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))]