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2
.gitignore vendored
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@ -4,5 +4,5 @@ internal_saves/
__pycache__/
results/
data/
config.ini
config2.ini
configs/

202
LICENSE.txt Normal file
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@ -0,0 +1,202 @@
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37
README.md
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@ -1,4 +1,6 @@
# dependency parsetree
# STARK: a tool for statisical analysis of dependency-parsed corpora
STARK is a python-based command-line tool for extraction of dependency trees from parsed corpora based on various user-defined criteria. It is primarily aimed at processing corpora based on the [Universal Dependencies](https://universaldependencies.org/) annotation scheme, but it also takes any other corpus in the [CONLL-U](https://universaldependencies.org/format.html) format as input.
## Linux installation and execution
### Installation
Install python 3 on your sistem.
@ -24,8 +26,37 @@ python3 dependency-parsetree.py --config_file=<PATH TO .ini file>
Example:
```bash
python3 dependency-parsetree.py --config_file=official_config.ini
python3 dependency-parsetree.py --config_file=config.ini
```
## Parameter settings
The type of trees to be extracted can be defined through several parameters in the `config.ini` configuration file.
[![alt text](https://gitea.cjvt.si/lkrsnik/dependency_parsing/raw/branch/master/Clarin-SI-logo.png)](http://www.clarin.si/info/about/)
- `input`: location of the input file or directory (parsed corpus in .conllu)
- `output`: location of the output file (extraction results)
- `internal_saves`: location of the folder with files for optimization during processing
- `cpu_cores`: number of CPU cores to be used during processing
- `tree_size`: number of nodes in the tree (integer or range)
- `tree_type`: extraction of all possible subtrees or full subtrees only (values *all* or *complete*)
- `dependency_type`: extraction of labeled or unlabeled trees (values *labeled* or *unlabeled*)
- `node_order`: extraction of trees by taking surface word order into account (values *free* or *fixed*)
- `node_type`: type of nodes under investigation (values *form*, *lemma*, *upos*, *xpos*, *feats* or *deprel*)
- `label_whitelist`: predefined list of dependency labels allowed in the extracted tree
- `root_whitelist`: predefined characteristics of the root node
- `query`: predefined tree structure based on the modified Turku NLP [query language](http://bionlp.utu.fi/searchexpressions-new.html).
- `print_root`: print root node information in the output file (values *yes* or *no*)
- `nodes_number`: print the number of nodes in the tree in the output file (values *yes* or *no*)
- `association_measures`: calculate the strength of association between nodes by MI, MI3, t-test, logDice, Dice and simple-LL scores (values *yes* or *no*)
- `frequency_threshold`: minimum frequency of occurrences of the tree in the corpus
- `lines_threshold`: maximum number of trees in the output
## Output
The tool returns the resulting list of all relevant trees in the form of a tab-separated `.tsv` file with information on the tree structure, its frequency and other relevant information in relation to specific parameter settings. The tool does not support any data visualization, however, the output structure of the tree is directly trasnferable to the [Dep_Search](http://bionlp-www.utu.fi/dep_search/) concordancing service giving access to specific examples in many corpora.
## Credits
This program was developed by Luka Krsnik in collaboration with Kaja Dobrovoljc and Marko Robnik Šikonja and with financial support from [CLARIN.SI](https://www.clarin.si/).
[![alt text](https://gitea.cjvt.si/lkrsnik/dependency_parsing/raw/branch/master/logos/CLARIN.png)](http://www.clarin.si/info/about/)
[![alt text](https://gitea.cjvt.si/lkrsnik/dependency_parsing/raw/branch/master/logos/CJVT.png)](https://www.cjvt.si/en/)
[![alt text](https://gitea.cjvt.si/lkrsnik/dependency_parsing/raw/branch/master/logos/FRI.png)](https://www.fri.uni-lj.si/en/about)
[![alt text](https://gitea.cjvt.si/lkrsnik/dependency_parsing/raw/branch/master/logos/FF.png)](http://www.ff.uni-lj.si/an/aboutFaculty/about_faculty)

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@ -1,140 +0,0 @@
import copy
import string
from generic import create_output_string_form, create_output_string_deprel, create_output_string_lemma, \
create_output_string_upos, create_output_string_xpos, create_output_string_feats, generate_key
class Result(object):
def __init__(self, node, architecture_order, create_output_strings):
# self.array = [[create_output_string(node) for create_output_string in create_output_strings]]
# if create_output_string_lemma in create_output_strings:
# key_array = [[create_output_string(node) if create_output_string != create_output_string_lemma else 'L=' + create_output_string(node) for create_output_string in create_output_strings]]
# else:
# key_array = self.array
# if len(self.array[0]) > 1:
# self.key = '&'.join(key_array[0])
# else:
# # output_string = create_output_strings[0](node)
# self.key = key_array[0][0]
self.array, self.key = generate_key(node, create_output_strings)
self.key_free = self.key
# self.array = [[output_string]]
self.order_key = str(architecture_order)
self.order = [architecture_order]
self.deprel = node.deprel.get_value()
# order with original numbers in sentences
# self.order = str([architecture_order])
# order with numbers from 0 to n of n-gram
self.root = ''
self.final_order = ''
self.separators = []
def __repr__(self):
return self.key
def add(self, string, architecture_order, separator, is_left):
if is_left:
self.array = [string] + self.array
self.order = [architecture_order] + self.order
# self.order = [architecture_order] + self.order
self.separators = [separator] + self.separators
self.key = string + ' ' + separator + ' ' + self.key
self.order_key = architecture_order + ' ' + separator + ' ' + self.order_key
else:
self.array += [string]
self.order += [architecture_order]
# self.order += [architecture_order]
self.separators += [separator]
self.key += ' ' + separator + ' ' + string
self.order_key += ' ' + separator + ' ' + architecture_order
def add_separator(self, separator, left=True):
self_copy = copy.copy(self)
if left:
self_copy.separators += [separator]
self_copy.key += separator
self_copy.order_key += separator
else:
self_copy.separators = [separator] + self_copy.separators
self_copy.key = separator + self_copy.key
self_copy.order_key = separator + self_copy.order_key
return self_copy
# def merge_results2(self):
def merge_results(self, right_t, separator, left=True):
left_tree = copy.copy(self)
right_tree = copy.copy(right_t)
if separator:
if left:
# merged_results.append(left_part + right_part + separator)
left_tree.key = left_tree.key + right_tree.key + separator
left_tree.order_key = left_tree.order_key + right_tree.order_key + separator
left_tree.array = left_tree.array + right_tree.array
left_tree.order = left_tree.order + right_tree.order
# left_tree.order = str([architecture_order])
left_tree.separators = left_tree.separators + right_tree.separators + [separator]
else:
# merged_results.append(left_part + separator + right_part)
left_tree.key = left_tree.key + separator + right_tree.key
left_tree.order_key = left_tree.order_key + separator + right_tree.order_key
left_tree.array = left_tree.array + right_tree.array
left_tree.order = left_tree.order + right_tree.order
# left_tree.order = str([architecture_order])
left_tree.separators = left_tree.separators + [separator] + right_tree.separators
else:
# merged_results.append(left_part + right_part)
left_tree.key = left_tree.key + right_tree.key
left_tree.order_key = left_tree.order_key + right_tree.order_key
left_tree.array = left_tree.array + right_tree.array
left_tree.order = left_tree.order + right_tree.order
# left_tree.order = str([architecture_order])
left_tree.separators = left_tree.separators + right_tree.separators
return left_tree
def extend_answer(self, other_answer, separator):
self.array.extend(other_answer.array)
self.order.extend(other_answer.order)
self.key += separator + other_answer.key
self.order_key += separator + other_answer.order_key
self.separators.extend(separator)
def put_in_bracelets(self, inplace=False):
if inplace:
self.key = ('(' + self.key + ')')
self.order_key = ('(' + self.order_key + ')')
return
result = copy.copy(self)
result.key = ('(' + result.key + ')')
result.order_key = ('(' + result.order_key + ')')
return result
def finalize_result(self):
result = copy.copy(self)
result.key = result.key[1:-1]
result.set_root()
# create order letters
order_letters = [''] * len(result.order)
for i in range(len(result.order)):
ind = result.order.index(min(result.order))
result.order[ind] = 10000
order_letters[ind] = string.ascii_uppercase[i]
result.order = ''.join(order_letters)
# result.order_key = result.order_key[1:-1]
# TODO When tree is finalized create relative word order (alphabet)!
return result
def set_root(self):
if len(self.array[0]) > 1:
self.root = '&'.join(self.array[0])
else:
# output_string = create_output_strings[0](node)
self.root = self.array[0][0]

14
ResultNode.py
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@ -1,3 +1,17 @@
# Copyright 2019 CJVT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from generic import generate_key, generate_name

14
ResultTree.py
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@ -1,3 +1,17 @@
# Copyright 2019 CJVT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import string

3
Tree.py
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@ -1,7 +1,6 @@
import sys
from copy import copy
from Result import Result
from ResultNode import ResultNode
from ResultTree import ResultTree
from Value import Value
@ -270,7 +269,7 @@ class Tree(object):
# TODO add order rearagement (TO KEY)
complete_answers[i].extend(new_complete_answers[i])
# if create_output_string_form(self) == 'Dogodek':
# if create_output_string_lemma(self) == 'drama':
# print('HERE!@@!')
# if create_output_string_form(self) == 'vpiti':
# print('HERE!@@!')

14
Value.py
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@ -1,3 +1,17 @@
# Copyright 2019 CJVT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class Value(object):
def __init__(self, value):
self.value = value

0
official_config.ini → config.ini
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280
dependency-parsetree.py
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@ -1,4 +1,18 @@
#!/usr/bin/env python
# Copyright 2019 CJVT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import configparser
import copy
@ -11,6 +25,16 @@ import string
import time
import timeit
from multiprocessing import Pool
import gzip
def save_zipped_pickle(obj, filename, protocol=-1):
with gzip.open(filename, 'wb') as f:
pickle.dump(obj, f, protocol)
def load_zipped_pickle(filename):
with gzip.open(filename, 'rb') as f:
loaded_object = pickle.load(f)
return loaded_object
import pyconll
@ -123,6 +147,8 @@ def decode_query(orig_query, dependency_type, feats_detailed_list):
def create_trees(config):
internal_saves = config.get('settings', 'internal_saves')
input_path = config.get('settings', 'input')
# 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)
@ -183,14 +209,16 @@ def create_trees(config):
raise Exception('No root element in sentence!')
all_trees.append(root)
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)
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)
else:
print('Reading trees:')
print('Completed')
with open(trees_read_outputfile, 'rb') as pkl_file:
(all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict) = pickle.load(pkl_file)
all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict = load_zipped_pickle(trees_read_outputfile)
# with open(trees_read_outputfile, 'rb') as pkl_file:
# (all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict) = pickle.load(pkl_file)
return all_trees, form_dict, lemma_dict, upos_dict, xpos_dict, deprel_dict, corpus_size, feats_detailed_dict
@ -333,123 +361,8 @@ def create_ngrams_query_trees(n, trees):
# tree['children'] = [{}]
return trees
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--config_file",
default=None,
type=str,
required=True,
help="The input config file.")
args = parser.parse_args()
config = configparser.ConfigParser()
config.read(args.config_file)
# a = args.config_file
# config.read('config.ini')
# create queries
tree_size = 0
# 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)
# 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': [{}, {}, {}]}]}]
tree_size_range = config.get('settings', 'tree_size', fallback='0').split('-')
tree_size_range = [int(r) for r in tree_size_range]
if tree_size_range[0] > 1:
if len(tree_size_range) == 1:
query_tree = create_ngrams_query_trees(tree_size_range[0], [{}])
elif len(tree_size_range) == 2:
query_tree = []
for i in range(tree_size_range[0], tree_size_range[1] + 1):
query_tree.extend(create_ngrams_query_trees(i, [{}]))
else:
query_tree = [decode_query('(' + config.get('settings', 'query') + ')', '', feats_detailed_list)]
# order_independent_queries(query_tree)
# set filters
node_types = config.get('settings', 'node_type').split('+')
create_output_string_functs = []
for node_type in node_types:
assert node_type in ['deprel', 'lemma', 'upos', 'xpos', 'form', 'feats'], '"node_type" is not set up correctly'
cpu_cores = config.getint('settings', 'cpu_cores')
if node_type == 'deprel':
create_output_string_funct = create_output_string_deprel
elif node_type == 'lemma':
create_output_string_funct = create_output_string_lemma
elif node_type == 'upos':
create_output_string_funct = create_output_string_upos
elif node_type == 'xpos':
create_output_string_funct = create_output_string_xpos
elif node_type == 'feats':
create_output_string_funct = create_output_string_feats
else:
create_output_string_funct = create_output_string_form
create_output_string_functs.append(create_output_string_funct)
result_dict = {}
unigrams_dict = {}
filters = {}
filters['node_order'] = config.get('settings', 'node_order') == 'fixed'
# filters['caching'] = config.getboolean('settings', 'caching')
filters['dependency_type'] = config.get('settings', 'dependency_type') == 'labeled'
if config.has_option('settings', 'label_whitelist'):
filters['label_whitelist'] = config.get('settings', 'label_whitelist').split('|')
else:
filters['label_whitelist'] = []
if config.has_option('settings', 'root_whitelist'):
# test
filters['root_whitelist'] = []
for option in config.get('settings', 'root_whitelist'). split('|'):
attribute_dict = {}
for attribute in option.split('&'):
value = attribute.split('=')
# assert value[0] in ['deprel', 'lemma', 'upos', 'xpos', 'form',
# 'feats'], '"root_whitelist" is not set up correctly'
attribute_dict[value[0]] = value[1]
filters['root_whitelist'].append(attribute_dict)
# filters['root_whitelist'] = [{'upos': 'NOUN', 'Case': 'Nom'}, {'upos': 'ADJ', 'Degree': 'Sup'}]
else:
filters['root_whitelist'] = []
filters['complete_tree_type'] = config.get('settings', 'tree_type') == 'complete'
filters['association_measures'] = config.getboolean('settings', 'association_measures')
filters['nodes_number'] = config.getboolean('settings', 'nodes_number')
filters['frequency_threshold'] = config.getfloat('settings', 'frequency_threshold', fallback=0)
filters['lines_threshold'] = config.getint('settings', 'lines_threshold', fallback=0)
filters['print_root'] = config.getboolean('settings', 'print_root')
# for tree in all_trees[2:]:
# for tree in all_trees[1205:]:
def count_trees(cpu_cores, all_trees, query_tree, create_output_string_functs, filters, unigrams_dict, result_dict):
with Pool(cpu_cores) as p:
start_exe_time = time.time()
# 1.25 s (16 cores)
# chunked_trees = list(chunkify(all_trees, cpu_cores))
# if cpu_cores > 1:
@ -531,6 +444,131 @@ def main():
else:
result_dict[key] = {'object': r, 'number': 1}
def read_filters(config, feats_detailed_list):
tree_size_range = config.get('settings', 'tree_size', fallback='0').split('-')
tree_size_range = [int(r) for r in tree_size_range]
if tree_size_range[0] > 1:
if len(tree_size_range) == 1:
query_tree = create_ngrams_query_trees(tree_size_range[0], [{}])
elif len(tree_size_range) == 2:
query_tree = []
for i in range(tree_size_range[0], tree_size_range[1] + 1):
query_tree.extend(create_ngrams_query_trees(i, [{}]))
else:
query_tree = [decode_query('(' + config.get('settings', 'query') + ')', '', feats_detailed_list)]
# order_independent_queries(query_tree)
# set filters
node_types = config.get('settings', 'node_type').split('+')
create_output_string_functs = []
for node_type in node_types:
assert node_type in ['deprel', 'lemma', 'upos', 'xpos', 'form', 'feats'], '"node_type" is not set up correctly'
cpu_cores = config.getint('settings', 'cpu_cores')
if node_type == 'deprel':
create_output_string_funct = create_output_string_deprel
elif node_type == 'lemma':
create_output_string_funct = create_output_string_lemma
elif node_type == 'upos':
create_output_string_funct = create_output_string_upos
elif node_type == 'xpos':
create_output_string_funct = create_output_string_xpos
elif node_type == 'feats':
create_output_string_funct = create_output_string_feats
else:
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')
filters['node_order'] = config.get('settings', 'node_order') == 'fixed'
# filters['caching'] = config.getboolean('settings', 'caching')
filters['dependency_type'] = config.get('settings', 'dependency_type') == 'labeled'
if config.has_option('settings', 'label_whitelist'):
filters['label_whitelist'] = config.get('settings', 'label_whitelist').split('|')
else:
filters['label_whitelist'] = []
if config.has_option('settings', 'root_whitelist'):
# test
filters['root_whitelist'] = []
for option in config.get('settings', 'root_whitelist').split('|'):
attribute_dict = {}
for attribute in option.split('&'):
value = attribute.split('=')
# assert value[0] in ['deprel', 'lemma', 'upos', 'xpos', 'form',
# 'feats'], '"root_whitelist" is not set up correctly'
attribute_dict[value[0]] = value[1]
filters['root_whitelist'].append(attribute_dict)
# filters['root_whitelist'] = [{'upos': 'NOUN', 'Case': 'Nom'}, {'upos': 'ADJ', 'Degree': 'Sup'}]
else:
filters['root_whitelist'] = []
filters['complete_tree_type'] = config.get('settings', 'tree_type') == 'complete'
filters['association_measures'] = config.getboolean('settings', 'association_measures')
filters['nodes_number'] = config.getboolean('settings', 'nodes_number')
filters['frequency_threshold'] = config.getfloat('settings', 'frequency_threshold', fallback=0)
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
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--config_file",
default=None,
type=str,
required=True,
help="The input config file.")
args = parser.parse_args()
config = configparser.ConfigParser()
config.read(args.config_file)
# a = args.config_file
# config.read('config.ini')
# create queries
# 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)
filters, query_tree, create_output_string_functs, cpu_cores, unigrams_dict, result_dict, tree_size_range, node_types = read_filters(config, feats_detailed_list)
# 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': [{}, {}, {}]}]}]
# 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))
# test 1 layer queries

14
generic.py
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@ -1,3 +1,17 @@
# Copyright 2019 CJVT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import sys

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