def run():
# parse arguments
args = parse_arguments()
workflow_dir = args.workflow_dir
task_dir = args.task_dir
# get branch meta data
branch_meta_file = os.path.join(workflow_dir, 'branch_meta.json')
with open(branch_meta_file, 'r') as f_in:
branch_meta = json.load(f_in)
# get predictor, training/test
# datasets data
task_meta = get_task_meta_data(task_dir)
# get evaluation data
evaluation_results_file = os.path.join(task_dir, 'evaluation_results.json')
with open(evaluation_results_file, 'r') as f_in:
evaluation_results = json.load(f_in)
# compile and add timestamp, date
timestamp = time.time()
record = {
"timestamp": time.time(),
"date": datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
}
record.update(branch_meta)
record.update(task_meta)
record.update({"evaluation_results": evaluation_results})
# save record to json for backup
with open('record_to_push.json', 'w') as f_out:
json.dump(record, f_out, indent=4, sort_keys=True)
# connect to db and table
performance_db = connector.connect_to_db('PredictorPerformanceDatabase')
task_name = task_meta['predictor_input']['prediction_task'].split('(')[0]
performance_table = getattr(performance_db,
'cnn_{0}_table'.format(task_name))
# insert the record
performance_table.insert_one(record)
from connector import connect_to_db con = connect_to_db() cur = con.cursor() insert_in_test1 = "UPDATE TEST1 set NAME = 'Vasya' where ADMISSION = 1" cur.execute(insert_in_test1) con.commit() con.close()