Random Forest for Regression by TreeBagger

Here is an exampleRF using a Random Forest (TreeBagger) in matlab.

1. Loads a matlab test dataset.

2. Finds the capabilities of computer so we can best utilize them.

3. Trains a TreeBagger (Random Forest).

4. Creates a scatter diagram.

5. Estimates the relative importance of the inputs.

6. Examines how many trees are needed.

%--------------------------------------------------------------------------
clear;clc;close all

%--------------------------------------------------------------------------
% Load an example dataset provided with matlab
load house_dataset
In = houseInputs';
Out = houseTargets';

%--------------------------------------------------------------------------
% Find capabilities of computer so we can best utilize them.

% Find if gpu is present
ngpus=gpuDeviceCount;
disp([num2str(ngpus) ' GPUs found'])
if ngpus>0
    lgpu=1;
    disp('GPU found')
    useGPU='yes';
else
    lgpu=0;
    disp('No GPU found')
    useGPU='no';
end

% Find number of cores
ncores=feature('numCores');
disp([num2str(ncores) ' cores found'])

% Find number of cpus
import java.lang.*;
r=Runtime.getRuntime;
ncpus=r.availableProcessors;
disp([num2str(ncpus) ' cpus found'])

if ncpus>1
    useParallel='yes';
else
    useParallel='no';
end

[archstr,maxsize,endian]=computer;
disp([...
    'This is a ' archstr ...
    ' computer that can have up to ' num2str(maxsize) ...
    ' elements in a matlab array and uses ' endian ...
    ' byte ordering.'...
    ])

% Set up the size of the parallel pool if necessary
npool=ncores;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Opening parallel pool 开启方法1
CoreNum=npool; %调用的处理器个数
if matlabpool('size')<=0  %之前没有打开
    matlabpool('open','local',CoreNum);
else  %之前已经打开
    disp('matlab pool already started');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Opening parallel pool 开启方法2
if ncpus>1
    tic
    disp('Opening parallel pool')

    % first check if there is a current pool
    poolobj=gcp('nocreate');

    % If there is no pool create one
    if isempty(poolobj)
        command=['parpool(' num2str(npool) ');'];
        disp(command);
        eval(command);
    else
        poolsize= poolobj.NumWorkers;
        disp(['A pool of ' poolsize ' workers already exists.'])
    end

    % Set parallel options
    paroptions = statset('UseParallel',true);
    toc

end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%--------------------------------------------------------------------------
tic
leaf=5;
ntrees=200;
fboot=1;
surrogate='on';
disp('Training the tree bagger')
b = TreeBagger(...
        ntrees,...
        In,Out,... 
        'Method','regression',...
        'oobvarimp','on',...
        'surrogate',surrogate,...
        'minleaf',leaf,...
        'FBoot',fboot,...
        'Options',paroptions...
    );
toc

%--------------------------------------------------------------------------
% Estimate Output using tree bagger
disp('Estimate Output using tree bagger')
x=Out;
y=predict(b, In);
name='Bagged Decision Trees Model';
toc

%--------------------------------------------------------------------------
% calculate the training data correlation coefficient
cct=corrcoef(x,y);
cct=cct(2,1);

%--------------------------------------------------------------------------
% Create a scatter Diagram
disp('Create a scatter Diagram')

% plot the 1:1 line
plot(x,x,'LineWidth',3);

hold on
scatter(x,y,'filled');
hold off
grid on

set(gca,'FontSize',18)
xlabel('Actual','FontSize',25)
ylabel('Estimated','FontSize',25)
title(['Training Dataset, R^2=' num2str(cct^2,2)],'FontSize',30)

drawnow

fn='ScatterDiagram';
fnpng=[fn,'.png'];
print('-dpng',fnpng);

%--------------------------------------------------------------------------
% Calculate the relative importance of the input variables
tic
disp('Sorting importance into descending order')
weights=b.OOBPermutedVarDeltaError;
[B,iranked] = sort(weights,'descend');
toc

%--------------------------------------------------------------------------
disp(['Plotting a horizontal bar graph of sorted labeled weights.']) 

%--------------------------------------------------------------------------
figure
barh(weights(iranked),'g');
xlabel('Variable Importance','FontSize',30,'Interpreter','latex');
ylabel('Variable Rank','FontSize',30,'Interpreter','latex');
title(...
    ['Relative Importance of Inputs in estimating Redshift'],...
    'FontSize',17,'Interpreter','latex'...
    );
hold on
barh(weights(iranked(1:10)),'y');
barh(weights(iranked(1:5)),'r');

%--------------------------------------------------------------------------
grid on 
xt = get(gca,'XTick');    
xt_spacing=unique(diff(xt));
xt_spacing=xt_spacing(1);    
yt = get(gca,'YTick');    
ylim([0.25 length(weights)+0.75]);
xl=xlim;
xlim([0 2.5*max(weights)]);

%--------------------------------------------------------------------------
% Add text labels to each bar
for ii=1:length(weights)
    text(...
        max([0 weights(iranked(ii))+0.02*max(weights)]),ii,...
        ['Column ' num2str(iranked(ii))],'Interpreter','latex','FontSize',11);
end

%--------------------------------------------------------------------------
set(gca,'FontSize',16)
set(gca,'XTick',0:2*xt_spacing:1.1*max(xl));
set(gca,'YTick',yt);
set(gca,'TickDir','out');
set(gca, 'ydir', 'reverse' )
set(gca,'LineWidth',2);   
drawnow

%--------------------------------------------------------------------------
fn='RelativeImportanceInputs';
fnpng=[fn,'.png'];
print('-dpng',fnpng);

%--------------------------------------------------------------------------
% Ploting how weights change with variable rank
disp('Ploting out of bag error versus the number of grown trees')

figure
plot(b.oobError,'LineWidth',2);
xlabel('Number of Trees','FontSize',30)
ylabel('Out of Bag Error','FontSize',30)
title('Out of Bag Error','FontSize',30)
set(gca,'FontSize',16)
set(gca,'LineWidth',2);   
grid on
drawnow
fn='EroorAsFunctionOfForestSize';
fnpng=[fn,'.png'];
print('-dpng',fnpng);

本文转自：
https://davidlary.info/blog/category/machine-learning/