// --------------------------------------------------------------------------
//                   OpenMS -- Open-Source Mass Spectrometry               
// --------------------------------------------------------------------------
// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
// ETH Zurich, and Freie Universitaet Berlin 2002-2013.
// 
// This software is released under a three-clause BSD license:
//  * Redistributions of source code must retain the above copyright
//    notice, this list of conditions and the following disclaimer.
//  * Redistributions in binary form must reproduce the above copyright
//    notice, this list of conditions and the following disclaimer in the
//    documentation and/or other materials provided with the distribution.
//  * Neither the name of any author or any participating institution 
//    may be used to endorse or promote products derived from this software 
//    without specific prior written permission.
// For a full list of authors, refer to the file AUTHORS. 
// --------------------------------------------------------------------------
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING 
// INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 
// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 
// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 
// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 
// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
// 
// --------------------------------------------------------------------------
// $Maintainer: Hannes Roest $
// $Authors: Hannes Roest $
// --------------------------------------------------------------------------

#include <OpenMS/CONCEPT/ClassTest.h>

///////////////////////////

#include <OpenMS/ANALYSIS/OPENSWATH/ConfidenceScoring.h>

///////////////////////////

using namespace OpenMS;

std::vector<TargetedExperiment::RetentionTime> get_rts_(double rt_val)
{
  // add retention time for the peptide
  CVTerm rt;
  std::vector<TargetedExperiment::RetentionTime> retention_times;
  TargetedExperiment::RetentionTime retention_time;
  OpenMS::DataValue dtype(rt_val);
  rt.setCVIdentifierRef("MS");
  rt.setAccession("MS:1000896"); // normalized RT
  rt.setName("normalized retention time");
  rt.setValue(dtype);
  retention_time.addCVTerm(rt);
  retention_times.push_back(retention_time);
  return retention_times;
}

START_TEST(ConfidenceScoring<D>, "$Id: ConfidenceScoring_test.C 11560 2013-07-16 07:27:17Z hroest $")

/////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////

ConfidenceScoring* confidence_scoring_ptr = 0;
ConfidenceScoring* confidence_scoring_nullPointer = 0;

START_SECTION((ConfidenceScoring()))
  confidence_scoring_ptr = new ConfidenceScoring;
  TEST_NOT_EQUAL(confidence_scoring_ptr, confidence_scoring_nullPointer)
END_SECTION

START_SECTION((virtual ~ConfidenceScoring()))
    delete confidence_scoring_ptr;
END_SECTION


START_SECTION((void initialize(TargetedExperiment library, Size n_decoys, Size n_transitions, TransformationDescription rt_trafo)))
  ConfidenceScoring scoring;
  TargetedExperiment library;
  TransformationDescription rt_trafo;
  scoring.initialize(library, 0, 0, rt_trafo);
  TEST_NOT_EQUAL(&scoring, confidence_scoring_nullPointer)
END_SECTION

START_SECTION((void initializeGlm(double intercept, double rt_coef, double int_coef)))
  ConfidenceScoring scoring;
  scoring.initializeGlm(0.0, -1.0, -1.0);
  TEST_NOT_EQUAL(&scoring, confidence_scoring_nullPointer)
END_SECTION

START_SECTION((void scoreMap(FeatureMap<> & features)))
{
  ConfidenceScoring scoring(true); // initialize with test mode
  TargetedExperiment library;
  TransformationDescription rt_trafo;
  scoring.initialize(library, 0, 0, rt_trafo);
  scoring.initializeGlm(0.0, -1.0, -1.0);
  FeatureMap<> features;
  TEST_EXCEPTION(Exception::IllegalArgument, scoring.scoreMap(features))

  // The input to the program is 
  // - a transition library which contains peptides with corresponding assays
  // - a feature map where each feature corresponds to an assay (mapped with
  //   MetaValue "PeptideRef") and each feature has as many subordinates as the
  //   assay has transitions (mapped with MetaValue "native_id")

  // In this case we have 2 assays (pep_1 and pep_2) with 1 transition each
  // (tr_10 for pep_1 and tr_20 for pep_2).
  {
    TargetedExperiment::Peptide p;

    p.id = "pep_1";
    p.rts = get_rts_(50.0);
    library.addPeptide(p);

    ReactionMonitoringTransition rm_trans;
    rm_trans.setNativeID("tr_10");
    rm_trans.setPrecursorMZ(400.0);
    rm_trans.setProductMZ(500.0);
    rm_trans.setPeptideRef(p.id);
    rm_trans.setLibraryIntensity(500.0);
    library.addTransition(rm_trans);
  }
  {
    TargetedExperiment::Peptide p;
    p.id = "pep_2";
    p.rts = get_rts_(60.0);
    library.addPeptide(p);


    ReactionMonitoringTransition rm_trans;
    rm_trans.setNativeID("tr_20");
    rm_trans.setPrecursorMZ(400.0);
    rm_trans.setProductMZ(500.0);
    rm_trans.setPeptideRef(p.id);
    rm_trans.setLibraryIntensity(500.0);
    library.addTransition(rm_trans);

  }

  {
    Feature f;
    f.setRT(60.0);
    f.setMetaValue("PeptideRef", "pep_1");
    f.setOverallQuality(-1);

    std::vector<Feature> subordinates;
    Feature sub;
    sub.setIntensity(1);
    sub.setMZ(500);
    sub.setMetaValue("native_id", "tr_10");
    subordinates.push_back(sub);
    f.setSubordinates(subordinates);

    features.push_back(f);
  }
  {
    Feature f;
    f.setRT(60.0);
    f.setMetaValue("PeptideRef", "pep_2");
    f.setOverallQuality(-1);

    std::vector<Feature> subordinates;
    Feature sub;
    sub.setIntensity(1);
    sub.setMZ(500);
    sub.setMetaValue("native_id", "tr_20");
    subordinates.push_back(sub);
    f.setSubordinates(subordinates);

    features.push_back(f);
  }

  scoring.initialize(library, 0, 0, rt_trafo);
  scoring.scoreMap(features);

  TEST_REAL_SIMILAR(features[0].getOverallQuality(), 0.0);
  TEST_REAL_SIMILAR(features[1].getOverallQuality(), 1.0);

  // the absolute computed score for each feature
  TEST_REAL_SIMILAR(features[0].getMetaValue("GLM_score"), 0.0);
  TEST_REAL_SIMILAR(features[1].getMetaValue("GLM_score"), 0.5);
  // the local fdr score (1-quality)
  TEST_REAL_SIMILAR(features[0].getMetaValue("local_FDR"), 1.0);
  TEST_REAL_SIMILAR(features[1].getMetaValue("local_FDR"), 0.0);
}
END_SECTION


START_SECTION(([EXTRA] test exceptions))
{
  ConfidenceScoring scoring(true); // initialize with test mode
  TargetedExperiment library;
  TransformationDescription rt_trafo;
  scoring.initialize(library, 0, 0, rt_trafo);
  scoring.initializeGlm(0.0, -1.0, -1.0);
  FeatureMap<> features;

  {
    TargetedExperiment::Peptide p;

    p.id = "pep_1";
    p.rts = get_rts_(50.0);
    library.addPeptide(p);

    ReactionMonitoringTransition rm_trans;
    rm_trans.setNativeID("tr_10");
    rm_trans.setPrecursorMZ(400.0);
    rm_trans.setProductMZ(500.0);
    rm_trans.setPeptideRef(p.id);
    rm_trans.setLibraryIntensity(500.0);
    library.addTransition(rm_trans);
  }
  {
    TargetedExperiment::Peptide p;
    p.id = "pep_2";
    p.rts = get_rts_(60.0);
    library.addPeptide(p);

    ReactionMonitoringTransition rm_trans;
    rm_trans.setNativeID("tr_20");
    rm_trans.setPrecursorMZ(400.0);
    rm_trans.setProductMZ(500.0);
    rm_trans.setPeptideRef(p.id);
    rm_trans.setLibraryIntensity(500.0);
    library.addTransition(rm_trans);

  }

  // If no meta value is present for the featuere, we cannot map it to the assay
  {
    Feature f;
    f.setRT(60.0);
    f.setOverallQuality(-1);
    //f.setMetaValue("PeptideRef", "pep_1");
    features.push_back(f);
  }
  {
    Feature f;
    f.setRT(60.0);
    f.setOverallQuality(-1);
    //f.setMetaValue("PeptideRef", "pep_2");
    features.push_back(f);
  }

  scoring.initialize(library, 0, 0, rt_trafo);
  TEST_EXCEPTION_WITH_MESSAGE(Exception::IllegalArgument, scoring.scoreMap(features), "Feature does not contain meta value 'PeptideRef' (reference to assay)")

  // After we add the meta value, we still should get an exception
  features[0].setMetaValue("PeptideRef", "pep_1");
  features[1].setMetaValue("PeptideRef", "pep_2");
  TEST_EXCEPTION_WITH_MESSAGE(Exception::IllegalArgument, scoring.scoreMap(features), "Feature intensities were empty - please provide feature subordinate with intensities")

  // An exception should be thrown if the sub-features cannot be mapped to the
  // transitions (e.g. the metavalue "native_id" is missing)
  {
    std::vector<Feature> subordinates;
    Feature sub;
    sub.setIntensity(1);
    sub.setMZ(500);
    // sub.setMetaValue("native_id", "tr_10");
    subordinates.push_back(sub);
    features[0].setSubordinates(subordinates);
  }
  {
    std::vector<Feature> subordinates;
    Feature sub;
    sub.setIntensity(1);
    sub.setMZ(500);
    //sub.setMetaValue("native_id", "tr_20");
    subordinates.push_back(sub);
    features[1].setSubordinates(subordinates);
  }
  TEST_EXCEPTION_WITH_MESSAGE(Exception::IllegalArgument, scoring.scoreMap(features), "Did not find a feature for each assay provided - each feature needs to have n subordinates with the meta-value 'native_id' set to the corresponding transition.")

  {
    std::vector<Feature> subordinates;
    Feature sub;
    sub.setIntensity(1);
    sub.setMZ(500);
    sub.setMetaValue("native_id", "tr_10");
    subordinates.push_back(sub);
    features[0].setSubordinates(subordinates);
  }
  {
    std::vector<Feature> subordinates;
    Feature sub;
    sub.setIntensity(1);
    sub.setMZ(500);
    sub.setMetaValue("native_id", "tr_20");
    subordinates.push_back(sub);
    features[1].setSubordinates(subordinates);
  }
  scoring.scoreMap(features);
  TEST_REAL_SIMILAR(features[0].getOverallQuality(), 0.0);
  TEST_REAL_SIMILAR(features[1].getOverallQuality(), 1.0);


}
END_SECTION

/////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////
END_TEST