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// dataset = ds

// Deleting all the nodes
MATCH (n)
DETACH DELETE n;

// Deleteing all the edges
MATCH ()-[r]->()
DELETE r;

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# Download the 'train_embeddings.pkl' from https://files.fm/f/76h5d4229
# Run the code in the folder where 'train_embeddings.pkl' is located.

import io

import compress_pickle as pickle
import numpy as np
import tensorflow as tf
from keras import Sequential
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping

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\RequirePackage{tikz}


\documentclass[varwidth]{standalone}

\usepackage{import}
\usepackage{pgfplots}

\usepgfplotslibrary{groupplots}
\pgfplotsset{compat=newest}

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-- Dataset available from http://multimedia-commons.s3-website-us-west-2.amazonaws.com/?prefix=tools/etc/ in Sqlite3 database format ('yfcc100m_dataset.sql' file)

SET NAMES utf8;
SET time_zone = '+00:00';
SET foreign_key_checks = 0;
SET sql_mode = 'NO_AUTO_VALUE_ON_ZERO';

DROP TABLE IF EXISTS `yfcc100m_dataset`;
CREATE TABLE `yfcc100m_dataset` (
  `photoid` int NOT NULL,

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# Loading PySpark modules

from pyspark.sql import DataFrame
from pyspark.sql.types import *

#from pyspark.context import SparkContext
#from pyspark.sql.session import SparkSession

# sc = SparkContext('local')
# spark = SparkSession(sc)

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-- 'xxx.stackexchange.com' is the name of the database

-- `xxx.stackexchange.com`.badges definition

CREATE TABLE `badges` (
  `Id` int(11) NOT NULL,
  `UserId` int(11) NOT NULL,
  `Name` varchar(30) NOT NULL,
  `Date` datetime NOT NULL,
  `Class` int(11) NOT NULL,

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// Loading the posts
LOAD CSV WITH HEADERS FROM 'file:///posts_all_csv.csv' AS row
WITH toInteger(row[0]) AS postId, row[5] AS postBody, toInteger(row[3]) AS postScore
RETURN count(row);

LOAD CSV WITH HEADERS FROM 'file:///posts_all_csv.csv' AS row FIELDTERMINATOR '\t'
WITH row[0] AS postId, row[3] AS postScore, row[5] AS postBody
MERGE (p:Post {postId: postId})
SET p.postBody = postBody, p.postScore = postScore
RETURN p;

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