mndrake

library(miniCRAN)
library(remotes)

# CRAN mirror to use (recommend checkpoint date for installed version of R https://mran.microsoft.com/timemachine)
cran_repo <- c(CRAN = "https://cran.microsoft.com/snapshot/2018-11-30")

# local path to create miniCRAN repo
miniCRAN_dir <- "/data/dataiku/miniCRAN"

mndrake

from io import StringIO
import logging

class StreamingLog(object):
    def __init__(self, logger_name, level=logging.INFO):
        self.stream = StringIO()
        self.handler = logging.StreamHandler(self.stream)
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        self.handler.setFormatter(formatter)
        self.log = logging.getLogger(logger_name)

mndrake

from urllib.parse import urlparse
import re
import dataiku
import pandas as pd

PROJECT_ID = 'CUSTOMERSEGMENTATION'
ANALYSIS_ID = 'UjW24hJ1'
ML_TASK_ID = 'LsiobCLw'
MODEL_ID = 'A-CUSTOMERSEGMENTATION-UjW24hJ1-LsiobCLw-s1-pp1-m1'

mndrake

#!/bin/sh

# Exit immediately if a command exits with a non-zero status.
# Treat unset variables as an error when substituting.
set -eu

# Function to be called on script exit for cleanup purposes.
cleanup() {
    # Clean up any temporary directories
    if [ -n "${td:-}" ]; then