private void btnAprioriNew_Click(object sender, EventArgs e)
{
richAssociationAprioriNew.Text = string.Empty;
richLargeAprioriNew.Text = string.Empty;
//do apriori
double supportThreshold = double.Parse(txtSupportThreshold.Text);
System.Diagnostics.Stopwatch calcTime = System.Diagnostics.Stopwatch.StartNew();
ItemsetCollection L = AprioriMining.DoAprioriNew(db, supportThreshold);
richLargeAprioriNew.Text = (L.Count + " Large Itemsets (by Apriori)") + "\r\n";
txtTimesAprioriNew.Text = ((calcTime.ElapsedMilliseconds)) + " Milliseconds";
foreach (Itemset itemset in L)
{
richLargeAprioriNew.Text += itemset.ToString() + "\r\n";
}
//do mining
double confidenceThreshold = double.Parse(txtConfidenceThreshold.Text);
List <AssociationRule> allRules = AprioriMining.Mine(db, L, confidenceThreshold);
richAssociationAprioriNew.Text = (allRules.Count + " Association Rules \n");
foreach (AssociationRule rule in allRules)
{
richAssociationAprioriNew.Text += rule.ToString() + "\r\n";
}
}
public void ExecuteCommand(UserRecientBlock item)
{
var trainingDB = PerformCollection(item);
var aprioriResult = AprioriMining.DoApriori(trainingDB, 40.0);
var rules = AprioriMining.Mine(trainingDB, aprioriResult, 70.0);
UpdateConcertSuggestions(rules);
}
//[Test]
public void TestMethod1()
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.Apriori(collection, 50);
ItemsetCollection Check = GetItems();
Assert.AreEqual(Check, L);
}
private void btnAprioriNewSS_Click(object sender, EventArgs e)
{
richAssociationAprioriNewSS.Text = string.Empty;
richLargeAprioriNewSS.Text = string.Empty;
// do FP-Growth
double supportThreshold = double.Parse(txtSupportThreshold.Text);
System.Diagnostics.Stopwatch calcTime = System.Diagnostics.Stopwatch.StartNew();
ReturnLApriori();//FPGrowthMining.DoFPGrowthParallel(db, supportThreshold);
txtTimesAprioriNewSS.Text = ((calcTime.ElapsedMilliseconds)) + " Milliseconds";
//
string[] database = null;
try { database = System.IO.File.ReadAllLines("OutputAprioriNew.txt"); }
catch { }
ItemsetCollection L = new ItemsetCollection();
Itemset items;
foreach (string item in database)
{
items = new Itemset();
string[] itemsupport = item.Split(':');
//items.AddRange(itemsupport[0].Split(','));
foreach (string it in itemsupport[0].Split(','))
{
items.Add(int.Parse(it));
}
//items.Remove("");
items.Support = double.Parse(itemsupport[1]);
L.Add(items);
}
//
richLargeAprioriNewSS.Text = (L.Count + " Large Itemsets (by Apriori cải tiến song song)") + "\r\n";
foreach (Itemset itemset in L)
{
richLargeAprioriNewSS.Text += itemset.ToString() + "\r\n";
}
//do mining
double confidenceThreshold = double.Parse(txtConfidenceThreshold.Text);
List <AssociationRule> allRules = AprioriMining.Mine(db, L, confidenceThreshold);
richAssociationAprioriNewSS.Text = (allRules.Count + " Association Rules \n");
foreach (AssociationRule rule in allRules)
{
richAssociationAprioriNewSS.Text += rule.ToString() + "\r\n";
}
}
public void AprioriTid() //Test of AprioriTid for support = 50%
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.AprioriTid(collection, 50);
ItemsetCollection Check = GetItems();
Assert.AreEqual(Check.Count, L.Count); //Checking items count in itemsetcollection
for (int k = 0; k < Check.Count; k++) //Loop for all itmesets
{
Assert.AreEqual(Check[k].Support, L[k].Support); //Checking support of itemset
Assert.AreEqual(Check[k].Count, L[k].Count); // Checking number count of itmes in itemset
Assert.AreEqual(Check[k].Sum(), L[k].Sum()); //Checking summary of all numbers in list
}
}
public static void DoApriori(double support, double confidence, Itemset _items, ItemsetCollection _db)
{
Itemset uniqueItems = _db.GetUniqueItems();
ItemsetCollection L = AprioriMining.DoApriori(_db, support);
Console.WriteLine((L.Count + " Large Itemsets (by Apriori)\n"));
foreach (Itemset itemset in L)
{
Console.WriteLine(itemset.ToString() + " Длина " + itemset.Count);
}
Console.WriteLine("\n");
int maxLSize = L[0].Count;
Console.WriteLine("maxLSize=" + maxLSize);
foreach (Itemset itemset in L)
{
if (maxLSize < itemset.Count)
{
maxLSize = itemset.Count;
}
}
Console.WriteLine("maxLSize=" + maxLSize);
foreach (Itemset itemset in L)
{
if (itemset.Count < maxLSize)
{
itemset.Clear();
}
}
foreach (Itemset itemset in L)
{
Console.WriteLine(itemset.ToString() + " Длина " + itemset.Count);
}
List <AssociationRule> allRules = AprioriMining.Mine(_db, L, confidence);
Console.WriteLine(allRules.Count + " Association Rules\n");
foreach (AssociationRule rule in allRules)
{
Console.WriteLine(rule.ToString() + "Длина=" + rule.getTotalLength() + "\n");
}
}
public void Mine() //Test of minig asscociationrules for conffidence = 80
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.Apriori(collection, 50);
List <AssociationRule> AllRules = AprioriMining.Mine(collection, L, 80);
List <AssociationRule> Check = GetRules();
Assert.AreEqual(Check.Count, AllRules.Count); //Checking rules count in rules collection
for (int k = 0; k < Check.Count; k++) //Loop for all rules
{
Assert.AreEqual(Check[k].Confidence, AllRules[k].Confidence); //Checking confidence of rule
Assert.AreEqual(Check[k].X.Count, AllRules[k].X.Count); // Checking numbers count of itmes in X
Assert.AreEqual(Check[k].X.Sum(), AllRules[k].X.Sum()); //Checking summary of all numbers in X
Assert.AreEqual(Check[k].Y.Count, AllRules[k].Y.Count); // Checking numbers count of itmes in Y
Assert.AreEqual(Check[k].Y.Sum(), AllRules[k].Y.Sum()); //Checking summary of all numbers in Y
}
}
private void TestApriori()
{
ItemsetCollection collection = new ItemsetCollection {
list1, list2, list3, list4
};
ItemsetCollection L = AprioriMining.Apriori(collection, 50);
ItemsetCollection Check = GetItems();
Itemset i = new Itemset();
i.Add(1);
i.Support = 10;
i.Tid = 0;
Itemset j = new Itemset();
j.Add(1);
j.Support = 10;
j.Tid = 0;
Assert.AreEqual(Check[0].Sum(), j.ToList());
}
/// <summary>
/// Connects to MySQL in order to run AprioriMiner
/// </summary>
private static void DataFromMysql()
{
double[] minsupminconf = PromptForMinsupAndConf();
var tablechosen = PromptForDatasetSize();
var tableToMine = string.Empty;
switch (tablechosen)
{
case 1:
tableToMine = "itemsets";
break;
case 5:
tableToMine = "itemsets5k";
break;
case 20:
tableToMine = "itemsets20k";
break;
case 75:
tableToMine = "itemsets75k";
break;
default:
Console.WriteLine("Whooops something very bad has happened... Try again.");
return;
}
Console.WriteLine("Connecting to mysql server.... Trying to find table with {0}K transactions", tablechosen);
var database = new ItemsetCollection();
var timeStartQuery = DateTime.Now;
Console.Write("Started querying DB at " + timeStartQuery.ToLocalTime());
using (var conn = new MySqlConnection("server=localhost;user=dmuser;database=DataMiningDb;port=3306;password=data;"))
{
try
{
conn.Open();
var rows = 0;
rows = CountTransactions(conn, tableToMine);
var cmd = conn.CreateCommand();
for (var i = 1; i <= rows; i++)
{
cmd.CommandText = i == 1
? (@"set profiling=1; SELECT itemID FROM dataminingdb." + tableToMine + " WHERE transId="
+ i)
: @"SELECT itemID FROM dataminingdb." + tableToMine + " WHERE transId=" + i;
var reader = cmd.ExecuteReader();
var listofItemsInRow = new Itemset();
while (reader.Read())
{
listofItemsInRow.Add(reader.GetInt32(0));
}
database.Add(listofItemsInRow);
reader.Close();
// ============== LOG PROFILER DATA
cmd.CommandText = "SHOW PROFILES";
var pfdata = cmd.ExecuteReader();
var rs = 0;
var list = new List <object[]>();
while (pfdata.Read())
{
var rowArray = new object[pfdata.FieldCount];
rs = pfdata.GetValues(rowArray);
list.Add(rowArray);
}
using (var swrtr = new StreamWriter(@"..\..\..\MysqlProfilerLog.csv", true))
{
// swrtr.WriteLine(DateTime.Now + " = LOG Success = {0}--------------------\n", rs);
// Query Number,Time,SQLStatement
foreach (var stringse in list)
{
if (stringse[2].Equals("SHOW WARNINGS"))
{
continue;
}
foreach (var s in stringse)
{
swrtr.Write(s + ",");
if (stringse.ElementAt(2) == s)
{
swrtr.Write("\n");
}
}
// swrtr.WriteLine(Environment.NewLine);
}
}
// ==============
pfdata.Close();
// Writing profiler info to log file
// RecordLog(conn);
} // End of For Loop
}
catch (Exception e)
{
Console.WriteLine("There was an error: {0}\n\n Consult the help menu.", e.Message);
}
}
// database (of ItemsetCollection should now be populated and ready for mining...
var endQueryT = DateTime.Now;
Console.WriteLine("\aEnded queries at: {0}\nTime elapsed: {1}\n\nNow running Apriori on fetched data...", endQueryT.ToLocalTime(), endQueryT.Subtract(timeStartQuery));
var large = AprioriMining.DoApriori(database, CreateSetOfUniqueItems(), minsupminconf[0]);
var results = "Results: \n\n " + large.Count + " supported Itemsets obtained by Apriori\n\n";
foreach (var itemset in large)
{
results += itemset.ToString() + "\n";
}
Console.WriteLine("DONE! Now mining association rules...");
var allRules = AprioriMining.Mine(database, large, minsupminconf[1]);
results += "\nAssociation Rules Found: \n";
if (allRules.Count == 0)
{
results += "No rules were found over minconf of " + minsupminconf[1] + "%";
}
else
{
foreach (var associationRule in allRules)
{
results += associationRule.ToString() + "\n";
}
}
Console.WriteLine(results);
var finaltimeEnd = DateTime.Now;
Console.Write("Total Apriori time was: {0}", endQueryT.Subtract(finaltimeEnd));
}
private static void DataFromMongo()
{
double[] minsupminconf = PromptForMinsupAndConf();
var collneeded = PromptForDatasetSize();
var collectionNeeded = string.Empty;
switch (collneeded)
{
case 1:
collectionNeeded = "receipts1k";
break;
case 5:
collectionNeeded = "receipts5k";
break;
case 20:
collectionNeeded = "receipts20k";
break;
case 75:
collectionNeeded = "receipts75k";
break;
case 0:
collectionNeeded = "receipts";
break;
default:
Console.WriteLine("Whooops something very bad has happened... Try again.");
return;
}
Console.WriteLine("\nConnecting to mongoDB...");
var database = new ItemsetCollection();
var start = DateTime.Now;
try
{
const string ConnStr = "mongodb://localhost:27017";
var clt = new MongoClient(ConnStr);
var svr = clt.GetServer();
var dblearn = svr.GetDatabase("learn");
// var tlist = dblearn.GetCollection("receipts");
var list = dblearn.GetCollection <Transaction>(collectionNeeded);
Console.WriteLine("Found Data!\nCollection Name: {0}\nSize: {1}", collectionNeeded, list.Count());
var all = list.FindAll();
foreach (var transaction in all)
{
var itemset = new Itemset();
foreach (var item in transaction.Items)
{
itemset.Add(item);
}
database.Add(itemset);
}
}
catch (Exception e)
{
Console.WriteLine("There was an error, here are the details: \n=====================Error Stuff Here=====================\n" + e.Message + "\n==========================================================");
Console.WriteLine("Is the mongodb server running?? See the Help/Info section for help...");
return;
}
var endqueryT = DateTime.Now;
var totalQueryT = start.Subtract(endqueryT);
var itemsunique = CreateSetOfUniqueItems();
Console.WriteLine("Disconnected from MongoDB server at {0}.\n Total query time was: {1}", endqueryT.ToLocalTime(), totalQueryT);
Console.WriteLine("Now running Apriori on fetched data...");
var large = AprioriMining.DoApriori(database, itemsunique, minsupminconf[0]);
var results = "Results: \n\n " + large.Count + " supported Itemsets obtained by Apriori\n\n";
foreach (var itemset in large)
{
results += itemset.ToString() + "\n";
}
Console.WriteLine("DONE! Now mining association rules...");
var allRules = AprioriMining.Mine(database, large, minsupminconf[1]);
results += "\nAssociation Rules Found: \n";
if (allRules.Count == 0)
{
results += "No rules were found over minconf of " + minsupminconf[1] + "%";
}
else
{
foreach (var associationRule in allRules)
{
results += associationRule.ToString() + "\n";
}
}
Console.WriteLine(results);
var finaltimeEnd = DateTime.Now;
Console.Write("It's now: {0}.\n\nTotal Apriori time was: {1}", finaltimeEnd.ToLocalTime(), endqueryT.Subtract(finaltimeEnd));
}
private void PopulateChart()
{
try
{
var filmList = db.Movies.ToList();
Itemset myItemset = new Itemset();
foreach (var i in filmList)
{
myItemset.Add(i.MyMovieID);
}
ItemsetCollection myItemsetCollection = new ItemsetCollection();
var MovieList = db.UserMovieLists.Select(u => u);
foreach (var u in MovieList)
{
var query = from a in db.Ratings
join b in db.UserMovieLists
on a.UserMovieLIstID equals b.UserMovieListID
into temp
from b in temp.DefaultIfEmpty()
where a.UserMovieLIstID == u.UserMovieListID
where a.UserRatingID >= 5
select new
{
a.MyMovieID,
};
Itemset tempItemset = new Itemset();
;
if (myItemset != null)
{
if (query.Count() > 1)
{
foreach (var r in query.Take(25))
{
tempItemset.Add(r.MyMovieID);
}
myItemsetCollection.Add(tempItemset);
}
}
}
List <double> supportList = new List <double>();
double support1 = 30;
double support2 = 27.5;
double support3 = 25;
double support4 = 22.5;
double support5 = 20;
supportList.Add(support5);
supportList.Add(support4);
supportList.Add(support3);
supportList.Add(support2);
supportList.Add(support1);
foreach (var ele in supportList)
{
int timeStart, timeStop, timeElapsed;
timeStart = Environment.TickCount;
ItemsetCollection L = AprioriMining.Apriori(myItemsetCollection, ele);
timeStop = Environment.TickCount;
timeElapsed = (timeStop - timeStart) / 1000;
this.chart1.Series["Apriori"].Points.AddXY(ele, timeElapsed);
}
foreach (var ele in supportList)
{
int timeStart, timeStop, timeElapsed;
timeStart = Environment.TickCount;
ItemsetCollection L = AprioriMining.AprioriTid(myItemsetCollection, ele);
timeStop = Environment.TickCount;
timeElapsed = (timeStop - timeStart) / 1000;
this.chart1.Series["AprioriTid"].Points.AddXY(ele, timeElapsed);
}
}
catch (Exception exp)
{
MessageBox.Show(exp.ToString());
}
}
private void AddRules2()
{
try
{
var filmList = db.Movies.ToList();
Itemset myItemset = new Itemset();
foreach (var i in filmList)
{
myItemset.Add(i.MyMovieID);
}
ItemsetCollection myItemsetCollection = new ItemsetCollection();
var MovieList = db.UserMovieLists.Select(u => u);
foreach (var u in MovieList)
{
var query = from a in db.Ratings
join b in db.UserMovieLists
on a.UserMovieLIstID equals b.UserMovieListID
into temp
from b in temp.DefaultIfEmpty()
where a.UserMovieLIstID == u.UserMovieListID
where a.UserRatingID >= 5
select new
{
a.MyMovieID,
};
Itemset tempItemset = new Itemset();
;
if (myItemset != null)
{
if (query.Count() > 1)
{
foreach (var r in query.Take(25))
{
tempItemset.Add(r.MyMovieID);
}
myItemsetCollection.Add(tempItemset);
}
}
}
ItemsetCollection L = AprioriMining.AprioriTid(myItemsetCollection, 20);
List <AssociationRule> allRules = AprioriMining.Mine(myItemsetCollection, L, 20);
var ruleX = db.RulesX.Select(x => x);
db.RulesX.RemoveRange(ruleX);
db.SaveChanges();
var ruleY = db.RulesY.Select(x => x);
db.RulesY.RemoveRange(ruleY);
db.SaveChanges();
var myRule = db.Rules.Select(x => x);
db.Rules.RemoveRange(myRule);
db.SaveChanges();
var ruleXmovie = db.RuleXMovie.Select(x => x);
db.RuleXMovie.RemoveRange(ruleXmovie);
db.SaveChanges();
var ruleYMovie = db.RuleYMovie.Select(x => x);
db.RuleYMovie.RemoveRange(ruleYMovie);
foreach (var r in allRules)
{
var rule = new MyRule();
var X = new RuleX();
var Y = new RuleY();
db.RulesX.Add(X);
db.SaveChanges();
db.RulesY.Add(Y);
db.SaveChanges();
rule.Support = r.Support;
rule.Confidene = r.Confidence;
rule.RuleXID = X.RuleXID;
rule.RuleYID = Y.RuleYID;
db.Rules.Add(rule);
db.SaveChanges();
foreach (var x in r.X)
{
var rm = new RuleXMyMovie();
rm.RuleX_RuleXID = X.RuleXID;
rm.MyMovie_MyMovieID = x;
db.RuleXMovie.Add(rm);
db.SaveChanges();
}
foreach (var y in r.Y)
{
var rm = new RuleYMyMovie();
rm.RuleY_RuleYID = Y.RuleYID;
rm.MyMovie_MyMovieID = y;
db.RuleYMovie.Add(rm);
db.SaveChanges();
}
}
}
catch (Exception exp)
{
MessageBox.Show(exp.ToString());
}
}