private static string GetModelName(string dataset, RunType runType, TaskSelectionMethod taskSelectionMetric, WorkerSelectionMethod workerSelectionMetric, bool online, int taskSamples = -1, int workerSamples = -1, int numCommunities = -1)
{
return(dataset + "_" + Enum.GetName(typeof(RunType), runType)
+ "_" + Enum.GetName(typeof(TaskSelectionMethod), taskSelectionMetric)
+ "_" + Enum.GetName(typeof(WorkerSelectionMethod), workerSelectionMetric)
+ (online ? "Online" : "") + (taskSamples > 0 ? "_T" + taskSamples.ToString() : "")
+ (workerSamples > 0 ? "_W" + workerSamples.ToString() : "")
+ (numCommunities > 0 ? "_Comm" + numCommunities.ToString() : ""));
}
/// <summary>
/// Runs the active learning experiment presented in Venanzi et.al (WWW14) on a single data set.
/// </summary>
/// <param name="dataSet">The data.</param>
/// <param name="runType">The model run type.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="model">The model instance.</param>
/// <param name="communityCount">The number of communities (only for CBCC).</param>
static void RunWWWActiveLearning(string dataSet, RunType runType, TaskSelectionMethod taskSelectionMethod, BCC model, int communityCount = 4)
{
// Reset the random seed so results can be duplicated for the paper
Rand.Restart(12347);
var workerSelectionMethod = WorkerSelectionMethod.RandomWorker;
var data = Datum.LoadData(@"Data\" + dataSet + ".csv");
string modelName = GetModelName(dataSet, runType, taskSelectionMethod, workerSelectionMethod, communityCount);
ActiveLearning.RunActiveLearning(data, modelName, runType, model, taskSelectionMethod, workerSelectionMethod, ResultsDir, communityCount);
}
/// <summary>
/// Runs the active learning experiment presented in Venanzi et.al (WWW14) on a single data set.
/// </summary>
/// <param name="dataSet">The data.</param>
/// <param name="runType">The model run type.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="model">The model instance.</param>
/// <param name="communityCount">The number of communities (only for CBCC).</param>
public static void RunHCOMPActiveLearning(string dataSet, RunType runType, TaskSelectionMethod taskSelectionMethod, int InitialNumLabelsPerTask, BCC model, int communityCount = 4)
{
var data = Datum.LoadData(@"Data/" + dataSet + ".csv");
string modelName = Program.GetModelName(dataSet, runType, taskSelectionMethod, WorkerSelectionMethod.RandomWorker);
//initial Number of Label Per Task
//int initialNumLabelsPerTask = 1;
int initialNumLabelsPerTask = InitialNumLabelsPerTask;
ActiveLearning.RunActiveLearning(data, modelName, runType, model, taskSelectionMethod, WorkerSelectionMethod.RandomWorker, ResultsDir, communityCount, initialNumLabelsPerTask);
}
} //End AddModel
/// <summary>
/// Initial the experimentItem according to the previous setting
/// </summary>
/// <param name="currentRunType"></param>
/// <param name="currentTaskSelectionMethod"></param>
/// <param name="currentWorkerSelectionMethod"></param>
/// <param name="labelStartingPoints"></param>
/// <param name="totalNumberOfLabels"></param>
/// <returns></returns>
private ExperimentModel getExperimentItem(RunType currentRunType, TaskSelectionMethod currentTaskSelectionMethod, WorkerSelectionMethod currentWorkerSelectionMethod, int[] labelStartingPoints)
{
//if the RunType is MajorityVote, no TaskSelectionMethods would be selected
if (currentRunType == RunType.MajorityVote)
{
return(new ExperimentModel(currentTaskSelectionMethod, WorkerSelectionMethod.RandomWorker, currentRunType, 1, labelStartingPoints[0]));
}
//if it is an entropy task, add the different labelling rounds
if (currentTaskSelectionMethod == TaskSelectionMethod.EntropyTask)
{
int currentLabellingRound = trackBarNumberOfLabellingRounds.Value;
return(new ExperimentModel(currentTaskSelectionMethod, currentWorkerSelectionMethod, currentRunType, currentLabellingRound, labelStartingPoints[currentLabellingRound - 1]));
}
else//other taskSelectionMethods, or empty in the batch running
{
return(new ExperimentModel(currentTaskSelectionMethod, currentWorkerSelectionMethod, currentRunType, 1, labelStartingPoints[0]));
}//end if
}
} //End AddModel
/// <summary>
/// Initial the experimentItem according to the previous setting
/// </summary>
/// <param name="currentRunType"></param>
/// <param name="currentTaskSelectionMethod"></param>
/// <param name="currentWorkerSelectionMethod"></param>
/// <param name="labelStartingPoints"></param>
/// <param name="totalNumberOfLabels"></param>
/// <returns></returns>
private ExperimentModel getExperimentItem(RunType currentRunType, TaskSelectionMethod currentTaskSelectionMethod, WorkerSelectionMethod currentWorkerSelectionMethod, int[] labelStartingPoints)
{
//if the RunType is MajorityVote, no TaskSelectionMethods would be selected
if (currentRunType == RunType.MajorityVote)
{
return new ExperimentModel(currentTaskSelectionMethod, WorkerSelectionMethod.RandomWorker, currentRunType, 1, labelStartingPoints[0]);
}
//if it is an entropy task, add the different labelling rounds
if (currentTaskSelectionMethod == TaskSelectionMethod.EntropyTask)
{
int currentLabellingRound = trackBarNumberOfLabellingRounds.Value;
return new ExperimentModel(currentTaskSelectionMethod, currentWorkerSelectionMethod, currentRunType, currentLabellingRound, labelStartingPoints[currentLabellingRound - 1]);
}
else//other taskSelectionMethods, or empty in the batch running
{
return new ExperimentModel(currentTaskSelectionMethod, currentWorkerSelectionMethod, currentRunType, 1, labelStartingPoints[0]);
}//end if
}
/// <summary>
/// Runs the standard active learning procedure in parallel on an array of model instances and an input data set.
/// </summary>
/// <param name="data">The data.</param>
/// <param name="modelName">The model name.</param>
/// <param name="runType">The model run type.</param>
/// <param name="model">The model instance.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="workerSelectionMethod">The method for selecting workers (only Random is implemented).</param>
/// <param name="numIncremData">The number of data points to add at each iteration.</param>
/// <param name="communityCount">The number of communities (only for CBCC).</param>
/// <param name="initialNumLabelsPerTask">The initial number of exploratory labels that are randomly selected for each task.</param>
public static void RunParallelActiveLearning(IList<Datum> data,
string[] modelName,
RunType[] runType,
BCC[] model,
TaskSelectionMethod[] taskSelectionMethod,
WorkerSelectionMethod[] workerSelectionMethod,
int communityCount = -1,
int initialNumLabelsPerTask = 1,
int numIncremData = 1)
{
int numModels = runType.Length;
Stopwatch stopWatch = new Stopwatch();
int totalLabels = data.Count();
// Dictionary keyed by task Id, with randomly order labelings
var groupedRandomisedData =
data.GroupBy(d => d.TaskId).
Select(g =>
{
var arr = g.ToArray();
int cnt = arr.Length;
var perm = Rand.Perm(cnt);
return new
{
key = g.Key,
arr = g.Select((t, i) => arr[perm[i]]).ToArray()
};
}).ToDictionary(a => a.key, a => a.arr);
// Dictionary keyed by task Id, with label counts
Dictionary<string, int> totalCounts = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => kvp.Value.Length);
Dictionary<string, int> currentCounts = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => initialNumLabelsPerTask);
// Keyed by task, value is a HashSet containing all the remaining workers with a label - workers are removed after adding a new datum
Dictionary<string, HashSet<string>> remainingWorkersPerTask = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => new HashSet<string>(kvp.Value.Select(dat => dat.WorkerId)));
int numTaskIds = totalCounts.Count();
int totalInstances = data.Count - initialNumLabelsPerTask * numTaskIds;
//throw an exception if the totalInstances is less than or equals to zero
if (totalInstances <= 0)
{
throw new System.Exception("The variable 'totalInstances' should be greater than zero");
}
//only creates accuracy list when it's null (for GUI Use)
if (accuracyArray == null)
{
accuracyArray = Util.ArrayInit<List<double>>(numModels, i => new List<double>());
}
List<double>[] avgRecallArray = Util.ArrayInit(numModels, i => new List<double>());
taskValueListArray = Util.ArrayInit(numModels, i => new List<ActiveLearningResult>());
int[] indexArray = new int[numModels];
Debug.WriteLine("Parallel Active Learning");
Debug.WriteLine("\tModel\tAcc\tAvgRec");
// Get initial data
//make the results variable be global for GUi
results = Util.ArrayInit<Results>(numModels, i => new Results());
List<Datum> subData = GetSubdata(groupedRandomisedData, currentCounts, remainingWorkersPerTask);
List<Datum>[] subDataArray = Util.ArrayInit<List<Datum>>(numModels, i => new List<Datum>(subData));
List<Datum>[] nextData = new List<Datum>[numModels];
ActiveLearning[] activeLearning = new ActiveLearning[numModels];
isExperimentCompleted = false;
// Main loop
for (int iter = 0; ; iter++)
{
bool calculateAccuracy = true;
bool doSnapShot = iter % 100 == 0; // Frequency of snapshots
//stop Active Learning if the user requests to stop
if (isExperimentCompleted) {
return;
}
// Run all the models
for (int indexModel = 0; indexModel < numModels; indexModel++ )
{
if (subDataArray[indexModel] != null || nextData[indexModel] != null)
{
switch (runType[indexModel])
{
case RunType.VoteDistribution:
results[indexModel].RunMajorityVote(subDataArray[indexModel], data, calculateAccuracy, true);
break;
case RunType.MajorityVote:
results[indexModel].RunMajorityVote(subDataArray[indexModel], data, calculateAccuracy, false);
break;
case RunType.DawidSkene:
results[indexModel].RunDawidSkene(subDataArray[indexModel], data, calculateAccuracy);
break;
default: // Run BCC models
results[indexModel].RunBCC(modelName[indexModel], subDataArray[indexModel], data, model[indexModel], RunMode.ClearResults, calculateAccuracy, communityCount, false);
break;
}
} //end for running all the data
if (activeLearning[indexModel] == null)
{
activeLearning[indexModel] = new ActiveLearning(data, model[indexModel], results[indexModel], communityCount);
}
else
{
activeLearning[indexModel].UpdateActiveLearningResults(results[indexModel]);
}
// Select next task
Dictionary<string, ActiveLearningResult> TaskUtility = new Dictionary<string, ActiveLearningResult>();
switch (taskSelectionMethod[indexModel])
{
case TaskSelectionMethod.EntropyTask:
TaskUtility = activeLearning[indexModel].EntropyTrueLabel();
break;
case TaskSelectionMethod.RandomTask:
TaskUtility = data.GroupBy(d => d.TaskId).ToDictionary(a => a.Key, a => new ActiveLearningResult
{
TaskValue = Rand.Double()
});
break;
case TaskSelectionMethod.UniformTask:
//add task value according to the count left
TaskUtility = currentCounts.OrderBy(kvp => kvp.Value).ToDictionary(a => a.Key, a => new ActiveLearningResult
{
TaskValue = 1
});
break;
default: // Entropy task selection
TaskUtility = activeLearning[indexModel].EntropyTrueLabel();
break;
}
// We create a list of worker utilities
Dictionary<string, double> WorkerAccuracy = null;
// Best worker selection is only allowed for methods that infer worker confusion matrices.
if (results[indexModel].WorkerConfusionMatrix == null)
workerSelectionMethod[indexModel] = WorkerSelectionMethod.RandomWorker;
switch (workerSelectionMethod[indexModel])
{
case WorkerSelectionMethod.BestWorker:
// Assign worker accuracies to the maximum value on the diagonal of the confusion matrix (conservative approach).
// Alternative ways are also possible.
WorkerAccuracy = results[indexModel].WorkerConfusionMatrixMean.ToDictionary(
kvp => kvp.Key,
kvp => Results.GetConfusionMatrixDiagonal(kvp.Value).Max());
break;
case WorkerSelectionMethod.RandomWorker:
// Assign worker accuracies to random values
WorkerAccuracy = results[indexModel].FullMapping.WorkerIdToIndex.ToDictionary(kvp => kvp.Key, kvp => Rand.Double());
break;
default:
throw new ApplicationException("No worker selection method selected");
}
// Create a list of tuples (TaskId, WorkerId, ActiveLearningResult)
List<Tuple<string, string, ActiveLearningResult>> LabelValue = new List<Tuple<string, string, ActiveLearningResult>>();
foreach (var kvp in TaskUtility)
{
foreach (var workerId in remainingWorkersPerTask[kvp.Key])
{
var labelValue = new ActiveLearningResult
{
WorkerId = workerId,
TaskId = kvp.Key,
TaskValue = kvp.Value.TaskValue,
WorkerValue = WorkerAccuracy[workerId]
};
LabelValue.Add(Tuple.Create(labelValue.TaskId, labelValue.WorkerId, labelValue));
}
}
// Increment tha active set with new data
nextData[indexModel] = GetNextData(groupedRandomisedData, LabelValue, currentCounts, totalCounts, remainingWorkersPerTask, numIncremData);
if (nextData[indexModel] == null || nextData[indexModel].Count == 0)
break;
indexArray[indexModel] += nextData[indexModel].Count;
subDataArray[indexModel].AddRange(nextData[indexModel]);
// Logs
if (calculateAccuracy)
{
accuracyArray[indexModel].Add(results[indexModel].Accuracy);
avgRecallArray[indexModel].Add(results[indexModel].AvgRecall);
if (TaskUtility == null)
{
var sortedLabelValue = LabelValue.OrderByDescending(kvp => kvp.Item3.TaskValue).ToArray();
taskValueListArray[indexModel].Add(sortedLabelValue.First().Item3);
}
else
{
//Adding WorkerId into taskValueListArray
ActiveLearningResult nextTaskValueItem = TaskUtility[nextData[indexModel].First().TaskId];
nextTaskValueItem.WorkerId = nextData[indexModel].First().WorkerId;
nextTaskValueItem.TaskId = nextData[indexModel].First().TaskId;
taskValueListArray[indexModel].Add(nextTaskValueItem);
}
if (doSnapShot)
{
Debug.WriteLine("{0} of {1}:\t{2}\t{3:0.000}\t{4:0.0000}", indexArray[indexModel], totalInstances, modelName[indexModel], accuracyArray[indexModel].Last(), avgRecallArray[indexModel].Last());
}
}
}//end of models
}//end for all data
}
/// <summary>
/// Runs the standard active learning procedure on a model instance and an input data set.
/// </summary>
/// <param name="data">The data.</param>
/// <param name="modelName">The model name.</param>
/// <param name="runType">The model run type.</param>
/// <param name="model">The model instance.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="workerSelectionMethod">The method for selecting workers (only Random is implemented).</param>
/// <param name="resultsDir">The directory to save the log files.</param>
/// <param name="communityCount">The number of communities (only for CBCC).</param>
/// <param name="initialNumLabelsPerTask">The initial number of exploratory labels that are randomly selected for each task.</param>
/// <param name="numIncremData">The number of data points to add at each round.</param>
public static void RunActiveLearning(
IList<Datum> data,
string modelName,
RunType runType,
BCC model,
TaskSelectionMethod taskSelectionMethod,
WorkerSelectionMethod workerSelectionMethod,
string resultsDir,
int communityCount = -1,
int initialNumLabelsPerTask = 1,
int numIncremData = 1)
{
//Count elapsed time
Stopwatch stopWatchTotal = new Stopwatch();
stopWatchTotal.Start();
int totalLabels = data.Count();
// Dictionary keyed by task Id, with randomly order labelings
var groupedRandomisedData =
data.GroupBy(d => d.TaskId).
Select(g =>
{
var arr = g.ToArray();
int cnt = arr.Length;
var perm = Rand.Perm(cnt);
return new
{
key = g.Key,
arr = g.Select((t, i) => arr[perm[i]]).ToArray()
};
}).ToDictionary(a => a.key, a => a.arr);
// Dictionary keyed by task Id, with label counts
Dictionary<string, int> totalCounts = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => kvp.Value.Length);
// Keyed by task, value is a HashSet containing all the remaining workers with a label - workers are removed after adding a new datum
Dictionary<string, HashSet<string>> remainingWorkersPerTask = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => new HashSet<string>(kvp.Value.Select(dat => dat.WorkerId)));
int numTaskIds = totalCounts.Count();
int totalInstances = initialNumLabelsPerTask > 0 ? data.Count - initialNumLabelsPerTask * numTaskIds : data.Count - numIncremData;
//throw an exception if the totalInstances is less than or equals to zero
if (totalInstances <= 0)
{
throw new System.Exception("The variable 'totalInstances' should be greater than zero");
}
string[] WorkerIds = data.Select(d => d.WorkerId).Distinct().ToArray();
//only creat accuracy list when it's null (for GUI Use)
if (accuracy == null)
{
accuracy = new List<double>();
}
List<double> avgRecall = new List<double>();
//List<ActiveLearningResult> taskValueList = new List<ActiveLearningResult>();
taskValueList = new List<ActiveLearningResult>();
int index = 0;
Console.WriteLine("Active Learning: {0}", modelName);
Console.WriteLine("\t\t\t\t\t\tAcc\tAvgRec");
// Get initial data
Results results = new Results();
Dictionary<string, int> currentCounts = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => initialNumLabelsPerTask);
List<Datum> subData = GetSubdata(groupedRandomisedData, currentCounts, remainingWorkersPerTask);
var s = remainingWorkersPerTask.Select(w => w.Value.Count).Sum();
List<Datum> nextData = null;
ActiveLearning activeLearning = null;
isExperimentCompleted = false;
for (int iter = 0; ; iter++) //run until data run out
{
bool calculateAccuracy = true;
bool doSnapShot = iter % 1 == 0;
if (subData != null || nextData != null)
{
switch (runType)
{
case RunType.VoteDistribution:
results.RunMajorityVote(subData, data, calculateAccuracy, true);
break;
case RunType.MajorityVote:
results.RunMajorityVote(subData, data, calculateAccuracy, false);
break;
case RunType.DawidSkene:
results.RunDawidSkene(subData, data, calculateAccuracy);
break;
default: // Run BCC models
results.RunBCC(modelName, subData, data, model, RunMode.ClearResults, calculateAccuracy, communityCount, false);
break;
}
}
if (activeLearning == null)
{
activeLearning = new ActiveLearning(data, model, results, communityCount);
}
else
{
activeLearning.UpdateActiveLearningResults(results);
}
// We create a list of task utilities
// TaskValue: Dictionary keyed by task, the value is an active learning result.
Dictionary<string, ActiveLearningResult> TaskUtility = null;
switch (taskSelectionMethod)
{
case TaskSelectionMethod.EntropyTask:
TaskUtility = activeLearning.EntropyTrueLabel();
break;
case TaskSelectionMethod.RandomTask:
TaskUtility = data.GroupBy(d => d.TaskId).ToDictionary(a => a.Key, a => new ActiveLearningResult
{
TaskValue = Rand.Double()
});
break;
case TaskSelectionMethod.UniformTask:
// Reproduce uniform task selection by picking the task with the lowest number of current labels. That is, minus the current count.
TaskUtility = currentCounts.OrderBy(kvp => kvp.Value).ToDictionary(a => a.Key, a => new ActiveLearningResult
{
TaskId = a.Key,
TaskValue = -a.Value
});
break;
default:
TaskUtility = activeLearning.EntropyTrueLabel();
break;
}
// We create a list of worker utilities.
Dictionary<string, double> WorkerAccuracy = null;
// Best worker selection is only allowed for methods that infer worker confusion matrices.
if (results.WorkerConfusionMatrix == null)
workerSelectionMethod = WorkerSelectionMethod.RandomWorker;
switch (workerSelectionMethod)
{
case WorkerSelectionMethod.BestWorker:
// Assign worker accuracies to the maximum value on the diagonal of the confusion matrix (conservative approach).
// Alternative ways are also possible.
WorkerAccuracy = results.WorkerConfusionMatrixMean.ToDictionary(
kvp => kvp.Key,
kvp => Results.GetConfusionMatrixDiagonal(kvp.Value).Max());
break;
case WorkerSelectionMethod.RandomWorker:
// Assign worker accuracies to random values
WorkerAccuracy = results.FullMapping.WorkerIdToIndex.ToDictionary(kvp => kvp.Key, kvp => Rand.Double());
break;
default:
throw new ApplicationException("No worker selection method selected");
}
// Create a list of tuples (TaskIds, WorkerId, ActiveLearningResult).
List<Tuple<string, string, ActiveLearningResult>> LabelValue = new List<Tuple<string,string,ActiveLearningResult>>();
foreach (var kvp in TaskUtility)
{
foreach (var workerId in remainingWorkersPerTask[kvp.Key])
{
var labelValue = new ActiveLearningResult
{
WorkerId = workerId,
TaskId = kvp.Key,
TaskValue = kvp.Value.TaskValue,
WorkerValue = WorkerAccuracy[workerId]
};
LabelValue.Add(Tuple.Create(labelValue.TaskId, labelValue.WorkerId, labelValue));
}
}
// Increment tha active set with new data
nextData = GetNextData(groupedRandomisedData, LabelValue, currentCounts, totalCounts, remainingWorkersPerTask, numIncremData);
if (nextData == null || nextData.Count == 0)
break;
index += nextData.Count;
subData.AddRange(nextData);
// Logs
if (calculateAccuracy)
{
accuracy.Add(results.Accuracy);
avgRecall.Add(results.AvgRecall);
if (TaskUtility == null)
{
var sortedLabelValue = LabelValue.OrderByDescending(kvp => kvp.Item3.TaskValue).ToArray();
taskValueList.Add(sortedLabelValue.First().Item3);
}
else
{
//Adding WorkerId into taskValueList
ActiveLearningResult nextTaskValueItem = TaskUtility[nextData.First().TaskId];
nextTaskValueItem.WorkerId = nextData.First().WorkerId;
//add taskID
nextTaskValueItem.TaskId = nextData.First().TaskId;
taskValueList.Add(nextTaskValueItem);
}
if (doSnapShot)
{
Console.WriteLine("{0} (label {1} of {2}):\t{3:0.000}\t{4:0.0000}", modelName, index, totalInstances, accuracy.Last(), avgRecall.Last());
//DoSnapshot(accuracy, nlpd, avgRecall, taskValueList, results, modelName, "interim", resultsDir, initialNumLabelsPerTask);
}
}//end if logs
}//end for all data
isExperimentCompleted = true;
stopWatchTotal.Stop();
DoSnapshot(accuracy, avgRecall, taskValueList, results, modelName, "final", resultsDir, initialNumLabelsPerTask);
ResetAccuracyList();
Console.WriteLine("Elapsed time: {0}\n", stopWatchTotal.Elapsed);
}
/// <summary>
/// Returns the model name as a string.
/// </summary>
/// <param name="dataset">The name of the data set.</param>
/// <param name="runType">The model run type.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="workerSelectionMethod">The method for selecting workers (only Random is implemented).</param>
/// <param name="numCommunities">The number of communities (only for CBCC).</param>
/// <returns>The model name</returns>
public static string GetModelName(string dataset, RunType runType, TaskSelectionMethod taskSelectionMethod, WorkerSelectionMethod workerSelectionMethod, int numCommunities = -1)
{
return(dataset + "_" + Enum.GetName(typeof(RunType), runType)
+ "_" + Enum.GetName(typeof(TaskSelectionMethod), taskSelectionMethod));
}
/// <summary>
/// Runs the active learning experiment presented in Venanzi et.al (WWW14)
/// for all the models with an array of data sets.
/// </summary>
/// <param name="startIndex">First instance of the data set array.</param>
/// <param name="endIndex">Last instance of the data set array.</param>
/// <param name="whichModel">Model to run.</param>
public static void RunHCOMPExperiments(int startIndex, int endIndex, int whichModel, TaskSelectionMethod currentTaskSelectionMethod, int InitialNumLabelsPerTask)
{
//Select current task selection method(Entropy/Random)
//TaskSelectionMethod currentTaskSelectionMethod = TaskSelectionMethod.EntropyTask;
for (int ds = startIndex; ds <= endIndex; ds++)
{
switch (whichModel)
{
case 1: RunHCOMPActiveLearning(Program.Datasets[ds], RunType.MajorityVote, currentTaskSelectionMethod, InitialNumLabelsPerTask, null); break;
case 2: RunHCOMPActiveLearning(Program.Datasets[ds], RunType.DawidSkene, currentTaskSelectionMethod, InitialNumLabelsPerTask, null); break;
case 3: RunHCOMPActiveLearning(Program.Datasets[ds], RunType.BCC, currentTaskSelectionMethod, InitialNumLabelsPerTask, new BCC()); break;
case 4: RunHCOMPActiveLearning(Program.Datasets[ds], RunType.CBCC, currentTaskSelectionMethod, InitialNumLabelsPerTask, new CBCC(), Program.NumCommunities[ds]); break;
default: // Run all
RunHCOMPActiveLearning(Program.Datasets[ds], RunType.MajorityVote, currentTaskSelectionMethod, InitialNumLabelsPerTask, null);
RunHCOMPActiveLearning(Program.Datasets[ds], RunType.DawidSkene, currentTaskSelectionMethod, InitialNumLabelsPerTask, null);
RunHCOMPActiveLearning(Program.Datasets[ds], RunType.BCC, currentTaskSelectionMethod, InitialNumLabelsPerTask, new BCC());
RunHCOMPActiveLearning(Program.Datasets[ds], RunType.CBCC, currentTaskSelectionMethod, InitialNumLabelsPerTask, new CBCC(), Program.NumCommunities[ds]);
RunHCOMPActiveLearning(Program.Datasets[ds], RunType.BCC, currentTaskSelectionMethod, InitialNumLabelsPerTask, new CBCC());
break;
}
}
}
/// <summary>
/// Constructor for non-EntropyMABTask Selection Method
/// </summary>
/// <param name="taskSelectionMethod"></param>
/// <param name="runType"></param>
/// <param name="numberOfLabellingRound"></param>
/// <param name="labelStartingPoint"></param>
public ExperimentModel(TaskSelectionMethod taskSelectionMethod, WorkerSelectionMethod workerSelectionMethod, RunType runType, int numberOfLabellingRound, int labelStartingPoint)
: this(runType, numberOfLabellingRound, labelStartingPoint)
{
this.taskSelectionMethod = taskSelectionMethod;
this.WorkerSelectionMethod = workerSelectionMethod;
}
private static string GetModelName(string dataset, RunType runType, TaskSelectionMethod taskSelectionMetric, WorkerSelectionMethod workerSelectionMetric, bool online, int taskSamples = -1, int workerSamples = -1, int numCommunities = -1)
{
return dataset + "_" + Enum.GetName(typeof(RunType), runType)
+ "_" + Enum.GetName(typeof(TaskSelectionMethod), taskSelectionMetric)
+ "_" + Enum.GetName(typeof(WorkerSelectionMethod), workerSelectionMetric)
+ (online ? "Online" : "") + (taskSamples > 0 ? "_T" + taskSamples.ToString() : "")
+ (workerSamples > 0 ? "_W" + workerSamples.ToString() : "")
+ (numCommunities > 0 ? "_Comm" + numCommunities.ToString() : "");
}
/// <summary>
/// Background Thread for running the active learning experiment
/// <param name="worker"></param>
/// <param name="e"></param>
public void RunParallelActiveLearning(
System.ComponentModel.BackgroundWorker worker,
System.ComponentModel.DoWorkEventArgs e)
{
//Create a state of the Thread
CurrentParallelState currentState = new CurrentParallelState();
//Set setting in the experimentSetting Class
int totalNumberOfModels = GetNumberOfExperiemntModels();
//Clear previous results
ActiveLearning.ResetParallelAccuracyList(totalNumberOfModels);
//obtain the accuracy list reference
accuracyArrayOfAllExperimentModels = ActiveLearning.accuracyArray;
//The RunTypes that have Worker Confusion Matrices
RunType[] runTypesHaveWorkerMatrices = { RunType.DawidSkene, RunType.BCC, RunType.CBCC };
//Set the models selected in the setting pane
string[] currentModelNames = new string[totalNumberOfModels];
RunType[] currentRunTypes = new RunType[totalNumberOfModels];
TaskSelectionMethod[] currentTaskSelectionMethods = new TaskSelectionMethod[totalNumberOfModels];
WorkerSelectionMethod[] currentWorkerSelectionMethods = new WorkerSelectionMethod[totalNumberOfModels];
BCC[] currentBCCModels = new BCC[totalNumberOfModels];
//for each ExperimentModel, set runTypeArray, taskSelectionMethodArray, workerSelectionMethodArray...
for (int i = 0; i < totalNumberOfModels; i++)
{
ExperimentModel currentExperimentModel = GetExperimentModel(i);
RunType currentRunType = currentExperimentModel.runType;
currentRunTypes[i] = currentRunType;
//set the task selection method
currentTaskSelectionMethods[i] = currentExperimentModel.taskSelectionMethod;
//Add into worker selection method array if the runType can have worker selection
if (runTypesHaveWorkerMatrices.Contains(currentRunType))
{
currentWorkerSelectionMethods[i] = currentExperimentModel.WorkerSelectionMethod;
//Add corresponding model
//if the RunType is BCC, add into BCC model array
if (currentRunType == RunType.BCC)
{
currentBCCModels[i] = new BCC();
}//CBCC Model
else if(currentRunType == RunType.CBCC)
{
CBCC currentBCCmodel = new CBCC();
currentBCCModels[i] = currentBCCmodel;
}
} //end if the runType has worker confusion matrices
} //end for
currentModelNames = currentModelNames.Select((s, i) => CrowdsourcingModels.Program.GetModelName(currentDataset.GetDataSetNameWithoutExtension(), currentRunTypes[i])).ToArray();
//run RunParallelActiveLearning in the ActiveLearning
ActiveLearning.RunParallelActiveLearning(currentDataset.LoadData(), currentModelNames, currentRunTypes,
currentBCCModels, currentTaskSelectionMethods, currentWorkerSelectionMethods,
communityCount, numberOfLabellingRound);
currentState.isRunningComplete = true;
Debug.WriteLine("RunParallelActiveLearning Complete");
//isSimulationComplete = true;
//worker.ReportProgress(0, currentState);
}//end function RunParallelActiveLearning
/// <summary>
/// Constructor for non-EntropyMABTask Selection Method
/// </summary>
/// <param name="taskSelectionMethod"></param>
/// <param name="runType"></param>
/// <param name="numberOfLabellingRound"></param>
/// <param name="labelStartingPoint"></param>
public ExperimentModel(TaskSelectionMethod taskSelectionMethod, WorkerSelectionMethod workerSelectionMethod, RunType runType, int numberOfLabellingRound, int labelStartingPoint)
: this(runType, numberOfLabellingRound, labelStartingPoint)
{
this.taskSelectionMethod = taskSelectionMethod;
this.WorkerSelectionMethod = workerSelectionMethod;
}
/// <summary>
/// Returns the model name as a string.
/// </summary>
/// <param name="dataset">The name of the data set.</param>
/// <param name="runType">The model run type.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="workerSelectionMethod">The method for selecting workers (only Random is implemented).</param>
/// <returns>The model name</returns>
public static string GetModelName(string dataset, RunType runType, TaskSelectionMethod taskSelectionMethod, WorkerSelectionMethod workerSelectionMethod)
{
return dataset + "_" + Enum.GetName(typeof(RunType), runType)
+ "_" + (!taskSelectionMethod.Equals("") ? Enum.GetName(typeof(TaskSelectionMethod), taskSelectionMethod) : "")
+ "_" + (!workerSelectionMethod.Equals("") ? Enum.GetName(typeof(WorkerSelectionMethod), workerSelectionMethod) : "");
}
/// <summary>
/// Background Thread for running the active learning experiment
/// <param name="worker"></param>
/// <param name="e"></param>
public void RunParallelActiveLearning(
System.ComponentModel.BackgroundWorker worker,
System.ComponentModel.DoWorkEventArgs e)
{
//Create a state of the Thread
CurrentParallelState currentState = new CurrentParallelState();
//Set setting in the experimentSetting Class
int totalNumberOfModels = GetNumberOfExperiemntModels();
//Clear previous results
ActiveLearning.ResetParallelAccuracyList(totalNumberOfModels);
//obtain the accuracy list reference
accuracyArrayOfAllExperimentModels = ActiveLearning.accuracyArray;
//The RunTypes that have Worker Confusion Matrices
RunType[] runTypesHaveWorkerMatrices = { RunType.DawidSkene, RunType.BCC, RunType.CBCC };
//Set the models selected in the setting pane
string[] currentModelNames = new string[totalNumberOfModels];
RunType[] currentRunTypes = new RunType[totalNumberOfModels];
TaskSelectionMethod[] currentTaskSelectionMethods = new TaskSelectionMethod[totalNumberOfModels];
WorkerSelectionMethod[] currentWorkerSelectionMethods = new WorkerSelectionMethod[totalNumberOfModels];
BCC[] currentBCCModels = new BCC[totalNumberOfModels];
//for each ExperimentModel, set runTypeArray, taskSelectionMethodArray, workerSelectionMethodArray...
for (int i = 0; i < totalNumberOfModels; i++)
{
ExperimentModel currentExperimentModel = GetExperimentModel(i);
RunType currentRunType = currentExperimentModel.runType;
currentRunTypes[i] = currentRunType;
//set the task selection method
currentTaskSelectionMethods[i] = currentExperimentModel.taskSelectionMethod;
//Add into worker selection method array if the runType can have worker selection
if (runTypesHaveWorkerMatrices.Contains(currentRunType))
{
currentWorkerSelectionMethods[i] = currentExperimentModel.WorkerSelectionMethod;
//Add corresponding model
//if the RunType is BCC, add into BCC model array
if (currentRunType == RunType.BCC)
{
currentBCCModels[i] = new BCC();
}//CBCC Model
else if (currentRunType == RunType.CBCC)
{
CBCC currentBCCmodel = new CBCC();
currentBCCModels[i] = currentBCCmodel;
}
} //end if the runType has worker confusion matrices
} //end for
currentModelNames = currentModelNames.Select((s, i) => CrowdsourcingModels.Program.GetModelName(currentDataset.GetDataSetNameWithoutExtension(), currentRunTypes[i])).ToArray();
//run RunParallelActiveLearning in the ActiveLearning
ActiveLearning.RunParallelActiveLearning(currentDataset.LoadData(), currentModelNames, currentRunTypes,
currentBCCModels, currentTaskSelectionMethods, currentWorkerSelectionMethods,
communityCount, numberOfLabellingRound);
currentState.isRunningComplete = true;
Debug.WriteLine("RunParallelActiveLearning Complete");
//isSimulationComplete = true;
//worker.ReportProgress(0, currentState);
}//end function RunParallelActiveLearning
/// <summary>
/// Runs the standard active learning procedure on a model instance and an input data set.
/// </summary>
/// <param name="data">The data.</param>
/// <param name="modelName">The model name.</param>
/// <param name="runType">The model run type.</param>
/// <param name="model">The model instance.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="workerSelectionMethod">The method for selecting workers (only Random is implemented).</param>
/// <param name="resultsDir">The directory to save the log files.</param>
/// <param name="communityCount">The number of communities (only for CBCC).</param>
/// <param name="initialNumLabelsPerTask">The initial number of exploratory labels that are randomly selected for each task.</param>
public static void RunActiveLearning(IList <Datum> data, string modelName, RunType runType, BCC model, TaskSelectionMethod taskSelectionMethod, WorkerSelectionMethod workerSelectionMethod, string resultsDir, int communityCount = -1, int initialNumLabelsPerTask = 1)
{
//Count elapsed time
Stopwatch stopWatchTotal = new Stopwatch();
stopWatchTotal.Start();
int totalLabels = data.Count();
// Dictionary keyed by task Id, with randomly order labelings
var groupedRandomisedData =
data.GroupBy(d => d.TaskId).
Select(g =>
{
var arr = g.ToArray();
int cnt = arr.Length;
var perm = Rand.Perm(cnt);
return(new
{
key = g.Key,
arr = g.Select((t, i) => arr[perm[i]]).ToArray()
});
}).ToDictionary(a => a.key, a => a.arr);
// Dictionary keyed by task Id, with label counts
Dictionary <string, int> totalCounts = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => kvp.Value.Length);
Dictionary <string, int> currentCounts = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => initialNumLabelsPerTask);
// Keyed by task, value is a HashSet containing all the remaining workers with a label - workers are removed after adding a new datum
Dictionary <string, HashSet <string> > remainingWorkersPerTask = groupedRandomisedData.ToDictionary(kvp => kvp.Key, kvp => new HashSet <string>(kvp.Value.Select(dat => dat.WorkerId)));
int numTaskIds = totalCounts.Count();
int totalInstances = data.Count - initialNumLabelsPerTask * numTaskIds;
string[] WorkerIds = data.Select(d => d.WorkerId).Distinct().ToArray();
// Log structures
List <double> accuracy = new List <double>();
List <double> nlpd = new List <double>();
List <double> avgRecall = new List <double>();
List <ActiveLearningResult> taskValueList = new List <ActiveLearningResult>();
int index = 0;
Console.WriteLine("Active Learning: {0}", modelName);
Console.WriteLine("\t\tAcc\tAvgRec");
// Get initial data
Results results = new Results();
List <Datum> subData = null;
subData = GetSubdata(groupedRandomisedData, currentCounts, remainingWorkersPerTask);
var s = remainingWorkersPerTask.Select(w => w.Value.Count).Sum();
List <Datum> nextData = null;
int numIncremData = 3;
ActiveLearning activeLearning = null;
for (int iter = 0; iter < 500; iter++)
{
bool calculateAccuracy = true;
////bool doSnapShot = iter % 100 == 0; // Frequency of snapshots
bool doSnapShot = true;
if (subData != null || nextData != null)
{
switch (runType)
{
case RunType.VoteDistribution:
results.RunMajorityVote(subData, calculateAccuracy, true);
break;
case RunType.MajorityVote:
results.RunMajorityVote(subData, calculateAccuracy, false);
break;
case RunType.DawidSkene:
results.RunDawidSkene(subData, calculateAccuracy);
break;
default: // Run BCC models
results.RunBCC(modelName, subData, data, model, Results.RunMode.ClearResults, calculateAccuracy, communityCount, false);
break;
}
}
if (activeLearning == null)
{
activeLearning = new ActiveLearning(data, model, results, communityCount);
}
else
{
activeLearning.UpdateActiveLearningResults(results);
}
// Select next task
Dictionary <string, ActiveLearningResult> TaskValue = null;
List <Tuple <string, string, ActiveLearningResult> > LabelValue = null;
switch (taskSelectionMethod)
{
case TaskSelectionMethod.EntropyTask:
TaskValue = activeLearning.EntropyTrueLabelPosterior();
break;
case TaskSelectionMethod.RandomTask:
TaskValue = data.GroupBy(d => d.TaskId).ToDictionary(a => a.Key, a => new ActiveLearningResult
{
TaskValue = Rand.Double()
});
break;
default: // Entropy task selection
TaskValue = activeLearning.EntropyTrueLabelPosterior();
break;
}
nextData = GetNextData(groupedRandomisedData, TaskValue, currentCounts, totalCounts, numIncremData);
if (nextData == null || nextData.Count == 0)
{
break;
}
index += nextData.Count;
subData.AddRange(nextData);
// Logs
if (calculateAccuracy)
{
accuracy.Add(results.Accuracy);
nlpd.Add(results.NegativeLogProb);
avgRecall.Add(results.AvgRecall);
if (TaskValue == null)
{
var sortedLabelValue = LabelValue.OrderByDescending(kvp => kvp.Item3.TaskValue).ToArray();
taskValueList.Add(sortedLabelValue.First().Item3);
}
else
{
taskValueList.Add(TaskValue[nextData.First().TaskId]);
}
if (doSnapShot)
{
Console.WriteLine("{0} of {1}:\t{2:0.000}\t{3:0.0000}", index, totalInstances, accuracy.Last(), avgRecall.Last());
DoSnapshot(accuracy, nlpd, avgRecall, taskValueList, results, modelName, "interim", resultsDir);
}
}
}
stopWatchTotal.Stop();
DoSnapshot(accuracy, nlpd, avgRecall, taskValueList, results, modelName, "final", resultsDir);
Console.WriteLine("Elapsed time: {0}\n", stopWatchTotal.Elapsed);
}
/// <summary>
/// Returns the model name as a string.
/// </summary>
/// <param name="dataset">The name of the data set.</param>
/// <param name="runType">The model run type.</param>
/// <param name="taskSelectionMethod">The method for selecting tasks (Random / Entropy).</param>
/// <param name="workerSelectionMethod">The method for selecting workers (only Random is implemented).</param>
/// <returns>The model name</returns>
public static string GetModelName(string dataset, RunType runType, TaskSelectionMethod taskSelectionMethod, WorkerSelectionMethod workerSelectionMethod)
{
return(dataset + "_" + Enum.GetName(typeof(RunType), runType)
+ "_" + (!taskSelectionMethod.Equals("") ? Enum.GetName(typeof(TaskSelectionMethod), taskSelectionMethod) : "")
+ "_" + (!workerSelectionMethod.Equals("") ? Enum.GetName(typeof(WorkerSelectionMethod), workerSelectionMethod) : ""));
}
