def _transform(self, raw_submission):
sections = extract_sections(raw_submission)
if sections is None:
return [0]
else:
section_stats = map(lambda x: self.__transform(x), sections)
return [max(section_stats)]
def _transform(self, raw_submission):
stat = get_stat_function(self.stat)
sections = extract_sections(raw_submission)
section_stats = map(
lambda x: self.__transform(x),
sections) # Be aware that a class might contain no functions
return [stat(map(lambda x: sum(x), section_stats))]
def _transform(self, raw_submission):
stat = get_stat_function(self.stat)
sections = extract_sections(raw_submission)
section_stats = map(lambda x: self.__transform(x), sections)
stats = map(lambda x: stat(x), section_stats)
a = [np.mean(stats)]
return a
def _transform(self, raw_submission):
stat = get_stat_function(self.stat)
sections = extract_sections(raw_submission)
methods = map(lambda s: self.__transform(s), sections)
return [
stat(
map(lambda x: stat(x),
map(lambda x: map(lambda x: stat(x), x), methods)))
]
def _transform(self, raw_submission):
sections = extract_sections(raw_submission)
if sections is None:
return [1]
else:
section_stats = map(
lambda x: self.__transform(x),
sections) # Be aware that a class might contain no functions
return [sum(section_stats) / float(len(section_stats))]
def make_score_function(score):
if score == "RMSE":
return make_scorer(mse, greater_is_better=False)
elif score == 'PC':
return make_scorer(pearson, greater_is_better=True)
else:
raise ValueError('Scoring {} is not supported!'.format(score))
# FEATURES = powerset(FEATURES)
FEATURES = [FEATURES]
X, Y = load(corpus_path=os.path.join(BASEPATH, 'data/training'),
labels=DIMENSIONS)
for x in X:
sections = extract_sections(x)
for section in sections:
classes(section)
recognizer = RECOGNIZER[0]
result = {'recognizer_name': recognizer[0]}
number_features = args.nfeatures
output_filename = os.path.join(
BASEPATH, 'result_{}_{}_{}_{}.json'.format(DIMENSIONS[0], SCORE,
recognizer[0], number_features))
# print(output_filename)
with open(output_filename, 'w') as outfile:
outfile.write('Job started')
def _transform(self, raw_submission):
stat = get_stat_function(self.stat)
sections = extract_sections(raw_submission)
clazz_stats = map(lambda x: self.__transform(x), sections)
return [np.mean(map(lambda x: stat(x), clazz_stats))]
