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get_multiple_ara_scores_with_json.py
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get_multiple_ara_scores_with_json.py
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import xlsxwriter
import pandas as pd
import json
import ijson
# Index in Array where to place the score of each ARA
# Index + 1 will store the normalized score of the ARA
# Index 0 will store Disease
# Index 1 will store Drug
ara_indexing = {
"arax" : 2,
"explanatory_agent" : 4,
"ia" : 6,
"unsecret_agent" : 8,
"aragorn" : 10,
"robokop" : 12,
"bte" : 14
}
def get_ara_provider(row):
"""
Function that returns the ARA a query was sent to
Will return None if no ARA could be tagged to the query
"""
if "message" not in row['data']:
return
message = row['data']['message']
if 'results' not in message:
return
elif not message['results']:
return
elif "reasoner_id" in message['results'][0]:
if message['results'][0]["reasoner_id"] == "ARAX":
return "arax"
elif message['results'][0]["reasoner_id"] == "Explanatory Agent":
return "explanatory_agent"
edges = message["knowledge_graph"]["edges"]
key = list(edges.keys())[0]
attribute_list = edges[key]['attributes']
for attribute in attribute_list:
if 'value' not in attribute:
continue
label = attribute['value']
if isinstance(label, str):
if label == "infores:openpredict":
return "op"
elif label == "infores:improving-agent":
return "ia"
elif label == "infores:molepro":
return "molepro"
elif label == "infores:unsecret-agent":
return "unsecret_agent"
elif label == "infores:genetics-data-provider":
return "geneticskp"
elif label == "infores:aragorn" in label:
return "aragorn"
elif isinstance(label, list):
if "infores:biothings-explorer" in label:
return "bte"
elif "infores:automat-robokop" in label:
return "robokop"
#Print ids of queries which weren't tagged to any ARA
print(row['id'])
def convert_to_dataframe(data):
final_output = []
for disease, drugs in data.items():
for drug,values in drugs.items():
final_output.append(values)
return final_output
def write_to_excel(data):
headers = ["Disease", "Drug", "ARAX", "ARAX NS", "EA", "EA NS", "IA", "IA NS", "UA", "UA NS",
"AR", "AR NS", "ROBO", "ROBO NS", "BTE", "BTE NS"]
writer = pd.ExcelWriter('results-script.xlsx', engine='xlsxwriter')
data.to_excel(writer, sheet_name='Sheet1', header=headers, index=False)
writer.save()
def score_parser(json_data,ara):
query_node = json_data['data']['message']['query_graph']['nodes']
if 'on' in query_node:
disease_idx = query_node['on']['ids'][0]
elif 'n0' in query_node:
disease_idx = query_node['n0']['ids'][0]
elif 'disease' in query_node:
disease_idx = query_node['disease']['ids'][0]
else:
print(query_node)
raise Exception("Could not get Disease")
if disease_idx not in output_data:
output_data[disease_idx] = dict()
for result in json_data['data']['message']['results']:
node_bindings = result['node_bindings']
if 'sn' in node_bindings:
drug_idx = node_bindings['sn'][0]['id']
elif 'n1' in node_bindings:
drug_idx = node_bindings['n1'][0]['id']
elif 'drug' in node_bindings:
drug_idx = node_bindings['drug'][0]['id']
elif "chemical" in node_bindings:
drug_idx = node_bindings['chemical'][0]['id']
else:
print(node_bindings)
raise Exception("Could not get Drug")
if drug_idx not in output_data[disease_idx]:
output_data[disease_idx][drug_idx] = [disease_idx, drug_idx, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
#Noticed some Aragorn queries did not have scores in the results
if 'score' not in result:
return "error"
score = result['score']
if 'normalized_score' in result:
normalized_score = result['normalized_score']
else:
normalized_score = 0
output_data[disease_idx][drug_idx][ara_indexing[ara]] = score
output_data[disease_idx][drug_idx][ara_indexing[ara] + 1] = normalized_score
return "success"
if __name__ == "__main__":
# output_data will store all the scores from all ARAs during processing
output_data = dict()
# data.json is the sqldump in json format
with open('data.json') as fp:
for x in ijson.items(fp, "item"):
try:
if 'message' not in x['data']:
continue
#Some queries aren't about Drugs to treat Disease
if "biolink:treats" not in str(x['data']['message']['query_graph']):
continue
ara = get_ara_provider(x)
if ara not in list(ara_indexing.keys()):
continue
response = score_parser(x,ara)
if "response" == "error":
continue
except Exception as err:
print(f"ID: {x['id']} Unexpected {err=}, {type(err)=}")
raise
y = convert_to_dataframe(output_data)
df = pd.DataFrame(y)
write_to_excel(df)