def main(argv):
if len(argv) == 0:
# print 'Usage: my_program command --option <argument>'
print(__doc__)
else:
args = docopt(__doc__, argv=argv)
if args['aggregate_labels']:
with tempfile.TemporaryDirectory() as tmpdir:
scrapping.scrap(args['<bundles_dir>'], tmpdir)
notcut_trees = args['<out_dir>'] + '/' + args[
'<prefix>'] + '_notcut_trees.txt'
notcut_priors = args['<out_dir>'] + '/' + args[
'<prefix>'] + '_notcut_priors.csv'
cut_trees = args['<out_dir>'] + '/' + args[
'<prefix>'] + '_cut_trees.txt'
split_trees = args['<out_dir>'] + '/' + args[
'<prefix>'] + '_split_trees.txt'
withdummy_trees = args['<out_dir>'] + '/' + args[
'<prefix>'] + '_withdummy_trees.txt'
cut_priors = args['<out_dir>'] + '/' + args[
'<prefix>'] + '_cut_priors.csv'
split_priors = args['<out_dir>'] + '/' + args[
'<prefix>'] + '_split_priors.csv'
out_df = args['<out_dir>'] + '/' + args['<prefix>'] + '_df.csv'
lt.aggregate_labels_from_bundles_to_trees(
args['<all_trees.txt>'], tmpdir, notcut_trees)
tt.remove_duplicate_nodes(notcut_trees, notcut_trees)
print('Duplicate nodes were removed.')
tt.translate_list_of_trees(notcut_trees, notcut_trees)
print('Trees were translated.')
lt.remove_2nd_tags(notcut_trees)
print('2nd tags were removed.')
lt.print_label_priors(notcut_trees, notcut_priors)
lt.cut_non_labeled_branches(notcut_trees, cut_trees)
print('Non-labeled branches were cut.')
lt.apply_split_labels_to_trees(cut_trees, split_trees)
lt.print_label_priors(split_trees, split_priors)
print('Split labels were applied.')
lt.apply_start_split_end_labels_to_trees(
cut_trees, withdummy_trees)
print('Start-Split-End labels were applied.')
lt.print_label_priors(cut_trees, cut_priors)
lt.create_data_csv(split_trees, out_df)
print('Labels aggregated successfully.')
if args['rename_labels']:
lt.rename_labels(args['<in_file>'], args['<out_file>'])
if args['rename_tags_in_trees']:
lt.rename_tags_in_trees(args['<trees.txt>'],
args['<out_trees.txt>'])
if args['rename_tags_in_df']:
lt.rename_tags_in_df(args['<df.csv>'], args['<out_df.csv>'])
if args['create_bundles']:
tt.create_bundles(args['<all_trees.txt>'], args['<tree_ids>'],
args['<out_dir>'])
if args['rework_labels']:
lt.rework_labels(args['<labeled_trees.txt>'],
args['<rework_settings.txt>'],
args['<out_trees.txt>'])
if args['create_df']:
lt.create_data_csv(args['<labeled_trees.txt>'],
args['<out_df.csv>'], args['--rework_settings'],
args['--ignore_deleted'])
if args['print']:
if args['dis_branch']:
dst.print_branch(trees_path=args['<trees.txt>'],
probas_path=args['<probas.dispr>'],
branch_atlas_id=args['<branch_atlas_id>'],
out_file=args['<out_file.csv>'])
if args['dis_tags_npmi']:
dst.print_dis_tags_npmi(trees_path=args['<trees.txt>'],
probas_path=args['<probas.dispr>'],
out_file=args['<out.csv>'],
just_count=args['--just_count'],
just_pmi=args['--just_pmi'])
if args['tags_npmi']:
lt.print_tags_npmi_table(args['<labeled_trees.txt>'],
args['<out.csv>'],
args['--just_count'], args['--log'])
if args['label_priors']:
lt.print_label_priors(args['<labeled_trees.txt>'],
args['<out.csv>'], args['--per_tree'])
if args['label_details']:
lt.print_label_details(args['<labeled_trees.txt>'],
args['<out.csv>'])
if args['label_cooc_lists']:
lt.print_label_cooc_lists(args['<labeled_trees.txt>'],
args['<out.csv>'])
if args['label_passes']:
lt.print_label_passes(args['<labeled_trees.txt>'],
args['<out_dir>'])
if args['label_ngrams']:
lt.print_label_ngrams(
args['<labeled_trees.txt>'], args['<out_dir>'],
[int(n) for n in args['<n1,n2,n3>'].split(',')])
if args['forward_backward_transitions']:
lt.print_forward_backward_transitions(
args['<labeled_trees.txt>'], args['<out_dir>'],
[int(n) for n in args['<n1,n2,n3>'].split(',')])
if args['label_ngram_lists']:
lt.print_label_ngram_lists(
args['<labeled_trees.txt>'], args['<out_file>'],
[int(n) for n in args['<n1,n2,n3>'].split(',')],
args['<min_count>'])
if args['trees_statistics']:
tt.create_list_of_trees_statistics(args['<trees.txt>'],
args['<stats.csv>'])
if args['label_stats']:
trees_path = args['<trees.txt>']
out_dir = args['<stats_dir>']
priors_csv = out_dir + '/priors.csv'
npmi = out_dir + '/npmi.csv'
correlation_log = out_dir + '/corr_log.txt'
pmi = out_dir + '/pmi.csv'
matthews = out_dir + '/matthews_correlation.csv'
together_counts = out_dir + '/together_counts.csv'
general_stats = out_dir + '/general_stats.csv'
lt.print_label_ngrams(trees_path, out_dir, [2, 3, 4, 5, 6, 7])
lt.print_label_priors(trees_path, priors_csv)
lt.print_tags_npmi_table(trees_path,
npmi,
log_file=correlation_log)
lt.print_tags_npmi_table(trees_path, pmi, just_pmi=True)
lt.print_tags_npmi_table(trees_path,
together_counts,
just_count=True)
tt.create_list_of_trees_statistics(trees_path, general_stats)
lt.print_tags_matthews(trees_path, matthews)
if args['grid_search']:
cv = None
if args['--cv']:
cv = int(args['--cv'])
target_tags = None
if args['--target_tags']:
target_tags = args['--target_tags'].split(',')
split_tags = None
if args['--split_tags']:
split_tags = [int(i) for i in args['--split_tags'].split(',')]
adders = args['--adders']
ct.grid_search(args['<data_prefix>'],
args['<pipe_prefix>'],
args['<params_prefix>'],
adders=adders,
cv=cv,
split_tags=split_tags,
target_tags=target_tags)
if args['train_test']:
target_tags = None
if args['--target_tags']:
target_tags = args['--target_tags'].split(',')
adders = args['--adders']
cls = args['--cls']
ct.train_test(args['<train>'],
args['<test>'],
args['<pipe>'],
args['<params>'],
args['<mode>'],
classifier=cls,
target_tags=target_tags,
adders=adders)
if args['aggregate_train_test']:
target_tags = None
if args['--target_tags']:
target_tags = args['--target_tags'].split(',')
adders = args['--adders']
cls = args['--cls']
ct.aggregate_traintest(args['<train>'],
args['<test>'],
args['<pipe>'],
args['<params>'],
args['<mode>'],
classifier=cls,
target_tags=target_tags,
adders=adders)
if args['lstmsa']:
ct.lstm_sa(data_prefix=args['<data_prefix>'],
target_tags=args['--target_tags'],
pipes=args['<pipes>'],
hidden_dims=args['<hidden_dims>'])
if args['aggregate_scores']:
ct.aggregate_scores(args['<data_prefix>'], args['<pipe_prefix>'])
if args['aggregate_scores_lstmsa']:
ct.aggregate_scores_lstmsa(args['<data_prefix>'], args['--tags'])
if args['create_doc2vec_train_file']:
ft.create_d2vtrain_lines(args['<trees.txt>'],
args['<out_file.txt>'])
if args['train_doc2vec']:
ft.train_doc2vec(args['<train_file.txt>'], args['<out_model.txt>'],
int(args['<epochs>']), int(args['<dim>']))
if args['prefit_adders']:
ft.prefit_adders(args['<data_prefix>'], args['--adders'])
if args['prepare_dissent']:
dst.run_trees(args['<trees.txt>'], args['<out_path>'])
if args['merge_disprobas']:
dst.merge_probas(probas_dir=args['<probas_dir>'],
out_path=args['<out_file.dispr>'])
