PLearn 0.1
Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | Protected Attributes | Private Types | Private Member Functions | Static Private Attributes | Friends
PLearn::RegressionTreeLeave Class Reference

#include <RegressionTreeLeave.h>

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List of all members.

Public Member Functions

 RegressionTreeLeave ()
virtual ~RegressionTreeLeave ()
virtual string classname () const
virtual OptionListgetOptionList () const
virtual OptionMapgetOptionMap () const
virtual RemoteMethodMapgetRemoteMethodMap () const
virtual RegressionTreeLeavedeepCopy (CopiesMap &copies) const
virtual void makeDeepCopyFromShallowCopy (CopiesMap &copies)
 Does the necessary operations to transform a shallow copy (this) into a deep copy by deep-copying all the members that need to be.
virtual void build ()
 Post-constructor.
virtual int outputsize () const
void initLeave (PP< RegressionTreeRegisters > the_train_set, RTR_type_id the_id, bool the_missing_leave=false)
virtual void initStats ()
virtual void addRow (int row)
virtual void addRow (int row, real target, real weight)
virtual void addRow (int row, Vec outputv, Vec errorv)
virtual void addRow (int row, real target, real weight, Vec outputv, Vec errorv)
virtual void removeRow (int row, real target, real weight)
virtual void removeRow (int row, Vec outputv, Vec errorv)
virtual void removeRow (int row, real target, real weight, Vec outputv, Vec errorv)
void registerRow (int row)
int getId () const
int length () const
virtual void getOutputAndError (Vec &output, Vec &error) const
virtual TVec< string > getOutputNames () const
virtual void printStats ()
real getWeightsSum ()
real getTargetsSum ()
virtual bool uniqTarget ()
virtual void addLeave (PP< RegressionTreeLeave > leave)
virtual void removeLeave (PP< RegressionTreeLeave > leave)

Static Public Member Functions

static string _classname_ ()
static OptionList_getOptionList_ ()
static RemoteMethodMap_getRemoteMethodMap_ ()
static Object_new_instance_for_typemap_ ()
static bool _isa_ (const Object *o)
static void _static_initialize_ ()
static const PPathdeclaringFile ()
static void declareOptions (OptionList &ol)
 Declare options (data fields) for the class.

Public Attributes

bool missing_leave
real loss_function_weight
RTR_type_id id
PP< RegressionTreeRegisterstrain_set

Static Public Attributes

static int verbosity = 0
static bool output_confidence_target = false
static StaticInitializer _static_initializer_

Protected Attributes

int length_
real weights_sum
real targets_sum
real weighted_targets_sum
real weighted_squared_targets_sum
real loss_function_factor

Private Types

typedef Object inherited

Private Member Functions

void build_ ()
 Object-specific post-constructor.
void verbose (string msg, int level)

Static Private Attributes

static Vec dummy_vec

Friends

class RegressionTreeNode
class RegressionTreeRegisters

Detailed Description

Definition at line 50 of file RegressionTreeLeave.h.


Member Typedef Documentation


Constructor & Destructor Documentation

PLearn::RegressionTreeLeave::RegressionTreeLeave ( )

Definition at line 58 of file RegressionTreeLeave.cc.

References build().

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PLearn::RegressionTreeLeave::~RegressionTreeLeave ( ) [virtual]

Definition at line 72 of file RegressionTreeLeave.cc.

{
}

Member Function Documentation

string PLearn::RegressionTreeLeave::_classname_ ( ) [static]
OptionList & PLearn::RegressionTreeLeave::_getOptionList_ ( ) [static]
RemoteMethodMap & PLearn::RegressionTreeLeave::_getRemoteMethodMap_ ( ) [static]
bool PLearn::RegressionTreeLeave::_isa_ ( const Object o) [static]
Object * PLearn::RegressionTreeLeave::_new_instance_for_typemap_ ( ) [static]
StaticInitializer RegressionTreeLeave::_static_initializer_ & PLearn::RegressionTreeLeave::_static_initialize_ ( ) [static]
void PLearn::RegressionTreeLeave::addLeave ( PP< RegressionTreeLeave leave) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 274 of file RegressionTreeLeave.cc.

References classname(), length_, PLERROR, targets_sum, weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

                                                               {
    if(leave->classname()=="RegressionTreeLeave" && classname()=="RegressionTreeLeave"){
        length_ += leave->length_;
        weights_sum += leave->weights_sum;
        targets_sum += leave->targets_sum;
        weighted_targets_sum += leave->weighted_targets_sum;
        weighted_squared_targets_sum += leave->weighted_squared_targets_sum;        
    }else
        PLERROR("In RegressionTreeLeave::addLeave subclass %s or %s must reimplement it.",
                classname().c_str(), leave->classname().c_str());
}

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void PLearn::RegressionTreeLeave::addRow ( int  row) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 164 of file RegressionTreeLeave.cc.

References train_set.

Referenced by addRow().

{
    real weight = train_set->getWeight(row);
    real target = train_set->getTarget(row);
    addRow(row, target, weight);
}

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void PLearn::RegressionTreeLeave::addRow ( int  row,
real  target,
real  weight 
) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 154 of file RegressionTreeLeave.cc.

References length_, PLearn::pow(), targets_sum, weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

{
    length_ += 1;
    weights_sum += weight;
    targets_sum += target;
    real squared_target = pow(target, 2);
    weighted_targets_sum += weight * target;
    weighted_squared_targets_sum += weight * squared_target;  
}

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void PLearn::RegressionTreeLeave::addRow ( int  row,
Vec  outputv,
Vec  errorv 
) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 178 of file RegressionTreeLeave.cc.

References addRow(), and getOutputAndError().

{
    addRow(row);
    getOutputAndError(outputv,errorv);
}

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void PLearn::RegressionTreeLeave::addRow ( int  row,
real  target,
real  weight,
Vec  outputv,
Vec  errorv 
) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 171 of file RegressionTreeLeave.cc.

References addRow(), and getOutputAndError().

{
    addRow(row, target, weight);
    getOutputAndError(outputv,errorv);
}

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void PLearn::RegressionTreeLeave::build ( ) [virtual]

Post-constructor.

The normal implementation should call simply inherited::build(), then this class's build_(). This method should be callable again at later times, after modifying some option fields to change the "architecture" of the object.

Reimplemented from PLearn::Object.

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 125 of file RegressionTreeLeave.cc.

References PLearn::Object::build(), and build_().

Referenced by PLearn::RegressionTreeMulticlassLeaveProb::build(), PLearn::RegressionTreeMulticlassLeaveFast::build(), PLearn::RegressionTreeMulticlassLeave::build(), and RegressionTreeLeave().

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void PLearn::RegressionTreeLeave::build_ ( ) [private]

Object-specific post-constructor.

This method should be redefined in subclasses and do the actual building of the object according to previously set option fields. Constructors can just set option fields, and then call build_. This method is NOT virtual, and will typically be called only from three places: a constructor, the public virtual build() method, and possibly the public virtual read method (which calls its parent's read). build_() can assume that its parent's build_() has already been called.

Reimplemented from PLearn::Object.

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 131 of file RegressionTreeLeave.cc.

Referenced by build().

{
}

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string PLearn::RegressionTreeLeave::classname ( ) const [virtual]

Reimplemented from PLearn::Object.

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 52 of file RegressionTreeLeave.cc.

Referenced by addLeave(), removeLeave(), and uniqTarget().

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void PLearn::RegressionTreeLeave::declareOptions ( OptionList ol) [static]

Declare options (data fields) for the class.

Redefine this in subclasses: call declareOption(...) for each option, and then call inherited::declareOptions(options). Please call the inherited method AT THE END to get the options listed in a consistent order (from most recently defined to least recently defined).

  static void MyDerivedClass::declareOptions(OptionList& ol)
  {
      declareOption(ol, "inputsize", &MyObject::inputsize_,
                    OptionBase::buildoption,
                    "The size of the input; it must be provided");
      declareOption(ol, "weights", &MyObject::weights,
                    OptionBase::learntoption,
                    "The learned model weights");
      inherited::declareOptions(ol);
  }
Parameters:
olList of options that is progressively being constructed for the current class.

Reimplemented from PLearn::Object.

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 76 of file RegressionTreeLeave.cc.

References PLearn::OptionBase::buildoption, PLearn::declareOption(), PLearn::Object::declareOptions(), PLearn::declareStaticOption(), dummy_vec, id, PLearn::OptionBase::learntoption, length_, loss_function_factor, loss_function_weight, missing_leave, PLearn::OptionBase::nosave, output_confidence_target, targets_sum, train_set, verbosity, weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

Referenced by PLearn::RegressionTreeMulticlassLeaveProb::declareOptions(), PLearn::RegressionTreeMulticlassLeaveFast::declareOptions(), and PLearn::RegressionTreeMulticlassLeave::declareOptions().

{ 
    declareOption(ol, "id", &RegressionTreeLeave::id, OptionBase::buildoption,
                  "The id of this leave to register the rows of the RegressionTreeRegisters\n");
    declareOption(ol, "missing_leave", &RegressionTreeLeave::missing_leave, OptionBase::buildoption,
                  "The indicator that it is a leave with missing values for the split feature\n");
    declareOption(ol, "loss_function_weight", &RegressionTreeLeave::loss_function_weight, OptionBase::buildoption,
                  "The hyper parameter to balance the error and the confidence factor\n");
    declareStaticOption(ol, "verbosity", &RegressionTreeLeave::verbosity, OptionBase::buildoption,
                  "The desired level of verbosity\n");
    declareOption(ol, "train_set", &RegressionTreeLeave::train_set, 
                  OptionBase::buildoption | OptionBase::nosave,
                  "The train set with the sorted row index matrix and the leave id vector\n");
    declareOption(ol, "length", &RegressionTreeLeave::length_, OptionBase::learntoption,
                  "The number of rows in this leave\n");
    declareOption(ol, "weights_sum", &RegressionTreeLeave::weights_sum, OptionBase::learntoption,
                  "The sum of weights for the samples in this leave\n");
    declareOption(ol, "targets_sum", &RegressionTreeLeave::targets_sum, OptionBase::learntoption,
                  "The sum of targets for the samples in this leave\n");
    declareOption(ol, "weighted_targets_sum", &RegressionTreeLeave::weighted_targets_sum, OptionBase::learntoption,
                  "The sum of weighted targets for the samples in this leave\n");
    declareOption(ol, "weighted_squared_targets_sum", &RegressionTreeLeave::weighted_squared_targets_sum, OptionBase::learntoption,
                  "The sum of squared weighted target values for the samples in this leave\n");
    declareOption(ol, "loss_function_factor", &RegressionTreeLeave::loss_function_factor, OptionBase::learntoption,
                  "2 / pow(loss_function_weight, 2.0).\n");

    declareStaticOption(ol, "output_confidence_target",
                  &RegressionTreeLeave::output_confidence_target,
                  OptionBase::buildoption,
                  "If false the output size is 1 and contain only the predicted"
                  " target. Else output size is 2 and contain also the"
                  " confidence\n");

    declareStaticOption(ol, "output", &RegressionTreeLeave::dummy_vec, OptionBase::nosave,
                  "DEPRECATED");
    declareStaticOption(ol, "error", &RegressionTreeLeave::dummy_vec, OptionBase::nosave,
                  "DEPRECATED");

    inherited::declareOptions(ol);
}

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static const PPath& PLearn::RegressionTreeLeave::declaringFile ( ) [inline, static]
RegressionTreeLeave * PLearn::RegressionTreeLeave::deepCopy ( CopiesMap copies) const [virtual]
int PLearn::RegressionTreeLeave::getId ( ) const [inline]

Definition at line 102 of file RegressionTreeLeave.h.

{return id;}
OptionList & PLearn::RegressionTreeLeave::getOptionList ( ) const [virtual]
OptionMap & PLearn::RegressionTreeLeave::getOptionMap ( ) const [virtual]
void PLearn::RegressionTreeLeave::getOutputAndError ( Vec output,
Vec error 
) const [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 205 of file RegressionTreeLeave.cc.

References PLearn::TVec< T >::clear(), length_, loss_function_factor, missing_leave, MISSING_VALUE, output_confidence_target, RTR_HAVE_MISSING, weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

Referenced by addRow(), printStats(), and removeRow().

{
    real conf = 0;
    if(length_>0){
        output[0] = weighted_targets_sum / weights_sum;
        if (!RTR_HAVE_MISSING || missing_leave != true)
        {
            //we put the most frequent case first as an optimisation
            conf = 1.0;
            error[0] = ((weights_sum * output[0] * output[0]) - 
                        (2.0 * weighted_targets_sum * output[0]) + weighted_squared_targets_sum)
                * loss_function_factor;
            if (error[0] < 1E-10) {error[0] = 0.0;} //PLWARNING("E[0] <1e-10: %f",error[0]);}
            error[1] = 0.0;
            real weights_sum_factor  = weights_sum * loss_function_factor;
            if (error[0] > weights_sum_factor) error[2] = weights_sum_factor;
            else error[2] = error[0];
        }
        else
        {
            error[0] = 0.0;
            error[1] = weights_sum;
            error[2] = 0.0;
        }
    }else{
        output[0] = MISSING_VALUE;
        error.clear();
    }
    if(output_confidence_target) output[1] = conf;
}

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TVec< string > PLearn::RegressionTreeLeave::getOutputNames ( ) const [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 236 of file RegressionTreeLeave.cc.

References PLearn::TVec< T >::append(), and output_confidence_target.

{
    TVec<string> ret;
    ret.append("val_pred");
    if(output_confidence_target)
        ret.append("confidence");
    return ret;
}

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RemoteMethodMap & PLearn::RegressionTreeLeave::getRemoteMethodMap ( ) const [virtual]
real PLearn::RegressionTreeLeave::getTargetsSum ( ) [inline]

Definition at line 108 of file RegressionTreeLeave.h.

{return targets_sum;}
real PLearn::RegressionTreeLeave::getWeightsSum ( ) [inline]

Definition at line 107 of file RegressionTreeLeave.h.

{return weights_sum;}
void PLearn::RegressionTreeLeave::initLeave ( PP< RegressionTreeRegisters the_train_set,
RTR_type_id  the_id,
bool  the_missing_leave = false 
)

Definition at line 135 of file RegressionTreeLeave.cc.

References missing_leave, and train_set.

{
    train_set = the_train_set;
    id = the_id;
    missing_leave = the_missing_leave;
}
void PLearn::RegressionTreeLeave::initStats ( ) [virtual]
int PLearn::RegressionTreeLeave::length ( ) const [inline]

Definition at line 103 of file RegressionTreeLeave.h.

{return length_;}
void PLearn::RegressionTreeLeave::makeDeepCopyFromShallowCopy ( CopiesMap copies) [virtual]

Does the necessary operations to transform a shallow copy (this) into a deep copy by deep-copying all the members that need to be.

This needs to be overridden by every class that adds "complex" data members to the class, such as Vec, Mat, PP<Something>, etc. Typical implementation:

  void CLASS_OF_THIS::makeDeepCopyFromShallowCopy(CopiesMap& copies)
  {
      inherited::makeDeepCopyFromShallowCopy(copies);
      deepCopyField(complex_data_member1, copies);
      deepCopyField(complex_data_member2, copies);
      ...
  }
Parameters:
copiesA map used by the deep-copy mechanism to keep track of already-copied objects.

Reimplemented from PLearn::Object.

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 117 of file RegressionTreeLeave.cc.

References PLearn::Object::makeDeepCopyFromShallowCopy().

Referenced by PLearn::RegressionTreeMulticlassLeaveProb::makeDeepCopyFromShallowCopy(), PLearn::RegressionTreeMulticlassLeaveFast::makeDeepCopyFromShallowCopy(), and PLearn::RegressionTreeMulticlassLeave::makeDeepCopyFromShallowCopy().

{
    inherited::makeDeepCopyFromShallowCopy(copies);
    //we don't deep copy it as we don't modify it 
    //and this is a link to the RegressionTree train_set
//    deepCopyField(train_set, copies);
}

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virtual int PLearn::RegressionTreeLeave::outputsize ( ) const [inline, virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 89 of file RegressionTreeLeave.h.

    {return output_confidence_target?2:1;}
void PLearn::RegressionTreeLeave::printStats ( ) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 245 of file RegressionTreeLeave.cc.

References PLearn::endl(), getOutputAndError(), length_, output_confidence_target, PLearn::sqrt(), targets_sum, weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

{
    cout << " l " << length_;
    Vec output(2);
    Vec error(3);
    getOutputAndError(output,error);
    cout << " o0 " << output[0];
    if(output_confidence_target)
        cout << " o1 " << output[1];
    cout << " e0 " << error[0];
    cout << " e1 " << error[1];
    cout << " ws " << weights_sum;
    cout << " ts " << targets_sum;
    cout << " wts " << weighted_targets_sum;
    cout << " wsts " << weighted_squared_targets_sum;
    cout << " wts/ws " <<weighted_targets_sum/weights_sum;
    cout << " wsts/ws "<<weighted_squared_targets_sum/weights_sum;
    cout << " sqrt(wsts/ws) "<<sqrt(weighted_squared_targets_sum/weights_sum);
    cout << endl;
}

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void PLearn::RegressionTreeLeave::registerRow ( int  row) [inline]

Definition at line 100 of file RegressionTreeLeave.h.

    {train_set->registerLeave(id, row);}
void PLearn::RegressionTreeLeave::removeLeave ( PP< RegressionTreeLeave leave) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 286 of file RegressionTreeLeave.cc.

References classname(), length_, PLERROR, targets_sum, weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

                                                                  {
    if(leave->classname()=="RegressionTreeLeave" && classname()=="RegressionTreeLeave"){
        length_ -= leave->length_;
        weights_sum -= leave->weights_sum;
        targets_sum -= leave->targets_sum;
        weighted_targets_sum -= leave->weighted_targets_sum;
        weighted_squared_targets_sum -= leave->weighted_squared_targets_sum;
    }else
        PLERROR("In RegressionTreeLeave::removeLeave subclass %s or %s must reimplement it.",
                classname().c_str(), leave->classname().c_str());
}

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void PLearn::RegressionTreeLeave::removeRow ( int  row,
Vec  outputv,
Vec  errorv 
) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 183 of file RegressionTreeLeave.cc.

References removeRow(), and train_set.

{
    real weight = train_set->getWeight(row);
    real target = train_set->getTarget(row);
    removeRow(row, target, weight, output, error);
}

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void PLearn::RegressionTreeLeave::removeRow ( int  row,
real  target,
real  weight 
) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 189 of file RegressionTreeLeave.cc.

References length_, PLearn::pow(), targets_sum, weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

Referenced by removeRow().

{
    length_ -= 1;
    weights_sum -= weight;
    targets_sum -= target;
    real squared_target = pow(target, 2);
    weighted_targets_sum -= weight * target;
    weighted_squared_targets_sum -= weight * squared_target; 
}

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void PLearn::RegressionTreeLeave::removeRow ( int  row,
real  target,
real  weight,
Vec  outputv,
Vec  errorv 
) [virtual]

Reimplemented in PLearn::RegressionTreeMulticlassLeave, PLearn::RegressionTreeMulticlassLeaveFast, and PLearn::RegressionTreeMulticlassLeaveProb.

Definition at line 198 of file RegressionTreeLeave.cc.

References getOutputAndError(), and removeRow().

{
    removeRow(row,target,weight);
    getOutputAndError(outputv, errorv);
}

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bool PLearn::RegressionTreeLeave::uniqTarget ( ) [virtual]

Definition at line 265 of file RegressionTreeLeave.cc.

References classname(), PLearn::fast_is_equal(), PLERROR, PLearn::sqrt(), weighted_squared_targets_sum, weighted_targets_sum, and weights_sum.

                                    {
    if(classname()=="RegressionTreeLeave"){
        real wts_w = weighted_targets_sum/weights_sum;
        real wsts_w= sqrt(weighted_squared_targets_sum/weights_sum);
        return fast_is_equal(wts_w,wsts_w);
    }else
        PLERROR("In RegressionTreeLeave::uniqTarget subclass must reimplement it.");
}

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void PLearn::RegressionTreeLeave::verbose ( string  msg,
int  level 
) [private]

Definition at line 299 of file RegressionTreeLeave.cc.

References PLearn::endl(), and verbosity.

{
    if (verbosity >= the_level)
        cout << the_msg << endl;
}

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Friends And Related Function Documentation

friend class RegressionTreeNode [friend]

Definition at line 53 of file RegressionTreeLeave.h.

friend class RegressionTreeRegisters [friend]

Definition at line 54 of file RegressionTreeLeave.h.


Member Data Documentation

Definition at line 55 of file RegressionTreeLeave.h.

Referenced by declareOptions().

Definition at line 65 of file RegressionTreeLeave.h.

Referenced by declareOptions().

Definition at line 60 of file RegressionTreeLeave.h.

Referenced by declareOptions(), and verbose().


The documentation for this class was generated from the following files:
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