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- %**************************************************************
- %* mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
- %* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu )
- %* License: GPLv2
- %* Version: 0.02
- %
- % Calls Classification Random Forest
- % A wrapper matlab file that calls the mex file
- % This does training given the data and labels
- % Documentation copied from R-packages pdf
- % http://cran.r-project.org/web/packages/randomForest/randomForest.pdf
- % Tutorial on getting this working in tutorial_ClassRF.m
- %**************************************************************
- % function model = classRF_train(X,Y,ntree,mtry, extra_options)
- %
- %___Options
- % requires 2 arguments and the rest 3 are optional
- % X: data matrix
- % Y: target values
- % ntree (optional): number of trees (default is 500). also if set to 0
- % will default to 500
- % mtry (default is floor(sqrt(size(X,2))) D=number of features in X). also if set to 0
- % will default to 500
- %
- %
- % Note: TRUE = 1 and FALSE = 0 below
- % extra_options represent a structure containing various misc. options to
- % control the RF
- % extra_options.replace = 0 or 1 (default is 1) sampling with or without
- % replacement
- % extra_options.classwt = priors of classes. Here the function first gets
- % the labels in ascending order and assumes the
- % priors are given in the same order. So if the class
- % labels are [-1 1 2] and classwt is [0.1 2 3] then
- % there is a 1-1 correspondence. (ascending order of
- % class labels). Once this is set the freq of labels in
- % train data also affects.
- % extra_options.cutoff (Classification only) = A vector of length equal to number of classes. The ?winning?
- % class for an observation is the one with the maximum ratio of proportion
- % of votes to cutoff. Default is 1/k where k is the number of classes (i.e., majority
- % vote wins).
- % extra_options.strata = (not yet stable in code) variable that is used for stratified
- % sampling. I don't yet know how this works. Disabled
- % by default
- % extra_options.sampsize = Size(s) of sample to draw. For classification,
- % if sampsize is a vector of the length the number of strata, then sampling is stratified by strata,
- % and the elements of sampsize indicate the numbers to be
- % drawn from the strata.
- % extra_options.nodesize = Minimum size of terminal nodes. Setting this number larger causes smaller trees
- % to be grown (and thus take less time). Note that the default values are different
- % for classification (1) and regression (5).
- % extra_options.importance = Should importance of predictors be assessed?
- % extra_options.localImp = Should casewise importance measure be computed? (Setting this to TRUE will
- % override importance.)
- % extra_options.proximity = Should proximity measure among the rows be calculated?
- % extra_options.oob_prox = Should proximity be calculated only on 'out-of-bag' data?
- % extra_options.do_trace = If set to TRUE, give a more verbose output as randomForest is run. If set to
- % some integer, then running output is printed for every
- % do_trace trees.
- % extra_options.keep_inbag Should an n by ntree matrix be returned that keeps track of which samples are
- % 'in-bag' in which trees (but not how many times, if sampling with replacement)
- %
- % Options eliminated
- % corr_bias which happens only for regression ommitted
- % norm_votes - always set to return total votes for each class.
- %
- %___Returns model which has
- % importance = a matrix with nclass + 2 (for classification) or two (for regression) columns.
- % For classification, the first nclass columns are the class-specific measures
- % computed as mean decrease in accuracy. The nclass + 1st column is the
- % mean decrease in accuracy over all classes. The last column is the mean decrease
- % in Gini index. For Regression, the first column is the mean decrease in
- % accuracy and the second the mean decrease in MSE. If importance=FALSE,
- % the last measure is still returned as a vector.
- % importanceSD = The ?standard errors? of the permutation-based importance measure. For classification,
- % a p by nclass + 1 matrix corresponding to the first nclass + 1
- % columns of the importance matrix. For regression, a length p vector.
- % localImp = a p by n matrix containing the casewise importance measures, the [i,j] element
- % of which is the importance of i-th variable on the j-th case. NULL if
- % localImp=FALSE.
- % ntree = number of trees grown.
- % mtry = number of predictors sampled for spliting at each node.
- % votes (classification only) a matrix with one row for each input data point and one
- % column for each class, giving the fraction or number of ?votes? from the random
- % forest.
- % oob_times number of times cases are 'out-of-bag' (and thus used in computing OOB error
- % estimate)
- % proximity if proximity=TRUE when randomForest is called, a matrix of proximity
- % measures among the input (based on the frequency that pairs of data points are
- % in the same terminal nodes).
- % errtr = first column is OOB Err rate, second is for class 1 and so on
- function model=classRF_train(X,Y,ntree,mtry, extra_options)
- DEFAULTS_ON =0;
- %DEBUG_ON=0;
- TRUE=1;
- FALSE=0;
-
- orig_labels = sort(unique(Y));
- Y_new = Y;
- new_labels = 1:length(orig_labels);
-
- for i=1:length(orig_labels)
- Y_new(find(Y==orig_labels(i)))=Inf;
- Y_new(isinf(Y_new))=new_labels(i);
- end
-
- Y = Y_new;
-
- if exist('extra_options','var')
- if isfield(extra_options,'DEBUG_ON'); DEBUG_ON = extra_options.DEBUG_ON; end
- if isfield(extra_options,'replace'); replace = extra_options.replace; end
- if isfield(extra_options,'classwt'); classwt = extra_options.classwt; end
- if isfield(extra_options,'cutoff'); cutoff = extra_options.cutoff; end
- if isfield(extra_options,'strata'); strata = extra_options.strata; end
- if isfield(extra_options,'sampsize'); sampsize = extra_options.sampsize; end
- if isfield(extra_options,'nodesize'); nodesize = extra_options.nodesize; end
- if isfield(extra_options,'importance'); importance = extra_options.importance; end
- if isfield(extra_options,'localImp'); localImp = extra_options.localImp; end
- if isfield(extra_options,'nPerm'); nPerm = extra_options.nPerm; end
- if isfield(extra_options,'proximity'); proximity = extra_options.proximity; end
- if isfield(extra_options,'oob_prox'); oob_prox = extra_options.oob_prox; end
- %if isfield(extra_options,'norm_votes'); norm_votes = extra_options.norm_votes; end
- if isfield(extra_options,'do_trace'); do_trace = extra_options.do_trace; end
- %if isfield(extra_options,'corr_bias'); corr_bias = extra_options.corr_bias; end
- if isfield(extra_options,'keep_inbag'); keep_inbag = extra_options.keep_inbag; end
- end
- keep_forest=1; %always save the trees :)
-
- %set defaults if not already set
- if ~exist('DEBUG_ON','var') DEBUG_ON=FALSE; end
- if ~exist('replace','var'); replace = TRUE; end
- %if ~exist('classwt','var'); classwt = []; end %will handle these three later
- %if ~exist('cutoff','var'); cutoff = 1; end
- %if ~exist('strata','var'); strata = 1; end
- if ~exist('sampsize','var');
- if (replace)
- sampsize = size(X,1);
- else
- sampsize = ceil(0.632*size(X,1));
- end;
- end
- if ~exist('nodesize','var'); nodesize = 1; end %classification=1, regression=5
- if ~exist('importance','var'); importance = FALSE; end
- if ~exist('localImp','var'); localImp = FALSE; end
- if ~exist('nPerm','var'); nPerm = 1; end
- %if ~exist('proximity','var'); proximity = 1; end %will handle these two later
- %if ~exist('oob_prox','var'); oob_prox = 1; end
- %if ~exist('norm_votes','var'); norm_votes = TRUE; end
- if ~exist('do_trace','var'); do_trace = FALSE; end
- %if ~exist('corr_bias','var'); corr_bias = FALSE; end
- if ~exist('keep_inbag','var'); keep_inbag = FALSE; end
-
- if ~exist('ntree','var') | ntree<=0
- ntree=500;
- DEFAULTS_ON=1;
- end
- if ~exist('mtry','var') | mtry<=0 | mtry>size(X,2)
- mtry =floor(sqrt(size(X,2)));
- end
-
- addclass =isempty(Y);
-
- if (~addclass && length(unique(Y))<2)
- error('need atleast two classes for classification');
- end
- [N D] = size(X);
-
- if N==0; error(' data (X) has 0 rows');end
-
- if (mtry <1 || mtry > D)
- DEFAULTS_ON=1;
- end
-
- mtry = max(1,min(D,round(mtry)));
-
- if DEFAULTS_ON
- fprintf('\tSetting to defaults %d trees and mtry=%d\n',ntree,mtry);
- end
-
- if ~isempty(Y)
- if length(Y)~=N,
- error('Y size is not the same as X size');
- end
- addclass = FALSE;
- else
- if ~addclass,
- addclass=TRUE;
- end
- error('have to fill stuff here')
- end
-
- if ~isempty(find(isnan(X))); error('NaNs in X'); end
- if ~isempty(find(isnan(Y))); error('NaNs in Y'); end
-
- %now handle categories. Problem is that categories in R are more
- %enhanced. In this i ask the user to specify the column/features to
- %consider as categories, 1 if all the values are real values else
- %specify the number of categories here
- if exist ('extra_options','var') && isfield(extra_options,'categories')
- ncat = extra_options.categories;
- else
- ncat = ones(1,D);
- end
-
- maxcat = max(ncat);
- if maxcat>32
- error('Can not handle categorical predictors with more than 32 categories');
- end
- %classRF - line 88 in randomForest.default.R
- nclass = length(unique(Y));
- if ~exist('cutoff','var')
- cutoff = ones(1,nclass)* (1/nclass);
- else
- if sum(cutoff)>1 || sum(cutoff)<0 || length(find(cutoff<=0))>0 || length(cutoff)~=nclass
- error('Incorrect cutoff specified');
- end
- end
- if ~exist('classwt','var')
- classwt = ones(1,nclass);
- ipi=0;
- else
- if length(classwt)~=nclass
- error('Length of classwt not equal to the number of classes')
- end
- if ~isempty(find(classwt<=0))
- error('classwt must be positive');
- end
- ipi=1;
- end
- if ~exist('proximity','var')
- proximity = addclass;
- oob_prox = proximity;
- end
-
- if ~exist('oob_prox','var')
- oob_prox = proximity;
- end
-
- %i handle the below in the mex file
- % if proximity
- % prox = zeros(N,N);
- % proxts = 1;
- % else
- % prox = 1;
- % proxts = 1;
- % end
-
- %i handle the below in the mex file
- if localImp
- importance = TRUE;
- % impmat = zeors(D,N);
- else
- % impmat = 1;
- end
-
- if importance
- if (nPerm<1)
- nPerm = int32(1);
- else
- nPerm = int32(nPerm);
- end
-
- %classRF
- % impout = zeros(D,nclass+2);
- % impSD = zeros(D,nclass+1);
- else
- % impout = zeros(D,1);
- % impSD = 1;
- end
-
- %i handle the below in the mex file
- %somewhere near line 157 in randomForest.default.R
- if addclass
- % nsample = 2*n;
- else
- % nsample = n;
- end
-
- Stratify = (length(sampsize)>1);
- if (~Stratify && sampsize>N)
- error('Sampsize too large')
- end
-
- if Stratify
- if ~exist('strata','var')
- strata = Y;
- end
- nsum = sum(sampsize);
- if ( ~isempty(find(sampsize<=0)) || nsum==0)
- error('Bad sampsize specification');
- end
- else
- nsum = sampsize;
- end
- %i handle the below in the mex file
- %nrnodes = 2*floor(nsum/nodesize)+1;
- %xtest = 1;
- %ytest = 1;
- %ntest = 1;
- %labelts = FALSE;
- %nt = ntree;
-
-
-
-
- %[ldau,rdau,nodestatus,nrnodes,upper,avnode,mbest,ndtree]=
- %keyboard
-
-
-
- if Stratify
- strata = int32(strata);
- else
- strata = int32(1);
- end
-
- Options = int32([addclass, importance, localImp, proximity, oob_prox, do_trace, keep_forest, replace, Stratify, keep_inbag]);
-
- if DEBUG_ON
- %print the parameters that i am sending in
- fprintf('size(x) %d\n',size(X));
- fprintf('size(y) %d\n',size(Y));
- fprintf('nclass %d\n',nclass);
- fprintf('size(ncat) %d\n',size(ncat));
- fprintf('maxcat %d\n',maxcat);
- fprintf('size(sampsize) %d\n',size(sampsize));
- fprintf('sampsize[0] %d\n',sampsize(1));
- fprintf('Stratify %d\n',Stratify);
- fprintf('Proximity %d\n',proximity);
- fprintf('oob_prox %d\n',oob_prox);
- fprintf('strata %d\n',strata);
- fprintf('ntree %d\n',ntree);
- fprintf('mtry %d\n',mtry);
- fprintf('ipi %d\n',ipi);
- fprintf('classwt %f\n',classwt);
- fprintf('cutoff %f\n',cutoff);
- fprintf('nodesize %f\n',nodesize);
- end
-
-
- [nrnodes,ntree,xbestsplit,classwt,cutoff,treemap,nodestatus,nodeclass,bestvar,ndbigtree,mtry ...
- outcl, counttr, prox, impmat, impout, impSD, errtr, inbag] ...
- = mexClassRF_train(X',int32(Y_new),length(unique(Y)),ntree,mtry,int32(ncat), ...
- int32(maxcat), int32(sampsize), strata, Options, int32(ipi), ...
- classwt, cutoff, int32(nodesize),int32(nsum));
- model.nrnodes=nrnodes;
- model.ntree=ntree;
- model.xbestsplit=xbestsplit;
- model.classwt=classwt;
- model.cutoff=cutoff;
- model.treemap=treemap;
- model.nodestatus=nodestatus;
- model.nodeclass=nodeclass;
- model.bestvar = bestvar;
- model.ndbigtree = ndbigtree;
- model.mtry = mtry;
- model.orig_labels=orig_labels;
- model.new_labels=new_labels;
- model.nclass = length(unique(Y));
- model.outcl = outcl;
- model.counttr = counttr;
- if proximity
- model.proximity = prox;
- else
- model.proximity = [];
- end
- model.localImp = impmat;
- model.importance = impout;
- model.importanceSD = impSD;
- model.errtr = errtr';
- model.inbag = inbag;
- model.votes = counttr';
- model.oob_times = sum(counttr)';
- clear mexClassRF_train
- %keyboard
- 1;
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