def test_parse_object_string_sample(self):
"""Test parsing sample name from object string."""
obs = parse_object_string_sample('foo_bar123')
self.assertEqual(obs, 'foo')
with self.assertRaises(ValueError):
_ = parse_object_string_sample('foo')
def build_problem_data(group_map_files, mapping_file, prediction_field,
start_level, include_only, negate, n_processes):
#For each scope, build a map from group to object and vice versa
group_to_object = []
object_to_group = []
for map_file in group_map_files:
g_to_o, o_to_g = read_split_file(map_file)
group_to_object.append(g_to_o)
object_to_group.append(o_to_g)
#Find a list of sample names from our group names
#An alternative is 'samplenames = samplemap.keys()', but that may have records without features
samplenames = set()
for grp in group_to_object[start_level]:
l = group_to_object[start_level][grp]
for obj in l:
samplenames.add(parse_object_string_sample(obj))
samplenames = list(samplenames)
#get a map of sample name to it's properties
samplemap = read_mapping_file(mapping_file)
sample_to_response = {}
for samplename in samplenames:
if (include_only is None or
((samplemap[samplename][include_only[0]] in include_only[1]) ^ negate)):
sample_to_response[samplename] = samplemap[samplename][prediction_field]
problem_data = ProblemData(group_to_object, object_to_group, sample_to_response, n_processes)
feature_vector = FeatureVector([FeatureRecord(group, start_level,
len(group_to_object[start_level][group]))
for group in group_to_object[start_level].keys()])
return problem_data, feature_vector
