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csv_to_form.py
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csv_to_form.py
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import ast
import csv
import json
import os
import re
import string
from os import path, remove
import requests
valid = list(string.digits)
valid.extend(list(string.ascii_letters))
valid.append('_')
nlpql_template = '''
// Phenotype library name
phenotype "Form {}, Question {}" version "1";
// # Referenced libraries #
include ClarityCore version "1.0" called Clarity;
// Data Entities
{}
// Operations
{}
// Comments
/*
{}
*/
'''
nlpql_template2 = '''
// Phenotype library name
phenotype "{}" version "1";
// # Referenced libraries #
include ClarityCore version "1.0" called Clarity;
// Termsets
{}
// Data Entities
{}
// Operations
{}
'''
termset_template = '''
termset {}_terms: [
{}
];
'''
basic_data_entity_template = '''
define final {}:
{}
'''
cql_vsac_header = '''
valueset "{}_valueset": '{}'
'''
pt_define = '''
define "Pt": [Patient]
'''
cql_template = '''
library Retrieve2 version '1.0'
using FHIR version '3.0.0'
include FHIRHelpers version '3.0.0' called FHIRHelpers
codesystem "LOINC": 'http://loinc.org'
codesystem "SNOMED": '2.16.840.1.113883.6.96'
codesystem "RxNorm": 'http://www.nlm.nih.gov/research/umls/rxnorm'
codesystem "CPT": 'http://www.ama-assn.org/go/cpt'
codesystem "ICD9": '2.16.840.1.113883.6.42'
codesystem "ICD10": '2.16.840.1.113883.6.3'
codesystem USCoreEthnicitySystem: '2.16.840.1.113883.6.238'
codesystem RelationshipType: '2.16.840.1.113883.4.642.3.449'
{}
context Patient
{}
{}
{}
'''
# +
cql_concept_template = '''
define "%s_concepts": Concept {
%s
}
'''
# -
# Code '26464-8' from "LOINC",
# Code '804-5' from "LOINC",
# Code '6690-2' from "LOINC",
# Code '49498-9' from "LOINC"
# +
cql_result_template = '''
define "{}":
{}
'''
cql_result_template_res = ''' [{}]'''
cql_result_template_cs = ''' [{}: Code in "{}_concepts"] {}'''
cql_result_template_vs = '''[{}:"{}_valueset"]'''
cql_task_template = '''
define final %s:
Clarity.CQLExecutionTask({
"task_index": 0,
cql: \"\"\"
%s
\"\"\"
});
'''
def format_answer(a):
return '_'.join(a.split(' ')).lower().replace('"', '').replace('(', '').replace(')', '').replace('.', '') \
.replace('\n', ' ').strip()
def question_number(line):
question_string = ''
spl = line.split(':')
try:
n = int(spl[-1])
except ValueError:
n = -1
if len(spl) > 1:
question_string = spl[0]
if n != -1 and n < 10000:
return question_string, n
else:
return None, None
def value_set(set_name, final_str, *args):
args_str = '{ \n \t\t'
for k, v in args[0].items():
if isinstance(v, float):
v = '"{}"'.format(v)
args_str += '{}: {}, \n \t\t'.format(k, v)
args_str += "}"
val_set = """
define {}:
Clarity.ValueExtraction({});
""".format(set_name, args_str)
val_set += final_str
return val_set
def gen_feature_name(rhs, comparator, lhs):
op_name = "AnyVal"
if comparator == "<":
op_name = "Lt"
elif comparator == ">":
op_name = "Gt"
elif comparator == "<=":
op_name = "Leq"
elif comparator == ">=":
op_name = "Geq"
elif comparator == "==":
op_name = "Equals"
feature_name = "{}{}{}".format(rhs[0], op_name, str(lhs))
final_str = """
define final has{}:
where {}.value {} {};
""".format(feature_name, feature_name, comparator, lhs)
return feature_name, final_str
def convert_expr_to_value_extraction(expr, feature_name=None):
kwargs_to_pass = {}
val_extr_ast = ast.parse(expr)
code_ast = ast.parse(expr)
lhs = list()
rhs = None
comparator = None
for node in ast.walk(code_ast):
# we need to be able to handle n-grams that are actually a single concept/measurement, etc.
if isinstance(node, ast.Name):
lhs.append(node.id)
# this is our (numeric) LHS
elif isinstance(node, ast.Num):
rhs = node.n
# grab our operator and convert it to a string representation
elif isinstance(node, ast.Compare):
op = node.ops[0]
if isinstance(op, ast.Lt):
comparator = "<"
elif isinstance(op, ast.Gt):
comparator = ">"
elif isinstance(op, ast.LtE):
comparator = "<="
elif isinstance(op, ast.GtE):
comparator = ">="
elif isinstance(op, ast.Eq):
comparator = "=="
kwargs_to_pass["termset"] = "{}".format([x.replace("_", " ") for x in lhs])
# leq and geq are handled the same as >; < per clarity value extraction docs
if comparator in ("<", "<="):
kwargs_to_pass["maximum_value"] = '"{}"'.format(rhs)
elif comparator in (">", ">="):
kwargs_to_pass["minimum_value"] = '"{}"'.format(rhs)
elif comparator == "==":
kwargs_to_pass["minimum_value"] = '"{}"'.format(rhs)
kwargs_to_pass["maximum_value"] = '"{}"'.format(rhs)
if feature_name is None:
feature_name, final_str = gen_feature_name(lhs, comparator, rhs)
return value_set(feature_name, final_str, {k: v for k, v in kwargs_to_pass.items()})
# input_str = [["ANC >= 500" ],["ANC == 200"], ["FiO2 < 0.4"]]
# json_obj = json.loads(json.dumps(input_str))
# out = ''
# for x in json_obj:
# test = convert_expr_to_value_extraction("""{}""".format(str(x[0])))
# out += test
# -
def is_numeric(test):
try:
int(test)
float(test)
return True
except ValueError:
return False
def merger(dict1, dict2):
res = {**dict1, **dict2}
return res
def cleanup_row(r):
output_row = dict()
row = json.loads(json.dumps(r, indent=4, sort_keys=True).replace('\\u00a0', ' ').replace('\\u00ad', '-')
.replace('\\u2265', '>=').replace('\\u2264', '<=').replace('\\u00b3', '3').replace(
'\\u00b0', ' degrees')
.replace('\\u03b3', 'gamma').replace('\\u03b1', 'alpha').replace('\\u00b5', 'u'))
for ro in row.keys():
try:
new_key = ro.strip().lower()
output_row[new_key] = row[ro].strip()
except:
print('failure to cleanup row')
return output_row
def write_nlpql_file(output_dir, folder_prefix,
group_formatted, termsets, entities, operations, form_name, old_grouping, comment):
if len(group_formatted.strip()) == 0:
print('empty group name; nothing to save')
return
filename = '{}/{}/{}.nlpql'.format(output_dir, folder_prefix, group_formatted)
if len(termsets) == 0 and len(entities) == 0 and len(operations) == 0:
print('no NLPQL attributes found for {}'.format(filename))
termsets = list(set(termsets))
entities = list(set(entities))
operations = list(set(operations))
if len(entities) > 0:
with open(filename, 'w') as f:
ts_string = '\n\n'.join(termsets)
de_string = '\n\n'.join(entities)
op_string = '\n\n'.join(operations)
query = nlpql_template2.format(form_name, ts_string, de_string, op_string)
f.write(query)
def write_questions_file(output_dir, folder_prefix, form_data, groups, evidence_bundles, evidence_count, suffix=''):
question_file_name = '{}/{}/questions{}.json'.format(output_dir, folder_prefix, suffix)
form_data['groups'] = list(groups.keys())
form_data['evidence_bundles'] = list(evidence_bundles.keys())
form_data['version'] = get_nlpql_version(question_file_name)
form_data['questions_with_evidence_count'] = evidence_count
with open(question_file_name, 'w') as f:
f.write(json.dumps(form_data, indent=4))
return form_data
def write_questions_file_v2(output_dir, folder_prefix, form_data, groups, evidence_bundles, evidence_count,
all_features, all_rows,
suffix='_v2'):
question_file_name = '{}/{}/questions{}.json'.format(output_dir, folder_prefix, suffix)
# form_data['groups'] = list(groups.keys())
# form_data['evidence_bundles'] = list(evidence_bundles.keys())
# form_data['version'] = get_nlpql_version(question_file_name)
# form_data['questions_with_evidence_count'] = evidence_count
# group_formatted = '_'.join(grouping.lower().split(' ')).replace(',', '').replace('_/_', '_')
new_groupings = dict()
new_evidences = dict()
for q in form_data.get('questions', list()):
group = q.get('group', '')
group_id = '_'.join(group.lower().split(' ')).replace(',', '').replace('_/_', '_').replace('.', '')
if group_id not in new_groupings:
new_groupings[group_id] = list()
new_q = dict()
# question type
q_type = q.get('question_type')
# get answers
answers = q.get('answers', list())
options = list()
valid_answers = list()
for a in answers:
v = a.get('value', '').strip()
t = a.get('text', '').strip()
options.append({
'label': t,
'value': v
})
if len(t) > 0:
valid_answers.append(t)
new_q['options'] = options
# get autofill and defaults
evidence_bundle = q.get('evidence_bundle', dict())
autofill = dict()
default_answer = None
case_values = dict()
for evidence in evidence_bundle.keys():
features = evidence_bundle[evidence]
rows = all_rows.get(q.get('question_number'))
if rows:
for row in rows:
if row:
row_feature = row.get('feature', row.get('feature_name', ''))
default = row.get('default_answer', row.get('default', '')).strip()
autofill_criteria = row.get('autofill', '')
autofill_mc = row.get('autofill_mc_answer', row.get('autofill_mc', ''))
if len(default) > 0 and (
default in valid_answers or (q_type != 'RADIO' or q_type != 'CHECKBOX')):
formatted_default = format_answer(default)
if default and formatted_default and formatted_default != default_answer and \
(q_type == 'RADIO' or q_type == 'CHECKBOX'):
print('WARNING: duplicate default on Question {}: {} != {}'.format(
q.get('question_number', -1000),
formatted_default,
default_answer))
default_answer = formatted_default
if len(autofill_criteria) and autofill_criteria.upper() != 'EXISTS':
print(autofill_criteria)
query = dict()
fld = "{}.{}.{}".format(evidence, row_feature, autofill_criteria)
query['field'] = fld
query['operator'] = '$exists'
query['criteria'] = True
case_values[fld] = {
'queries': [query],
"value": '$$' + fld
}
elif (autofill_criteria.strip().lower() == 'exists' or len(autofill_criteria) == 0) and len(
autofill_mc) > 0:
autofill_exist_answer = format_answer(autofill_mc)
query = dict()
query['field'] = "{}.{}.pipeline_id".format(evidence, row_feature)
query['operator'] = '$exists'
query['criteria'] = True
if autofill_exist_answer not in case_values:
case_values[autofill_exist_answer] = {
'queries': [query],
"value": autofill_exist_answer
}
else:
case_values[autofill_exist_answer]['queries'].append(query)
if default_answer:
autofill['default'] = default_answer
default_cases = list(case_values.values())
cases = list()
for default_case in default_cases:
case = dict()
default_queries = default_case.get('queries', list())
if len(default_queries) > 1:
# default operator is and - this forces or
or_op = dict()
or_op['operator'] = '$or'
or_op['clauses'] = default_queries
case['queries'] = [or_op]
case['value'] = default_case.get('value')
else:
case = default_case
cases.append(case)
autofill['cases'] = cases
# map to new data structure
new_q['id'] = 'question{}'.format(q.get('question_number', -1000))
new_q['number'] = int(q.get('question_number', -1000))
new_q['name'] = q.get('question_name')
new_q['type'] = q_type
new_q['value'] = ''
q_evidence = q.get('nlpql_grouping', '')
if len(q_evidence) > 0:
new_q['evidence'] = q_evidence
if len(cases) > 0:
new_q['autofill'] = autofill
new_groupings[group_id].append(new_q)
evidence_bundle = q.get('evidence_bundle', dict())
for k in evidence_bundle.keys():
new_evidences[k] = evidence_bundle[k]
new_form_data = dict()
new_form_data['name'] = form_data['name']
new_form_data['slug'] = folder_prefix
new_form_data['description'] = form_data['description']
new_form_data['allocated_users'] = form_data['allocated_users']
new_form_data['groups'] = dict()
new_form_data['evidences'] = dict()
new_form_data['groups']['byId'] = dict()
group_evidences = list()
question_all_ids = list()
group_ids = list()
for g in list(groups.keys()):
id_name = '_'.join(g.lower().split(' ')).replace(',', '').replace('_/_', '_').replace('.', '')
new_grouping_mapping = new_groupings.get(id_name, list())
mapped_group = dict()
qs = dict()
id_list = list()
evidences = set()
for ngm in new_grouping_mapping:
the_id = ngm.get('id')
evidence = ngm.get('evidence', '')
qs[the_id] = ngm
id_list.append(the_id)
if len(evidence) > 0:
evidences.add(evidence)
mapped_group['name'] = g
mapped_group['questions'] = dict()
mapped_group['questions']['byId'] = qs
mapped_group['questions']['allIds'] = list(qs.keys())
mapped_group['evidences'] = list(evidences)
new_form_data['groups']['byId'][id_name] = mapped_group
group_ids.append(id_name)
question_all_ids.extend(id_list)
group_evidences.extend(list(evidences))
# new_form_data['groups']['evidences'] = group_evidences
# new_form_data['groups']['allIds'] = question_all_ids
new_form_data['groups']['allIds'] = group_ids
for k in new_evidences.keys():
new_evidence = dict()
new_evidence['allIds'] = new_evidences.get(k, list())
byid = dict()
for l in new_evidence.get('allIds'):
byid_item = dict()
# TODO display stuff
feature_info = all_features.get(l, dict())
feature_name_display_name = feature_info.get('feature_name', feature_info.get('feature', '')). \
replace('_', ' ').strip().title()
display_type = feature_info.get('display_type', '')
fhir_resource_type = feature_info.get('fhir_resource_type', '')
if len(display_type) > 0:
byid_item['displayType'] = display_type
else:
nlp_task_type = feature_info.get('nlp_task_type', '')
if 'CQL' in nlp_task_type:
if fhir_resource_type == 'Observation':
byid_item['displayType'] = 'table'
byid_item['cols'] = [
{
"label": "Date",
"value": "r.result_display.date"
},
{
"label": "Name",
"value": "r.code_coding_0_display"
},
{
"label": "Val",
"value": "`${r.valueQuantity_value} ${r.valueQuantity_code}`"
}
]
elif fhir_resource_type == 'Condition':
byid_item['displayType'] = 'table'
byid_item['cols'] = [
{
"label": "Date",
"value": "r.result_display.date"
},
{
"label": "Name",
"value": "r.code_coding_0_display"
},
{
"label": "Code",
"value": "r.code_coding_0_code"
}
]
else:
byid_item['displayType'] = 'cards'
elif 'ValueExtraction' in nlp_task_type:
byid_item['displayType'] = 'table'
byid_item['cols'] = [
{
"label": "Date",
"value": "r.result_display.date"
},
{
"label": "Name",
"value": "r.text"
},
{
"label": "Val",
"value": "`${r.value}`"
}
]
else:
byid_item['displayType'] = 'cards'
display_name = feature_info.get('feature_display_name', '')
if len(display_name) > 0:
byid_item['title'] = display_name
else:
if len(fhir_resource_type) > 0:
byid_item['title'] = '{} {}'.format(feature_name_display_name, fhir_resource_type).strip()
else:
byid_item['title'] = '{} Text Mentions'.format(feature_name_display_name).strip()
byid[l] = byid_item
new_evidence['byId'] = byid
new_form_data['evidences'][k] = new_evidence
with open(question_file_name, 'w') as f:
f.write(json.dumps(new_form_data, indent=4))
def save_question_to_form_data(q_type, answers, name, question_num, group, evidence, grouping, map_qs, form_data):
# if not grouping or grouping == '':
# grouping = grouping_alt
print('saving question ', question_num, ' ', name)
answer_sets = list()
if q_type != 'DATE' and q_type != 'TEXT':
for a in answers:
txt = a.replace('\n', ' ').replace('"', '').strip()
val = format_answer(a)
if " = " in txt:
kv = txt.replace('"', '').split('=')
if len(kv) == 2:
answer_sets.append({
'text': kv[1].strip(),
'value': kv[0].strip()
})
else:
answer_sets.append({
'text': txt,
'value': val
})
else:
answer_sets.append({
'text': txt,
'value': val
})
this_evidence = dict()
if evidence and len(grouping) > 0:
for k in evidence.keys():
if k == grouping:
this_evidence[k] = list(set(evidence[k]))
for v in evidence[k]:
if v == grouping:
this_evidence[k] = list(set(evidence[k]))
new_q = {
"question_name": name,
"question_type": q_type,
"question_number": question_num,
"group": group,
"answers": answer_sets,
"evidence_bundle": this_evidence,
"nlpql_grouping": grouping
}
new_question = True
question_num = str(question_num)
old_q = dict()
for q_ in form_data.get('questions', list()):
old_q_num = str(q_.get('question_number', ''))
if old_q_num == question_num:
new_question = False
old_q = q_
break
if new_question:
map_qs.append(question_num)
form_data['questions'].append(new_q)
else:
merged_q = merger(old_q, new_q)
i = 0
for q_ in form_data.get('questions', list()):
old_q_num = str(q_.get('question_number', ''))
if old_q_num == question_num:
form_data['questions'][i] = merged_q
break
i += 1
evidence = dict()
def get_term_string(_terms):
if len(_terms) < 1:
return ''
_terms = [i.replace('"', '') for i in _terms]
term_string = '", "'.join(_terms)
if term_string.strip() != '':
term_string = '"' + term_string + '"'
term_string = term_string.replace(', " unspecified",', ',').replace('"",', '')
return term_string
def map_provider_assertion(terms, termsets, feature_name, features, entities):
term_string = get_term_string(terms)
if term_string != '':
termsets.append(termset_template.format(feature_name, term_string))
pq = '''Clarity.ProviderAssertion({
termset: [%s_terms]
});
''' % feature_name
pa = basic_data_entity_template.format(feature_name, pq)
features.append(feature_name)
entities.append(pa)
def map_term_proximity(terms, terms2, termsets, feature_name, features, entities, word_distance=3):
term_string = get_term_string(terms)
term_string2 = get_term_string(terms2)
if term_string != '' and term_string2 != '':
termsets.append(termset_template.format(feature_name, term_string))
f2 = feature_name + '2'
termsets.append(termset_template.format(f2, term_string2))
pq = ''' Clarity.TermProximityTask({
documentset: [Docs],
"termset1": [%s_terms],
"termset2": [%s_terms],
"word_distance": %d,
"any_order": "True"
});
''' % (feature_name, f2, word_distance)
pa = basic_data_entity_template.format(feature_name, pq)
features.append(feature_name)
entities.append(pa)
def map_logic(logic, feature_name, features, entities):
if not logic.startswith('where '):
logic = 'where ' + logic
if not logic.endswith(';'):
logic = logic + ';\n'
pa = basic_data_entity_template.format(feature_name, logic)
features.append(feature_name)
entities.append(pa)
def map_value_extraction(terms, termsets, feature_name, value_min, value_max, value_enum_set, features, entities, values_before_terms):
map_generic_task('ValueExtraction', terms, termsets, feature_name, value_min, value_max, value_enum_set,
features, entities, values_before_terms=values_before_terms)
def map_generic_task(nlp_task_type, terms, termsets, feature_name, value_min, value_max, value_enum_set,
features, entities, values_before_terms=False):
term_string = get_term_string(terms)
if term_string.strip() != '':
termsets.append(termset_template.format(feature_name, term_string))
v_min = ''
v_max = ''
v_values_before_terms = ''
v_enum_string = ''
if len(value_min) > 0:
v_min = ', minimum_value: "{}"'.format(value_min)
if len(value_max) > 0:
v_max = ', maximum_value: "{}"'.format(value_max)
if nlp_task_type == 'ValueExtraction':
if values_before_terms:
v_values_before_terms = ', "values_before_terms": "True"'
if len(value_enum_set) > 0:
v_enum = ''
for v in value_enum_set:
if len(v) == 0:
continue
if len(v_enum) > 0:
v_enum += ', '
v = v.replace('?', '').replace('"', '').replace("'", '').strip()
v_enum += ('"{}"'.format(v))
if len(v_enum) > 0:
v_enum_string = ', enum_list: [{}],'.format(v_enum)
if len(terms) > 0:
terms_attr_string = 'termset: [%s_terms]' % feature_name
else:
terms_attr_string = ''
query_params = ('''
%s
%s
%s
%s
%s
''' % (terms_attr_string, v_min, v_max, v_values_before_terms, v_enum_string)).strip()
pq = '''Clarity.%s({
%s});
''' % (nlp_task_type, query_params)
pq = pq.replace(',});', '});').replace(""",
});""", '});')
pa = basic_data_entity_template.format(feature_name, pq)
features.append(feature_name)
entities.append(pa)
def map_cql(codes, code_sys, feature_name, concepts, fhir_resource_type, entities, features, value_set_oid,
cql_expression, cql_folder, value_min=None, value_max=None):
cql_concept = ''
cql_header = ''
c_string = ''
if not codes:
codes = list()
if len(codes) == 1 and codes[0] == '':
codes = list()
if len(codes) > 0:
for c in codes:
if len(c_string) > 0:
c_string += ', \n '
code = c.replace('?', '').replace('"', '').replace("'", '')
c_string += 'Code \'{}\' from "{}"'.format(code, code_sys)
cql_concept = cql_concept_template % (feature_name, c_string)
concepts.append(cql_concept)
if value_set_oid and len(value_set_oid) > 0:
value_set_oid = value_set_oid.replace('?', '').replace('"', '').replace("'", '')
cql_define_name = feature_name
cql_header = cql_vsac_header.format(cql_define_name, value_set_oid)
if len(cql_expression) > 0:
cql_res = cql_expression
else:
c_string = ''
resource = fhir_resource_type
if not resource or len(resource) == 0:
resource = 'Observation'
cql_result_members = list()
#
# define "Conditions Indicating Sexual Activity":
# ["Condition": "Other Female Reproductive Conditions"]
# union ["Condition": "Genital Herpes"]
# union ["Condition": "Genococcal Infections and Venereal Diseases"]
# union ["Condition": "Inflammatory Diseases of Female Reproductive Organs"]
# union ["Condition": "Chlamydia"]
# union ["Condition": "HIV"]
# union ["Condition": "Syphilis"]
# union ["Condition": "Complications of Pregnancy, Childbirth and the Puerperium"]
value_template = 'O where O.value.value > {:.1f} {}'
value_template_upper = ' and O.value.value < {:.1f}'
value_template_upper_only = 'O where O.value.value < {:.1f} {}'
upper_string = ''
value_string = ''
if resource == 'Observation':
if value_max and value_max != '' and (not value_min or value_min == ''):
try:
value_string = value_template_upper_only.format((float(value_max)), '')
except:
value_string = ''
else:
if value_max and value_max != '':
try:
upper_string = value_template_upper.format((float(value_max)))
except Exception as ex:
upper_string = ''
if value_min and value_min != '':
try:
value_string = value_template.format((float(value_min)), upper_string)
except:
value_string = ''
if not codes:
codes = list()
if len(codes) == 1 and codes[0] == '':
codes = list()
if len(codes) > 0:
cql_result_members.append(cql_result_template_cs.format(resource, feature_name, value_string))
if value_set_oid and len(value_set_oid) > 0:
cql_result_members.append(cql_result_template_vs.format(resource, feature_name))
if len(cql_concept) == 0 and len(cql_result_members) == 0:
cql_result_members.append(cql_result_template_res.format(resource))
if len(cql_result_members) == 1:
cql_res = '\t' + cql_result_members[0]
else:
cql_res = '''\n\t\t\t\tunion '''.join(cql_result_members)
if len(cql_res) > 0:
cql_res = cql_result_template.format(feature_name, cql_res)
pt_context = ''
if '"Pt"' in cql_concept or '"Pt"' in cql_res:
pt_context = pt_define
cql = cql_template.format(cql_header, pt_context, cql_concept, cql_res)
entities.append(cql_task_template % (feature_name, cql))
features.append(feature_name)
if len(cql_folder) > 0:
filename = '{}/{}.cql'.format(cql_folder, feature_name)
with open(filename, 'w') as f:
f.write(cql)
def get_nlpql_version(question_file_name):
if path.exists(question_file_name):
with open(question_file_name) as json_file:
prev_form_data = json.load(json_file)
prev_version = prev_form_data.get('version')
if not prev_version:
version = "0.0.1"
else:
version_segments = prev_version.split('.')
version_segments = [i for i in version_segments if i]
version = ''
try:
version_int = int(version_segments[-1]) + 1
except Exception as ex:
version_int = 1
print(ex)
for s in version_segments[:-1]:
version = version + s
version = version + '.'
version += str(version_int)
else:
version = "0.0.1"
return version
def get_feature_name(name, all_features):
remove_it = string.punctuation
remove_it = remove_it.replace("_", "")
pattern = r"[{}]".format(remove_it)
name = re.sub(pattern, "", name)
# if name in all_features:
# sep = name.split('_')
# if len(sep) <= 1:
# name = name + '_1'
# else:
# last = sep[-1]
# if last.isdigit():
# next_int = int(last) + 1
# sep = sep[0:-1]
# name = '_'.join(sep) + '_' + str(next_int)
# if name in all_features:
# return get_feature_name(name, all_features)
# else:
# name = name + '_1'
# if name in all_features:
# return get_feature_name(name, all_features)
return name
def parse_questions_from_feature_csv(folder_prefix='4100r4',
form_name="Form 4100 R4.0",
file_name='/Users/charityhilton/Downloads/feature2question.csv',
output_dir='/Users/charityhilton/repos/CIBMTR_knowledge_base',
description=None):
if not description:
description = form_name
output_folder_path = os.path.join(output_dir, folder_prefix)
print(output_folder_path)
if not os.path.exists(output_folder_path):
os.mkdir(output_folder_path)
cql_folder = os.path.join(output_folder_path, 'cql')
if not os.path.exists(cql_folder):
os.mkdir(cql_folder)
feature_names = set()
temp = False
if file_name.startswith('http'):
r = requests.get(file_name)
temp = True
file_name = '/tmp/{}.csv'.format(folder_prefix)
with open(file_name, 'wb') as f:
f.write(r.content)
with open(file_name, 'r', encoding='utf-8', errors='ignore') as csv_file:
reader = csv.DictReader(csv_file, delimiter=',', quotechar='"')
form_data = {
"name": form_name,
"owner": "gatech",
"description": description,
"allocated_users": ["admin"],
"groups": list(),
"questions": list(),
"evidence_bundles": list()
}
n = 0
groups = dict()
grouping = None
new_grouping = False
last_question = None
new_question = False
question_num = None
termsets = list()
entities = list()
operations = list()
concepts = list()
comment = ''
group_number = 1
evidence_count = 0
evidence = dict()
evidence_bundles = dict()
all_features = dict()
all_rows = dict()
name = None
group = None