Skip to content

Run every llse initialization and honour --nnls_init #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
ngamarra wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Run every llse initialization and honour --nnls_init #1

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 8 additions & 9 deletions python/nanomix/models.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
from scipy.optimize import minimize, nnls, Bounds
import os
import sys
from multiprocessing import Queue
import threading
from functools import partial

Expand Down Expand Up @@ -136,19 +135,19 @@ class my_thread(threading.Thread):
"""
thread class for parallel processing of fit_llse
"""
def __init__(self, data, p01, p11, queue):
def __init__(self, data, p01, p11, initializations):
super(my_thread, self).__init__()
self.data = data
self.p01 = p01
self.p11 = p11
self.queue = queue
self.initializations = initializations
self.res = []
def run(self):
alpha = self.queue.get()
f = lambda x: -1 * log_likelihood_sequencing_with_errors(self.data, x, self.p01, self.p11)
bnds = [ (0.0, 1.0) ] * self.data.K
cons = ({'type': 'eq', 'fun': eq_constraint})
self.res.append(minimize(f, alpha, method='SLSQP', options={'maxiter': 200, 'disp':False}, bounds=bnds, constraints=cons))
for alpha in self.initializations:
self.res.append(minimize(f, alpha, method='SLSQP', options={'maxiter': 200, 'disp':False}, bounds=bnds, constraints=cons))


def fit_llse_parallel(data, p01, p11, n_trials, threads, concentration, init_nnls):
Expand All @@ -157,11 +156,11 @@ def fit_llse_parallel(data, p01, p11, n_trials, threads, concentration, init_nnl
if init_nnls:
initializations.append(fit_nnls(data))

queues = [Queue() for i in range(threads)]
res = []
for i in range(n_trials):
queues[i % threads].put(initializations[i])
threads = [my_thread(data, p01, p11, queues[i]) for i in range(threads)]
# Deal every initialization out across the workers -- including the
# --nnls_init vector appended above, which lives at index n_trials.
chunks = [initializations[i::threads] for i in range(threads)]
threads = [my_thread(data, p01, p11, chunks[i]) for i in range(threads)]
for t in threads:
t.start()
for t in threads:
Expand Down