def explore_space_of_candidates(experiment, objective_function,
working_directory, chooser,
grid_size=1000,
grid_seed=1,
max_finished_jobs=100):
# Build the experiment grid.
expt_grid = ExperimentGrid(working_directory,
experiment.variables, grid_size, grid_seed)
next_jobid = 0
run_python_job = PythonRunner()
while next_jobid < max_finished_jobs:
best_val, best_job = expt_grid.get_best()
# Gets you everything - NaN for unknown values & durations.
grid, values, durations = expt_grid.get_grid()
# Returns lists of indices.
candidates = expt_grid.get_candidates()
pending = expt_grid.get_pending()
complete = expt_grid.get_complete()
n_candidates = candidates.shape[0]
n_pending = pending.shape[0]
n_complete = complete.shape[0]
logging.info("%d candidates %d pending %d complete", n_candidates,
n_pending, n_complete)
if n_candidates == 0:
logging.info("There are no candidates left. Exiting.")
return
# Ask the chooser to pick the next candidate
logging.info("Choosing next candidate... ")
job_id = chooser.next(grid, values, durations, candidates, pending, complete)
yield best_val, best_job, expt_grid.get_params(best_job), job_id
# If the job_id is a tuple, then the chooser picked a new job.
# We have to add this to our grid
if isinstance(job_id, tuple):
(job_id, candidate) = job_id
job_id = expt_grid.add_to_grid(candidate)
logging.info("selected job %d from the grid", job_id)
expt_grid.set_submitted(job_id, next_jobid)
expt_grid.set_running(job_id)
start_t = time.time()
result, memoized = run_python_job(job_id, objective_function,
expt_grid.get_params(job_id), working_directory)
duration = time.time() - start_t
expt_grid.set_complete(job_id, result, duration)
next_jobid += 1
if memoized:
max_finished_jobs += 1
