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#!/bin/bash

# Download a dataset or competition data from Kaggle using the Kaggle API
# Usage: download_kaggle_dataset.sh <type> <dataset-name> <output_dir>
# Arguments:
#  type: The type of the Kaggle data to download. Either '-d' or 'dataset' for dataset, or '-c' or 'competition' for competition.
#  dataset-name: The name of the dataset or competition on Kaggle. For datasets, it should be in the format 'username/dataset-name'.
#  output_dir: The directory where the data will be downloaded and extracted.

# Examples:

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#!/bin/bash

# List of the packages to be installed and their descriptions
packages=(
  "exa:A modern replacement for 'ls'"
  "duf:Disk Usage/Free Utility"
  "fzf:A general-purpose command-line fuzzy finder"
  "bpytop:Resource monitor that shows usage and stats for processor, memory, disks, network and processes"
  "htop:Interactive process viewer"
  "nano:Easy-to-use text editor"

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import argparse
from pathlib import Path

import duckdb

# Create the parser
parser = argparse.ArgumentParser('`init_database.py` creates a DuckDB database '
                                 'from the eICU dataset.')

# Add the arguments

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# EditorConfig is awesome: https://EditorConfig.org

# Top-most EditorConfig file
root = true

# Unix-style newlines with a newline ending every file
[*]
charset = utf-8
end_of_line = lf
insert_final_newline = true

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-- Description: This file contains the SQL statements to create the tables to store two geosocial datasets into a MySQL/MariaDB database
-- The datasets are available at http://snap.stanford.edu/data/loc-gowalla.html
-- and http://snap.stanford.edu/data/loc-brightkite.html

-- Create database and tables to store the Gowalla and Brightkite datasets
DROP DATABASE IF EXISTS geosocial_db; -- Drop the database if it exists (comment this line if you want to keep the old database
CREATE DATABASE geosocial_db; -- Create the geosocial_db database (comment this line if you want to keep the old database

-- Switch the geosocial_db database
USE geosocial_db;

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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