def run(self):
"""
Do all the calculation here.
:return:
"""
# for each FeatureExtractor_type key in feature_dict, find the subclass
# of FeatureExtractor, run, and put results in a dict
for fextractor_type in self.feature_dict:
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
if self.feature_option_dict is not None \
and fextractor_type in self.feature_option_dict:
optional_dict = self.feature_option_dict[fextractor_type]
else:
optional_dict = self.optional_dict # FIXME: hacky
runner = fextractor_class(
self.assets,
logger=None,
fifo_mode=self.fifo_mode,
delete_workdir=self.delete_workdir,
result_store=self.result_store,
optional_dict=optional_dict,
optional_dict2=self.optional_dict2,
)
runner.run(parallelize=self.parallelize, processes=self.processes)
results = runner.results
self.type2results_dict[fextractor_type] = results
# assemble an output dict with demanded atom features
# atom_features_dict = self.fextractor_atom_features_dict
result_dicts = list(map(lambda x: dict(), self.assets))
for fextractor_type in self.feature_dict:
assert fextractor_type in self.type2results_dict
for atom_feature in self._get_atom_features(fextractor_type):
scores_key = self._get_scores_key(fextractor_type,
atom_feature)
for result_index, result in enumerate(
self.type2results_dict[fextractor_type]):
try:
result_dicts[result_index][scores_key] = result[
scores_key]
except KeyError:
scores_key_alt = BasicResult.scores_key_wildcard_match(
result.result_dict, scores_key)
result_dicts[result_index][scores_key] = result[
scores_key_alt]
self.results = list(
map(lambda tasset: BasicResult(tasset[0], tasset[1]),
zip(self.assets, result_dicts)))
def run(self):
"""
Do all the calculation here.
:return:
"""
# for each FeatureExtractor_type key in feature_dict, find the subclass
# of FeatureExtractor, run, and put results in a dict
for fextractor_type in self.feature_dict:
# fextractor = self._get_fextractor_instance(fextractor_type)
# fextractor.run()
# results = fextractor.results
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
runner = fextractor_class(
self.assets,
logger=None,
fifo_mode=self.fifo_mode,
delete_workdir=self.delete_workdir,
result_store=self.result_store,
optional_dict=self.optional_dict,
optional_dict2=self.optional_dict2,
)
runner.run(parallelize=self.parallelize)
results = runner.results
self.type2results_dict[fextractor_type] = results
# assemble an output dict with demanded atom features
# atom_features_dict = self.fextractor_atom_features_dict
result_dicts = map(lambda x: dict(), self.assets)
for fextractor_type in self.feature_dict:
assert fextractor_type in self.type2results_dict
for atom_feature in self._get_atom_features(fextractor_type):
scores_key = self._get_scores_key(fextractor_type,
atom_feature)
for result_index, result in enumerate(
self.type2results_dict[fextractor_type]):
result_dicts[result_index][scores_key] = result[scores_key]
self.results = map(
lambda (asset, result_dict): BasicResult(asset, result_dict),
zip(self.assets, result_dicts))
def run(self):
"""
Do all the calculation here.
:return:
"""
# for each FeatureExtractor_type key in feature_dict, find the subclass
# of FeatureExtractor, run, and put results in a dict
for fextractor_type in self.feature_dict:
runner = self._get_fextractor_instance(fextractor_type)
runner.run(parallelize=self.parallelize, processes=self.processes)
results = runner.results
self.type2results_dict[fextractor_type] = results
# assemble an output dict with demanded atom features
# atom_features_dict = self.fextractor_atom_features_dict
result_dicts = list(map(lambda x: dict(), self.assets))
for fextractor_type in self.feature_dict:
assert fextractor_type in self.type2results_dict
for atom_feature in self._get_atom_features(fextractor_type):
scores_key = self._get_scores_key(fextractor_type,
atom_feature)
for result_index, result in enumerate(
self.type2results_dict[fextractor_type]):
try:
result_dicts[result_index][scores_key] = result[
scores_key]
except KeyError:
scores_key_alt = BasicResult.scores_key_wildcard_match(
result.result_dict, scores_key)
result_dicts[result_index][scores_key] = result[
scores_key_alt]
self.results = list(
map(lambda tasset: BasicResult(tasset[0], tasset[1]),
zip(self.assets, result_dicts)))