It means fast risk control with python. It's a lightweight tool that automatic recognize the outliers from a large data pool. This project aims to help people get easily method with abnormal recognition, especially forces password attacks. We wish it could be a nice Open Source which could simplify the complexity of the Data Feature Project.
@bolg:风控用户识别方法
We got the correctness around 29 data sets below,however the speed of Frcwp comes last.
U can get it easily download from Pypi with pip install Frcwp.
import pandas as pd
from Frcwp import Frcwp
path = '../data/data_all.csv'
traindata = pd.read_csv(path)
frc = Frcwp()
traindata = frc.changeformat(traindata, index=0)
params = {
'na_rate': 0.4,
'single_dealed': 1,
'is_scale': 0,
'distince_method': 'Maha',
'outlier_rate': 0.05,
'strange_rate': 0.15,
'nestimators': 150,
'contamination': 0.2
}
frc.fit(traindata, **params)
predict_params = {
'output': 20,
'is_whole': 1
}
frc.predict(frc.potentialdata_set, **predict_params)Frcwp is implemented in Python 3.6, use Pandas.DataFrame to store data. These package can be easily installed using pip.
- feature scanning
- increase new outliers distinguishing methods