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vigra/random_forest/rf_preprocessing.hxx |

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    00002 /*                                                                      */
    00003 /*        Copyright 2008-2009 by  Ullrich Koethe and Rahul Nair         */
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    00035
    00036 #ifndef VIGRA_RF_PREPROCESSING_HXX
    00037 #define VIGRA_RF_PREPROCESSING_HXX
    00038
    00039 #include <limits>
    00040 #include "rf_common.hxx"
    00041
    00042 namespace vigra
    00043 {
    00044
    00045 /** Class used while preprocessing  (currently used only during learn)
    00046  *
    00047  * This class is internally used by the Random Forest learn function.
    00048  * Different split functors may need to process the data in different manners
    00049  * (i.e., regression labels that should not be touched and classification
    00050  * labels that must be converted into a integral format)
    00051  *
    00052  * This Class only exists in specialized versions, where the Tag class is
    00053  * fixed.
    00054  *
    00055  * The Tag class is determined by Splitfunctor::Preprocessor_t . Currently
    00056  * it can either be ClassificationTag or RegressionTag.  look At the
    00057  * RegressionTag specialisation for the basic interface if you ever happen
    00058  * to care.... - or need some sort of vague new preprocessor.
    00059  * new preprocessor ( Soft labels or whatever)
    00060  */
    00061 template<class Tag, class LabelType, class T1, class C1, class T2, class C2>
    00062 class Processor;
    00063
    00064 namespace detail
    00065 {
    00066
    00067     /* Common helper function used in all Processors.
    00068      * This function analyses the options struct and calculates the real
    00069      * values needed for the current problem (data)
    00070      */
    00071     template<class T>
    00072     void fill_external_parameters(RandomForestOptions const  & options,
    00073                                   ProblemSpec<T> & ext_param)
    00074     {
    00075         // set correct value for mtry
    00076         switch(options.mtry_switch_)
    00077         {
    00078             case RF_SQRT:
    00079                 ext_param.actual_mtry_ =
    00080                     int(std::floor(
    00081                             std::sqrt(double(ext_param.column_count_))
    00082                             + 0.5));
    00083                 break;
    00084             case RF_LOG:
    00085                 // this is in Breimans original paper
    00086                 ext_param.actual_mtry_ =
    00087                     int(1+(std::log(double(ext_param.column_count_))
    00088                            /std::log(2.0)));
    00089                 break;
    00090             case RF_FUNCTION:
    00091                 ext_param.actual_mtry_ =
    00092                     options.mtry_func_(ext_param.column_count_);
    00093                 break;
    00094             case RF_ALL:
    00095                 ext_param.actual_mtry_ = ext_param.column_count_;
    00096                 break;
    00097             default:
    00098                 ext_param.actual_mtry_ =
    00099                     options.mtry_;
    00100         }
    00101         // set correct value for msample
    00102         switch(options.training_set_calc_switch_)
    00103         {
    00104             case RF_CONST:
    00105                 ext_param.actual_msample_ =
    00106                     options.training_set_size_;
    00107                 break;
    00108             case RF_PROPORTIONAL:
    00109                 ext_param.actual_msample_ =
    00110                     (int)std::ceil(  options.training_set_proportion_ *
    00111                                      ext_param.row_count_);
    00112                     break;
    00113             case RF_FUNCTION:
    00114                 ext_param.actual_msample_ =
    00115                     options.training_set_func_(ext_param.row_count_);
    00116                 break;
    00117             default:
    00118                 vigra_precondition(1!= 1, "unexpected error");
    00119
    00120         }
    00121
    00122     }
    00123
    00124     /* Returns true if MultiArray contains NaNs
    00125      */
    00126     template<unsigned int N, class T, class C>
    00127     bool contains_nan(MultiArrayView<N, T, C> const & in)
    00128     {
    00129         for(int ii = 0; ii < in.size(); ++ii)
    00130             if(in[ii] != in[ii])
    00131                 return true;
    00132         return false;
    00133     }
    00134
    00135     /* Returns true if MultiArray contains Infs
    00136      */
    00137     template<unsigned int N, class T, class C>
    00138     bool contains_inf(MultiArrayView<N, T, C> const & in)
    00139     {
    00140          if(!std::numeric_limits<T>::has_infinity)
    00141              return false;
    00142          for(int ii = 0; ii < in.size(); ++ii)
    00143             if(in[ii] == std::numeric_limits<T>::infinity())
    00144                 return true;
    00145          return false;
    00146     }
    00147 } // namespace detail
    00148
    00149
    00150
    00151 /** Preprocessor used during Classification
    00152  *
    00153  * This class converts the labels int Integral labels which are used by the
    00154  * standard split functor to address memory in the node objects.
    00155  */
    00156 template<class LabelType, class T1, class C1, class T2, class C2>
    00157 class Processor<ClassificationTag, LabelType, T1, C1, T2, C2>
    00158 {
    00159     public:
    00160     typedef Int32 LabelInt;
    00161     typedef MultiArrayView<2, T1, C1> Feature_t;
    00162     typedef MultiArray<2, T1> FeatureWithMemory_t;
    00163     typedef MultiArrayView<2,LabelInt> Label_t;
    00164     MultiArrayView<2, T1, C1>const &    features_;
    00165     MultiArray<2, LabelInt>             intLabels_;
    00166     MultiArrayView<2, LabelInt>         strata_;
    00167
    00168     template<class T>
    00169     Processor(MultiArrayView<2, T1, C1>const & features,
    00170               MultiArrayView<2, T2, C2>const & response,
    00171               RandomForestOptions &options,
    00172               ProblemSpec<T> &ext_param)
    00173     :
    00174         features_( features) // do not touch the features.
    00175     {
    00176         vigra_precondition(!detail::contains_nan(features), "Processor(): Feature Matrix "
    00177                                                            "Contains NaNs");
    00178         vigra_precondition(!detail::contains_nan(response), "Processor(): Response "
    00179                                                            "Contains NaNs");
    00180         vigra_precondition(!detail::contains_inf(features), "Processor(): Feature Matrix "
    00181                                                            "Contains inf");
    00182         vigra_precondition(!detail::contains_inf(response), "Processor(): Response "
    00183                                                            "Contains inf");
    00184         // set some of the problem specific parameters
    00185         ext_param.column_count_  = features.shape(1);
    00186         ext_param.row_count_     = features.shape(0);
    00187         ext_param.problem_type_  = CLASSIFICATION;
    00188         ext_param.used_          = true;
    00189         intLabels_.reshape(response.shape());
    00190
    00191         //get the class labels
    00192         if(ext_param.class_count_ == 0)
    00193         {
    00194             // fill up a map with the current labels and then create the
    00195             // integral labels.
    00196             std::set<T2>                    labelToInt;
    00197             for(MultiArrayIndex k = 0; k < features.shape(0); ++k)
    00198                 labelToInt.insert(response(k,0));
    00199             std::vector<T2> tmp_(labelToInt.begin(), labelToInt.end());
    00200             ext_param.classes_(tmp_.begin(), tmp_.end());
    00201         }
    00202         for(MultiArrayIndex k = 0; k < features.shape(0); ++k)
    00203         {
    00204             if(std::find(ext_param.classes.begin(), ext_param.classes.end(), response(k,0)) == ext_param.classes.end())
    00205             {
    00206                 throw std::runtime_error("unknown label type");
    00207             }
    00208             else
    00209                 intLabels_(k, 0) = std::find(ext_param.classes.begin(), ext_param.classes.end(), response(k,0))
    00210                                     - ext_param.classes.begin();
    00211         }
    00212         // set class weights
    00213         if(ext_param.class_weights_.size() == 0)
    00214         {
    00215             ArrayVector<T2>
    00216                 tmp((std::size_t)ext_param.class_count_,
    00217                     NumericTraits<T2>::one());
    00218             ext_param.class_weights(tmp.begin(), tmp.end());
    00219         }
    00220
    00221         // set mtry and msample
    00222         detail::fill_external_parameters(options, ext_param);
    00223
    00224         // set strata
    00225         strata_ = intLabels_;
    00226
    00227     }
    00228
    00229     /** Access the processed features
    00230      */
    00231     MultiArrayView<2, T1, C1>const & features()
    00232     {
    00233         return features_;
    00234     }
    00235
    00236     /** Access processed labels
    00237      */
    00238     MultiArrayView<2, LabelInt>& response()
    00239     {
    00240         return intLabels_;
    00241     }
    00242
    00243     /** Access processed strata
    00244      */
    00245     ArrayVectorView < LabelInt>  strata()
    00246     {
    00247         return ArrayVectorView<LabelInt>(intLabels_.size(), intLabels_.data());
    00248     }
    00249
    00250     /** Access strata fraction sized - not used currently
    00251      */
    00252     ArrayVectorView< double> strata_prob()
    00253     {
    00254         return ArrayVectorView< double>();
    00255     }
    00256 };
    00257
    00258
    00259
    00260 /** Regression Preprocessor - This basically does not do anything with the
    00261  * data.
    00262  */
    00263 template<class LabelType, class T1, class C1, class T2, class C2>
    00264 class Processor<RegressionTag,LabelType, T1, C1, T2, C2>
    00265 {
    00266 public:
    00267     // only views are created - no data copied.
    00268     MultiArrayView<2, T1, C1>   features_;
    00269     MultiArrayView<2, T2, C2>   response_;
    00270     RandomForestOptions const & options_;
    00271     ProblemSpec<LabelType> const &
    00272                                 ext_param_;
    00273     // will only be filled if needed
    00274     MultiArray<2, int>      strata_;
    00275     bool strata_filled;
    00276
    00277     // copy the views.
    00278     template<class T>
    00279     Processor(  MultiArrayView<2, T1, C1>   features,
    00280                 MultiArrayView<2, T2, C2>   response,
    00281                 RandomForestOptions const & options,
    00282                 ProblemSpec<T>& ext_param)
    00283     :
    00284         features_(features),
    00285         response_(response),
    00286         options_(options),
    00287         ext_param_(ext_param)
    00288     {
    00289         // set some of the problem specific parameters
    00290         ext_param.column_count_  = features.shape(1);
    00291         ext_param.row_count_     = features.shape(0);
    00292         ext_param.problem_type_  = REGRESSION;
    00293         ext_param.used_          = true;
    00294         detail::fill_external_parameters(options, ext_param);
    00295         vigra_precondition(!detail::contains_nan(features), "Processor(): Feature Matrix "
    00296                                                            "Contains NaNs");
    00297         vigra_precondition(!detail::contains_nan(response), "Processor(): Response "
    00298                                                            "Contains NaNs");
    00299         vigra_precondition(!detail::contains_inf(features), "Processor(): Feature Matrix "
    00300                                                            "Contains inf");
    00301         vigra_precondition(!detail::contains_inf(response), "Processor(): Response "
    00302                                                            "Contains inf");
    00303         strata_ = MultiArray<2, int> (MultiArrayShape<2>::type(response_.shape(0), 1));
    00304         ext_param.response_size_ = response.shape(1);
    00305         ext_param.class_count_ = response_.shape(1);
    00306         std::vector<T2> tmp_(ext_param.class_count_, 0);
    00307             ext_param.classes_(tmp_.begin(), tmp_.end());
    00308     }
    00309
    00310     /** access preprocessed features
    00311      */
    00312     MultiArrayView<2, T1, C1> & features()
    00313     {
    00314         return features_;
    00315     }
    00316
    00317     /** access preprocessed response
    00318      */
    00319     MultiArrayView<2, T2, C2> & response()
    00320     {
    00321         return response_;
    00322     }
    00323
    00324     /** access strata - this is not used currently
    00325      */
    00326     MultiArray<2, int> & strata()
    00327     {
    00328         return strata_;
    00329     }
    00330 };
    00331 }
    00332 #endif //VIGRA_RF_PREPROCESSING_HXX
    00333
    00334
    00335

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)