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Why not trying a Kaggle Challenge (Titanic) !

(This is a work in progress, I will update this article as soon as I get more free time. Hope you will enjoy it !)

Let's create the corresponding database first :

CREATE DATABASE IF NOT EXISTS kaggle_titanic;

And then load the data :

USE kaggle_titanic;
DROP TABLE IF EXISTS train_tmp;
ADD JAR csv-serde-1.1.2-0.11.0-all.jar;
CREATE TABLE train_tmp (
 PassengerId DOUBLE COMMENT 'regex : 999', 
 Survived DOUBLE COMMENT 'regex : 9', 
 Pclass INT COMMENT 'regex : 9', 
 Name STRING COMMENT 'regex : _Zzz!_Zzzzz_!Zzzzzzzzz_!!Zzzzzz!!_Zzzzzz_Zzzzz!!', 
 Sex STRING COMMENT 'regex : zzzzzz', 
 Age DOUBLE COMMENT 'regex : 99!9', 
 SibSp DOUBLE COMMENT 'regex : 9', 
 Parch DOUBLE COMMENT 'regex : 9', 
 Ticket STRING COMMENT 'regex : ZZZZZ!Z!Z!_9999999', 
 Fare DOUBLE COMMENT 'regex : 999!9999', 
 Cabin STRING COMMENT 'Z99_Z99_Z99_Z99', 
 Embarked STRING COMMENT 'regex : Z'
)
ROW FORMAT SERDE 'com.bizo.hive.serde.csv.CSVSerde'
with serdeproperties (
 "separatorChar" = ",",
 "quoteChar" = "\"",
 "escapeChar" = "\"
 ) 
STORED AS TEXTFILE;
LOAD DATA LOCAL INPATH 'train.csv' INTO TABLE train_tmp;
DROP TABLE IF EXISTS train;
CREATE TABLE train STORED AS ORC AS SELECT * FROM train_tmp;
USE kaggle_titanic;
DROP TABLE IF EXISTS test_tmp;
ADD JAR csv-serde-1.1.2-0.11.0-all.jar;
CREATE TABLE test_tmp (
 PassengerId DOUBLE COMMENT 'regex : 999', 
 Pclass INT COMMENT 'regex : 9', 
 Name STRING COMMENT 'regex : _Zzz!_Zzzzz_!Zzzzzzzzz_!!Zzzzzz!!_Zzzzzz_Zzzzz!!', 
 Sex STRING COMMENT 'regex : zzzzzz', 
 Age DOUBLE COMMENT 'regex : 99!9', 
 SibSp DOUBLE COMMENT 'regex : 9', 
 Parch DOUBLE COMMENT 'regex : 9', 
 Ticket STRING COMMENT 'regex : ZZZZZ!Z!Z!_9999999', 
 Fare DOUBLE COMMENT 'regex : 999!9999', 
 Cabin STRING COMMENT 'Z99_Z99_Z99_Z99', 
 Embarked STRING COMMENT 'regex : Z'
)
ROW FORMAT SERDE 'com.bizo.hive.serde.csv.CSVSerde'
with serdeproperties (
 "separatorChar" = ",",
 "quoteChar" = "\"",
 "escapeChar" = "\"
 ) 
STORED AS TEXTFILE;
LOAD DATA LOCAL INPATH 'test.csv' INTO TABLE test_tmp;
DROP TABLE IF EXISTS test;
CREATE TABLE test STORED AS ORC AS SELECT * FROM test_tmp;
USE kaggle_titanic;
DROP TABLE IF EXISTS train_and_test;
CREATE TABLE train_and_test STORED AS ORC AS 
SELECT CAST(PassengerId AS INT) AS PassengerId, Survived , CAST(Pclass AS INT) , Name, Sex, CAST(Age AS DOUBLE) AS Age, CAST(SibSp AS INT) AS SibSp, CAST(Parch AS INT) AS Parch, Ticket , CAST(Fare AS DOUBLE) AS Fare , Cabin , Embarked 
FROM train
UNION ALL
SELECT CAST(PassengerId AS INT) AS PassengerId, CAST(NULL AS Double) AS Survived , CAST(Pclass AS INT) , Name, Sex, CAST(Age AS DOUBLE) AS Age , CAST(SibSp AS INT) AS SibSp, CAST(Parch AS INT) AS Parch, Ticket , CAST(Fare AS DOUBLE) AS Fare , Cabin , Embarked 
FROM test
;
USE kaggle_titanic;
DROP TABLE IF EXISTS train_and_test;
CREATE TABLE train_and_test STORED AS ORC AS
SELECT PassengerId , Survived , Pclass , Name, Sex, Age , SibSp , Parch  , Ticket , Fare , Cabin , Embarked
FROM train
UNION ALL
SELECT PassengerId , CAST(NULL AS Double) AS Survived , Pclass , Name, Sex, Age , SibSp , Parch  , Ticket , Fare , Cabin , Embarked
FROM test
;

With some quick SQL queries we can already get some good overview of the data, make sure your zeppelin is configured (as well as security)

7921-screenshot-2016-09-23-092715.png

7927-screenshot-2016-09-23-103014.png

Let's now clean the dataset :

USE kaggle_titanic;
DROP TABLE IF EXISTS train_and_test_transform;
CREATE TABLE train_and_test_transform STORED AS ORC AS 
SELECT PassengerId , Survived , Pclass , Name, regexp_extract(name, '([^,]*), ([^ ]*)(.*)', 2) AS title, Sex, CASE WHEN age IS NULL THEN 30 ELSE age END AS age , 
SibSp , Parch  , Ticket , Fare , Cabin , COALESCE(embarked, 'embarked_is_NULL') AS embarked, substring(cabin,1,1) AS cabin_letter, LENGTH(regexp_replace(cabin, '[^ ]', '')) AS nbr_of_space_cabin,
CASE WHEN age IS NULL THEN true ELSE false END AS c_flag_age_null,
CASE WHEN Cabin IS NULL THEN true ELSE false END AS c_flag_cabin_null, 
CASE WHEN embarked IS NULL THEN true ELSE false END AS c_flag_embarked_null
FROM train_and_test
;

----------------------------------------------------------------------------------------------------

(I will update this very soon)

And switch to spark-shell !

import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer, VectorAssembler}
import org.apache.spark.ml.classification.GBTClassificationModel;
import org.apache.spark.ml.classification.GBTClassifier;
val data_prep_1 = sqlContext.sql("SELECT * FROM kaggle_titanic.train_and_test_transform");
data_prep_1.show(10);

Let's do the data transformation and prepare the feature column

val indexerSex = new StringIndexer().setInputCol("sex").setOutputCol("sexIndexed")
val indexerTitle = new StringIndexer().setInputCol("title").setOutputCol("titleIndexed")
val indexerEmbarked = new StringIndexer().setInputCol("embarked").setOutputCol("embarkedIndexed")
val data_prep_2 = indexerSex.fit(data_prep_1).transform(data_prep_1)
val data_prep_3 = indexerTitle.fit(data_prep_2).transform(data_prep_2)
val data_prep_4 = indexerEmbarked.fit(data_prep_3).transform(data_prep_3)
val vectorAssembler = new VectorAssembler().setInputCols(Array("sexIndexed", "titleIndexed", "embarkedIndexed", "age", "fare", "pclass", "parch", "sibsp", "c_flag_cabin_null", "c_flag_null_embarked")).setOutputCol("features")
val data_prep_5 = vectorAssembler.transform(data_prep_4)
data_prep_5.show()
val data_prep_6 = data_prep_5.filter($"survived".isNotNull)
val indexerSurvived = new StringIndexer().setInputCol("survived").setOutputCol("survivedIndexed")
val data_prep_7 = indexerSurvived.fit(data_prep_6).transform(data_prep_6)

And now let's build the model

val gbt = new GBTClassifier().setLabelCol("survivedIndexed").setFeaturesCol("features").setMaxIter(50)
var model = gbt.fit(data_prep_7)

Finally we can gather the data to predict and use the model :

val data_test_1 = data_prep_5.filter($"survived".isNull)
var result = model.transform(data_test_1)
result.select("passengerid", "prediction").show(1000)

screenshot-2016-09-23-103014.png
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Revision #:
2 of 2
Last update:
‎08-17-2019 10:04 AM
Updated by:
 
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