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Iris multi class classification using RandomForest

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Makoto YUI edited this page Dec 31, 2015 · 35 revisions

NOTE: RandomForest is being supported from Hivemall v0.4 or later.

Dataset

Attribute Information:
   1. sepal length in cm
   2. sepal width in cm
   3. petal length in cm
   4. petal width in cm
   5. class: 
      -- Iris Setosa
      -- Iris Versicolour
      -- Iris Virginica

Table preparation

create database iris;
use iris;

create external table raw (
  sepal_length int,
  sepal_width int,
  petal_length int,
  petak_width int,
  class string
)
ROW FORMAT DELIMITED
  FIELDS TERMINATED BY ','
  LINES TERMINATED BY '\n'
STORED AS TEXTFILE LOCATION '/dataset/iris/raw';

$ sed '/^$/d' iris.data | hadoop fs -put - /dataset/iris/raw/iris.data
create table label_mapping 
as
select
  class,
  rank - 1 as label
from (
select
  distinct class,
  dense_rank() over (order by class) as rank
from 
  raw
) t
;
create table training
as
select
  rowid() as rowid,
  array(t1.sepal_length, t1.sepal_width, t1.petal_length, t1.petak_width) as features,
  t2.label
from
  raw t1
  JOIN label_mapping t2 ON (t1.class = t2.class)
;

Training

train_randomforest_classifier takes a dense features in double[] and a label starting from 0.

CREATE TABLE model 
STORED AS SEQUENCEFILE 
AS
select 
  train_randomforest_classifier(features, label) 
  -- hivemall v0.4.1-alpha.2 and before
  -- train_randomforest_classifier(features, label) as (pred_model, var_importance, oob_errors, oob_tests)
  -- hivemall v0.4.1 and later
  -- train_randomforest_classifier(features, label) as (model_id, model_type, pred_model, var_importance, oob_errors, oob_tests)
from
  training;

Note: The default TEXTFILE should not be used for model table when using Javascript output through "-output javascript" option.

hive> desc model;
model_id                int                                         
model_type              int                                         
pred_model              string                                      
var_importance          array<double>                               
oob_errors              int                                         
oob_tests               int

Training options

"-help" option shows usage of the function.

select train_randomforest_classifier(features, label, "-help") from training;

> FAILED: UDFArgumentException 
usage: train_randomforest_classifier(double[] features, int label [,
       string options]) - Returns a relation consists of <int model_id,
       int model_type, string pred_model, array<double> var_importance,
       int oob_errors, int oob_tests> [-attrs <arg>] [-depth <arg>]
       [-disable_compression] [-help] [-leafs <arg>] [-output <arg>]
       [-rule <arg>] [-seed <arg>] [-splits <arg>] [-trees <arg>] [-vars
       <arg>]
 -attrs,--attribute_types <arg>   Comma separated attribute types (Q for
                                  quantitative variable and C for
                                  categorical variable. e.g., [Q,C,Q,C])
 -depth,--max_depth <arg>         The maximum number of the tree depth
                                  [default: Integer.MAX_VALUE]
 -disable_compression             Whether to disable compression of the
                                  output script [default: false]
 -help                            Show function help
 -leafs,--max_leaf_nodes <arg>    The maximum number of leaf nodes
                                  [default: Integer.MAX_VALUE]
 -output,--output_type <arg>      The output type (serialization/ser or
                                  opscode/vm or javascript/js) [default:
                                  serialization]
 -rule,--split_rule <arg>         Split algorithm [default: GINI, ENTROPY]
 -seed <arg>                      seed value in long [default: -1
                                  (random)]
 -splits,--min_split <arg>        A node that has greater than or equals
                                  to `min_split` examples will split
                                  [default: 2]
 -trees,--num_trees <arg>         The number of trees for each task
                                  [default: 50]
 -vars,--num_variables <arg>      The number of random selected features
                                  [default: ceil(sqrt(x[0].length))].
                                  int(num_variables * x[0].length) is
                                  considered if num_variable is (0,1]

Caution: "-num_trees" controls the number of trees for each task, not the total number of trees.

Parallelize Training

To parallelize RandomForest training, you can use UNION ALL as follows:

CREATE TABLE model 
STORED AS SEQUENCEFILE 
AS
select 
  train_randomforest_classifier(features, label, '-trees 25') 
from
  training
UNION ALL
select 
  train_randomforest_classifier(features, label, '-trees 25')
from
  training
;

Learning stats

Variable importance and Out Of Bag (OOB) error rate of RandomForest can be shown as follows:

select
  array_sum(var_importance) as var_importance,
  sum(oob_errors) / sum(oob_tests) as oob_err_rate
from
  model;

[2.81010338879605,0.4970357753626371,23.790369091407698,14.315316390235273] 0.05333333333333334

Output prediction model by Javascipt

CREATE TABLE model_javascript
STORED AS SEQUENCEFILE 
AS
select train_randomforest_classifier(features, label, "-output_type js -disable_compression")
from training;

select model from model_javascript limit 1;
if(x[3] <= 0.5) {
  0;
} else  {
  if(x[2] <= 4.5) {
    if(x[3] <= 1.5) {
      if(x[0] <= 4.5) {
        1;
      } else  {
        if(x[0] <= 5.5) {
          1;
        } else  {
          if(x[1] <= 2.5) {
            1;
          } else  {
            1;
          }
        }
      }
    } else  {
      2;
    }
  } else  {
    if(x[3] <= 1.5) {
      2;
    } else  {
      2;
    }
  }
}

Prediction

set hivevar:classification=true;
set hive.auto.convert.join=true;
set hive.mapjoin.optimized.hashtable=false;

create table predicted_vm
as
SELECT
  rowid,
  rf_ensemble(predicted) as predicted
FROM (
  SELECT
    rowid, 
    -- hivemall v0.4.1-alpha.2 and before
    -- tree_predict(p.model, t.features, ${classification}) as predicted
    -- hivemall v0.4.1 and later
    tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted
  FROM
    model p
    LEFT OUTER JOIN -- CROSS JOIN
    training t
) t1
group by
  rowid
;

Note: Javascript outputs can be evaluated by js_tree_predict.

Parallelize Prediction

The following query runs predictions in N-parallel. It would reduce elapsed time for prediction almost by N.

SET hivevar:classification=true;
set hive.auto.convert.join=true;
SET hive.mapjoin.optimized.hashtable=false;
SET mapred.reduce.tasks=8;

create table predicted_vm
as
SELECT
  rowid,
  rf_ensemble(predicted) as predicted
FROM (
  SELECT
    t.rowid, 
    -- hivemall v0.4.1-alpha.2 and before
    -- tree_predict(p.pred_model, t.features, ${classification}) as predicted
    -- hivemall v0.4.1 and later
    tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted
  FROM (
    SELECT model_id, model_type, pred_model
    FROM model
    DISTRIBUTE BY rand(1)
  ) p 
  LEFT OUTER JOIN training t
) t1
group by
  rowid
;

Evaluation

select count(1) from training;
> 150

set hivevar:total_cnt=150;

WITH t1 as (
SELECT
  t.rowid,
  t.label as actual,
  p.predicted.label as predicted
FROM
  predicted_vm p
  LEFT OUTER JOIN training t ON (t.rowid = p.rowid)
)
SELECT
  count(1) / ${total_cnt}
FROM
  t1
WHERE
  actual = predicted
;

0.9533333333333334

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