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feat: Adds monte carlo cycles to trade off space and cpu

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This commit is contained in:
Deepak Mallubhotla
2022-03-06 17:18:24 -06:00
parent e00dc95f02
commit e6d8d33c27
1 changed files with 14 additions and 4 deletions
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18
deepdog/alt_bayes_run.py
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@@ -4,6 +4,7 @@ import pdme.util.fast_v_calc
from typing import Sequence, Tuple, List from typing import Sequence, Tuple, List
import datetime import datetime
import csv import csv
import multiprocessing
import logging import logging
import numpy import numpy
@@ -19,6 +20,13 @@ DotInput = Tuple[numpy.typing.ArrayLike, float]
_logger = logging.getLogger(__name__) _logger = logging.getLogger(__name__)
def get_a_result(input) -> int:
discretisation, dot_inputs, lows, highs, monte_carlo_count, max_frequency = input
sample_dipoles = discretisation.get_model().get_n_single_dipoles(monte_carlo_count, max_frequency)
vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(dot_inputs, sample_dipoles)
return numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))
class AltBayesRun(): class AltBayesRun():
''' '''
A single Bayes run for a given set of dots. A single Bayes run for a given set of dots.
@@ -36,7 +44,7 @@ class AltBayesRun():
run_count: int run_count: int
The number of runs to do. The number of runs to do.
''' '''
def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, low_error: float = 0.9, high_error: float = 1.1, monte_carlo_count: int = 10000, max_frequency: float = 20, end_threshold: float = None) -> None: def __init__(self, dot_inputs: Sequence[DotInput], discretisations_with_names: Sequence[Tuple[str, pdme.model.Discretisation]], actual_model: pdme.model.Model, filename_slug: str, run_count: int, low_error: float = 0.9, high_error: float = 1.1, monte_carlo_count: int = 10000, monte_carlo_cycles: int = 10, max_frequency: float = 20, end_threshold: float = None) -> None:
self.dot_inputs = dot_inputs self.dot_inputs = dot_inputs
self.dot_inputs_array = pdme.measurement.oscillating_dipole.dot_inputs_to_array(dot_inputs) self.dot_inputs_array = pdme.measurement.oscillating_dipole.dot_inputs_to_array(dot_inputs)
self.discretisations = [disc for (_, disc) in discretisations_with_names] self.discretisations = [disc for (_, disc) in discretisations_with_names]
@@ -44,6 +52,7 @@ class AltBayesRun():
self.actual_model = actual_model self.actual_model = actual_model
self.model_count = len(self.discretisations) self.model_count = len(self.discretisations)
self.monte_carlo_count = monte_carlo_count self.monte_carlo_count = monte_carlo_count
self.monte_carlo_cycles = monte_carlo_cycles
self.run_count = run_count self.run_count = run_count
self.low_error = low_error self.low_error = low_error
self.high_error = high_error self.high_error = high_error
@@ -87,9 +96,10 @@ class AltBayesRun():
_logger.debug("Going to iterate over discretisations now") _logger.debug("Going to iterate over discretisations now")
for disc_count, discretisation in enumerate(self.discretisations): for disc_count, discretisation in enumerate(self.discretisations):
_logger.debug(f"Doing discretisation #{disc_count}") _logger.debug(f"Doing discretisation #{disc_count}")
sample_dipoles = discretisation.get_model().get_n_single_dipoles(self.monte_carlo_count, self.max_frequency) with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
vals = pdme.util.fast_v_calc.fast_vs_for_dipoles(self.dot_inputs_array, sample_dipoles) results.append(sum(
results.append(numpy.count_nonzero(pdme.util.fast_v_calc.between(vals, lows, highs))) pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles)
))
_logger.debug("Done, constructing output now") _logger.debug("Done, constructing output now")
row = { row = {