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# This is example, how to use MLJAR service for automatic machine learning and its R-wrapper for churn prediction.
# Example is based on data from https://github.com/WLOGSolutions/telco-customer-churn-in-r-and-h2o/tree/master/data
# Example by Dominik Krzemiński
library(mljar)
library(data.table)

# Read and clean the dataset
all_data <- fread("data/edw_cdr.csv")
all_data <- all_data[, !c("month", "year"), with = FALSE]
all_data <- all_data[complete.cases(all_data)]

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import os
import numpy as np
import lightgbm as lgb

# directory with your models
dir_with_models = './tmp'

# 100 samples of random data (with 70 columns), just for testing
X = np.random.rand(100, 70)

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import openml
import pandas as pd

openml.config.apikey = "aaas8a89sd87as87d8as7d98a" # it is some fake API key, please set your key here!

dataset_id = 1590  # Adults data set, see https://www.openml.org/d/1590

# get data directly fom openml
dataset = openml.datasets.get_dataset(dataset_id)
(X, y, categorical, names) = dataset.get_data(

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-- create a group
CREATE ROLE readaccess;

-- grant access to existing tables
GRANT USAGE ON SCHEMA public TO readaccess;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO readaccess;

-- grant access to future tables
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO readaccess;

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import numpy as np
import pandas as pd
from sqlalchemy import create_engine
import psycopg2
import time

host = "host-address-here"
user = "db-user-here"
password = "db-password"
db = "db-name"

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Adding "Copy" (to Clipboard) Button

If you would like to add a button to your fenced code blocks so that users may copy the code, you can do so in Docusaurus. You will have to add some code to your Docusaurus project, as seen below.

Under static/js, create a file called code-block-buttons.js with the following:

// Turn off ESLint for this file because it's sent down to users as-is.
/* eslint-disable */
window.addEventListener('load', function() {

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'''
MLJAR AutoML can work in 4 modes:
- Explain
- Perform
- Compete
- Optuna
'''

# training in Explain mode, perfect for data exploration
automl = AutoML(mode="Explain")