Editing Machine Learning/Kaggle Social Network Contest/load data

=Python=

== How to load the network into networkx ==
There is a network analysis package for Python called [http://networkx.lanl.gov/ networkx]. This package can be installed using easy_install.

The network can be loaded using the [http://networkx.lanl.gov/reference/generated/networkx.read_edgelist.html read_edgelist] function in networkx or by manually adding edges

NOTE: John found that it took up about 5.5GB of memory to load the entire network. We may need to process it in chunks - or maybe decompose it into smaller sub networks.

'''Method 1'''
<pre>
import networkx as nx
DG = nx.read_edgelist('social_train.csv', create_using=nx.DiGraph(), nodetype=int, delimiter=',')
</pre>

'''Method 2'''
<pre>
import networkx as nx
import csv
import time

t0 = time.clock()
DG = nx.DiGraph()

netcsv = csv.reader(open('social_train.csv', 'rb'), delimiter=',')

for row in netcsv:
    tmp1 = int(row[0])
    tmp2 = int(row[1])
    DG.add_edge(tmp1, tmp2)


print "Loaded in ", str(time.clock() - t0), "s"
</pre>

Below is the time to load different numbers of row using the two methods on a 2.8Ghz Quad core  machine with 3GB RAM. The second method seems quicker. Note that these are just based on single loads and are intended to be a guide rather than a rigorous  analysis of the methods!
{| border="1" 
|-
!|Rows
!| 1M
!| 2M
!| 3M
|-
!|Method 1
| 20s
| 53s
| 103s
|-
!|Method 2
| 15s
| 41s
| 86s
|}


= Ruby =

== Note on CSV Libraries ==
If you happen to be using Ruby (like Jared) for loading data in and out of CSV files, you should definitely try [[ http://fastercsv.rubyforge.org/ | FasterCSV ]](require 'faster_csv') instead of the stock CSV (require 'csv').  For example, when loading the adjacency list it was literally ten times faster using FasterCSV than using the normal CSV.

== Loading Adjacency Lists ==
<pre>
require 'rubygems'
require 'faster_csv'
def load_adj_list_faster(filename)
  adj_list_hash={}
  FasterCSV.foreach(filename, :quote_char => '"', :col_sep =>',', :row_sep =>:auto) do |row|
    node_id=row.shift
    list_of_adj=row
    adj_list_hash[node_id] = list_of_adj
  end
  return adj_list_hash
end

adj_list_lookup = load_adj_list_faster('adj_list.out.csv')
rev_adj_list_lookup = load_adj_list_faster('reverse_adj_list.out.csv')
</pre>


= R =
== igraph ==
The full dataset loaded pretty fast using the R package igraph. With the full data set loaded R is using less than 900MB of RAM. 

Grab the package with:
<pre>
install.packages("igraph")
</pre>

Load the data using:
<pre>
data <-as.matrix(read.csv("social_train.csv", header = FALSE));
dg <- graph.edgelist(data, directed=TRUE)
</pre>