def put_one_record(key, val):
''' save one record into mongodb '''
DATABASE.smlog.insert({
'func_name': key,
'created_at': val.get('created_at'),
'server_name': get_server_name(),
'file_path': val['file_path'],
'file_line': val['file_line'],
'message': val['message'],
'content': val['content'],
})
def put_one_record(key, val):
'''
save one record into mongodb
'''
DATABASE.smlog.update(
{
'created_at': val['created_at'],
'func_name': key,
'server_name': get_server_name(),
},
{
'$set': {
'func_count': val['count'],
'file_path': val['file_path'],
'file_line': val['file_line'],
'message': val['message'],
'content': val['content'],
}
},
safe=True,
upsert=True,
)
# Store class frequencies results
class_frequency_file_name = 'Top{0}/Top{0}_class_frequency_of_'.format(
K) + prediction_file.split('.')[0]
write.save_obj(
novel, prediction_files_path + class_frequency_file_name)
res = dict(
sorted(novel.items(), key=itemgetter(1), reverse=True)[:N])
res = {
str(k)[:class_str_length]: v / N_USERS
for k, v in res.items()
}
keys = res.keys()
values = res.values()
ordered = pd.DataFrame(list(zip(keys, values)),
columns=['x', 'y'
]).sort_values(by=['y'],
ascending=False)
print('\nExperiment Name: {0}'.format(prediction_file))
print(ordered)
f.writelines('\nExperiment Name: {0}'.format(prediction_file))
f.writelines(ordered.to_string())
sendmail('Elaborate Predictions on {0}'.format(get_server_name()),
'Amazon Women')
