import analysisutil
import matplotlib.pyplot as plt
analysisutil.add_argument('-i', '--indices', nargs='*')
(args, setup, file_util) = analysisutil.init(use_base_dir=True)
monotonicities = file_util.load_dill('monotonicities_max.dill')
if args.indices is not None:
index_sets = []
for indices_name in args.indices:
index_sets.append(
set(
file_util.load_dill(
'{0}_expression_indices.dill'.format(indices_name))))
indices = set.intersection(*index_sets)
monotonicities = [monotonicities[i] for i in indices]
fig = plt.figure()
plt.hist(monotonicities, bins=30)
plt.show()
file_util.save_figure(
fig, 'monotonicity_hist{0}'.format(
'_{0}'.format(args.indices) if args.indices is not None else ''))
from pathos.pools import ProcessPool
import Generator
import analysisutil
from Languages import LanguageLoader
from Languages.InformativenessMeasurer import SimMaxInformativenessMeasurer, InformativenessMeasurer
analysisutil.add_argument('inf_strat')
(args, setup, file_util) = analysisutil.init()
languages = LanguageLoader.load_languages(file_util)
universe = Generator.generate_simplified_models(args.model_size)
if args.inf_strat == 'exact':
informativeness_measurer = InformativenessMeasurer(len(universe))
elif args.inf_strat == 'simmax':
informativeness_measurer = SimMaxInformativenessMeasurer(universe)
else:
raise ValueError('{0} is not a valid informativeness strategy.'.format(
args.inf_strat))
with ProcessPool(nodes=args.processes) as pool:
informativeness = pool.map(informativeness_measurer, languages)
file_util.dump_dill(informativeness,
'informativeness_{0}.dill'.format(args.inf_strat))
