private ModelAndFeatures CreateScoringModel(string modelName, bool decoys, bool secondBest)
{
_out.WriteLine(Resources.CommandLine_CreateScoringModel_Creating_scoring_model__0_, modelName);
try
{
// Create new scoring model using the default calculators.
var scoringModel = new MProphetPeakScoringModel(modelName, null as LinearModelParams, null, decoys, secondBest);
var progressMonitor = new CommandProgressMonitor(_out, new ProgressStatus(String.Empty));
var targetDecoyGenerator = new TargetDecoyGenerator(_doc, scoringModel, progressMonitor);
// Get scores for target and decoy groups.
List<IList<float[]>> targetTransitionGroups;
List<IList<float[]>> decoyTransitionGroups;
targetDecoyGenerator.GetTransitionGroups(out targetTransitionGroups, out decoyTransitionGroups);
// If decoy box is checked and no decoys, throw an error
if (decoys && decoyTransitionGroups.Count == 0)
{
_out.WriteLine(Resources.CommandLine_CreateScoringModel_Error__There_are_no_decoy_peptides_in_the_document__Failed_to_create_scoring_model_);
return null;
}
// Use decoys for training only if decoy box is checked
if (!decoys)
decoyTransitionGroups = new List<IList<float[]>>();
// Set intial weights based on previous model (with NaN's reset to 0)
var initialWeights = new double[scoringModel.PeakFeatureCalculators.Count];
// But then set to NaN the weights that have unknown values for this dataset
for (int i = 0; i < initialWeights.Length; ++i)
{
if (!targetDecoyGenerator.EligibleScores[i])
initialWeights[i] = double.NaN;
}
var initialParams = new LinearModelParams(initialWeights);
// Train the model.
scoringModel = (MProphetPeakScoringModel)scoringModel.Train(targetTransitionGroups,
decoyTransitionGroups, initialParams, secondBest, true, progressMonitor);
Settings.Default.PeakScoringModelList.SetValue(scoringModel);
return new ModelAndFeatures(scoringModel, targetDecoyGenerator.PeakGroupFeatures);
}
catch (Exception x)
{
_out.WriteLine(Resources.CommandLine_CreateScoringModel_Error__Failed_to_create_scoring_model_);
_out.WriteLine(x.Message);
return null;
}
}
public void TestMProphetScoringModel()
{
var testFilesDir = new TestFilesDir(TestContext, ZIP_FILE);
// Test our MProphet implementation against known good results.
foreach (var fileWeights in _fileWeights)
{
// Load transition groups from data file.
var filePath = testFilesDir.GetTestPath(fileWeights._fileName);
ScoredGroupPeaksSet targetTransitionGroups;
ScoredGroupPeaksSet decoyTransitionGroups;
LoadData(filePath, out targetTransitionGroups, out decoyTransitionGroups);
// Discard half the transition groups that are used for testing.
targetTransitionGroups.DiscardHalf();
decoyTransitionGroups.DiscardHalf();
// Calculate weights for peak features.
var scoringModel = new MProphetPeakScoringModel("mProphet", fileWeights._weights); // Not L10N
scoringModel = (MProphetPeakScoringModel)scoringModel.Train(targetTransitionGroups.ToList(), decoyTransitionGroups.ToList(), new LinearModelParams(fileWeights._weights), false, false);
Assert.AreEqual(scoringModel.Parameters.Weights.Count, fileWeights._weights.Length);
for (int i = 0; i < scoringModel.Parameters.Weights.Count; i++)
Assert.AreEqual(fileWeights._weights[i], scoringModel.Parameters.Weights[i], 1e-6);
}
}