PLearn 0.1
RegressionTreeMulticlassLeaveProb.cc
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00001 // -*- C++ -*-
00002 
00003 // RegressionTreeMulticlassLeaveProb.cc
00004 // Copyright (c) 1998-2002 Pascal Vincent
00005 // Copyright (C) 1999-2002 Yoshua Bengio and University of Montreal
00006 // Copyright (c) 2002 Jean-Sebastien Senecal, Xavier Saint-Mleux, Rejean Ducharme
00007 //
00008 // Redistribution and use in source and binary forms, with or without
00009 // modification, are permitted provided that the following conditions are met:
00010 // 
00011 //  1. Redistributions of source code must retain the above copyright
00012 //     notice, this list of conditions and the following disclaimer.
00013 // 
00014 //  2. Redistributions in binary form must reproduce the above copyright
00015 //     notice, this list of conditions and the following disclaimer in the
00016 //     documentation and/or other materials provided with the distribution.
00017 // 
00018 //  3. The name of the authors may not be used to endorse or promote
00019 //     products derived from this software without specific prior written
00020 //     permission.
00021 // 
00022 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00023 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00024 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00025 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00026 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00027 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00028 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00029 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00030 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00031 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00032 // 
00033 // This file is part of the PLearn library. For more information on the PLearn
00034 // library, go to the PLearn Web site at www.plearn.org
00035 
00036 
00037 /* ********************************************************************************    
00038  * $Id: RegressionTreeMulticlassLeaveProb.cc, v 1.0 2004/07/19 10:00:00 Bengio/Kegl/Godbout    *
00039  * This file is part of the PLearn library.                                     *
00040  ******************************************************************************** */
00041 
00042 #include "RegressionTreeMulticlassLeaveProb.h"
00043 #include "RegressionTreeRegisters.h"
00044 #include <plearn/math/TMat_maths_impl.h>
00045 
00046 namespace PLearn {
00047 using namespace std;
00048 
00049 PLEARN_IMPLEMENT_OBJECT(RegressionTreeMulticlassLeaveProb,
00050                         "Object to represent the leaves of a regression tree.",
00051                         "It maintains the necessary statistics to compute the output and the train error\n"
00052                         "of the samples in the leave.\n"
00053     );
00054 
00055 RegressionTreeMulticlassLeaveProb::RegressionTreeMulticlassLeaveProb()
00056     : nb_class(-1),
00057       objective_function("l1")
00058 {
00059     build();
00060 }
00061 
00062 RegressionTreeMulticlassLeaveProb::~RegressionTreeMulticlassLeaveProb()
00063 {
00064 }
00065 
00066 void RegressionTreeMulticlassLeaveProb::declareOptions(OptionList& ol)
00067 { 
00068     inherited::declareOptions(ol);
00069 
00070     declareOption(ol, "nb_class", 
00071                   &RegressionTreeMulticlassLeaveProb::nb_class,
00072                   OptionBase::buildoption,
00073                   "The number of class. Should be numbered from 0 to nb_class -1.\n"
00074         );
00075 
00076     declareOption(ol, "objective_function",
00077                   &RegressionTreeMulticlassLeaveProb::objective_function,
00078                   OptionBase::buildoption,
00079                   "The function to be used to compute the leave error.\n"
00080                   "Current supported values are l1 and l2 (default is l1).");
00081       
00082     declareOption(ol, "multiclass_weights_sum",
00083                   &RegressionTreeMulticlassLeaveProb::multiclass_weights_sum,
00084                   OptionBase::learntoption,
00085                   "A vector to count the weight sum of each possible output "
00086                   "for the sample in this leave.\n");
00087     redeclareOption(ol, "loss_function_factor",
00088                   &RegressionTreeMulticlassLeaveProb::loss_function_factor,
00089                   OptionBase::learntoption,
00090                   "The loss fct factor. Depend of the objective_function.\n");
00091 }
00092 
00093 void RegressionTreeMulticlassLeaveProb::makeDeepCopyFromShallowCopy(CopiesMap& copies)
00094 {
00095     inherited::makeDeepCopyFromShallowCopy(copies);
00096     deepCopyField(objective_function, copies);
00097     deepCopyField(multiclass_weights_sum, copies);
00098 }
00099 
00100 void RegressionTreeMulticlassLeaveProb::build()
00101 {
00102     inherited::build();
00103     build_();
00104 }
00105 
00106 void RegressionTreeMulticlassLeaveProb::build_()
00107 {
00108 }
00109 
00110 void RegressionTreeMulticlassLeaveProb::initStats()
00111 {
00112     length_ = 0;
00113     weights_sum = 0.0;
00114     if (loss_function_weight != 0.0)
00115     {
00116         if(objective_function == "l1")
00117             loss_function_factor = 2.0 / loss_function_weight;
00118         else
00119             loss_function_factor = 2.0 / pow(loss_function_weight, 2);
00120     }
00121     else
00122     {
00123         loss_function_factor = 1.0;
00124     }
00125     multiclass_weights_sum.resize(nb_class);
00126     multiclass_weights_sum.fill(0);
00127 }
00128 
00129 void RegressionTreeMulticlassLeaveProb::addRow(int row)
00130 {
00131     real weight = train_set->getWeight(row);
00132     real target = train_set->getTarget(row);
00133     RegressionTreeMulticlassLeaveProb::addRow(row, target, weight);
00134 }
00135 
00136 void RegressionTreeMulticlassLeaveProb::addRow(int row, real target, real weight,
00137                                  Vec outputv, Vec errorv)
00138 {
00139     RegressionTreeMulticlassLeaveProb::addRow(row, target, weight);
00140     RegressionTreeMulticlassLeaveProb::getOutputAndError(outputv,errorv);
00141 }
00142 
00143 void RegressionTreeMulticlassLeaveProb::addRow(int row, real target, real weight)
00144 {
00145     length_ += 1;
00146     weights_sum += weight;
00147     multiclass_weights_sum[int(target)] += weight;
00148 }
00149 
00150 void RegressionTreeMulticlassLeaveProb::addRow(int row, Vec outputv, Vec errorv)
00151 {
00152     RegressionTreeMulticlassLeaveProb::addRow(row);
00153     RegressionTreeMulticlassLeaveProb::getOutputAndError(outputv,errorv);    
00154 }
00155 
00156 void RegressionTreeMulticlassLeaveProb::removeRow(int row, Vec outputv, Vec errorv)
00157 {
00158     real weight = train_set->getWeight(row);
00159     real target = train_set->getTarget(row);
00160     RegressionTreeMulticlassLeaveProb::removeRow(row,target,weight,outputv,errorv);
00161 }
00162 
00163 void RegressionTreeMulticlassLeaveProb::removeRow(int row, real target, real weight,
00164                                  Vec outputv, Vec errorv){
00165     RegressionTreeMulticlassLeaveProb::removeRow(row,target,weight);
00166     RegressionTreeMulticlassLeaveProb::getOutputAndError(outputv,errorv);
00167 }
00168 
00169 void RegressionTreeMulticlassLeaveProb::removeRow(int row, real target, real weight)
00170 {
00171     length_ -= 1;
00172     weights_sum -= weight;
00173     PLASSERT(length_>=0);
00174     PLASSERT(weights_sum>=0);
00175     PLASSERT(length_>0 || weights_sum==0);
00176     multiclass_weights_sum[int(target)] -= weight;
00177 }
00178 
00179 void RegressionTreeMulticlassLeaveProb::getOutputAndError(Vec& output, Vec& error)const
00180 {
00181 #ifdef BOUNDCHECK
00182     if(nb_class<=0)
00183         PLERROR("In RegressionTreeMulticlassLeaveProb::getOutputAndError() -"
00184                 " nb_class must be set.");
00185 #endif
00186     if(length_==0){        
00187         output.fill(MISSING_VALUE);
00188         error.clear();
00189         return;
00190     }
00191     int mc_winer = 0;
00192     real conf = 0;
00193     //index of the max. Is their an optimized version?
00194     output[1] = multiclass_weights_sum[0] / weights_sum;
00195     for (int mc_ind = 1; mc_ind < nb_class; mc_ind++)
00196     {
00197         output[mc_ind+1]=multiclass_weights_sum[mc_ind] / weights_sum;
00198         if (multiclass_weights_sum[mc_ind] > multiclass_weights_sum[mc_winer])
00199             mc_winer = mc_ind;
00200     }
00201     output[0] = mc_winer;
00202     if (missing_leave)
00203     {
00204         error[0] = 0.0;
00205         error[1] = weights_sum;
00206         error[2] = 0.0;
00207     }
00208     else
00209     {
00210         conf = multiclass_weights_sum[mc_winer] / weights_sum;
00211         error[0] = 0.0;
00212         if (objective_function == "l1")
00213         {
00214             for (int mc_ind = 0; mc_ind < nb_class;mc_ind++)
00215             {
00216                 error[0] += abs(mc_winer - mc_ind) 
00217                     * multiclass_weights_sum[mc_ind];
00218             }
00219         }
00220         else
00221         {
00222             for (int mc_ind = 0; mc_ind < nb_class;mc_ind++)
00223             {
00224                 error[0] += pow(mc_winer - mc_ind, 2.) 
00225                     * multiclass_weights_sum[mc_ind];
00226             }
00227         }
00228         error[0] *= loss_function_factor * length_ / weights_sum;
00229         if (error[0] < 1E-10) error[0] = 0.0;
00230         if (error[0] > weights_sum * loss_function_factor)
00231             error[2] = weights_sum * loss_function_factor;
00232         else error[2] = error[0];
00233         error[1] = (1.0 - conf) * length_;
00234     }
00235 }
00236 
00237 TVec<string> RegressionTreeMulticlassLeaveProb::getOutputNames() const
00238 {
00239     TVec<string> ret(nb_class+1);
00240     ret[0]="class_pred";
00241     for (int mc_ind = 0; mc_ind < nb_class;mc_ind++)
00242     {
00243         ret[mc_ind+1]="prob_class_"+tostring(mc_ind);
00244     }
00245     return ret;
00246 }
00247 
00248 void RegressionTreeMulticlassLeaveProb::addLeave(PP<RegressionTreeLeave> leave_){
00249     PP<RegressionTreeMulticlassLeaveProb> leave = (PP<RegressionTreeMulticlassLeaveProb>) leave_;
00250 
00251     if(leave->classname() == classname()){
00252         length_ += leave->length_;
00253         weights_sum += leave->weights_sum;
00254         multiclass_weights_sum += leave->multiclass_weights_sum;
00255     }else
00256         PLERROR("In %s::addLeave the leave to add should have the same class. It have %s.",
00257                 classname().c_str(), leave->classname().c_str());
00258 }
00259 
00260 void RegressionTreeMulticlassLeaveProb::removeLeave(PP<RegressionTreeLeave> leave_){
00261     PP<RegressionTreeMulticlassLeaveProb> leave = (PP<RegressionTreeMulticlassLeaveProb>) leave_;
00262 
00263     if(leave->classname() == classname()){
00264         length_ -= leave->length_;
00265         weights_sum -= leave->weights_sum;
00266         multiclass_weights_sum -= leave->multiclass_weights_sum;
00267     }else
00268         PLERROR("In %s::addLeave the leave to add should have the same class. It have %s.",
00269                 classname().c_str(), leave->classname().c_str());
00270 }
00271 
00272 void RegressionTreeMulticlassLeaveProb::printStats()
00273 {
00274     cout << " l " << length_;
00275     Vec output(2);
00276     Vec error(3);
00277     getOutputAndError(output,error);
00278     cout << " o0 " << output[0];
00279     cout << " o1 " << output[1];
00280     cout << " e0 " << error[0];
00281     cout << " e1 " << error[1];
00282     cout << " ws " << weights_sum;
00283     cout << endl;
00284     cout << " mws " << multiclass_weights_sum << endl;
00285 }
00286 
00287 } // end of namespace PLearn
00288 
00289 
00290 /*
00291   Local Variables:
00292   mode:c++
00293   c-basic-offset:4
00294   c-file-style:"stroustrup"
00295   c-file-offsets:((innamespace . 0)(inline-open . 0))
00296   indent-tabs-mode:nil
00297   fill-column:79
00298   End:
00299 */
00300 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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