/// <summary>
/// Generate the decision rules based on the specisied FP-tree and the minimal
/// confidence.
/// </summary>
/// <param name="tree">FP-tree</param>
/// <param name="minConfidence">The minimal confidence</param>
/// <returns></returns>
public List <DecisionRule <T> > GenerateRuleSet(FpTree <T> tree, double minConfidence)
{
FpGrowth(tree, new List <T>());
var decisionRules = new List <DecisionRule <T> >();
foreach (var frequentItemSet in FrequentItemSets.Keys)
{
if (frequentItemSet.ItemSet.Count < 2)
{
continue;
}
var subSets = EnumerableHelper.GetSubsets(frequentItemSet.ItemSet);
foreach (var t in subSets)
{
var leftSide = new FrequentItemSet <T>(t);
for (var j = 0; j < subSets.Count; j++)
{
var rightSide = new FrequentItemSet <T>(subSets[j]);
if (rightSide.ItemSet.Count != 1 || !FrequentItemSet <T> .SetsSeparated(rightSide, leftSide))
{
continue;
}
if (FrequentItemSets.ContainsKey(leftSide))
{
var confidence = (double)FrequentItemSets[frequentItemSet] / FrequentItemSets[leftSide];
if (confidence >= minConfidence)
{
var rule = new DecisionRule <T>(leftSide.ItemSet, rightSide.ItemSet, FrequentItemSets[frequentItemSet], confidence);
decisionRules.Add(rule);
}
}
}
}
}
return(decisionRules);
}
public override void Run(ExecutionSettings executionSettings, bool printRules)
{
builder = new MsDataBuilder();
var data = builder.BuildInstance(executionSettings);
var frequentSets = data.Elements.Keys.Select(element => new List <int> {
element
}).AsParallel().ToList();
frequentSets = frequentSets.Where(set => set.IsFrequent(data.Transactions, executionSettings.MinSup)).AsParallel().ToList();
var frequentItemSets = frequentSets.AsParallel().ToDictionary(set => new FrequentItemSet <int>(set),
set => set.GetSupport(data.Transactions));
List <List <int> > candidates;
while ((candidates = GenerateCandidates(frequentSets)).Count > 0)
{
//! sprawdź czy któryś podzbiór k-1 elementowy kadydatów nie jest w frequentSets => wywal go!
// leave only these sets which are frequent
candidates =
candidates.Where(set => set.IsFrequentParallel(data.Transactions, executionSettings.MinSup)).AsParallel().ToList();
if (candidates.Count > 0)
{
frequentSets = candidates;
foreach (var candidate in candidates)
{
frequentItemSets.Add(new FrequentItemSet <int>(candidate), candidate.GetSupportParallel(data.Transactions));
}
}
else
{
// we don't have any more candidates
break;
}
}
//here we should do something with the candidates
var decisionRules = new List <DecisionRule <int> >();
foreach (var frequentSet in frequentSets)
{
var subSets = EnumerableHelper.GetSubsets(frequentSet);
foreach (var t in subSets)
{
var leftSide = new FrequentItemSet <int>(t);
for (var j = 0; j < subSets.Count; j++)
{
var rightSide = new FrequentItemSet <int>(subSets[j]);
if (rightSide.ItemSet.Count != 1 || !FrequentItemSet <int> .SetsSeparated(rightSide, leftSide))
{
continue;
}
if (!frequentItemSets.ContainsKey(leftSide))
{
continue;
}
var confidence = (double)frequentItemSets[new FrequentItemSet <int>(frequentSet)] / frequentItemSets[leftSide];
if (confidence >= executionSettings.MinConf)
{
var rule = new DecisionRule <int>(leftSide.ItemSet, rightSide.ItemSet, frequentItemSets[new FrequentItemSet <int>(frequentSet)], confidence);
decisionRules.Add(rule);
}
}
}
}
if (!printRules)
{
return;
}
var result = PrintRules(decisionRules, executionSettings.DataSourcePath, executionSettings.MinSup, executionSettings.MinConf, data.Transactions.Keys.Count, data.Elements);
Console.WriteLine(result);
}
public override void Run(ExecutionSettings executionSettings, bool printRules)
{
builder = new MsDataBuilder();
var data = builder.BuildInstance(executionSettings);
var elementsList = data.Elements.Keys.ToList();
var transactionsList = data.Transactions.Keys.ToList();
var bitmapWrapper = PrepareBitmapWrapper(data, elementsList, transactionsList);
var elementsFrequencies = CalculateElementsFrequencies(bitmapWrapper);
var frequentSets = elementsList
.Where(e => elementsFrequencies[elementsList.IndexOf(e)] >= executionSettings.MinSup * transactionsList.Count)
.Select(element => new List <int> {
element
})
.ToList();
var frequentItemSets = frequentSets.ToDictionary(set => new FrequentItemSet <int>(set),
set => elementsFrequencies[elementsList.IndexOf(set[0])]);
List <List <int> > candidates;
if (frequentSets.Count == 0)
{
return;
}
var bitmapTransposed = new Bitmap(transactionsList.Count, frequentSets.Count);
var newElementsList = new List <int>(frequentSets.Count);
var jj = 0;
foreach (var set in frequentSets)
{
newElementsList.Add(set[0]);
for (var i = 0; i < transactionsList.Count; i++)
{
var pixel = bitmapWrapper.Bitmap.GetPixel(elementsList.IndexOf(set[0]), i);
bitmapTransposed.SetPixel(i, jj, pixel);
}
jj++;
}
var newBitmapWrapper = BitmapWrapper.ConvertBitmap(bitmapTransposed);
while ((candidates = GenerateCandidates(frequentSets)).Count > 0)
{
// 1. tranlate into elements Id's
foreach (var candidate in candidates)
{
for (var i = 0; i < candidate.Count; i++)
{
candidate[i] = newElementsList.IndexOf(candidate[i]);
}
}
// 2. execute CUDA counting
candidates = GetFrequentSets(candidates, executionSettings.MinSup, newBitmapWrapper, transactionsList.Count);
// 3. translate back from elements Id's
foreach (var candidate in candidates)
{
for (var i = 0; i < candidate.Count; i++)
{
candidate[i] = newElementsList[candidate[i]];
}
}
if (candidates.Count > 0)
{
var sw = new Stopwatch();
sw.Start();
frequentSets = candidates;
foreach (var candidate in candidates)
{
frequentItemSets.Add(new FrequentItemSet <int>(candidate), candidate.GetSupport(data.Transactions));
}
sw.Stop();
//Console.WriteLine("CAND: {0}", sw.ElapsedMilliseconds);
}
else
{
// we don't have any more candidates
break;
}
}
//here we should do something with the candidates
var decisionRules = new List <DecisionRule <int> >();
foreach (var frequentSet in frequentSets)
{
var subSets = EnumerableHelper.GetSubsets(frequentSet);
foreach (var t in subSets)
{
var leftSide = new FrequentItemSet <int>(t);
for (var j = 0; j < subSets.Count; j++)
{
var rightSide = new FrequentItemSet <int>(subSets[j]);
if (rightSide.ItemSet.Count != 1 || !FrequentItemSet <int> .SetsSeparated(rightSide, leftSide))
{
continue;
}
if (frequentItemSets.ContainsKey(leftSide))
{
var confidence = (double)frequentItemSets[new FrequentItemSet <int>(frequentSet)] / frequentItemSets[leftSide];
if (confidence >= executionSettings.MinConf)
{
var rule = new DecisionRule <int>(leftSide.ItemSet, rightSide.ItemSet, frequentItemSets[new FrequentItemSet <int>(frequentSet)], confidence);
decisionRules.Add(rule);
}
}
}
}
}
if (!printRules)
{
return;
}
var result = PrintRules(decisionRules, executionSettings.DataSourcePath, executionSettings.MinSup, executionSettings.MinConf, data.Transactions.Keys.Count, data.Elements);
Console.WriteLine(result);
}
