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feat: makes chunksize configurable
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Deepak Mallubhotla 2022-03-06 18:42:05 -06:00
parent fa82caa752
commit 88d961313c
Signed by: deepak
GPG Key ID: BEBAEBF28083E022
1 changed files with 4 additions and 3 deletions
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7
deepdog/alt_bayes_run.py
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@ -10,7 +10,7 @@ import numpy
# TODO: remove hardcode
CHUNKSIZE = 20
CHUNKSIZE = 50
# TODO: It's garbage to have this here duplicated from pdme.
DotInput = Tuple[numpy.typing.ArrayLike, float]
@ -43,7 +43,7 @@ class AltBayesRun():
run_count: int
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, monte_carlo_cycles: int = 10, 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, chunksize: int = CHUNKSIZE) -> None:
self.dot_inputs = 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]
@ -57,6 +57,7 @@ class AltBayesRun():
self.high_error = high_error
self.csv_fields = ["dipole_moment", "dipole_location", "dipole_frequency"]
self.compensate_zeros = True
self.chunksize = chunksize
for name in self.model_names:
self.csv_fields.extend([f"{name}_success", f"{name}_count", f"{name}_prob"])
@ -97,7 +98,7 @@ class AltBayesRun():
_logger.debug(f"Doing discretisation #{disc_count}")
with multiprocessing.Pool(multiprocessing.cpu_count() - 1 or 1) as pool:
results.append(sum(
pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, CHUNKSIZE)
pool.imap_unordered(get_a_result, [(discretisation, self.dot_inputs_array, lows, highs, self.monte_carlo_count, self.max_frequency)] * self.monte_carlo_cycles, self.chunksize)
))
_logger.debug("Done, constructing output now")