def run_combiner():
corpus = argmanager.parse_args().corpus
corpus_dir = argmanager.corpus_dir(corpus)
results_dir = argmanager.results_dir(corpus)
combine(corpus_dir)
combine(results_dir)
from preprocess import analyzer, argmanager
import sys
methods_index = list(sys.argv).index('-m')
# Checks to see if there are methods
if len(sys.argv) == (methods_index) + 1:
del sys.argv[methods_index]
args = argmanager.parse_args()
analyzer.retrieve_q(args.corpus, args.methods, args.train, args.seed)
'.pickle',
alz.generate_analyze_filename(corpus, methods, model, train_size,
seed))
if not os.path.isfile(results_file): return None
with open(results_file, 'rb') as f:
result = pickle.load(f)
return get_data_from_result(result, attribute)
label_dict = {
'traintime': 'Train Time',
'accuracy': 'Accuracy',
'vocabsize': 'Vocabulary Size',
'tokens': 'Corpus Size',
}
mode_dict = {
'progressive': pull_method_progressive_results,
'comparative': pull_method_comparative_results,
}
if __name__ == '__main__':
args = arg.parse_args()
if args.model:
create_result_box_plots(args.corpus, args.methods, args.model)
plot_results(args.corpus, args.methods, args.model)