public void PlayGolfExample()
{
//http://www.saedsayad.com/decision_tree.htm
var outlook = new int[] { 0, 0, 1, 2, 2, 2, 1, 0, 0, 2, 0, 1, 1, 2 };
var temp = new int[] { 0, 0, 0, 1, 2, 2, 2, 1, 2, 1, 1, 1, 0, 1 };
var humidity = new int[] { 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0 };
var wind = new int[] { 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1 };
var output = new int[] { 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0 };
DecisionTreeLearning <int> dtl = DecisionTreeLearning <int> .Create <int>(x => output[x], new DecisionTreeOptions()
{
MaxDepth = 3,
});
var outlookStrs = new string[] { "Rainy", "Overcast", "Sunny" };
dtl.AddDiscreteFeature <string>("outlook", a => outlookStrs[outlook[a]]);
var tempStrs = new string[] { "Hot", "Mild", "Cool" };
dtl.AddDiscreteFeature <string>("temp", a => tempStrs[temp[a]]);
dtl.AddDiscreteFeature <int>("humidity", a => humidity[a]);
dtl.AddDiscreteFeature <int>("wind", a => wind[a]);
var dt = dtl.Learn(Enumerable.Range(0, output.Length));
Assert.AreEqual(dt.Feature.Name, "outlook");
Assert.AreEqual(dt.ChildNodes.First(x => (x.Key as DiscreteFeatureValue).Value.ToString() == "Rainy").Value.Feature.Name, "humidity");
Assert.AreEqual(dt.ChildNodes.First(x => (x.Key as DiscreteFeatureValue).Value.ToString() == "Sunny").Value.Feature.Name, "wind");
Assert.IsNull(dt.ChildNodes.First(x => (x.Key as DiscreteFeatureValue).Value.ToString() == "Overcast").Value.Feature, "Overcast node should be leaf");
Assert.AreEqual(dt.GetOutput(0), 0);
Assert.AreEqual(dt.GetOutput(2), 1);
}
public ISolver simpleSeparation(int percentTrain)
{
int sizeTrainDataset = Convert.ToInt32(InputData.Length * ((double)percentTrain / 100));
int sizeTestDataset = InputData.Length - sizeTrainDataset;
float[][] trainInputDataset = new float[sizeTrainDataset][];
float[][] testInputDataset = new float[InputData.Length - sizeTrainDataset][];
float[] trainOutputDataset = new float[sizeTrainDataset];
float[] testOutputDataset = new float[InputData.Length - sizeTrainDataset];
Array.Copy(InputData, trainInputDataset, sizeTrainDataset);
Array.Copy(InputData, sizeTrainDataset, testInputDataset, 0, sizeTestDataset);
Array.Copy(OutputData, trainOutputDataset, sizeTrainDataset);
Array.Copy(OutputData, sizeTrainDataset, testOutputDataset, 0, sizeTestDataset);
if (ISolver is INeuralNetwork)
{
LearningAlgoManager la = new LearningAlgoManager()
{
usedAlgo = LS.LearningAlgorithmName,
GeneticParams = LS.LAParameters
};
ClosingError = la.startLearn(ISolver, trainInputDataset, trainOutputDataset);
}
else if (ISolver is DecisionTree)
{
DecisionTreeLearning la = new DecisionTreeLearning();
ClosingError = la.startLearn(ISolver, trainInputDataset, trainOutputDataset);
}
PreprocessingManager preprocessing = new PreprocessingManager();
mistakeTrain = 0;
List <string> expectedOutputValues = trainOutputDataset.Select(x => Convert.ToString(x)).ToList();
List <string> obtainedOutputValues = new List <string>();
for (int i = 0; i < sizeTrainDataset; i++)
{
obtainedOutputValues.Add(Convert.ToString(ISolver.Solve(trainInputDataset[i])[0]));
}
List <bool> comparisonOfResult = preprocessing.compareExAndObValues(expectedOutputValues, obtainedOutputValues, SelectionID, ParameterID);
var counts = comparisonOfResult.GroupBy(x => x).ToDictionary(x => x.Key, x => x.Count());
mistakeTrain = (float)counts[false] / (float)sizeTrainDataset;
mistakeTest = 0;
expectedOutputValues = testOutputDataset.Select(x => Convert.ToString(x)).ToList();
obtainedOutputValues.Clear();
for (int i = 0; i < sizeTestDataset; i++)
{
obtainedOutputValues.Add(Convert.ToString(ISolver.Solve(testInputDataset[i])[0]));
}
comparisonOfResult = preprocessing.compareExAndObValues(expectedOutputValues, obtainedOutputValues, SelectionID, ParameterID);
counts = comparisonOfResult.GroupBy(x => x).ToDictionary(x => x.Key, x => x.Count());
mistakeTest = (float)counts[false] / (float)sizeTestDataset;
return(ISolver);
}
public void ContiniousFeatureTest()
{
var data = Enumerable.Range(0, 1000).Select(x => new PatientRecord()).ToArray();
var dt = DecisionTreeLearning <PatientRecord> .Create <int>(x => x.Cancer? 1 : 0, new DecisionTreeOptions());
dt.AddContiniousFeature("Age", x => x.Age);
dt.AddContiniousFeature("Height", x => x.Height);
dt.AddContiniousFeature("Weight", x => x.Weight);
dt.AddDiscreteFeature("Smoke", x => x.Smoke ? 1 : 0);
var node = dt.Learn(data);
Assert.AreEqual(node.Feature.Name, "Smoke");
}
public static string LearnXpathFromTrainingFiles(string filesLocation)
{
DomPool.LoadDocuments(filesLocation);
DomPool.Initiate();
DomPool.ExtractAllFeatures();
DecisionNode dn = new DecisionNode();
dn.InitialNodeSet = new HashSet <HtmlNode>(DomPool.TargetNodes.Union(DomPool.NonTargetNodes));
dn.SelectedNegative = new HashSet <HtmlNode>(DomPool.NonTargetNodes.Except(DomPool.TargetNodesPrecision));
dn.SelectedPositive = new HashSet <HtmlNode>(DomPool.TargetNodes);
dn.FeatureSet = new HashSet <Feature>();
dn.CalculateEntropy();
DecisionTreeLearning.RecursiveTreeImprovement(dn);
return(XpathTools.GenerateAForgivingXpath(dn));
//"//*["+XpathTools.DecisionTreeToXpath(dn,new HashSet<Feature>())+"]";
}
public static void RunTest(string filesLocation)
{
string[] folders = Directory.GetDirectories(filesLocation);
foreach (string fldr in folders)
{
Console.WriteLine("Running for category:" + fldr);
string[] innerfolders = Directory.GetDirectories(fldr);
foreach (string innerdir in innerfolders)
{
Console.Out.Flush();
Console.WriteLine("Running for att:" + innerdir);
DomPool.LoadTestDocuments(innerdir.Replace(filesLocation, "testset"));
DomPool.LoadDocuments(innerdir);
//for(int i= (DomPool.allDocsNames.Count() - 1); i <= (DomPool.allDocsNames.Count()-1)/*DomPool.allDocsNames.Count()*/; i++)
for (int i = 1; i <= (DomPool.allDocsNames.Count() - 1); i++)
{
string[] tools = new string[] { "our", "our - not forgiving", "j48", "svm", "xpath-align", "svm" };
int toolStart = 5;
Dictionary <string, string> xpathNonForgiving = new Dictionary <string, string>();
for (int tool = toolStart; tool < 6; tool++)
{
Console.WriteLine("[-] running for training set size=" + i);
IEnumerable <IEnumerable <int> > subsetsIndexes = Subsets(DomPool.allDocsNames.Count(), i);
//Reduce size ...for testing only
//subsetsIndexes = subsetsIndexes.Take(30);
double totalAccuracy = 0;
double totalRecall = 0;
long totalTime = 0;
Console.WriteLine("[-] tool:" + tools[tool]);
Console.WriteLine("+ will run " + subsetsIndexes.Count() + " different iterations for the current set size");
int s = 0;
Dictionary <String, double> SiteTotalRecall = new Dictionary <string, double>();
Dictionary <String, double> SiteTotalPrecision = new Dictionary <string, double>();
Dictionary <String, double> SiteTotalTests = new Dictionary <string, double>();
foreach (string site in DomPool.allDocsNames)
{
SiteTotalPrecision[site] = 0;
SiteTotalRecall[site] = 0;
SiteTotalTests[site] = 0;
}
foreach (IEnumerable <int> currSubsetIndexes in subsetsIndexes)
{
List <int> listRep = new List <int>(currSubsetIndexes);
string stringRep = listRep.Aggregate("", (b, x) => b + "," + x);
s++;
if (s % 10 == 0)
{
//Console.Write("(" + s + "/" + subsetsIndexes.Count() + ") ");
Console.Write(".");
}
//if (tool == toolStart)
//{
HashSet <String> currSubset = GetSubSet(DomPool.allDocsNames, currSubsetIndexes);
DomPool.Initiate(currSubset);
DomPool.ExtractAllFeatures();
//}
var runres = new HashSet <HtmlNode>();
//our method
if (tool < 2)
{
string xpath = "";
if (tool == 0)
{
DecisionNode dn = new DecisionNode();
dn.InitialNodeSet = new HashSet <HtmlNode>(DomPool.TargetNodes.Union(DomPool.NonTargetNodes));
dn.SelectedNegative = new HashSet <HtmlNode>(DomPool.NonTargetNodes.Except(DomPool.TargetNodesPrecision));
dn.SelectedPositive = new HashSet <HtmlNode>(DomPool.TargetNodes);
dn.FeatureSet = new HashSet <Feature>();
dn.CalculateEntropy();
DecisionTreeLearning.RecursiveTreeImprovement(dn);
xpath = XpathTools.GenerateAForgivingXpath(dn);
xpathNonForgiving[stringRep] = XpathTools.DecisionTreeToXpath(dn, new HashSet <Feature>(), 1);
xpathNonForgiving[stringRep] = "//*" + (xpathNonForgiving[stringRep].Equals("") ? "" : ("[" + xpathNonForgiving[stringRep] + "]"));
}
if (tool == 1)
{
xpath = xpathNonForgiving[stringRep];
}
Console.WriteLine("Query:" + xpath);
var watch = Stopwatch.StartNew();
runres = DomPool.TESTRunXpathQuery(xpath);
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
totalTime = totalTime + elapsedMs;
}
else
{
if (tool == 2)
{
ModelLearner model = new ModelLearner();
model.LearnModel();
var watch = Stopwatch.StartNew();
runres = model.RunOnTestSet();
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
totalTime = totalTime + elapsedMs;
}
else
{
if (tool == 3)
{
NB model = new NB();
model.LearnModel();
var watch = Stopwatch.StartNew();
runres = model.RunOnTestSet();
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
totalTime = totalTime + elapsedMs;
}
else
{
if (tool == 4)
{
XpathAlignment model = new XpathAlignment();
model.LearnModel();
var watch = Stopwatch.StartNew();
runres = model.RunOnTestSet();
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
totalTime = totalTime + elapsedMs;
}
else
{
SVM model = new SVM();
model.LearnModel();
var watch = Stopwatch.StartNew();
runres = model.RunOnTestSet();
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
totalTime = totalTime + elapsedMs;
}
}
}
}
HashSet <HtmlNode> spos = new HashSet <HtmlNode>(DomPool.TESTTargetNodes.Intersect(runres));
HashSet <HtmlNode> sposprecision = new HashSet <HtmlNode>(DomPool.TESTTargetNodesPrecision.Intersect(runres));
foreach (var entry in DomPool.docsAndNames)
{
if (DomPool.trainingDocsNames.Contains(entry.Key))
{
continue;
}
HashSet <HtmlNode> docNodes = new HashSet <HtmlNode>(entry.Value.SelectNodes("//*"));
HashSet <HtmlNode> currspos = new HashSet <HtmlNode>(spos.Intersect(docNodes));
HashSet <HtmlNode> currrunres = new HashSet <HtmlNode>(runres.Intersect(docNodes));
HashSet <HtmlNode> currsposprecision = new HashSet <HtmlNode>(sposprecision.Intersect(docNodes));
HashSet <HtmlNode> currTargetNodes = new HashSet <HtmlNode>(DomPool.TESTTargetNodes.Intersect(docNodes));
double currSiteAccuracy = (currsposprecision.Count() / ((double)currrunres.Count()));
double currSiteRecall = (currspos.Count() / ((double)currTargetNodes.Count()));
if (((double)currrunres.Count()) > 0)
{
SiteTotalPrecision[entry.Key] = SiteTotalPrecision[entry.Key] + currSiteAccuracy;
SiteTotalRecall[entry.Key] = SiteTotalRecall[entry.Key] + currSiteRecall;
}
SiteTotalTests[entry.Key] = SiteTotalTests[entry.Key] + 1;
}
double currAccuracy = (sposprecision.Count() / ((double)runres.Count()));
double currRecall = (spos.Count() / ((double)DomPool.TESTTargetNodes.Count()));
if (runres.Count() > 0)
{
totalAccuracy = totalAccuracy + currAccuracy;
totalRecall = totalRecall + currRecall;
}
}
totalAccuracy = totalAccuracy / subsetsIndexes.Count();
totalRecall = totalRecall / subsetsIndexes.Count();
Console.WriteLine("########## Results " + tools[tool] + " for i=" + i + "##########");
Console.WriteLine("+++++++++ Detailed Results for i=" + i + "++++++++++#");
double count = 0;
double totalSumPrecision = 0;
double totalSumRecall = 0;
double avgRecall = 0;
double avgPrecision = 0;
double avgFscore = 0;
double numPrecision = 0;
foreach (string site in DomPool.allDocsNames)
{
if (SiteTotalTests[site] < 1)
{
SiteTotalTests[site]++;
}
else
{
numPrecision++;
}
double sitePrecision = SiteTotalPrecision[site] / SiteTotalTests[site];
double siteRecall = SiteTotalRecall[site] / SiteTotalTests[site];
double siteFscore = 2 * (sitePrecision * siteRecall) / (sitePrecision + siteRecall);
if (siteRecall == 0 && sitePrecision == 0)
{
siteFscore = 0;
}
count++;
avgRecall = avgRecall + siteRecall;
avgPrecision = avgPrecision + sitePrecision;
avgFscore = avgFscore + siteFscore;
Console.WriteLine(">" + site + ": Precision:" + sitePrecision + " , Recall:" + siteRecall + ", F-score:" + siteFscore);
}
Console.WriteLine("++++++++++++++++Total+++++++++++++++++");
avgRecall = avgRecall / count;
avgPrecision = avgPrecision / numPrecision;
avgFscore = avgFscore / count;
Console.WriteLine("Recall:" + avgRecall);
Console.WriteLine("Precision:" + avgPrecision);
Console.WriteLine("F-score:" + avgFscore);
Console.WriteLine("Time:" + totalTime);
}
}
}
}
Console.ReadLine();
}
public static void PorcessSites(List <SiteInfo> siteinfos)
{
foreach (SiteInfo si in siteinfos)
{
try {
int leftPages = pagesNum * si.attributeExtraction.Keys.Count();
List <HtmlNode> sitePages = new List <HtmlNode>(0);
if (!SiteDocuments.ContainsKey(si.SiteName))
{
SiteDocuments[si.SiteName] = new Dictionary <string, Dictionary <String, HtmlNode> >();
SiteLinks[si.SiteName] = new Dictionary <string, HashSet <String> >();
foreach (String attrName in si.attributeExtraction.Keys)
{
SiteDocuments[si.SiteName][attrName] = new Dictionary <string, HtmlNode>();
SiteLinks[si.SiteName][attrName] = new HashSet <string>();
}
}
//Download all URLs
foreach (String startURL in si.StartURLs)
{
try
{
String currURL = startURL;
while (currURL != null)
{
HtmlDocument doc = new HtmlDocument();
var currHTML = URLDownloader.GetHtmlOfURL(currURL);
doc.LoadHtml(currHTML);
if (!String.IsNullOrEmpty(si.PageExtractionXpath))
{
var links = doc.DocumentNode.SelectNodes(si.PageExtractionXpath);
foreach (HtmlNode lnk in links)
{
try
{
String pageLink = lnk.Attributes["href"].Value;
// var htmlstr = URLDownloader.GetHtmlOfURL(URLDownloader.UrlFixIfRelative(pageLink, currURL));
foreach (string attr in si.attributeExtraction.Keys)
{
var newURL = URLDownloader.UrlFixIfRelative(pageLink, currURL);
if (SiteLinks[si.SiteName][attr].Contains(newURL))
{
continue;
}
//HtmlDocument innerDoc = new HtmlDocument();
//innerDoc.LoadHtml(htmlstr);
//SiteDocuments[si.SiteName][attr].Add(pageLink, innerDoc.DocumentNode);
SiteLinks[si.SiteName][attr].Add(newURL);
if (--leftPages <= 0)
{
break;
}
}
if (leftPages <= 0)
{
break;
}
}
catch (Exception e) {
// Console.WriteLine(e.StackTrace);
}
}
}
else
{
foreach (string attr in si.attributeExtraction.Keys)
{
try
{
if (SiteLinks[si.SiteName][attr].Contains(currURL))
{
continue;
}
// HtmlDocument innerDoc = new HtmlDocument();
// innerDoc.LoadHtml(currHTML);
// SiteDocuments[si.SiteName][attr].Add(currURL, innerDoc.DocumentNode);
SiteLinks[si.SiteName][attr].Add(currURL);
if (--leftPages <= 0)
{
break;
}
}
catch
{
break;
}
}
}
if (leftPages <= 0)
{
break;
}
//get next page
String nextLink = null;
try
{
nextLink = HttpUtility.HtmlDecode(doc.DocumentNode.SelectSingleNode(si.NextPageXPath).Attributes["href"].Value);
}
catch { nextLink = null; }
if (nextLink != null)
{
nextLink = URLDownloader.UrlFixIfRelative(nextLink, currURL);
}
if (!currURL.ToLower().Trim().Equals(nextLink.ToLower().Trim()))
{
currURL = nextLink;
}
else
{
break;
}
}
}
catch { }
}
foreach (String attr in si.attributeExtraction.Keys)
{
var trainingkeys = new HashSet <String>(SiteLinks[si.SiteName][attr].Take(5)); //new HashSet<String>(SiteDocuments[si.SiteName][attr].Keys.Take(5));
var trainingDic = new Dictionary <String, HtmlNode>(); //SiteDocuments[si.SiteName][attr].Where(x => trainingkeys.Contains(x.Key)).ToDictionary(kv => kv.Key, kv => kv.Value);
foreach (String lnk in trainingkeys)
{
trainingDic.Add(lnk, GetHtmlNode(lnk));
}
var testDic = SiteDocuments[si.SiteName][attr].Where(x => !trainingkeys.Contains(x.Key)).ToDictionary(kv => kv.Key, kv => kv.Value);
foreach (var lnk in trainingDic.Keys)
{
HtmlNode adoc = trainingDic[lnk];
try
{
var gt = adoc.SelectNodes(si.attributeExtraction[attr]);
if (gt != null)
{
foreach (var targetNode in gt)
{
//Console.Write(":");
if (targetNode.Attributes.Contains("userselected"))
{
targetNode.SetAttributeValue("userselected", "yes");
}
else
{
targetNode.Attributes.Add("userselected", "yes");
}
}
}
}
catch { }
MD5 md5 = MD5.Create();
if (!File.Exists("huge/" + si.SiteName + "/training/" + attr + "/" + getMD5(lnk) + ".html"))
{
Directory.CreateDirectory("huge/" + si.SiteName + "/training/" + attr);
File.WriteAllText("huge/" + si.SiteName + "/training/" + attr + "/" + getMD5(lnk) + ".html", adoc.InnerHtml);
}
}
DomPool.LoadDocuments(trainingDic);
//DomPool.LoadTestDocuments();
DomPool.Initiate(new HashSet <string>(trainingDic.Keys));
DomPool.ExtractAllFeatures();
// Run code
DecisionNode dn = new DecisionNode();
dn.InitialNodeSet = new HashSet <HtmlNode>(DomPool.TargetNodes.Union(DomPool.NonTargetNodes));
dn.SelectedNegative = new HashSet <HtmlNode>(DomPool.NonTargetNodes.Except(DomPool.TargetNodesPrecision));
dn.SelectedPositive = new HashSet <HtmlNode>(DomPool.TargetNodes);
dn.FeatureSet = new HashSet <Feature>();
dn.CalculateEntropy();
DecisionTreeLearning.RecursiveTreeImprovement(dn);
var xpath = XpathTools.GenerateAForgivingXpath(dn);
var xpathNonForgiving = XpathTools.DecisionTreeToXpath(dn, new HashSet <Feature>(), 1);
xpathNonForgiving = "//*" + (xpathNonForgiving.Equals("") ? "" : ("[" + xpathNonForgiving + "]"));
XpathAlignment model = new XpathAlignment();
model.LearnModel();
var alignmentXpath = model.xpath;
CheckOnTest(new HashSet <string>(SiteLinks[si.SiteName][attr].Except(trainingkeys)), xpath, si.attributeExtraction[attr], si.SiteName, attr, "ForgivingXP");
CheckOnTest(new HashSet <string>(SiteLinks[si.SiteName][attr].Except(trainingkeys)), alignmentXpath, si.attributeExtraction[attr], si.SiteName, attr, "Alignment");
}
}finally{
}
SiteDocuments.Remove(si.SiteName);
}
Console.ReadLine();
}
