#include "opencv2/core/core_c.h" #include "opencv2/ml/ml.hpp" #include static void help() { printf("\nThis program demonstrated the use of OpenCV's decision tree function for learning and predicting data\n" "Usage :\n" "./mushroom \n" "\n" "The sample demonstrates how to build a decision tree for classifying mushrooms.\n" "It uses the sample base agaricus-lepiota.data from UCI Repository, here is the link:\n" "\n" "Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n" "UCI Repository of machine learning databases\n" "[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n" "Irvine, CA: University of California, Department of Information and Computer Science.\n" "\n" "// loads the mushroom database, which is a text file, containing\n" "// one training sample per row, all the input variables and the output variable are categorical,\n" "// the values are encoded by characters.\n\n"); } static int mushroom_read_database( const char* filename, CvMat** data, CvMat** missing, CvMat** responses ) { const int M = 1024; FILE* f = fopen( filename, "rt" ); CvMemStorage* storage; CvSeq* seq; char buf[M+2], *ptr; float* el_ptr; CvSeqReader reader; int i, j, var_count = 0; if( !f ) return 0; // read the first line and determine the number of variables if( !fgets( buf, M, f )) { fclose(f); return 0; } for( ptr = buf; *ptr != '\0'; ptr++ ) var_count += *ptr == ','; assert( ptr - buf == (var_count+1)*2 ); // create temporary memory storage to store the whole database el_ptr = new float[var_count+1]; storage = cvCreateMemStorage(); seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage ); for(;;) { for( i = 0; i <= var_count; i++ ) { int c = buf[i*2]; el_ptr[i] = c == '?' ? -1.f : (float)c; } if( i != var_count+1 ) break; cvSeqPush( seq, el_ptr ); if( !fgets( buf, M, f ) || !strchr( buf, ',' ) ) break; } fclose(f); // allocate the output matrices and copy the base there *data = cvCreateMat( seq->total, var_count, CV_32F ); *missing = cvCreateMat( seq->total, var_count, CV_8U ); *responses = cvCreateMat( seq->total, 1, CV_32F ); cvStartReadSeq( seq, &reader ); for( i = 0; i < seq->total; i++ ) { const float* sdata = (float*)reader.ptr + 1; float* ddata = data[0]->data.fl + var_count*i; float* dr = responses[0]->data.fl + i; uchar* dm = missing[0]->data.ptr + var_count*i; for( j = 0; j < var_count; j++ ) { ddata[j] = sdata[j]; dm[j] = sdata[j] < 0; } *dr = sdata[-1]; CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); } cvReleaseMemStorage( &storage ); delete [] el_ptr; return 1; } static CvDTree* mushroom_create_dtree( const CvMat* data, const CvMat* missing, const CvMat* responses, float p_weight ) { CvDTree* dtree; CvMat* var_type; int i, hr1 = 0, hr2 = 0, p_total = 0; float priors[] = { 1, p_weight }; var_type = cvCreateMat( data->cols + 1, 1, CV_8U ); cvSet( var_type, cvScalarAll(CV_VAR_CATEGORICAL) ); // all the variables are categorical dtree = new CvDTree; dtree->train( data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing, CvDTreeParams( 8, // max depth 10, // min sample count 0, // regression accuracy: N/A here true, // compute surrogate split, as we have missing data 15, // max number of categories (use sub-optimal algorithm for larger numbers) 10, // the number of cross-validation folds true, // use 1SE rule => smaller tree true, // throw away the pruned tree branches priors // the array of priors, the bigger p_weight, the more attention // to the poisonous mushrooms // (a mushroom will be judjed to be poisonous with bigger chance) )); // compute hit-rate on the training database, demonstrates predict usage. for( i = 0; i < data->rows; i++ ) { CvMat sample, mask; cvGetRow( data, &sample, i ); cvGetRow( missing, &mask, i ); double r = dtree->predict( &sample, &mask )->value; int d = fabs(r - responses->data.fl[i]) >= FLT_EPSILON; if( d ) { if( r != 'p' ) hr1++; else hr2++; } p_total += responses->data.fl[i] == 'p'; } printf( "Results on the training database:\n" "\tPoisonous mushrooms mis-predicted: %d (%g%%)\n" "\tFalse-alarms: %d (%g%%)\n", hr1, (double)hr1*100/p_total, hr2, (double)hr2*100/(data->rows - p_total) ); cvReleaseMat( &var_type ); return dtree; } static const char* var_desc[] = { "cap shape (bell=b,conical=c,convex=x,flat=f)", "cap surface (fibrous=f,grooves=g,scaly=y,smooth=s)", "cap color (brown=n,buff=b,cinnamon=c,gray=g,green=r,\n\tpink=p,purple=u,red=e,white=w,yellow=y)", "bruises? (bruises=t,no=f)", "odor (almond=a,anise=l,creosote=c,fishy=y,foul=f,\n\tmusty=m,none=n,pungent=p,spicy=s)", "gill attachment (attached=a,descending=d,free=f,notched=n)", "gill spacing (close=c,crowded=w,distant=d)", "gill size (broad=b,narrow=n)", "gill color (black=k,brown=n,buff=b,chocolate=h,gray=g,\n\tgreen=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y)", "stalk shape (enlarging=e,tapering=t)", "stalk root (bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r)", "stalk surface above ring (ibrous=f,scaly=y,silky=k,smooth=s)", "stalk surface below ring (ibrous=f,scaly=y,silky=k,smooth=s)", "stalk color above ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)", "stalk color below ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)", "veil type (partial=p,universal=u)", "veil color (brown=n,orange=o,white=w,yellow=y)", "ring number (none=n,one=o,two=t)", "ring type (cobwebby=c,evanescent=e,flaring=f,large=l,\n\tnone=n,pendant=p,sheathing=s,zone=z)", "spore print color (black=k,brown=n,buff=b,chocolate=h,green=r,\n\torange=o,purple=u,white=w,yellow=y)", "population (abundant=a,clustered=c,numerous=n,\n\tscattered=s,several=v,solitary=y)", "habitat (grasses=g,leaves=l,meadows=m,paths=p\n\turban=u,waste=w,woods=d)", 0 }; static void print_variable_importance( CvDTree* dtree ) { const CvMat* var_importance = dtree->get_var_importance(); int i; char input[1000]; if( !var_importance ) { printf( "Error: Variable importance can not be retrieved\n" ); return; } printf( "Print variable importance information? (y/n) " ); int values_read = scanf( "%1s", input ); CV_Assert(values_read == 1); if( input[0] != 'y' && input[0] != 'Y' ) return; for( i = 0; i < var_importance->cols*var_importance->rows; i++ ) { double val = var_importance->data.db[i]; char buf[100]; int len = (int)(strchr( var_desc[i], '(' ) - var_desc[i] - 1); strncpy( buf, var_desc[i], len ); buf[len] = '\0'; printf( "%s", buf ); printf( ": %g%%\n", val*100. ); } } static void interactive_classification( CvDTree* dtree ) { char input[1000]; const CvDTreeNode* root; CvDTreeTrainData* data; if( !dtree ) return; root = dtree->get_root(); data = dtree->get_data(); for(;;) { const CvDTreeNode* node; printf( "Start/Proceed with interactive mushroom classification (y/n): " ); int values_read = scanf( "%1s", input ); CV_Assert(values_read == 1); if( input[0] != 'y' && input[0] != 'Y' ) break; printf( "Enter 1-letter answers, '?' for missing/unknown value...\n" ); // custom version of predict node = root; for(;;) { CvDTreeSplit* split = node->split; int dir = 0; if( !node->left || node->Tn <= dtree->get_pruned_tree_idx() || !node->split ) break; for( ; split != 0; ) { int vi = split->var_idx, j; int count = data->cat_count->data.i[vi]; const int* map = data->cat_map->data.i + data->cat_ofs->data.i[vi]; printf( "%s: ", var_desc[vi] ); values_read = scanf( "%1s", input ); CV_Assert(values_read == 1); if( input[0] == '?' ) { split = split->next; continue; } // convert the input character to the normalized value of the variable for( j = 0; j < count; j++ ) if( map[j] == input[0] ) break; if( j < count ) { dir = (split->subset[j>>5] & (1 << (j&31))) ? -1 : 1; if( split->inversed ) dir = -dir; break; } else printf( "Error: unrecognized value\n" ); } if( !dir ) { printf( "Impossible to classify the sample\n"); node = 0; break; } node = dir < 0 ? node->left : node->right; } if( node ) printf( "Prediction result: the mushroom is %s\n", node->class_idx == 0 ? "EDIBLE" : "POISONOUS" ); printf( "\n-----------------------------\n" ); } } int main( int argc, char** argv ) { CvMat *data = 0, *missing = 0, *responses = 0; CvDTree* dtree; const char* base_path = argc >= 2 ? argv[1] : "agaricus-lepiota.data"; help(); if( !mushroom_read_database( base_path, &data, &missing, &responses ) ) { printf( "\nUnable to load the training database\n\n"); help(); return -1; } dtree = mushroom_create_dtree( data, missing, responses, 10 // poisonous mushrooms will have 10x higher weight in the decision tree ); cvReleaseMat( &data ); cvReleaseMat( &missing ); cvReleaseMat( &responses ); print_variable_importance( dtree ); interactive_classification( dtree ); delete dtree; return 0; }