/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// 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 the Intel Corporation or contributors 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.
//
//M*/
#include "_ml.h"
CvForestTree::CvForestTree()
{
forest = NULL;
}
CvForestTree::~CvForestTree()
{
clear();
}
bool CvForestTree::train( CvDTreeTrainData* _data,
const CvMat* _subsample_idx,
CvRTrees* _forest )
{
bool result = false;
CV_FUNCNAME( "CvForestTree::train" );
__BEGIN__;
clear();
forest = _forest;
data = _data;
data->shared = true;
CV_CALL(result = do_train(_subsample_idx));
__END__;
return result;
}
bool
CvForestTree::train( const CvMat*, int, const CvMat*, const CvMat*,
const CvMat*, const CvMat*, const CvMat*, CvDTreeParams )
{
assert(0);
return false;
}
bool
CvForestTree::train( CvDTreeTrainData*, const CvMat* )
{
assert(0);
return false;
}
CvDTreeSplit* CvForestTree::find_best_split( CvDTreeNode* node )
{
int vi;
CvDTreeSplit *best_split = 0, *split = 0, *t;
CV_FUNCNAME("CvForestTree::find_best_split");
__BEGIN__;
CvMat* active_var_mask = 0;
if( forest )
{
int var_count;
CvRNG* rng = forest->get_rng();
active_var_mask = forest->get_active_var_mask();
var_count = active_var_mask->cols;
CV_ASSERT( var_count == data->var_count );
for( vi = 0; vi < var_count; vi++ )
{
uchar temp;
int i1 = cvRandInt(rng) % var_count;
int i2 = cvRandInt(rng) % var_count;
CV_SWAP( active_var_mask->data.ptr[i1],
active_var_mask->data.ptr[i2], temp );
}
}
for( vi = 0; vi < data->var_count; vi++ )
{
int ci = data->var_type->data.i[vi];
if( node->num_valid[vi] <= 1
|| (active_var_mask && !active_var_mask->data.ptr[vi]) )
continue;
if( data->is_classifier )
{
if( ci >= 0 )
split = find_split_cat_class( node, vi );
else
split = find_split_ord_class( node, vi );
}
else
{
if( ci >= 0 )
split = find_split_cat_reg( node, vi );
else
split = find_split_ord_reg( node, vi );
}
if( split )
{
if( !best_split || best_split->quality < split->quality )
CV_SWAP( best_split, split, t );
if( split )
cvSetRemoveByPtr( data->split_heap, split );
}
}
__END__;
return best_split;
}
void CvForestTree::read( CvFileStorage* fs, CvFileNode* fnode, CvRTrees* _forest, CvDTreeTrainData* _data )
{
CvDTree::read( fs, fnode, _data );
forest = _forest;
}
void CvForestTree::read( CvFileStorage*, CvFileNode* )
{
assert(0);
}
void CvForestTree::read( CvFileStorage* _fs, CvFileNode* _node,
CvDTreeTrainData* _data )
{
CvDTree::read( _fs, _node, _data );
}
//////////////////////////////////////////////////////////////////////////////////////////
// Random trees //
//////////////////////////////////////////////////////////////////////////////////////////
CvRTrees::CvRTrees()
{
nclasses = 0;
oob_error = 0;
ntrees = 0;
trees = NULL;
data = NULL;
active_var_mask = NULL;
var_importance = NULL;
rng = cvRNG(0xffffffff);
default_model_name = "my_random_trees";
}
void CvRTrees::clear()
{
int k;
for( k = 0; k < ntrees; k++ )
delete trees[k];
cvFree( &trees );
delete data;
data = 0;
cvReleaseMat( &active_var_mask );
cvReleaseMat( &var_importance );
ntrees = 0;
}
CvRTrees::~CvRTrees()
{
clear();
}
CvMat* CvRTrees::get_active_var_mask()
{
return active_var_mask;
}
CvRNG* CvRTrees::get_rng()
{
return &rng;
}
bool CvRTrees::train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx,
const CvMat* _sample_idx, const CvMat* _var_type,
const CvMat* _missing_mask, CvRTParams params )
{
bool result = false;
CV_FUNCNAME("CvRTrees::train");
__BEGIN__;
int var_count = 0;
clear();
CvDTreeParams tree_params( params.max_depth, params.min_sample_count,
params.regression_accuracy, params.use_surrogates, params.max_categories,
params.cv_folds, params.use_1se_rule, false, params.priors );
data = new CvDTreeTrainData();
CV_CALL(data->set_data( _train_data, _tflag, _responses, _var_idx,
_sample_idx, _var_type, _missing_mask, tree_params, true));
var_count = data->var_count;
if( params.nactive_vars > var_count )
params.nactive_vars = var_count;
else if( params.nactive_vars == 0 )
params.nactive_vars = (int)sqrt((double)var_count);
else if( params.nactive_vars < 0 )
CV_ERROR( CV_StsBadArg, "<nactive_vars> must be non-negative" );
params.term_crit = cvCheckTermCriteria( params.term_crit, 0.1, 1000 );
// Create mask of active variables at the tree nodes
CV_CALL(active_var_mask = cvCreateMat( 1, var_count, CV_8UC1 ));
if( params.calc_var_importance )
{
CV_CALL(var_importance = cvCreateMat( 1, var_count, CV_32FC1 ));
cvZero(var_importance);
}
{ // initialize active variables mask
CvMat submask1, submask2;
cvGetCols( active_var_mask, &submask1, 0, params.nactive_vars );
cvGetCols( active_var_mask, &submask2, params.nactive_vars, var_count );
cvSet( &submask1, cvScalar(1) );
cvZero( &submask2 );
}
CV_CALL(result = grow_forest( params.term_crit ));
result = true;
__END__;
return result;
}
bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
{
bool result = false;
CvMat* sample_idx_mask_for_tree = 0;
CvMat* sample_idx_for_tree = 0;
CvMat* oob_sample_votes = 0;
CvMat* oob_responses = 0;
float* oob_samples_perm_ptr= 0;
float* samples_ptr = 0;
uchar* missing_ptr = 0;
float* true_resp_ptr = 0;
CV_FUNCNAME("CvRTrees::grow_forest");
__BEGIN__;
const int max_ntrees = term_crit.max_iter;
const double max_oob_err = term_crit.epsilon;
const int dims = data->var_count;
float maximal_response = 0;
// oob_predictions_sum[i] = sum of predicted values for the i-th sample
// oob_num_of_predictions[i] = number of summands
// (number of predictions for the i-th sample)
// initialize these variable to avoid warning C4701
CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 );
CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 );
nsamples = data->sample_count;
nclasses = data->get_num_classes();
trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*max_ntrees );
memset( trees, 0, sizeof(trees[0])*max_ntrees );
if( data->is_classifier )
{
CV_CALL(oob_sample_votes = cvCreateMat( nsamples, nclasses, CV_32SC1 ));
cvZero(oob_sample_votes);
}
else
{
// oob_responses[0,i] = oob_predictions_sum[i]
// = sum of predicted values for the i-th sample
// oob_responses[1,i] = oob_num_of_predictions[i]
// = number of summands (number of predictions for the i-th sample)
CV_CALL(oob_responses = cvCreateMat( 2, nsamples, CV_32FC1 ));
cvZero(oob_responses);
cvGetRow( oob_responses, &oob_predictions_sum, 0 );
cvGetRow( oob_responses, &oob_num_of_predictions, 1 );
}
CV_CALL(sample_idx_mask_for_tree = cvCreateMat( 1, nsamples, CV_8UC1 ));
CV_CALL(sample_idx_for_tree = cvCreateMat( 1, nsamples, CV_32SC1 ));
CV_CALL(oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
CV_CALL(samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
CV_CALL(missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims ));
CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ));
CV_CALL(data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr ));
{
double minval, maxval;
CvMat responses = cvMat(1, nsamples, CV_32FC1, true_resp_ptr);
cvMinMaxLoc( &responses, &minval, &maxval );
maximal_response = (float)MAX( MAX( fabs(minval), fabs(maxval) ), 0 );
}
ntrees = 0;
while( ntrees < max_ntrees )
{
int i, oob_samples_count = 0;
double ncorrect_responses = 0; // used for estimation of variable importance
CvMat sample, missing;
CvForestTree* tree = 0;
cvZero( sample_idx_mask_for_tree );
for( i = 0; i < nsamples; i++ ) //form sample for creation one tree
{
int idx = cvRandInt( &rng ) % nsamples;
sample_idx_for_tree->data.i[i] = idx;
sample_idx_mask_for_tree->data.ptr[idx] = 0xFF;
}
trees[ntrees] = new CvForestTree();
tree = trees[ntrees];
CV_CALL(tree->train( data, sample_idx_for_tree, this ));
// form array of OOB samples indices and get these samples
sample = cvMat( 1, dims, CV_32FC1, samples_ptr );
missing = cvMat( 1, dims, CV_8UC1, missing_ptr );
oob_error = 0;
for( i = 0; i < nsamples; i++,
sample.data.fl += dims, missing.data.ptr += dims )
{
CvDTreeNode* predicted_node = 0;
// check if the sample is OOB
if( sample_idx_mask_for_tree->data.ptr[i] )
continue;
// predict oob samples
if( !predicted_node )
CV_CALL(predicted_node = tree->predict(&sample, &missing, true));
if( !data->is_classifier ) //regression
{
double avg_resp, resp = predicted_node->value;
oob_predictions_sum.data.fl[i] += (float)resp;
oob_num_of_predictions.data.fl[i] += 1;
// compute oob error
avg_resp = oob_predictions_sum.data.fl[i]/oob_num_of_predictions.data.fl[i];
avg_resp -= true_resp_ptr[i];
oob_error += avg_resp*avg_resp;
resp = (resp - true_resp_ptr[i])/maximal_response;
ncorrect_responses += exp( -resp*resp );
}
else //classification
{
double prdct_resp;
CvPoint max_loc;
CvMat votes;
cvGetRow(oob_sample_votes, &votes, i);
votes.data.i[predicted_node->class_idx]++;
// compute oob error
cvMinMaxLoc( &votes, 0, 0, 0, &max_loc );
prdct_resp = data->cat_map->data.i[max_loc.x];
oob_error += (fabs(prdct_resp - true_resp_ptr[i]) < FLT_EPSILON) ? 0 : 1;
ncorrect_responses += cvRound(predicted_node->value - true_resp_ptr[i]) == 0;
}
oob_samples_count++;
}
if( oob_samples_count > 0 )
oob_error /= (double)oob_samples_count;
// estimate variable importance
if( var_importance && oob_samples_count > 0 )
{
int m;
memcpy( oob_samples_perm_ptr, samples_ptr, dims*nsamples*sizeof(float));
for( m = 0; m < dims; m++ )
{
double ncorrect_responses_permuted = 0;
// randomly permute values of the m-th variable in the oob samples
float* mth_var_ptr = oob_samples_perm_ptr + m;
for( i = 0; i < nsamples; i++ )
{
int i1, i2;
float temp;
if( sample_idx_mask_for_tree->data.ptr[i] ) //the sample is not OOB
continue;
i1 = cvRandInt( &rng ) % nsamples;
i2 = cvRandInt( &rng ) % nsamples;
CV_SWAP( mth_var_ptr[i1*dims], mth_var_ptr[i2*dims], temp );
// turn values of (m-1)-th variable, that were permuted
// at the previous iteration, untouched
if( m > 1 )
oob_samples_perm_ptr[i*dims+m-1] = samples_ptr[i*dims+m-1];
}
// predict "permuted" cases and calculate the number of votes for the
// correct class in the variable-m-permuted oob data
sample = cvMat( 1, dims, CV_32FC1, oob_samples_perm_ptr );
missing = cvMat( 1, dims, CV_8UC1, missing_ptr );
for( i = 0; i < nsamples; i++,
sample.data.fl += dims, missing.data.ptr += dims )
{
double predct_resp, true_resp;
if( sample_idx_mask_for_tree->data.ptr[i] ) //the sample is not OOB
continue;
predct_resp = tree->predict(&sample, &missing, true)->value;
true_resp = true_resp_ptr[i];
if( data->is_classifier )
ncorrect_responses_permuted += cvRound(true_resp - predct_resp) == 0;
else
{
true_resp = (true_resp - predct_resp)/maximal_response;
ncorrect_responses_permuted += exp( -true_resp*true_resp );
}
}
var_importance->data.fl[m] += (float)(ncorrect_responses
- ncorrect_responses_permuted);
}
}
ntrees++;
if( term_crit.type != CV_TERMCRIT_ITER && oob_error < max_oob_err )
break;
}
if( var_importance )
CV_CALL(cvConvertScale( var_importance, var_importance, 1./ntrees/nsamples ));
result = true;
__END__;
cvReleaseMat( &sample_idx_mask_for_tree );
cvReleaseMat( &sample_idx_for_tree );
cvReleaseMat( &oob_sample_votes );
cvReleaseMat( &oob_responses );
cvFree( &oob_samples_perm_ptr );
cvFree( &samples_ptr );
cvFree( &missing_ptr );
cvFree( &true_resp_ptr );
return result;
}
const CvMat* CvRTrees::get_var_importance()
{
return var_importance;
}
float CvRTrees::get_proximity( const CvMat* sample1, const CvMat* sample2,
const CvMat* missing1, const CvMat* missing2 ) const
{
float result = 0;
CV_FUNCNAME( "CvRTrees::get_proximity" );
__BEGIN__;
int i;
for( i = 0; i < ntrees; i++ )
result += trees[i]->predict( sample1, missing1 ) ==
trees[i]->predict( sample2, missing2 ) ? 1 : 0;
result = result/(float)ntrees;
__END__;
return result;
}
float CvRTrees::predict( const CvMat* sample, const CvMat* missing ) const
{
double result = -1;
CV_FUNCNAME("CvRTrees::predict");
__BEGIN__;
int k;
if( nclasses > 0 ) //classification
{
int max_nvotes = 0;
int* votes = (int*)alloca( sizeof(int)*nclasses );
memset( votes, 0, sizeof(*votes)*nclasses );
for( k = 0; k < ntrees; k++ )
{
CvDTreeNode* predicted_node = trees[k]->predict( sample, missing );
int nvotes;
int class_idx = predicted_node->class_idx;
CV_ASSERT( 0 <= class_idx && class_idx < nclasses );
nvotes = ++votes[class_idx];
if( nvotes > max_nvotes )
{
max_nvotes = nvotes;
result = predicted_node->value;
}
}
}
else // regression
{
result = 0;
for( k = 0; k < ntrees; k++ )
result += trees[k]->predict( sample, missing )->value;
result /= (double)ntrees;
}
__END__;
return (float)result;
}
void CvRTrees::write( CvFileStorage* fs, const char* name )
{
CV_FUNCNAME( "CvRTrees::write" );
__BEGIN__;
int k;
if( ntrees < 1 || !trees || nsamples < 1 )
CV_ERROR( CV_StsBadArg, "Invalid CvRTrees object" );
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_RTREES );
cvWriteInt( fs, "nclasses", nclasses );
cvWriteInt( fs, "nsamples", nsamples );
cvWriteInt( fs, "nactive_vars", (int)cvSum(active_var_mask).val[0] );
cvWriteReal( fs, "oob_error", oob_error );
if( var_importance )
cvWrite( fs, "var_importance", var_importance );
cvWriteInt( fs, "ntrees", ntrees );
CV_CALL(data->write_params( fs ));
cvStartWriteStruct( fs, "trees", CV_NODE_SEQ );
for( k = 0; k < ntrees; k++ )
{
cvStartWriteStruct( fs, 0, CV_NODE_MAP );
CV_CALL( trees[k]->write( fs ));
cvEndWriteStruct( fs );
}
cvEndWriteStruct( fs ); //trees
cvEndWriteStruct( fs ); //CV_TYPE_NAME_ML_RTREES
__END__;
}
void CvRTrees::read( CvFileStorage* fs, CvFileNode* fnode )
{
CV_FUNCNAME( "CvRTrees::read" );
__BEGIN__;
int nactive_vars, var_count, k;
CvSeqReader reader;
CvFileNode* trees_fnode = 0;
clear();
nclasses = cvReadIntByName( fs, fnode, "nclasses", -1 );
nsamples = cvReadIntByName( fs, fnode, "nsamples" );
nactive_vars = cvReadIntByName( fs, fnode, "nactive_vars", -1 );
oob_error = cvReadRealByName(fs, fnode, "oob_error", -1 );
ntrees = cvReadIntByName( fs, fnode, "ntrees", -1 );
var_importance = (CvMat*)cvReadByName( fs, fnode, "var_importance" );
if( nclasses < 0 || nsamples <= 0 || nactive_vars < 0 || oob_error < 0 || ntrees <= 0)
CV_ERROR( CV_StsParseError, "Some <nclasses>, <nsamples>, <var_count>, "
"<nactive_vars>, <oob_error>, <ntrees> of tags are missing" );
rng = CvRNG( -1 );
trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*ntrees );
memset( trees, 0, sizeof(trees[0])*ntrees );
data = new CvDTreeTrainData();
data->read_params( fs, fnode );
data->shared = true;
trees_fnode = cvGetFileNodeByName( fs, fnode, "trees" );
if( !trees_fnode || !CV_NODE_IS_SEQ(trees_fnode->tag) )
CV_ERROR( CV_StsParseError, "<trees> tag is missing" );
cvStartReadSeq( trees_fnode->data.seq, &reader );
if( reader.seq->total != ntrees )
CV_ERROR( CV_StsParseError,
"<ntrees> is not equal to the number of trees saved in file" );
for( k = 0; k < ntrees; k++ )
{
trees[k] = new CvForestTree();
CV_CALL(trees[k]->read( fs, (CvFileNode*)reader.ptr, this, data ));
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
}
var_count = data->var_count;
CV_CALL(active_var_mask = cvCreateMat( 1, var_count, CV_8UC1 ));
{
// initialize active variables mask
CvMat submask1, submask2;
cvGetCols( active_var_mask, &submask1, 0, nactive_vars );
cvGetCols( active_var_mask, &submask2, nactive_vars, var_count );
cvSet( &submask1, cvScalar(1) );
cvZero( &submask2 );
}
__END__;
}
int CvRTrees::get_tree_count() const
{
return ntrees;
}
CvForestTree* CvRTrees::get_tree(int i) const
{
return (unsigned)i < (unsigned)ntrees ? trees[i] : 0;
}
// End of file.