The task is predicting the click through rate (CTR) of advertisement, meaning that we are to predict the probability of each ad being clicked.
https://www.kaggle.com/c/kddcup2012-track2

Caution: This example just shows a baseline result. Use token tables and amplifier to get better AUC score.

---

Logistic Regression

Training

use kdd12track2;

-- set mapred.max.split.size=134217728; -- [optional] set if OOM caused at mappers on training
-- SET mapred.max.split.size=67108864;
select count(1) from training_orcfile;

235582879

235582879 / 56 (mappers) = 4206837

set hivevar:total_steps=5000000;
-- set mapred.reduce.tasks=64; -- [optional] set the explicit number of reducers to make group-by aggregation faster

drop table lr_model;
create table lr_model 
as
select 
 feature,
 cast(avg(weight) as float) as weight
from 
 (select 
     logress(features, label, "-total_steps ${total_steps}") as (feature,weight)
     -- logress(features, label) as (feature,weight)
  from 
     training_orcfile
 ) t 
group by feature;

-- set mapred.max.split.size=-1; -- reset to the default value

Note: Setting the “-total_steps” option is optional.

Prediction

drop table lr_predict;
create table lr_predict
  ROW FORMAT DELIMITED 
    FIELDS TERMINATED BY "\t"
    LINES TERMINATED BY "\n"
  STORED AS TEXTFILE
as
select
  t.rowid, 
  sigmoid(sum(m.weight)) as prob
from 
  testing_exploded  t LEFT OUTER JOIN
  lr_model m ON (t.feature = m.feature)
group by 
  t.rowid
order by 
  rowid ASC;

Evaluation

You can download scoreKDD.py from KDD Cup 2012, Track 2 site. After logging-in to Kaggle, download scoreKDD.py.

hadoop fs -getmerge /user/hive/warehouse/kdd12track2.db/lr_predict lr_predict.tbl

gawk -F "\t" '{print $2;}' lr_predict.tbl > lr_predict.submit

pypy scoreKDD.py KDD_Track2_solution.csv  lr_predict.submit

Note: You can use python instead of pypy.

Measure	Score
AUC	0.741111
NWMAE	0.045493
WRMSE	0.142395

---

Passive Aggressive

Training

drop table pa_model;
create table pa_model 
as
select 
 feature,
 cast(avg(weight) as float) as weight
from 
 (select 
     train_pa1a_regr(features,label) as (feature,weight)
  from 
     training_orcfile
 ) t 
group by feature;

PA1a is recommended when using PA for regression.

Prediction

drop table pa_predict;
create table pa_predict
  ROW FORMAT DELIMITED 
    FIELDS TERMINATED BY "\t"
    LINES TERMINATED BY "\n"
  STORED AS TEXTFILE
as
select
  t.rowid, 
  sum(m.weight) as prob
from 
  testing_exploded  t LEFT OUTER JOIN
  pa_model m ON (t.feature = m.feature)
group by 
  t.rowid
order by 
  rowid ASC;

The “prob” of PA can be used only for ranking and can have a negative value. A higher weight means much likely to be clicked. Note that AUC is sort a measure for evaluating ranking accuracy.

Evaluation

hadoop fs -getmerge /user/hive/warehouse/kdd12track2.db/pa_predict pa_predict.tbl

gawk -F "\t" '{print $2;}' pa_predict.tbl > pa_predict.submit

pypy scoreKDD.py KDD_Track2_solution.csv  pa_predict.submit

Measure	Score
AUC	0.739722
NWMAE	0.049582
WRMSE	0.143698