offset=0, timeout=7200.0, trace_enabled=False): + self, + measurer, + trial_duration_per_trial, + packet_loss_ratio_target, + trial_number_offset=0, + timeout=7200.0, + trace_enabled=False, + ): """Store rate measurer and additional parameters. The measurer must never report negative loss count. @@ -176,7 +182,7 @@ class PLRsearch: f"Started search with min_rate {min_rate!r}, " f"max_rate {max_rate!r}" ) - trial_result_list = list() + trial_result_list = [] trial_number = self.trial_number_offset focus_trackers = (None, None) transmit_rate = (min_rate + max_rate) / 2.0 @@ -186,34 +192,54 @@ class PLRsearch: trial_number += 1 logging.info(f"Trial {trial_number!r}") results = self.measure_and_compute( - self.trial_duration_per_trial * trial_number, transmit_rate, - trial_result_list, min_rate, max_rate, focus_trackers + self.trial_duration_per_trial * trial_number, + transmit_rate, + trial_result_list, + min_rate, + max_rate, + focus_trackers, ) measurement, average, stdev, avg1, avg2, focus_trackers = results + # Workaround for unsent packets and other anomalies. + measurement.plr_loss_count = min( + measurement.intended_count, + int(measurement.intended_count * measurement.loss_ratio + 0.9), + ) + logging.debug( + f"loss ratio {measurement.plr_loss_count}" + f" / {measurement.intended_count}" + ) zeros += 1 # TODO: Ratio of fill rate to drain rate seems to have # exponential impact. Make it configurable, or is 4:3 good enough? - if measurement.loss_ratio >= self.packet_loss_ratio_target: + if measurement.plr_loss_count >= ( + measurement.intended_count * self.packet_loss_ratio_target + ): for _ in range(4 * zeros): - lossy_loads.append(measurement.target_tr) - if measurement.loss_count > 0: + lossy_loads.append(measurement.intended_load) + lossy_loads.sort() zeros = 0 - lossy_loads.sort() + logging.debug("High enough loss, lossy loads added.") + else: + logging.debug( + f"Not a high loss, zero counter bumped to {zeros}." + ) if stop_time <= time.time(): return average, stdev trial_result_list.append(measurement) if (trial_number - self.trial_number_offset) <= 1: next_load = max_rate elif (trial_number - self.trial_number_offset) <= 3: - next_load = (measurement.relative_receive_rate / ( - 1.0 - self.packet_loss_ratio_target)) + next_load = measurement.relative_forwarding_rate / ( + 1.0 - self.packet_loss_ratio_target + ) else: next_load = (avg1 + avg2) / 2.0 if zeros > 0: if lossy_loads[0] > next_load: diminisher = math.pow(2.0, 1 - zeros) next_load = lossy_loads[0] + diminisher * next_load - next_load /= (1.0 + diminisher) + next_load /= 1.0 + diminisher # On zero measurement, we need to drain obsoleted low losses # even if we did not use them to increase next_load, # in order to get to usable loses at higher loads. @@ -263,22 +289,22 @@ class PLRsearch: # TODO: chi is from https://en.wikipedia.org/wiki/Nondimensionalization chi = (load - mrr) / spread chi0 = -mrr / spread - trace(u"stretch: load", load) - trace(u"mrr", mrr) - trace(u"spread", spread) - trace(u"chi", chi) - trace(u"chi0", chi0) + trace("stretch: load", load) + trace("mrr", mrr) + trace("spread", spread) + trace("chi", chi) + trace("chi0", chi0) if chi > 0: log_lps = math.log( load - mrr + (log_plus(0, -chi) - log_plus(0, chi0)) * spread ) - trace(u"big loss direct log_lps", log_lps) + trace("big loss direct log_lps", log_lps) else: two_positive = log_plus(chi, 2 * chi0 - log_2) two_negative = log_plus(chi0, 2 * chi - log_2) if two_positive <= two_negative: log_lps = log_minus(chi, chi0) + log_spread - trace(u"small loss crude log_lps", log_lps) + trace("small loss crude log_lps", log_lps) return log_lps two = log_minus(two_positive, two_negative) three_positive = log_plus(two_positive, 3 * chi - log_3) @@ -286,11 +312,11 @@ class PLRsearch: three = log_minus(three_positive, three_negative) if two == three: log_lps = two + log_spread - trace(u"small loss approx log_lps", log_lps) + trace("small loss approx log_lps", log_lps) else: log_lps = math.log(log_plus(0, chi) - log_plus(0, chi0)) log_lps += log_spread - trace(u"small loss direct log_lps", log_lps) + trace("small loss direct log_lps", log_lps) return log_lps @staticmethod @@ -329,26 +355,26 @@ class PLRsearch: # TODO: The stretch sign is just to have less minuses. Worth changing? chi = (mrr - load) / spread chi0 = mrr / spread - trace(u"Erf: load", load) - trace(u"mrr", mrr) - trace(u"spread", spread) - trace(u"chi", chi) - trace(u"chi0", chi0) + trace("Erf: load", load) + trace("mrr", mrr) + trace("spread", spread) + trace("chi", chi) + trace("chi0", chi0) if chi >= -1.0: - trace(u"positive, b roughly bigger than m", None) + trace("positive, b roughly bigger than m", None) if chi > math.exp(10): first = PLRsearch.log_xerfcx_10 + 2 * (math.log(chi) - 10) - trace(u"approximated first", first) + trace("approximated first", first) else: first = math.log(PLRsearch.xerfcx_limit - chi * erfcx(chi)) - trace(u"exact first", first) + trace("exact first", first) first -= chi * chi second = math.log(PLRsearch.xerfcx_limit - chi * erfcx(chi0)) second -= chi0 * chi0 intermediate = log_minus(first, second) - trace(u"first", first) + trace("first", first) else: - trace(u"negative, b roughly smaller than m", None) + trace("negative, b roughly smaller than m", None) exp_first = PLRsearch.xerfcx_limit + chi * erfcx(-chi) exp_first *= math.exp(-chi * chi) exp_first -= 2 * chi @@ -359,17 +385,17 @@ class PLRsearch: second = math.log(PLRsearch.xerfcx_limit - chi * erfcx(chi0)) second -= chi0 * chi0 intermediate = math.log(exp_first - math.exp(second)) - trace(u"exp_first", exp_first) - trace(u"second", second) - trace(u"intermediate", intermediate) + trace("exp_first", exp_first) + trace("second", second) + trace("intermediate", intermediate) result = intermediate + math.log(spread) - math.log(erfc(-chi0)) - trace(u"result", result) + trace("result", result) return result @staticmethod def find_critical_rate( - trace, lfit_func, min_rate, max_rate, loss_ratio_target, - mrr, spread): + trace, lfit_func, min_rate, max_rate, loss_ratio_target, mrr, spread + ): """Given ratio target and parameters, return the achieving offered load. This is basically an inverse function to lfit_func @@ -411,12 +437,12 @@ class PLRsearch: loss_rate = math.exp(lfit_func(trace, rate, mrr, spread)) loss_ratio = loss_rate / rate if loss_ratio > loss_ratio_target: - trace(u"halving down", rate) + trace("halving down", rate) rate_hi = rate elif loss_ratio < loss_ratio_target: - trace(u"halving up", rate) + trace("halving up", rate) rate_lo = rate - trace(u"found", rate) + trace("found", rate) return rate @staticmethod @@ -441,7 +467,7 @@ class PLRsearch: Instead, the expected average loss is scaled according to the number of packets actually sent. - TODO: Copy ReceiveRateMeasurement from MLRsearch. + TODO: Copy MeasurementResult from MLRsearch. :param trace: A multiprocessing-friendly logging function (closure). :param lfit_func: Fitting function, typically lfit_spread or lfit_erf. @@ -450,40 +476,47 @@ class PLRsearch: :param spread: The spread parameter for the fitting function. :type trace: function (str, object) -> None :type lfit_func: Function from 3 floats to float. - :type trial_result_list: list of MLRsearch.ReceiveRateMeasurement + :type trial_result_list: list of MLRsearch.MeasurementResult :type mrr: float :type spread: float :returns: Logarithm of result weight for given function and parameters. :rtype: float """ log_likelihood = 0.0 - trace(u"log_weight for mrr", mrr) - trace(u"spread", spread) + trace("log_weight for mrr", mrr) + trace("spread", spread) for result in trial_result_list: - trace(u"for tr", result.target_tr) - trace(u"lc", result.loss_count) - trace(u"d", result.duration) - # _rel_ values use units of target_tr (transactions per second). + trace("for tr", result.intended_load) + trace("plc", result.plr_loss_count) + trace("d", result.intended_duration) + # _rel_ values use units of intended_load (transactions per second). log_avg_rel_loss_per_second = lfit_func( - trace, result.target_tr, mrr, spread + trace, result.intended_load, mrr, spread ) # _abs_ values use units of loss count (maybe packets). # There can be multiple packets per transaction. log_avg_abs_loss_per_trial = log_avg_rel_loss_per_second + math.log( - result.transmit_count / result.target_tr + result.offered_count / result.intended_load ) # Geometric probability computation for logarithms. log_trial_likelihood = log_plus(0.0, -log_avg_abs_loss_per_trial) - log_trial_likelihood *= -result.loss_count + log_trial_likelihood *= -result.plr_loss_count log_trial_likelihood -= log_plus(0.0, +log_avg_abs_loss_per_trial) log_likelihood += log_trial_likelihood - trace(u"avg_loss_per_trial", math.exp(log_avg_abs_loss_per_trial)) - trace(u"log_trial_likelihood", log_trial_likelihood) + trace("avg_loss_per_trial", math.exp(log_avg_abs_loss_per_trial)) + trace("log_trial_likelihood", log_trial_likelihood) return log_likelihood def measure_and_compute( - self, trial_duration, transmit_rate, trial_result_list, - min_rate, max_rate, focus_trackers=(None, None), max_samples=None): + self, + trial_duration, + transmit_rate, + trial_result_list, + min_rate, + max_rate, + focus_trackers=(None, None), + max_samples=None, + ): """Perform both measurement and computation at once. High level steps: Prepare and launch computation worker processes, @@ -524,7 +557,7 @@ class PLRsearch: :param max_samples: Limit for integrator samples, for debugging. :type trial_duration: float :type transmit_rate: float - :type trial_result_list: list of MLRsearch.ReceiveRateMeasurement + :type trial_result_list: list of MLRsearch.MeasurementResult :type min_rate: float :type max_rate: float :type focus_trackers: 2-tuple of None or stat_trackers.VectorStatTracker @@ -572,7 +605,7 @@ class PLRsearch: # See https://stackoverflow.com/questions/15137292/large-objects-and-multiprocessing-pipes-and-send worker = multiprocessing.Process( target=Integrator.try_estimate_nd, - args=(worker_pipe_end, 10.0, self.trace_enabled) + args=(worker_pipe_end, 5.0, self.trace_enabled), ) worker.daemon = True worker.start() @@ -616,8 +649,13 @@ class PLRsearch: ) value = math.log( self.find_critical_rate( - trace, fitting_function, min_rate, max_rate, - self.packet_loss_ratio_target, mrr, spread + trace, + fitting_function, + min_rate, + max_rate, + self.packet_loss_ratio_target, + mrr, + spread, ) ) return value, logweight @@ -664,14 +702,18 @@ class PLRsearch: raise RuntimeError(f"Worker {name} did not finish!") result_or_traceback = pipe.recv() try: - value_tracker, focus_tracker, debug_list, trace_list, sampls = ( - result_or_traceback - ) - except ValueError: + ( + value_tracker, + focus_tracker, + debug_list, + trace_list, + sampls, + ) = result_or_traceback + except ValueError as exc: raise RuntimeError( f"Worker {name} failed with the following traceback:\n" f"{result_or_traceback}" - ) + ) from exc logging.info(f"Logs from worker {name!r}:") for message in debug_list: logging.info(message) @@ -682,8 +724,8 @@ class PLRsearch: ) return _PartialResult(value_tracker, focus_tracker, sampls) - stretch_result = stop_computing(u"stretch", stretch_pipe) - erf_result = stop_computing(u"erf", erf_pipe) + stretch_result = stop_computing("stretch", stretch_pipe) + erf_result = stop_computing("erf", erf_pipe) result = PLRsearch._get_result(measurement, stretch_result, erf_result) logging.info( f"measure_and_compute finished with trial result " @@ -705,7 +747,7 @@ class PLRsearch: :param measurement: The trial measurement obtained during computation. :param stretch_result: Computation output for stretch fitting function. :param erf_result: Computation output for erf fitting function. - :type measurement: ReceiveRateMeasurement + :type measurement: MeasurementResult :type stretch_result: _PartialResult :type erf_result: _PartialResult :returns: Combined results. @@ -730,7 +772,7 @@ class PLRsearch: # Named tuples, for multiple local variables to be passed as return value. _PartialResult = namedtuple( - u"_PartialResult", u"value_tracker focus_tracker samples" + "_PartialResult", "value_tracker focus_tracker samples" ) """Two stat trackers and sample counter. @@ -743,8 +785,8 @@ _PartialResult = namedtuple( """ _ComputeResult = namedtuple( - u"_ComputeResult", - u"measurement avg stdev stretch_exp_avg erf_exp_avg trackers" + "_ComputeResult", + "measurement avg stdev stretch_exp_avg erf_exp_avg trackers", ) """Measurement, 4 computation result values, pair of trackers. @@ -754,7 +796,7 @@ _ComputeResult = namedtuple( :param stretch_exp_avg: Stretch fitting function estimate average exponentiated. :param erf_exp_avg: Erf fitting function estimate average, exponentiated. :param trackers: Pair of focus trackers to start next iteration with. -:type measurement: ReceiveRateMeasurement +:type measurement: MeasurementResult :type avg: float :type stdev: float :type stretch_exp_avg: float diff --git a/resources/libraries/python/PLRsearch/log_plus.py b/resources/libraries/python/PLRsearch/log_plus.py index 8ede2909c6..aabefdb5be 100644 --- a/resources/libraries/python/PLRsearch/log_plus.py +++ b/resources/libraries/python/PLRsearch/log_plus.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021 Cisco and/or its affiliates. +# Copyright (c) 2024 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -76,14 +76,14 @@ def log_minus(first, second): :raises RuntimeError: If the difference would be non-positive. """ if first is None: - raise RuntimeError(u"log_minus: does not support None first") + raise RuntimeError("log_minus: does not support None first") if second is None: return first if second >= first: - raise RuntimeError(u"log_minus: first has to be bigger than second") + raise RuntimeError("log_minus: first has to be bigger than second") factor = -math.expm1(second - first) if factor <= 0.0: - msg = u"log_minus: non-positive number to log" + msg = "log_minus: non-positive number to log" else: return first + math.log(factor) raise RuntimeError(msg) diff --git a/resources/libraries/python/PLRsearch/stat_trackers.py b/resources/libraries/python/PLRsearch/stat_trackers.py index e0b21dc3a9..e598fd840e 100644 --- a/resources/libraries/python/PLRsearch/stat_trackers.py +++ b/resources/libraries/python/PLRsearch/stat_trackers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021 Cisco and/or its affiliates. +# Copyright (c) 2024 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -64,8 +64,10 @@ class ScalarStatTracker: :returns: Expression constructing an equivalent instance. :rtype: str """ - return f"ScalarStatTracker(log_sum_weight={self.log_sum_weight!r}," \ + return ( + f"ScalarStatTracker(log_sum_weight={self.log_sum_weight!r}," f"average={self.average!r},log_variance={self.log_variance!r})" + ) def copy(self): """Return new ScalarStatTracker instance with the same state as self. @@ -110,7 +112,8 @@ class ScalarStatTracker: if absolute_shift > 0.0: log_square_shift = 2 * math.log(absolute_shift) log_variance = log_plus( - log_variance, log_square_shift + log_sample_ratio) + log_variance, log_square_shift + log_sample_ratio + ) if log_variance is not None: log_variance += old_log_sum_weight - new_log_sum_weight self.log_sum_weight = new_log_sum_weight @@ -133,10 +136,17 @@ class ScalarDualStatTracker(ScalarStatTracker): One typical use is for Monte Carlo integrator to decide whether the partial sums so far are reliable enough. """ + def __init__( - self, log_sum_weight=None, average=0.0, log_variance=None, - log_sum_secondary_weight=None, secondary_average=0.0, - log_secondary_variance=None, max_log_weight=None): + self, + log_sum_weight=None, + average=0.0, + log_variance=None, + log_sum_secondary_weight=None, + secondary_average=0.0, + log_secondary_variance=None, + max_log_weight=None, + ): """Initialize new tracker instance, empty by default. :param log_sum_weight: Natural logarithm of sum of weights @@ -177,12 +187,14 @@ class ScalarDualStatTracker(ScalarStatTracker): :rtype: str """ sec = self.secondary - return f"ScalarDualStatTracker(log_sum_weight={self.log_sum_weight!r},"\ - f"average={self.average!r},log_variance={self.log_variance!r}," \ - f"log_sum_secondary_weight={sec.log_sum_weight!r}," \ - f"secondary_average={sec.average!r}," \ - f"log_secondary_variance={sec.log_variance!r}," \ + return ( + f"ScalarDualStatTracker(log_sum_weight={self.log_sum_weight!r}," + f"average={self.average!r},log_variance={self.log_variance!r}," + f"log_sum_secondary_weight={sec.log_sum_weight!r}," + f"secondary_average={sec.average!r}," + f"log_secondary_variance={sec.log_variance!r}," f"max_log_weight={self.max_log_weight!r})" + ) def add(self, scalar_value, log_weight=0.0): """Return updated both stats after addition of another sample. @@ -197,7 +209,7 @@ class ScalarDualStatTracker(ScalarStatTracker): """ # Using super() as copy() and add() are not expected to change # signature, so this way diamond inheritance will be supported. - primary = super(ScalarDualStatTracker, self) + primary = super() if self.max_log_weight is None or log_weight >= self.max_log_weight: self.max_log_weight = log_weight self.secondary = primary.copy() @@ -242,8 +254,12 @@ class VectorStatTracker: """ def __init__( - self, dimension=2, log_sum_weight=None, averages=None, - covariance_matrix=None): + self, + dimension=2, + log_sum_weight=None, + averages=None, + covariance_matrix=None, + ): """Initialize new tracker instance, two-dimensional empty by default. If any of latter two arguments is None, it means @@ -272,10 +288,12 @@ class VectorStatTracker: :returns: Expression constructing an equivalent instance. :rtype: str """ - return f"VectorStatTracker(dimension={self.dimension!r}," \ - f"log_sum_weight={self.log_sum_weight!r}," \ - f"averages={self.averages!r}," \ + return ( + f"VectorStatTracker(dimension={self.dimension!r}," + f"log_sum_weight={self.log_sum_weight!r}," + f"averages={self.averages!r}," f"covariance_matrix={self.covariance_matrix!r})" + ) def copy(self): """Return new instance with the same state as self. @@ -287,8 +305,10 @@ class VectorStatTracker: :rtype: VectorStatTracker """ return VectorStatTracker( - self.dimension, self.log_sum_weight, self.averages[:], - copy.deepcopy(self.covariance_matrix) + self.dimension, + self.log_sum_weight, + self.averages[:], + copy.deepcopy(self.covariance_matrix), ) def reset(self): |