classRF_train.m
15832 ワード
%**************************************************************
%* mex interface to Andy Liaw et al.'s C code (used in R package randomForest)
%* Added by Abhishek Jaiantilal ( [email protected] )
%* 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
',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
',size(X));
fprintf('size(y) %d
',size(Y));
fprintf('nclass %d
',nclass);
fprintf('size(ncat) %d
',size(ncat));
fprintf('maxcat %d
',maxcat);
fprintf('size(sampsize) %d
',size(sampsize));
fprintf('sampsize[0] %d
',sampsize(1));
fprintf('Stratify %d
',Stratify);
fprintf('Proximity %d
',proximity);
fprintf('oob_prox %d
',oob_prox);
fprintf('strata %d
',strata);
fprintf('ntree %d
',ntree);
fprintf('mtry %d
',mtry);
fprintf('ipi %d
',ipi);
fprintf('classwt %f
',classwt);
fprintf('cutoff %f
',cutoff);
fprintf('nodesize %f
',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;
直接コピーすればいいです.どういたしまして.