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replace multiprocessing.Pool with concurrent.futures.ProcessPoolExecutor #4

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replace multiprocessing.Pool with concurrent.futures.ProcessPoolExecutor #4

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69 changes: 34 additions & 35 deletions ldpop/lookup_table.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
from .moran_finite import MoranStatesFinite
from .compute_stationary import stationary

from multiprocessing import Pool
from concurrent.futures import ProcessPoolExecutor
import logging
import time
import pandas
Expand Down Expand Up @@ -75,40 +75,39 @@ def computeLikelihoods(n, exact, popSizes, theta, timeLens, rhoGrid, cores,

# make the pool first to avoid copying large objects.
# maxtasksperchild=1 to avoid memory issues
executor = Pool(cores, maxtasksperchild=1)

# make the states and the rates
states = get_states(n, exact)
moranRates = MoranRates(states)

# compute initial distributions and likelihoods
prevInit = states.getUnlinkedStationary(popSize=popSizes[-1], theta=theta)
inits = []

if load_stationary:
stationary_dists = numpy.load(load_stationary)
for stationary_dist in stationary_dists:
inits.append(stationary_dist)
else:
for rho in reversed(rhoGrid):
rates = moranRates.getRates(rho=rho,
popSize=popSizes[-1],
theta=theta)
prevInit = stationary(Q=rates,
init=prevInit,
norm_order=float('inf'),
epsilon=1e-2)
inits.append(prevInit)
ret = executor.map(getColumnHelper,
[(moranRates, rho, theta, popSizes, timeLens, prevInit,
stationaryNormOrder)
for rho, prevInit in zip(reversed(rhoGrid), inits)])
logging.info('Cleaning up results...')
if store_stationary:
full_inits = numpy.array([result[1] for result in ret])
numpy.save(store_stationary, full_inits)
ret = [states.ordered_log_likelihoods(result[0]) for result in ret]
executor.close()
with ProcessPoolExecutor(cores, max_tasks_per_child=1) as executor:

# make the states and the rates
states = get_states(n, exact)
moranRates = MoranRates(states)

# compute initial distributions and likelihoods
prevInit = states.getUnlinkedStationary(popSize=popSizes[-1], theta=theta)
inits = []

if load_stationary:
stationary_dists = numpy.load(load_stationary)
for stationary_dist in stationary_dists:
inits.append(stationary_dist)
else:
for rho in reversed(rhoGrid):
rates = moranRates.getRates(rho=rho,
popSize=popSizes[-1],
theta=theta)
prevInit = stationary(Q=rates,
init=prevInit,
norm_order=float('inf'),
epsilon=1e-2)
inits.append(prevInit)
ret = executor.map(getColumnHelper,
[(moranRates, rho, theta, popSizes, timeLens, prevInit,
stationaryNormOrder)
for rho, prevInit in zip(reversed(rhoGrid), inits)])
logging.info('Cleaning up results...')
if store_stationary:
full_inits = numpy.array([result[1] for result in ret])
numpy.save(store_stationary, full_inits)
ret = [states.ordered_log_likelihoods(result[0]) for result in ret]

return ([(rho, lik) for rho, lik in zip(rhoGrid, reversed(ret))],
states.ordered_indexes())
Expand Down
19 changes: 9 additions & 10 deletions ldpop/proposal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
from .moran_finite import MoranStatesFinite
from .moran_augmented import MoranRates

from multiprocessing import Pool
from concurrent.futures import ProcessPoolExecutor
import math
import pandas
import logging
Expand Down Expand Up @@ -72,15 +72,14 @@ def __init__(self, n, theta, rhos, pop_sizes,
states = MoranStatesFinite(2*n)
moranRates = MoranRates(states)

executor = Pool(processes)
likelihoodDictList = list(map(
states.ordered_log_likelihoods,
executor.map(ordered_wrapper, [(moranRates, rho, theta,
pop_sizes, epochLengths,
numTimePointsPerEpoch)
for rho in rhos])))
executor.close()
executor.join()
with ProcessPoolExecutor(processes) as executor:
likelihoodDictList = list(map(
states.ordered_log_likelihoods,
executor.map(ordered_wrapper, [(moranRates, rho, theta,
pop_sizes, epochLengths,
numTimePointsPerEpoch)
for rho in rhos])))

indexer = states.ordered_indexes()
data = {}
for rho, likelihoodDict in zip(rhos, likelihoodDictList):
Expand Down