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Posted to commits@mahout.apache.org by co...@apache.org on 2012/05/05 12:59:00 UTC
[CONF] Apache Mahout > Breiman Example
Space: Apache Mahout (https://cwiki.apache.org/confluence/display/MAHOUT)
Page: Breiman Example (https://cwiki.apache.org/confluence/display/MAHOUT/Breiman+Example)
Edited by Frank Scholten:
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h1. Introduction
This quick start page shows how to run the Breiman example. It implements the test procedure described in Breiman's paper [1].
The basic algorithm is as follows :
* repeat I iterations
* foreach iteration do
** 10% of the dataset is kept apart as a testing set
** build two forests using the training set, one with m=int(log2(M)+1) (called Random-Input) and one with m=1 (called Single-Input)
** choose the forest that gave the lowest oob error estimation to compute the test set error
** compute the test set error using the Single Input Forest (test error), this demonstrates that even with m=1, Decision Forests give comparable results to greater values of m
** compute the mean test set error using every tree of the chosen forest (tree error). This should indicate how well a single Decision Tree performs
* compute the mean test error for all iterations
* compute the mean tree error for all iterations
h1. Steps
h2. Download the data
* The current implementation is compatible with the UCI repository file format. Here are links to some of the datasets used in Breiman's paper:
** glass : http://archive.ics.uci.edu/ml/datasets/Glass+Identification
** breast cancer : http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
** diabetes : http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
** sonar : http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)
** ionosphere : http://archive.ics.uci.edu/ml/datasets/Ionosphere
** vehicle : http://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes)
** german : http://archive.ics.uci.edu/ml/datasets/Statlog+\(German+Credit+Data\)
* Put the data in HDFS: {code}$HADOOP_HOME/bin/hadoop fs -put <PATH TO DATA> testdata{code}
h2. Build the Job files
* In $MAHOUT_HOME/ run: {code}mvn install -DskipTests{code}
h2. Generate a file descriptor for the dataset:
for the glass dataset (glass.data), run :
{code}
$HADOOP_HOME/bin/hadoop jar $MAHOUT_HOME/core/target/mahout-core-<VERSION>-job.jar org.apache.mahout.df.tools.Describe -p testdata/glass.data -f testdata/glass.info -d I 9 N L
{code}
The "I 9 N L" string indicates the nature of the variables. which means 1 ignored(I) attribute, followed by 9 numerical(N) attributes, followed by the label(L)
* you can also use C for categorical (nominal) attributes
h2. Run the example
{code}
$HADOOP_HOME/hadoop jar $MAHOUT_HOME/examples/target/mahout-examples-<VERSION>-job.jar org.apache.mahout.df.BreimanExample -d testdata/glass.data -ds testdata/glass.info -i 10 -t 100
{code}
which builds 100 trees (-t argument) and repeats the test 10 iterations (-i argument)
* The example outputs the following results:
** Selection error : mean test error for the selected forest on all iterations
** Single Input error : mean test error for the single input forest on all iterations
** One Tree error : mean single tree error on all iterations
** Mean Random Input Time : mean build time for random input forests on all iterations
** Mean Single Input Time : mean build time for single input forests on all iterations
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