internal MetaResults GetMetaResults()
{
var metaData = new MetaResults();
if (_meta != null)
{
metaData = GetMetaResults(_meta);
}
return(metaData);
}
internal MetaResults GetMetaResults(JObject meta)
{
var metaData = new MetaResults();
if (meta["results"] != null)
{
metaData.Limit = (int)meta["results"]["limit"];
metaData.Skip = (int)meta["results"]["skip"];
metaData.Total = (int)meta["results"]["total"];
}
return(metaData);
}
/// <summary>
/// Converts JSON Data
/// </summary>
/// <param name="jsondata">JSON Data as Object</param>
/// <returns>Meta Results Data Object</returns>
/// <remarks></remarks>
public static MetaResults CnvJsonData(JObject jsondata)
{
var metaData = new MetaResults();
var obj = jsondata.GetValue("meta");
if (obj["results"] != null)
{
metaData.Limit = (int)obj["results"]["limit"];
metaData.Skip = (int)obj["results"]["skip"];
metaData.Total = (int)obj["results"]["total"];
}
return(metaData);
}
internal MetaResults GetMetaResults(string searchResults)
{
var metaData = new MetaResults();
if (!string.IsNullOrEmpty(searchResults))
{
var jo = JObject.Parse(searchResults);
var meta = (JObject)(jo.GetValue("meta"));
metaData = GetMetaResults(meta);
}
return(metaData);
}
/// <summary>
/// Converts JSON Data
/// </summary>
/// <param name="jsondata">JSON Data as String</param>
/// <returns>Meta Results Data Object</returns>
/// <remarks></remarks>
public static MetaResults CnvJsonData(string jsondata)
{
MetaResults result = null;
if (string.IsNullOrEmpty(jsondata))
{
result = new MetaResults();
}
else
{
var jo = JObject.Parse(jsondata);
result = CnvJsonData(jo);
}
return(result);
}
public static void WriteMetaResultsJSONFile(
MetaResults metaResults,
string wantedFileName = "")
{
string output = JsonConvert.SerializeObject(metaResults, Formatting.Indented);
//Choose the name
string fileName = "";
if (wantedFileName != "")
{
fileName = wantedFileName;
}
else
{
fileName = $"metaResults_{metaResults.bestInfos.TimeStamp.ToString("yyyy-MM-dd_HH-mm-ss")}.json";
}
File.WriteAllText(ConfigurationManager.Instance.config.ResultsFolder + fileName, output);
}
/*
* public static MetaResults MetaEvaluate(DiseasesData PredictionData, DiseasesData RealData, Tuple<TFType, IDFType> WeightCombinaison, double minWeight, double maxWeight, double step, Criterion criterion)
* {
* //Create MetaResult
* MetaResults metaResults = new MetaResults(WeightCombinaison.Item1, WeightCombinaison.Item2);
*
* //Compute all results and put them in metaResults
* List<Results> listResults = new List<Results>();
* for (double i = minWeight; i <= maxWeight; i+=step)
* {
* Results currentRes = Evaluate(PredictionData, RealData, WeightCombinaison, i);
* listResults.Add(currentRes);
* metaResults.perThreshold.Add(
* new PerThreshold(
* i,
* currentRes.general.Type,
* currentRes.general.RealPositives,
* currentRes.general.FalsePositives,
* currentRes.general.FalseNegatives,
* currentRes.general.Precision,
* currentRes.general.Recall,
* currentRes.general.F_Score
* ));
* }
*
* //Find best results
* Results Best_Result;
* switch (criterion)
* {
* case Criterion.F_Score:
* Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.F_Score > savedRes.general.F_Score ? currentRes : savedRes);
* break;
* case Criterion.Precision:
* Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.Precision > savedRes.general.Precision ? currentRes : savedRes);
* break;
* case Criterion.Recall:
* Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.Recall > savedRes.general.Recall ? currentRes : savedRes);
* break;
* default:
* Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.F_Score > savedRes.general.F_Score ? currentRes : savedRes);
* break;
* }
*
* //Complete metaResults
* metaResults.bestInfos = new BestInfos(
* Best_Result.general.TimeStamp,
* Best_Result.general.Type,
* Best_Result.general.Threshold,
* Best_Result.general.Precision,
* Best_Result.general.Recall,
* Best_Result.general.F_Score,
* criterion
* );
*
* return metaResults;
* }*/
public static MetaResults MetaEvaluate(DiseasesData PredictionData, DiseasesData RealData, Criterion criterion, params Tuple <TFType, IDFType>[] WeightCombinaisons)
{
//Create MetaResult
MetaResults metaResults = new MetaResults();
//Compute all results and put them in metaResults
List <Results> listResults = new List <Results>();
//If not precised, we generate
if (WeightCombinaisons.Length == 0)
{
WeightCombinaisons = GenerateDisctinctsTupleForWeightComputation().ToArray();
}
foreach (var tuple in WeightCombinaisons)
{
Results currentRes = Evaluate(PredictionData, RealData, tuple);
listResults.Add(currentRes);
metaResults.perCombinaison.Add(
new PerCombinaison(
currentRes.general.TimeStamp,
currentRes.general.NumberOfDiseasesWithKnownPhenotypes,
currentRes.general.NumberOfDiseasesWithPublicationsInPredictionData,
currentRes.general.NumberOfDiseasesEvaluatedForReal,
currentRes.general.Type,
currentRes.general.MeanNumberOfRelatedEntitiesFound,
currentRes.general.StandardDeviationNumberOfRelatedEntitiesFound,
currentRes.general.TFType,
currentRes.general.IDFType,
currentRes.general.RealPositives,
currentRes.general.FalsePositives,
currentRes.general.FalseNegatives,
currentRes.general.Precision,
currentRes.general.Recall,
currentRes.general.F_Score,
currentRes.general.MeanRankRealPositives,
currentRes.general.StandardDeviationRankRealPositivesGeneral,
criterion
));
}
//Find best results and sort by perCombinaison
Results Best_Result;
switch (criterion)
{
case Criterion.MeanRankRealPositives:
Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.MeanRankRealPositives < savedRes.general.MeanRankRealPositives ? currentRes : savedRes);
metaResults.perCombinaison = metaResults.perCombinaison.OrderBy(pc => pc.MeanRankRealPositives).ToList();
break;
case Criterion.F_Score:
Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.F_Score > savedRes.general.F_Score ? currentRes : savedRes);
metaResults.perCombinaison = metaResults.perCombinaison.OrderByDescending(pc => pc.F_Score).ToList();
break;
case Criterion.Precision:
Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.Precision > savedRes.general.Precision ? currentRes : savedRes);
metaResults.perCombinaison = metaResults.perCombinaison.OrderByDescending(pc => pc.Precision).ToList();
break;
case Criterion.Recall:
Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.Recall > savedRes.general.Recall ? currentRes : savedRes);
metaResults.perCombinaison = metaResults.perCombinaison.OrderByDescending(pc => pc.Recall).ToList();
break;
default:
Best_Result = listResults.Aggregate((savedRes, currentRes) => currentRes.general.MeanRankRealPositives < savedRes.general.MeanRankRealPositives ? currentRes : savedRes);
metaResults.perCombinaison = metaResults.perCombinaison.OrderBy(pc => pc.MeanRankRealPositives).ToList();
break;
}
//Complete metaResults
metaResults.bestInfos = new BestInfos(
Best_Result.general.TimeStamp,
Best_Result.general.NumberOfDiseasesWithKnownPhenotypes,
Best_Result.general.NumberOfDiseasesWithPublicationsInPredictionData,
Best_Result.general.NumberOfDiseasesEvaluatedForReal,
Best_Result.general.Type,
Best_Result.general.MeanNumberOfRelatedEntitiesFound,
Best_Result.general.StandardDeviationNumberOfRelatedEntitiesFound,
Best_Result.general.TFType,
Best_Result.general.IDFType,
Best_Result.general.RealPositives,
Best_Result.general.FalsePositives,
Best_Result.general.FalseNegatives,
Best_Result.general.Precision,
Best_Result.general.Recall,
Best_Result.general.F_Score,
Best_Result.general.MeanRankRealPositives,
Best_Result.general.StandardDeviationRankRealPositivesGeneral,
criterion
);
return(metaResults);
}
static void Main(string[] args)
{
//Environnement variables
//Environment.SetEnvironmentVariable("RD_AGGREGATOR_SETTINGS", @"C:\Users\Psycho\Source\Repos\RDSearch4\settings.json");
Environment.SetEnvironmentVariable("RD_AGGREGATOR_SETTINGS", @"C:\Users\CharlesCOUSYN\source\repos\Aggregator\settings.json");
var path = Environment.GetEnvironmentVariable("RD_AGGREGATOR_SETTINGS");
ConfigurationManager.Instance.Init(path);
//Obtain all symptoms/phenotypes
PhenotypeEngine phenotypeEngine = new PhenotypeEngine();
phenotypeEngine.GetSymptomsList();
/*
* //TESTED AND DONE
* //Update Orphanet (diseases/real datasets)
* OrphaEngine orphaEngine = new OrphaEngine(phenotypeEngine);
* orphaEngine.Start();*/
//Retrieving diseases from DB
List <Disease> lst_diseases = new List <Disease>();
using (var db = new MongoRepository.DiseaseRepository())
{
//lst_diseases = db.selectAll().Take(50).ToList();
lst_diseases = db.selectAll();
}
//TESTED AND DONE
/*
* //Update Publications
* PubmedEngine pubmedEngine = new PubmedEngine();
* Console.WriteLine("Starting requests at PMC this can take some time...");
* pubmedEngine.Start2(lst_diseases);
*/
/*
* //Update number of publications per disease
* Console.WriteLine("Update number of publications per disease.....");
* using (var dbDisease = new MongoRepository.DiseaseRepository())
* using (var dbPublication = new MongoRepository.PublicationRepository())
* {
* //Update all diseases
* foreach (var disease in lst_diseases)
* {
* long numberPublications = dbPublication.countForOneDisease(disease.OrphaNumber);
* disease.NumberOfPublications = (int)numberPublications;
* dbDisease.updateDisease(disease);
* }
* }
* Console.WriteLine("Update number of publications per disease finished");
*/
//Retrieving related entities by disease AND TextMine
/*
* TextMiningEngine textMiningEngine = new TextMiningEngine(phenotypeEngine);
* RecupSymptomsAndTextMine(lst_diseases, textMiningEngine);*/
//Retrieving PredictionData and RealData from DB (DiseasesData with type Symptom)
DiseasesData PredictionData = null;
DiseasesData RealData = null;
using (var dbPred = new MongoRepository.PredictionDataRepository())
using (var dbReal = new MongoRepository.RealDataRepository())
{
PredictionData = dbPred.selectByType(type.Symptom);
RealData = dbReal.selectByType(type.Symptom);
}
//Evaluation...
if (PredictionData != null && RealData != null)
{
Console.WriteLine("Evaluation....");
//Testing all combinaisons
MetaResults metaResults = Evaluator.MetaEvaluate(PredictionData, RealData, Evaluation.entities.Criterion.MeanRankRealPositives);
Evaluator.WriteMetaResultsJSONFile(metaResults);
//Having best combinaison and evaluate with it
Tuple <TFType, IDFType> tupleToTest = new Tuple <TFType, IDFType>(metaResults.bestInfos.TFType, metaResults.bestInfos.IDFType);
//Evaluate basically
Results resultsOfBestCombinaison = Evaluator.Evaluate(PredictionData, RealData, tupleToTest);
Evaluator.WriteResultsJSONFile(resultsOfBestCombinaison);
//Evaluate best combinaison with threshold search
MetaResultsWeight metaResultsWeight = Evaluator.MetaWeightEvaluate(PredictionData, RealData, tupleToTest, 0.0005, Evaluation.entities.Criterion.F_Score);
Evaluator.WriteMetaResultsWeightJSONFile(metaResultsWeight);
Console.WriteLine("Evaluation finished!");
}
Console.WriteLine("Finished :)");
Console.ReadLine();
}
internal MetaResults GetMetaResults(JObject meta)
{
var metaData = new MetaResults();
if (meta["results"] != null)
{
metaData.Limit = (int) meta["results"]["limit"];
metaData.Skip = (int) meta["results"]["skip"];
metaData.Total = (int) meta["results"]["total"];
}
return metaData;
}
internal MetaResults GetMetaResults(string searchResults)
{
var metaData = new MetaResults();
if (!string.IsNullOrEmpty(searchResults))
{
var jo = JObject.Parse(searchResults);
var meta = (JObject) (jo.GetValue("meta"));
metaData = GetMetaResults(meta);
}
return metaData;
}
internal MetaResults GetMetaResults()
{
var metaData = new MetaResults();
if (_meta != null)
{
metaData = GetMetaResults(_meta);
}
return metaData;
}
