Flatten complex nested parquet files on Hadoop with Herringbone


Herringbone is a suite of tools for working with parquet files on hdfs, and with impala and hive.https://github.com/stripe/herringbone

Please visit my github and this specific page for more details.


Note: You must be using a Hadoop machine and herringbone needs Hadoop environmet.

Pre-requsite : Thrift

  • Thrift 0.9.1 (MUST have 0.9.1 as 0.9.3 and 0.10.0 will give error while packaging)
  • Get thrift 0.9.1 Link

Pre-requsite : Impala

  • First setup Cloudera repo in your machine:
  • Install Impala
    • Install impala : $ sudo apt-get install impala
    • Install impala Server : $ sudo apt-get install impala-server
    • Install impala stat-store : $ sudo apt-get install impala-state-store
    • Install impala shell : $ sudo apt-get install impala-shell
    • Verify : impala : $ impala-shell
Starting Impala Shell without Kerberos authentication
Connected to mr-0xd7-precise1.0xdata.loc:21000
Server version: impalad version 2.6.0-cdh5.8.4 RELEASE (build 207450616f75adbe082a4c2e1145a2384da83fa6)
Welcome to the Impala shell. Press TAB twice to see a list of available commands.

Copyright (c) 2012 Cloudera, Inc. All rights reserved.

(Shell build version: Impala Shell v1.4.0-cdh4-INTERNAL (08fa346) built on Mon Jul 14 15:52:52 PDT 2014)

Building : Herringbone source

Here is the successful herringbone “mvn package” command log for your review:

[INFO] Scanning for projects...
[INFO] ------------------------------------------------------------------------
[INFO] Reactor Build Order:
[INFO] Herringbone Impala
[INFO] Herringbone Main
[INFO] Herringbone
[INFO] ------------------------------------------------------------------------
[INFO] Building Herringbone Impala 0.0.2
[INFO] ------------------------------------------------------------------------
[INFO] ------------------------------------------------------------------------
[INFO] Building Herringbone 0.0.1
[INFO] ------------------------------------------------------------------------
[INFO] ------------------------------------------------------------------------
[INFO] Reactor Summary:
[INFO] Herringbone Impala ................................. SUCCESS [ 2.930 s]
[INFO] Herringbone Main ................................... SUCCESS [ 13.012 s]
[INFO] Herringbone ........................................ SUCCESS [ 0.000 s]
[INFO] ------------------------------------------------------------------------
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 16.079 s
[INFO] Finished at: 2017-10-06T11:27:20-07:00
[INFO] Final Memory: 90M/1963M
[INFO] ------------------------------------------------------------------------

Using Herringbone

Note: You must have fiels on Hadoop, not on local file system

Verify the file on Hadoop:

  • ~/herringbone$ hadoop fs -ls /user/avkash/file-test1.parquet
  • -rw-r–r– 3 avkash avkash 1463376 2017-09-13 16:56 /user/avkash/file-test1.parquet
  • ~/herringbone$ bin/herringbone flatten -i /user/avkash/file-test1.parquet
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/avkash/herringbone/herringbone-main/target/herringbone-0.0.1-jar-with-dependencies.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/cloudera/parcels/CDH-5.8.4-1.cdh5.8.4.p0.5/jars/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
17/10/06 12:06:44 INFO client.RMProxy: Connecting to ResourceManager at mr-0xd1-precise1.0xdata.loc/
17/10/06 12:06:45 INFO Configuration.deprecation: mapred.max.split.size is deprecated. Instead, use mapreduce.input.fileinputformat.split.maxsize
17/10/06 12:06:45 INFO input.FileInputFormat: Total input paths to process : 1
17/10/06 12:06:45 INFO Configuration.deprecation: mapred.min.split.size is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize
1 initial splits were generated.
  Max: 1.34M
  Min: 1.34M
  Avg: 1.34M
1 merged splits were generated.
  Max: 1.34M
  Min: 1.34M
  Avg: 1.34M
17/10/06 12:06:45 INFO mapreduce.JobSubmitter: number of splits:1
17/10/06 12:06:45 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1499294366934_0707
17/10/06 12:06:45 INFO impl.YarnClientImpl: Submitted application application_1499294366934_0707
17/10/06 12:06:46 INFO mapreduce.Job: The url to track the job: http://mr-0xd1-precise1.0xdata.loc:8088/proxy/application_1499294366934_0707/
17/10/06 12:06:46 INFO mapreduce.Job: Running job: job_1499294366934_0707
17/10/06 12:06:52 INFO mapreduce.Job: Job job_1499294366934_0707 running in uber mode : false
17/10/06 12:06:52 INFO mapreduce.Job:  map 0% reduce 0%
17/10/06 12:07:22 INFO mapreduce.Job:  map 100% reduce 0%

Now verify the file:

~/herringbone$ hadoop fs -ls /user/avkash/file-test1.parquet-flat

Found 2 items
-rw-r--r--   3 avkash avkash          0 2017-10-06 12:07 /user/avkash/file-test1.parquet-flat/_SUCCESS
-rw-r--r--   3 avkash avkash    2901311 2017-10-06 12:07 /user/avkash/file-test1.parquet-flat/part-m-00000.parquet

Thats it, enjoy!!


H2O Word2Vec Tutorial with example in Scala

If you would like to know what is word2vec and why you should use it, there is lots of material available to scan.  You can learn more about H2O implementation of Word2Vec here, along with its configuration and interpretation.

In this Scala example we will use H2O Word2Vec algorithm to build a model using the given Text (as text file, or an Array) and then build Word2vec model from it.

Here is the full Scala code of the following example at my github.

Lets start H2O cluster first:

import org.apache.spark.h2o._
val h2oContext = H2OContext.getOrCreate(spark)

Now we will be importing required libraries to get our job done:

import scala.io.Source
import _root_.hex.word2vec.{Word2Vec, Word2VecModel}
import _root_.hex.word2vec.Word2VecModel.Word2VecParameters
import water.fvec.Vec

Now we will be creating a stop words list which are not useful for text mining and removed from the word source:

val STOP_WORDS = Set("ourselves", "hers", "between", "yourself", "but", "again", "there", "about", 
    "once", "during", "out", "very", "having", "with", "they", "own", "an", "be", "some", "for", "do", 
    "its", "yours", "such", "into", "of", "most", "itself", "other", "off", "is", "s", "am", "or", "who", "as", 
     "from", "him", "each", "the", "themselves", "until", "below", "are", "we", "these", "your", "his", "through", "don", "nor", "me", "were", "her", 
    "more", "himself", "this", "down", "should", "our", "their", "while", "above", "both", "up", 
    "to", "ours", "had", "she", "all", "no", "when", "at", "any", "before", "them", "same", "and", "been", "have", "in", "will", "on", "does", "yourselves", "then", "that", "because", "what", "over", "why", "so", "can", 
    "did", "not", "now", "under", "he", "you", "herself", "has", "just", "where", "too", "only", "myself", "which", "those", "i", "after", "few", "whom", "t", "being", "if", "theirs", "my", "against", "a", "by", "doing", 
    "it", "how", "further", "was", "here", "than")


Now lets ingest the text data we would want to run Word2Vec algorithms to vectorize the data first and then run machine learning experiment to it.

I have downloaded a free story “The Adventure of Sherlock Holmes” from Internet and using that as my source.  

val filename = "/Users/avkashchauhan/Downloads/TheAdventuresOfSherlockHolmes.txt"
val lines = Source.fromFile(filename).getLines.toArray
val sparkframe = sc.parallelize(lines)

Now lets defined the tokenize function which will convert out input text to tokens:

def tokenize(line: String) = {
 //get rid of nonWords such as punctuation as opposed to splitting by just " "

//Lets remove stopwords defined above
 .filterNot(word => STOP_WORDS.contains(word)) :+ null

Now we will be calling the tokenize function to create a list of labeled words:

val allLabelledWords = sparkframe.flatMap(d => tokenize(d))

Note: You can also use your own or a custom tokenize function from a library as well, you just need to map the function to the DataFrame.

Now lets convert the collection of label words into an H2O DataFrame:

val h2oFrame = h2oContext.asH2OFrame(allLabelledWords)

Here is the time now to use the H2O Word2Vec algorithm by configuring the parameters first:

val w2vParams = new Word2VecParameters
w2vParams._train = h2oFrame._key
w2vParams._epochs = 500
w2vParams._min_word_freq = 0
w2vParams._init_learning_rate = 0.05f
w2vParams._window_size = 20
w2vParams._vec_size = 20
w2vParams._sent_sample_rate = 0.0001f

Now we will perform the real action, building the model:

val w2v = new Word2Vec(w2vParams).trainModel().get()

Now we can apply the model to perform some actions on it:

Lets start first test by finding synonyms using this given word2vec model. We will be calling findSynonyms method by passing a given word  to find N synonyms, the results will be the top ‘count’ synonyms with their distance values:

w2v.findSynonyms("love", 3)
w2v.findSynonyms("help", 2)
w2v.findSynonyms("hate", 1)

Lets Transform words using w2v model and aggregate method average:

The transform() function takes an H2O Vec as the first parameter, where the vector needs to be extracted from the H2O frame h2oFrame.

val newSparkFrame = w2v.transform(h2oFrame.vec(0), Word2VecModel.AggregateMethod.NONE).toTwoDimTable()

Thats it, enjoy!!


Full working example of connecting Netezza from Java and python

Before start connecting you must make sure you can access the Netezza database and table from the machine where you are trying to run Java and or Python samples.

Connecting Netezza server from Python Sample

Check out my Ipython Jupyter Notebook with Python Sample

Step 1: Importing python jaydebeapi library

import jaydebeapi

Step 2: Setting Database connection settings

dsn_database = "avkash"            
dsn_hostname = "" 
dsn_port = "5480"                
dsn_uid = "admin"        
dsn_pwd = "password"      
jdbc_driver_name = "org.netezza.Driver"
jdbc_driver_loc = "/Users/avkashchauhan/learn/customers/netezza/nzjdbc3.jar"
###jdbc:netezza://" + server + "/" + dbName ;
url = '{0}:user={1};password={2}'.format(connection_string, dsn_uid, dsn_pwd)
print("URL: " + url)
print("Connection String: " + connection_string)

Step 3:Creating Database Connection

conn = jaydebeapi.connect("org.netezza.Driver", connection_string, {'user': dsn_uid, 'password': dsn_pwd},
                         jars = "/Users/avkashchauhan/learn/customers/netezza/nzjdbc3.jar")
curs = conn.cursor()

Step 4:Processing SQL Query

curs.execute("select * from allusers")
result = curs.fetchall()
print("Total records: " + str(len(result)))

Step 5: Printing all records

for i in range(len(result)):

Step 6: Closing all connections


Connecting Netezza server from Java Code Sample

Step 1: Have the Netezza driver as nzjdbc3.jar in a folder.

Step 2: Create netezzaJdbcMain.java as below in the same folder where nzjdbc3.jar is placed.

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.sql.Statement;
public class netezzaJdbcMain {
    public static void main(String[] args) {
        String server = "x.x.x.x";
        String port = "5480";
        String dbName = "_db_name_";
        String url = "jdbc:netezza://" + server + "/" + dbName ;
        String user = "admin";
        String pwd = "password";
        String schema = "db_schema";
        Connection conn = null;
        Statement st = null;
        ResultSet rs = null;
        try {
            System.out.println(" Connecting ... ");
            conn = DriverManager.getConnection(url, user, pwd);
            System.out.println(" Connected "+conn);
            String sql = "select * from allusers";
            st = conn.createStatement();
            rs = st.executeQuery(sql);

            System.out.println("Printing result...");
            int i = 0;
            while (rs.next()) {
                String userName = rs.getString("name");
                int year = rs.getInt("age");
                System.out.println("User: " + userName +
                        ", age is: " + year);
            if (i==0){
                System.out.println(" No data found");
        } catch (Exception e) {
        } finally {
            try {
                if( rs != null) 
                if( st!= null)
                if( conn != null)
            } catch (SQLException e1) {

Step 3: Compile code as below:

$ javac -cp nzjdbc3.jar -J-Xmx2g -J-XX:MaxPermSize=128m netezzaJdbcMin.java                                                                                                                                
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0

Note: You should see your main class is compiled without any problem.

Step 4: Run compiled class as below:

$ java -cp .:nzjdbc3.jar netezzaJdbcMain

 Connecting ...
 Connected org.netezza.sql.NzConnection@3feba861
Printing result...
User: John                , age is: 30
User: Jason               , age is: 26
User: Jim                 , age is: 20
User: Kyle                , age is: 21
User: Kim                 , age is: 27

Note: You will see results something as above.

Thats it, enjoy!!

Visualizing H2O GBM and Random Forest MOJO Models Trees in python

In this example we will build a tree based model first using H2O machine learning library and the save that model as MOJO. Using GraphViz/Dot library we will extract individual trees/cross validated model trees from the MOJO and visualize them. If you are new to H2O MOJO model, learn here.

You can also get full working Ipython Notebook for this example from here.

Lets build the model first using H2O GBM algorithm. You can also use Distributed Random Forest Model as well for tree visualization.

Let’s first import key python models:

import h2o
import subprocess
from IPython.display import Image

Now we will be building GBM Model using a public PROSTATE dataset:

df = h2o.import_file('https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate.csv')
x = df.col_names
df[y] = df[y].asfactor()
train, valid, test = df.split_frame(ratios=[.8,.1])
from h2o.estimators.gbm import H2OGradientBoostingEstimator
gbm_cv3 = H2OGradientBoostingEstimator(nfolds=3)
gbm_cv3.train(x=x, y=y, training_frame=train)

## Getting all cross validated models 
all_models = gbm_cv3.cross_validation_models()
print("Total cross validation models: " + str(len(all_models)))

Now lets set all the default parameters to create the graph tree first and then tree images (in PNG format) in the local disk. Make sure you have a writable path where you can create and save these intermediate files. You also need to provide the path for latest H2O (h2o.jar) which is used to generate MOJO Model.

mojo_file_name = "/Users/avkashchauhan/Downloads/my_gbm_mojo.zip"
h2o_jar_path= '/Users/avkashchauhan/tools/h2o-3/h2o-'
mojo_full_path = mojo_file_name
gv_file_path = "/Users/avkashchauhan/Downloads/my_gbm_graph.gv"

Now lets definie Image file name which we will generate from the Tree ID.  Based on Tree ID the image file will have my_gbm_tree_ID.png file name

image_file_name = "/Users/avkashchauhan/Downloads/my_gbm_tree"
Now we will be downloading GBM MOJO Model by saving to disk:

Now lets define the function to generate graphViz tree from the saved MOJO model:

def generateTree(h2o_jar_path, mojo_full_path, gv_file_path, image_file_path, tree_id = 0):
    image_file_path = image_file_path + "_" + str(tree_id) + ".png"
    result = subprocess.call(["java", "-cp", h2o_jar_path, "hex.genmodel.tools.PrintMojo", "--tree", str(tree_id), "-i", mojo_full_path , "-o", gv_file_path ], shell=False)
    result = subprocess.call(["ls",gv_file_path], shell = False)
    if result is 0:
        print("Success: Graphviz file " + gv_file_path + " is generated.")
        print("Error: Graphviz file " + gv_file_path + " could not be generated.")

Now lets defined the method to generate Tree image as PNG from the saved GraphViz tree:

def generateTreeImage(gv_file_path, image_file_path, tree_id):
    image_file_path = image_file_path + "_" + str(tree_id) + ".png"
    result = subprocess.call(["dot", "-Tpng", gv_file_path, "-o", image_file_path], shell=False)
    result = subprocess.call(["ls",image_file_path], shell = False)
    if result is 0:
        print("Success: Image File " + image_file_path + " is generated.")
        print("Now you can execute the follow line as-it-is to see the tree graph:") 
        print("Image(filename='" + image_file_path + "\')")
        print("Error: Image file " + image_file_path + " could not be generated.")

Note: I had to write 2 steps process above because If I put all in 1 step the process hung after graphviz is created.

Now lets generate tree by passing all parameters defined above and proper TREE ID as the last parameter.

#Just change the tree id in the function below to get which particular tree you want
generateTree(h2o_jar_path, mojo_full_path, gv_file_path, image_file_name, 3)

Now we will be generating PNG Tree Image from the saved GraphViz content.

generateTreeImage(gv_file_path, image_file_name, 3)
# Note: If this step hangs, you can look at "dot" active process in osx and try killing it

Lets visualize the main model tree:

# Just pass the Tree Image file name depending on your tree


Lets Visualize the first Cross Validation tree (Cross Validation ID- 1)

# Just pass the Tree Image file name depending on your tree


Lets Visualize the first Cross Validation tree (Cross Validation ID- 2)

# Just pass the Tree Image file name depending on your tree


Lets Visualize the first Cross Validation tree (Cross Validation ID- 3)

Just pass the Tree Image file name depending on your tree



After looking at these tree, you can visualize how the decision are made.

Helpful documentation:

Thats it, enjoy!!

Stacked Ensemble Model in Scala using H2O GBM and Deep Learning Models

In this full Scala sample we will be using H2O Stacked Ensembles algorithm. Stacked ensemble is a process of building models of various types first with cross-validation and keep fold columns for each model. In the next step building the stacked ensemble model using all the CV folds. You can learn more about Stacked Ensembles here.

In this Stacked Ensemble we will be using GBM and Deep Learning Algorithms and then finally building the Stacked Ensemble model using the GBM and Deep Learning models.

First lets import key classes specific to H2O:

import org.apache.spark.h2o._
import water.Key
import java.io.File

Now we will create H2O context so we can call key H2O function specific to data ingest and Deep Learning algorithms:

val h2oContext = H2OContext.getOrCreate(sc)
import h2oContext._
import h2oContext.implicits._

Lets import data from local file system as H2O Data Frame:

val prostateData = new H2OFrame(new File("/Users/avkashchauhan/src/github.com/h2oai/sparkling-water/examples/smalldata/prostate.csv"))

In this Stacked Ensemble we will be using GBM and Deep Learning Algorithms so lets first build the deep learning model:

import _root_.hex.deeplearning.DeepLearning
import _root_.hex.deeplearning.DeepLearningModel.DeepLearningParameters

val dlParams = new DeepLearningParameters()
dlParams._epochs = 100
dlParams._train = prostateData
dlParams._response_column = 'CAPSULE
dlParams._variable_importances = true
dlParams._nfolds = 5
dlParams._seed = 1111
dlParams._keep_cross_validation_predictions = true;
val dl = new DeepLearning(dlParams, Key.make("dlProstateModel.hex"))
val dlModel = dl.trainModel.get

Now lets build the GBM model:

import _root_.hex.tree.gbm.GBM
import _root_.hex.tree.gbm.GBMModel.GBMParameters

val gbmParams = new GBMParameters()
gbmParams._train = prostateData
gbmParams._response_column = 'CAPSULE
gbmParams._nfolds = 5
gbmParams._seed = 1111
gbmParams._keep_cross_validation_predictions = true;
val gbm = new GBM(gbmParams,Key.make("gbmRegModel.hex"))
val gbmModel = gbm.trainModel().get()

Now build the Stacked Ensemble Models so first we need classes required for Stacked Ensembles as below:

import _root_.hex.Model
import _root_.hex.StackedEnsembleModel
import _root_.hex.ensemble.StackedEnsemble

Now we will define Stacked Ensembles parameters as below:

val stackedEnsembleParameters = new StackedEnsembleModel.StackedEnsembleParameters()
stackedEnsembleParameters._train = prostateData._key
stackedEnsembleParameters._response_column = 'CAPSULE

Now we need to pass all the different algorithms we would want to use in the Stacked Ensemble by passing their keys as below:

type T_MODEL_KEY = Key[Model[_, _ <: Model.Parameters, _ <:Model.Output]]

// Option 1
stackedEnsembleParameters._base_models = Array(gbmRegModel._key.asInstanceOf[T_MODEL_KEY], dlModel._key.asInstanceOf[T_MODEL_KEY])
// Option 2 
stackedEnsembleParameters._base_models = Array(gbmRegModel, dlModel).map(model => model._key.asInstanceOf[T_MODEL_KEY])

// Note: You can choose any of the above option to pass the model keys

Finally defining the stacked ensemble job as below:

val stackedEnsembleJob = new StackedEnsemble(stackedEnsembleParameters)

And as the last steps let build the stacked ensemble model:

val stackedEnsembleModel = stackedEnsembleJob.trainModel().get();

Now we can take a look at our Stacked Ensemble model as below:


Thats it, enjoy!!

Helpful content: https://github.com/h2oai/h2o-3/blob/a554bffabda6770386a31d47e05f00543d7b9ac3/h2o-algos/src/test/java/hex/ensemble/StackedEnsembleTest.java


Logistic Regression with H2O Deep Learning in Scala

Here is the sample code which show using Feed Forward Network based Deep Learning algorithms from H2O to perform a logistic regression .

First lets import key classes specific to H2O

import org.apache.spark.h2o._
import water.Key
import java.io.File

Now we will create H2O context so we can call key H2O function specific to data ingest and Deep Learning algorithms:

val h2oContext = H2OContext.getOrCreate(sc)
import h2oContext._
import h2oContext.implicits._

Lets import data from local file system as H2O Data Frame:

val prostateData = new H2OFrame(new File("/Users/avkashchauhan/src/github.com/h2oai/sparkling-water/examples/smalldata/prostate.csv"))

Now lets import Deep Learning classes:

import root.hex.deeplearning.DeepLearning
import root.hex.deeplearning.DeepLearningModel.DeepLearningParameters

Now we will define all key parameters specific to H2O Deep Learning Algorithm

val dlParams = new DeepLearningParameters()
dlParams._epochs = 100
dlParams._train = prostateData
dlParams._response_column = 'CAPSULE
dlParams._variable_importances = true
dlParams._nfolds = 5
dlParams._seed = 1111
dlParams._keep_cross_validation_predictions = true;

Now we will create the Deep Learning Algorithm key first and then start the deep learning algorithm in blocking mode:

val dl = new DeepLearning(dlParams, Key.make("dlProstateModel.hex"))
val dlModel = dl.trainModel.get()

Lets learn more about our model:


Now we can perform the prediction by passing an H2O Dataframe (Here I am simply passing the original data frame however you can load your test  data frame and pass it as H2O frame to perform prediction.):

val predictionH2OFrame = dlModel.score(prostateData)('predict)
val predictionsFromModel = asRDD[DoubleHolder](predictionH2OFrame).collect.map(_.result.getOrElse(Double.NaN))

Thats it, enjoy!!



Scala Example with Grid Search and Hyperparameters for GBM in H2O

Here is the full source code for GBM Scala code to perform Grid Search and Hyper parameters optimization using H2O (here is the github code as well):

import org.apache.spark.SparkFiles
import org.apache.spark.h2o._
import org.apache.spark.examples.h2o._
import org.apache.spark.sql.{DataFrame, SQLContext}
import water.Key
import java.io.File

import water.support.SparkContextSupport.addFiles
import water.support.H2OFrameSupport._

// Create SQL support
implicit val sqlContext = spark.sqlContext
import sqlContext.implicits._

// Start H2O services
val h2oContext = H2OContext.getOrCreate(sc)
import h2oContext._
import h2oContext.implicits._

// Register files to SparkContext

// Import all year airlines data into H2O
val airlinesData = new H2OFrame(new File(SparkFiles.get("year2005.csv.gz")))

// Import weather data into Spark
val wrawdata = sc.textFile(SparkFiles.get("Chicago_Ohare_International_Airport.csv"),8).cache()
val weatherTable = wrawdata.map(_.split(",")).map(row => WeatherParse(row)).filter(!_.isWrongRow())

// Transfer data from H2O to Spark DataFrame
val airlinesTable = h2oContext.asDataFrame(airlinesData).map(row => AirlinesParse(row))
val flightsToORD = airlinesTable.filter(f => f.Dest==Some("ORD"))

// Use Spark SQL to join flight and weather data in spark

// Perform SQL Join on both tables
val bigTable = sqlContext.sql(
 |FROM FlightsToORD f
 |JOIN WeatherORD w
 |ON f.Year=w.Year AND f.Month=w.Month AND f.DayofMonth=w.Day""".stripMargin)

val trainFrame:H2OFrame = bigTable
withLockAndUpdate(trainFrame){ fr => fr.replace(19, fr.vec("IsDepDelayed").toCategoricalVec)}


import h2oContext.implicits._
import _root_.hex.tree.gbm.GBM
import _root_.hex.tree.gbm.GBMModel.GBMParameters

val gbmParams = new GBMParameters()

gbmParams._train = trainFrame
gbmParams._response_column = 'IsDepDelayed

import _root_.hex.genmodel.utils.DistributionFamily

gbmParams._distribution = DistributionFamily.bernoulli

val gbm = new GBM(gbmParams,Key.make("gbmModel.hex"))
val gbmModel = gbm.trainModel.get
// Same as above
val gbmModel = gbm.trainModel().get()

// Use model to estimate delay on training data
val predGBMH2OFrame = gbmModel.score(trainFrame)('predict)
val predGBMFromModel = asRDD[DoubleHolder](predGBMH2OFrame).collect.map(_.result.getOrElse(Double.NaN))

def let[A](in: A)(body: A => Unit) = {

import _root_.hex.grid.{GridSearch}
import _root_.hex.grid.GridSearch
import _root_.hex.ScoreKeeper

import water.Key
import scala.collection.JavaConversions._

val gbmHyperSpace: java.util.Map[String, Array[Object]] = Map[String, Array[AnyRef]](
 "_ntrees" -> (1 to 10).map(v => Int.box(100*v)).toArray,
 "_max_depth" -> (2 to 7).map(Int.box).toArray,
 "_learn_rate" -> Array(0.1, 0.01).map(Double.box),
 "_col_sample_rate" -> Array(0.3, 0.7, 1.0).map(Double.box),
 "_learn_rate_annealing" -> Array(0.8, 0.9, 0.95, 1.0).map(Double.box)

// @Snippet
import _root_.hex.grid.HyperSpaceSearchCriteria.RandomDiscreteValueSearchCriteria

val gbmHyperSpaceCriteria = let(new RandomDiscreteValueSearchCriteria) { c =>
 c.set_max_runtime_secs(4 * 60 /* seconds */)

// This step will create 
// If you will pass the code below it will run through also for long time
// val gs = GridSearch.startGridSearch(null, gbmParams, gbmHyperSpace);
val gbmGrid = GridSearch.startGridSearch(Key.make("gbmGridModel"),
 new GridSearch.SimpleParametersBuilderFactory[GBMParameters],

// Training Frame Info

// Looking at gird models by Keys
val mKeys = gbmGrid.getModelKeys()
gbmGrid.createSummaryTable(mKeys, "mse", true);
gbmGrid.createSummaryTable(mKeys, "rmse", true);

// Model Count

// All Models
val ms = gbmGrid.getModels()
val gbm =ms(0)
val gbm =ms(1)
val gbm =ms(2)

// All hyper parameters

Thats it, Enjoy!!


H2O backend and API processing through Rapids

H2O cluster support various frontend i.e. python, R, FLOW etc and all the functions at these various front ends are handled through H2O cluster backend through API. Frontend actions are translated into API and H2O backend handles these API through Rapid expressions. We will understand how these APIs are handled from backend.

Lets Start H2O from command line directly from h2o.jar

$ java -jar h2o.jar

Now use python to connect with H2O

> import h2o

> h2o.init()

> h2o.ls()

Note: You will see there are no keys as the result of h2o.ls()

> df = h2o.create_frame(cols=2, rows=5,integer_range=1,time_fraction=1)

> h2o.ls()

Note: Now you will see a new key shown as below:


0     py_32_sid_9613

Note: Above py_32_sid_9613 is the frame ID in H2O memory for the frame we just created using create_frame API.

> df

2013-09-26 19:47:37   1995-01-01 16:14:34

1983-12-04 04:05:07    1974-09-08 23:06:41

2015-03-03 01:56:36    1982-11-03 19:21:53

1979-10-20 08:35:22     1987-10-09 14:24:59

1990-09-26 11:56:17     1981-08-16 04:23:02

> df.sort([‘C1′,’C2’])

C1                                    C2

1979-10-20 08:35:22     1987-10-09 14:24:59

1983-12-04 04:05:07     1974-09-08 23:06:41

1990-09-26 11:56:17     1981-08-16 04:23:02

2013-09-26 19:47:37      1995-01-01 16:14:34

2015-03-03 01:56:36      1982-11-03 19:21:53

> h2o.ls()


0     py_32_sid_9613

1     py_34_sid_9613

Note: As we ran the sort operation on the given frame df, another temporary frame py_34_sid_9613 was created. If you have created a new data frame to store sorted records as below a new frame would have been created as well to store the results of frame ndf as below:

> ndf = df.sort([‘C1′,’C2’])

Now if you look at the H2O logs you will see how the Rapids are

09-08 11:10:33.204 20753 #02927-14 INFO: 
    POST /99/Rapids, parms: {ast=(tmp= py_34_sid_9613 
        (sort py_32_sid_9613 ['C1' 'C2'])), session_id=_sid_9613}

Looking into the above logs we can understand the following:

Function sort was applied on frame  py_32_sid_9613 with parameters as columns [‘C1′,’C2’] and the result of this operation is frame  py_34_sid_9613.

This is how you can decipher H2O Rapids for any H2O API you tried.

That’s all, enjoy!!

Getting all categorical for predictors in H2O POJO and MOJO models

Here is the Java/Scala code snippet which shows how you can get the categorical values for each enum/factor predictor from H2O POJO and MOJO Models:

to get the list of all column names in your POJO/MOJO model, you can try the following:


import java.io.*;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.prediction.*;
import hex.genmodel.MojoModel;
import java.util.Arrays;


## First use the POJO model class as below:
private static String modelClassName = "gbm_prostate_binomial";

##Then you can GenModel class to get info you are looking for as below:
hex.genmodel.GenModel rawModel;
rawModel = (hex.genmodel.GenModel) Class.forName(modelClassName).newInstance();

## Now you can get the results as below:
System.out.println("isSupervised : " + rawModel.isSupervised());
System.out.println("Columns Names :  " + Arrays.toString(rawModel.getNames()));
System.out.println("Response ID : " + rawModel.getResponseIdx());
System.out.println("Number of columns : " + rawModel.getNumCols());
System.out.println("Response Name : " + rawModel.getResponseName());

## Printing all categorical values for each predictors
for (int i = 0; i < rawModel.getNumCols(); i++) 
 String[] domainValues = rawModel.getDomainValues(i);
Output Results:
isSupervised : true
Response ID : 8
Number of columns : 8
[0, 1, 2]
Note: For all null values means the predictor was numeric values and all the categorical values are listed for the each enum/factor predictor.


## Lets assume you have MOJO model as gbm_prostate_binomial.zip
## You would need to load your model as below:
hex.genmodel.GenModel mojo = MojoModel.load("gbm_prostate_binomial.zip");

## Now you can get list of predictors as below:
System.out.println("isSupervised : " + mojo.isSupervised());
System.out.println("Columns Names : " + Arrays.toString(mojo.getNames()));
System.out.println("Number of columns : " + mojo.getNumCols());
System.out.println("Response ID : " + mojo.getResponseIdx());
System.out.println("Response Name : " + mojo.getResponseName());

## Printing all categorical values for each predictors
for (int i = 0; i < mojo.getNumCols(); i++) {
 String[] domainValues = mojo.getDomainValues(i);
Output Results:
isSupervised : true
Response ID : 8
Number of columns : 8
[0, 1, 2]
Note: For all null values means the predictor was numeric values and all the categorical values are listed for the each enum/factor predictor.

To can get help on using MOJO and POJO models visit the following:

That’s it, enjoy!!