def map_pspace(fn, pspace):
"""Maps a function to parameter space values.
Parameters
----------
fn : function
Function to evaluate on parameter space. Has to return a dictionary.
pspace : :class:`.pspace._PSpaceObj`
Parameter space providing parameter values to evaluate function on.
Returns
-------
dict
Dictionary with the input parameter values and the function return
values.
Examples
--------
>>> def fn(x):
... return {'y': x * x}
>>>
>>> from pprint import pprint
>>> from psyrun import Param
>>> pprint(map_pspace(fn, Param(x=[1, 2])))
{'x': [1, 2], 'y': [1, 4]}
"""
return dict_concat(list(get_result(fn, p) for p in pspace.iterate()))
def split(self):
"""Perform splitting of parameters space and save input files for
processing."""
items_remaining = len(self.pspace)
param_iter = self.pspace.iterate()
for i, filename in enumerate(self._iter_filenames()):
split_size = max(
self.min_items, items_remaining // (self.max_splits - i))
items_remaining -= split_size
block = dict_concat(
[row for row in self._iter_n(param_iter, split_size)])
save_dict_h5(os.path.join(self.indir, filename), block)
def map_pspace_parallel(fn, pspace, n_jobs=-1, backend='multiprocessing'):
"""Maps a function to parameter space values in parallel.
Requires `joblib <https://pythonhosted.org/joblib/>`_.
Parameters
----------
fn : function
Function to evaluate on parameter space. Has to return a dictionary.
pspace : :class:`.pspace._PSpaceObj`
Parameter space providing parameter values to evaluate function on.
n_jobs : int, optional
Number of parallel jobs. Set to -1 to automatically determine.
backend : str, optional
Backend to use. See `joblib documentation
<https://pythonhosted.org/joblib/parallel.html#using-the-threading-backend>`_
for details.
Returns
-------
dict
Dictionary with the input parameter values and the function return
values.
Examples
--------
>>> from pprint import pprint
>>> from psyrun import Param
>>> from psyrun.example import square
>>>
>>> pprint(map_pspace_parallel(square, Param(x=[1, 2])))
{'x': [1, 2], 'y': [1, 4]}
"""
import joblib
parallel = joblib.Parallel(n_jobs=n_jobs, backend=backend)
return dict_concat(parallel(
joblib.delayed(get_result)(fn, p) for p in pspace.iterate()))
def test_dict_concat(args, result):
assert dict_concat(args) == result
