private void runKfoldCrossValidation(object parameters)
{
try
{
// Setting environment for experiments
kFoldThreadParameter p = (kFoldThreadParameter)parameters;
DataLoader loader = p.loader;
Methodology methodology = p.methodology;
int fold = p.fold;
int nIteration = p.nIteration;
// #1 Core Part: 'EgoNetwork DB' --> 'Graph Strctures(Node, Edge)'
loader.setTrainTestSet(fold);
if (Program.isValidTrainSet == false)
{
return;
}
List <Feature> features = loader.getFeaturesOnMethodology(methodology);
loader.setGraphConfiguration(features);
// Graph Elements: Nodes and edges
Dictionary <int, Node> nodes = loader.allNodes;
Dictionary <int, List <ForwardLink> > edges = loader.allLinksFromNodes;
// Incase: Mention Count(O), Friendship(X)
if (methodology == Methodology.INCL_MENTIONCOUNT ||
methodology == Methodology.INCL_AUTHORSHIP_AND_MENTIONCOUNT ||
methodology == Methodology.INCL_FOLLOWSHIP_ON_THIRDPARTY_AND_MENTIONCOUNT ||
methodology == Methodology.EXCL_FRIENDSHIP)
{
foreach (List <ForwardLink> forwardLinks in edges.Values)
{
for (int i = 0; i < forwardLinks.Count; i++)
{
if (forwardLinks[i].type == EdgeType.FRIENDSHIP)
{
ForwardLink revisedForwardLink = forwardLinks[i];
revisedForwardLink.type = EdgeType.UNDEFINED; // FRIENDSHIP --> UNDEFINED
forwardLinks[i] = revisedForwardLink;
}
}
}
}
// #2 Core Part: 'Graph Strcture(Nodes, Edges)' --> 'PageRank Matrix(2D-ragged array)'
Graph graph = new Graph(nodes, edges);
graph.buildGraph();
// #3 Core Part: Recommendation list(Personalized PageRanking Algorithm)
Recommender recommender = new Recommender(graph);
var recommendation = recommender.Recommendation(0, 0.15f, nIteration); // '0': Ego Node's Index, '0.15f': Damping Factor
// #4 Core Part: Validation - AP(Average Precision)
DataSet testSet = loader.getTestSet();
HashSet <long> egoLikedTweets = testSet.getEgoLikedTweets();
int hit = 0, like = 0;
double AP = 0.0, recall = 0.0; // Average Precision
for (int i = 0; i < recommendation.Count; i++)
{
if (egoLikedTweets.Contains(((Tweet)recommendation[i]).ID))
{
hit += 1;
AP += (double)hit / (i + 1);
}
}
// LIKE
like = (int)egoLikedTweets.Count;
// Average Precision & Recall
if (hit != 0)
{
AP /= hit;
recall = (double)hit / like;
}
else
{
AP = 0.0;
recall = 0.0;
}
// Add current result to final one
lock (kFoldLocker)
{
foreach (EvaluationMetric metric in this.metrics)
{
switch (metric)
{
case EvaluationMetric.MAP:
this.finalResult[metric] += AP;
break;
case EvaluationMetric.RECALL:
this.finalResult[metric] += recall;
break;
case EvaluationMetric.LIKE:
this.finalResult[metric] += like;
break;
case EvaluationMetric.HIT:
this.finalResult[metric] += hit;
break;
}
}
}
}
catch (FileNotFoundException e)
{
Console.WriteLine(e);
}
finally
{
kFoldSemaphore.Release();
}
}
private void runKfoldCrossValidation(object parameters)
{
try
{
// Setting environment for experiments
kFoldThreadParameter p = (kFoldThreadParameter)parameters;
DataLoader loader = p.loader;
Methodology methodology = p.methodology;
int fold = p.fold;
int nIteration = p.nIteration;
// #1 Core Part: 'EgoNetwork DB' --> 'Graph Strctures(Node, Edge)'
loader.setTrainTestSet(fold);
if (Program.isValidTrainSet == false)
return;
List<Feature> features = loader.getFeaturesOnMethodology(methodology);
loader.setGraphConfiguration(features);
// Graph Elements: Nodes and edges
Dictionary<int, Node> nodes = loader.allNodes;
Dictionary<int, List<ForwardLink>> edges = loader.allLinksFromNodes;
// Incase: Mention Count(O), Friendship(X)
if (methodology == Methodology.INCL_MENTIONCOUNT
|| methodology == Methodology.INCL_AUTHORSHIP_AND_MENTIONCOUNT
|| methodology == Methodology.INCL_FOLLOWSHIP_ON_THIRDPARTY_AND_MENTIONCOUNT
|| methodology == Methodology.EXCL_FRIENDSHIP)
{
foreach (List<ForwardLink> forwardLinks in edges.Values)
{
for (int i = 0; i < forwardLinks.Count; i++)
{
if (forwardLinks[i].type == EdgeType.FRIENDSHIP)
{
ForwardLink revisedForwardLink = forwardLinks[i];
revisedForwardLink.type = EdgeType.UNDEFINED; // FRIENDSHIP --> UNDEFINED
forwardLinks[i] = revisedForwardLink;
}
}
}
}
// #2 Core Part: 'Graph Strcture(Nodes, Edges)' --> 'PageRank Matrix(2D-ragged array)'
Graph graph = new Graph(nodes, edges);
graph.buildGraph();
// #3 Core Part: Recommendation list(Personalized PageRanking Algorithm)
Recommender recommender = new Recommender(graph);
var recommendation = recommender.Recommendation(0, 0.15f, nIteration); // '0': Ego Node's Index, '0.15f': Damping Factor
// #4 Core Part: Validation - AP(Average Precision)
DataSet testSet = loader.getTestSet();
HashSet<long> egoLikedTweets = testSet.getEgoLikedTweets();
int hit = 0, like = 0;
double AP = 0.0, recall = 0.0; // Average Precision
for (int i = 0; i < recommendation.Count; i++)
{
if (egoLikedTweets.Contains(((Tweet)recommendation[i]).ID))
{
hit += 1;
AP += (double)hit / (i + 1);
}
}
// LIKE
like = (int)egoLikedTweets.Count;
// Average Precision & Recall
if (hit != 0)
{
AP /= hit;
recall = (double)hit / like;
}
else
{
AP = 0.0;
recall = 0.0;
}
// Add current result to final one
lock (kFoldLocker)
{
foreach (EvaluationMetric metric in this.metrics)
{
switch (metric)
{
case EvaluationMetric.MAP:
this.finalResult[metric] += AP;
break;
case EvaluationMetric.RECALL:
this.finalResult[metric] += recall;
break;
case EvaluationMetric.LIKE:
this.finalResult[metric] += like;
break;
case EvaluationMetric.HIT:
this.finalResult[metric] += hit;
break;
}
}
}
}
catch (FileNotFoundException e)
{
Console.WriteLine(e);
}
finally
{
kFoldSemaphore.Release();
}
}