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Added functionality for gigafida processing

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This commit is contained in:
Luka 2019-12-21 14:10:18 +01:00
parent f09b84540b
commit dd36388707
1 changed files with 77 additions and 36 deletions
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113
dependency-parsetree.py
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@ -25,6 +25,7 @@ import string
import time
import timeit
from multiprocessing import Pool
from pathlib import Path
import gzip
def save_zipped_pickle(obj, filename, protocol=-1):
@ -144,16 +145,14 @@ def decode_query(orig_query, dependency_type, feats_detailed_list):
return root
def create_trees(config):
internal_saves = config.get('settings', 'internal_saves')
input_path = config.get('settings', 'input')
def create_trees(input_path, internal_saves, feats_detailed_dict={}, save=True):
# internal_saves = filters['internal_saves']
# input_path = filters['input']
hash_object = hashlib.sha1(input_path.encode('utf-8'))
hex_dig = hash_object.hexdigest()
trees_read_outputfile = os.path.join(internal_saves, hex_dig)
if not os.path.exists(trees_read_outputfile):
if not os.path.exists(trees_read_outputfile) or not save:
train = pyconll.load_from_file(input_path)
@ -161,7 +160,6 @@ def create_trees(config):
all_trees = []
corpus_size = 0
feats_detailed_dict = {}
for sentence in train:
root = None
@ -206,10 +204,14 @@ def create_trees(config):
token.children_split = len(token.children)
if root == None:
raise Exception('No root element in sentence!')
# print(input_path)
print('No root: ' + sentence.id)
continue
# raise Exception('No root element in sentence!')
all_trees.append(root)
save_zipped_pickle((all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict), trees_read_outputfile, protocol=2)
if save:
save_zipped_pickle((all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict), trees_read_outputfile, protocol=2)
# with open(trees_read_outputfile, 'wb') as output:
#
# pickle.dump((all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict), output)
@ -479,8 +481,6 @@ def read_filters(config, feats_detailed_list):
create_output_string_funct = create_output_string_form
create_output_string_functs.append(create_output_string_funct)
result_dict = {}
unigrams_dict = {}
filters = {}
filters['internal_saves'] = config.get('settings', 'internal_saves')
filters['input'] = config.get('settings', 'input')
@ -515,7 +515,7 @@ def read_filters(config, feats_detailed_list):
filters['lines_threshold'] = config.getint('settings', 'lines_threshold', fallback=0)
filters['print_root'] = config.getboolean('settings', 'print_root')
return filters, query_tree, create_output_string_functs, cpu_cores, unigrams_dict, result_dict, tree_size_range, node_types
return filters, query_tree, create_output_string_functs, cpu_cores, tree_size_range, node_types
def main():
parser = argparse.ArgumentParser()
@ -531,46 +531,87 @@ def main():
config = configparser.ConfigParser()
config.read(args.config_file)
internal_saves = config.get('settings', 'internal_saves')
input_path = config.get('settings', 'input')
# a = args.config_file
# config.read('config.ini')
# create queries
if os.path.isdir(input_path):
checkpoint_path = Path(internal_saves, 'checkpoint.pkl')
continuation_processing = config.getboolean('settings', 'continuation_processing', fallback=False)
if not checkpoint_path.exists() or not continuation_processing:
already_processed = set()
result_dict = {}
unigrams_dict = {}
corpus_size = 0
feats_detailed_list = {}
if checkpoint_path.exists():
os.remove(checkpoint_path)
else:
already_processed, result_dict, unigrams_dict, corpus_size, feats_detailed_list = load_zipped_pickle(
checkpoint_path)
for path in os.listdir(input_path):
path_obj = Path(input_path, path)
pathlist = path_obj.glob('**/*.conllu')
if path_obj.name in already_processed:
continue
start_exe_time = time.time()
for path in pathlist:
# because path is object not string
path_str = str(path)
# print(path_in_str)
(all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, sub_corpus_size,
feats_detailed_list) = create_trees(path_str, internal_saves, feats_detailed_dict=feats_detailed_list, save=False)
corpus_size += sub_corpus_size
filters, query_tree, create_output_string_functs, cpu_cores, tree_size_range, node_types = read_filters(
config, feats_detailed_list)
count_trees(cpu_cores, all_trees, query_tree, create_output_string_functs, filters, unigrams_dict,
result_dict)
already_processed.add(path_obj.name)
print("Execution time:")
print("--- %s seconds ---" % (time.time() - start_exe_time))
save_zipped_pickle(
(already_processed, result_dict, unigrams_dict, corpus_size, feats_detailed_list),
checkpoint_path, protocol=2)
else:
# 261 - 9 grams
# 647 - 10 grams
# 1622 - 11 grams
# 4126 - 12 grams
# 10598 - 13 grams
(all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size,
feats_detailed_list) = create_trees(config)
(all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size,
feats_detailed_list) = create_trees(input_path, internal_saves)
filters, query_tree, create_output_string_functs, cpu_cores, unigrams_dict, result_dict, tree_size_range, node_types = read_filters(config, feats_detailed_list)
result_dict = {}
unigrams_dict = {}
# if config.getint('settings', 'tree_size') == 2:
# tree_size = 2
# query_tree = [{"children": [{}]}]
# elif config.getint('settings', 'tree_size') == 3:
# tree_size = 3
# query_tree = [{"children": [{}, {}]}, {"children": [{"children": [{}]}]}]
# elif config.getint('settings', 'tree_size') == 4:
# tree_size = 4
# query_tree = [{"children": [{}, {}, {}]}, {"children": [{"children": [{}, {}]}]}, {"children": [{"children": [{}]}, {}]}, {"children": [{"children": [{"children": [{}]}]}]}]
# elif config.getint('settings', 'tree_size') == 5:
# tree_size = 5
# query_tree = [{"children": [{}, {}, {}, {}]}, {"children": [{"children": [{}]}, {}, {}]}, {"children": [{"children": [{}, {}]}, {}]}, {"children": [{"children": [{}]}, {"children": [{}]}]},
# {"children": [{"children": [{"children": [{}]}]}, {}]}, {"children": [{"children": [{"children": [{}]}, {}]}]}, {"children": [{"children": [{"children": [{}, {}]}]}]},
# {"children": [{"children": [{"children": [{"children": [{}]}]}]}]}, {'children': [{'children': [{}, {}, {}]}]}]
filters, query_tree, create_output_string_functs, cpu_cores, tree_size_range, node_types = read_filters(config, feats_detailed_list)
start_exe_time = time.time()
count_trees(cpu_cores, all_trees, query_tree, create_output_string_functs, filters, unigrams_dict, result_dict)
# for tree in all_trees[2:]:
# for tree in all_trees[1205:]:
start_exe_time = time.time()
count_trees(cpu_cores, all_trees, query_tree, create_output_string_functs, filters, unigrams_dict, result_dict)
print("Execution time:")
print("--- %s seconds ---" % (time.time() - start_exe_time))
print("Execution time:")
print("--- %s seconds ---" % (time.time() - start_exe_time))
# test 1 layer queries
# # tree.r_children = []
# # tree.children[1].children = []