public bool Equals(MProphetPeakScoringModel other)
{
return(base.Equals(other) &&
ColinearWarning.Equals(other.ColinearWarning) &&
Lambda.Equals(other.Lambda));
}
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);
}
}
public bool Equals(MProphetPeakScoringModel other)
{
return (base.Equals(other) &&
ColinearWarning.Equals(other.ColinearWarning) &&
Lambda.Equals(other.Lambda));
}
// Test that the dialog behaves correctly when opening a model
// that is incompatible with the dataset (some or all composite scores are NaN's)
protected void TestIncompatibleDataSet()
{
// Define an incompatible model
var weights = new[] {0.5322, -1.0352, double.NaN, double.NaN, 1.4744, 0.0430, 0.0477, -0.2740, double.NaN,
2.0096, 7.7726, -0.0566, 0.4751, 0.5, 0.5, double.NaN, double.NaN,
double.NaN, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN};
var parameters = new LinearModelParams(weights, -2.5);
var incompatibleModel = new MProphetPeakScoringModel("incompatible", parameters, null, true);
Settings.Default.PeakScoringModelList.Add(incompatibleModel);
RunDlg<PeptideSettingsUI>(SkylineWindow.ShowPeptideSettingsUI, peptideSettingsDlg =>
{
peptideSettingsDlg.ComboPeakScoringModelSelected = "incompatible";
peptideSettingsDlg.OkDialog();
});
var reintegrateDlgIncompatible = ShowDialog<ReintegrateDlg>(SkylineWindow.ShowReintegrateDialog);
var editList = ShowDialog<EditListDlg<SettingsListBase<PeakScoringModelSpec>, PeakScoringModelSpec>>(
reintegrateDlgIncompatible.EditPeakScoringModel);
RunUI(() => editList.SelectItem("incompatible")); // Not L10N
RunDlg<EditPeakScoringModelDlg>(editList.EditItem, editDlgTemp =>
{
// All of the percentage fields should be null
VerifyCellValues(editDlgTemp, SCORES_AND_WEIGHTS[10], 0.0);
editDlgTemp.TrainModelClick();
// Cell values go back to the standard trained model after we train and enable calculators,
// despite having been loaded with weird values
editDlgTemp.SetChecked(3, true);
editDlgTemp.TrainModelClick();
VerifyCellValues(editDlgTemp, SCORES_AND_WEIGHTS[1], 1.0, false);
editDlgTemp.CancelDialog();
});
OkDialog(editList, editList.OkDialog);
// Trying to reintegrate gives an error because the model is incompatible
RunDlg<MessageDlg>(reintegrateDlgIncompatible.OkDialog, messageDlg =>
{
Assert.AreEqual(TextUtil.LineSeparate(string.Format(Resources.ReintegrateDlg_OkDialog_Failed_attempting_to_reintegrate_peaks_),
Resources.ReintegrateDlg_OkDialog_The_current_peak_scoring_model_is_incompatible_with_one_or_more_peptides_in_the_document___Please_train_a_new_model_),
messageDlg.Message);
messageDlg.OkDialog();
});
OkDialog(reintegrateDlgIncompatible, reintegrateDlgIncompatible.CancelDialog);
}
