Kingkha

import json
from datetime import datetime

import requests

import pickle
import os.path
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request

Kingkha

import groovy.json.JsonOutput
import static groovy.io.FileType.FILES

pipeline {
    agent any
    stages {
        stage('Build') {
            steps {
                catchError {
                    sh '$WORKSPACE/run_collection.sh'

Kingkha

import pandas as pd
from sklearn.linear_model import LogisticRegression


def build_model_input():
    train_df = pd.read_csv('data/train.csv')
    test_df = pd.read_csv('data/test.csv')

    df = pd.concat([train_df, test_df])
    for col in df.columns:

Kingkha

# Source: http://ethen8181.github.io/machine-learning/model_selection/auc/auc.html#Implementation
def _binary_clf_curve(y_true, y_score):
    """
    Calculate true and false positives per binary classification
    threshold (can be used for roc curve or precision/recall curve);
    the calcuation makes the assumption that the positive case
    will always be labeled as 1

    Parameters
    ----------