Scoring H2O MOJO models with spark UDF and Scala

With H2O machine learning the best case is that your machine learning models can be exported as Java code so you can use them for scoring in any platform which supports Java. H2O algorithms generates POJO and MOJO models which does not require H2O runtime to score which is great for any enterprise. You can learn more about H2O POJO and MOJO models here.

Here is the Spark Scala code which shows how to score the H2O MOJO model by loading it from the disk and then using RowData object to pass as row to H2O easyPredict class:

import _root_.hex.genmodel.GenModel
import _root_.hex.genmodel.easy.{EasyPredictModelWrapper, RowData}
import _root_.hex.genmodel.easy.prediction
import _root_.hex.genmodel.MojoModel
import _root_.hex.genmodel.easy.RowData

// Load Mojo
val mojo = MojoModel.load("/Users/avkashchauhan/learn/customers/mojo_bin/gbm_model.zip")
val easyModel = new EasyPredictModelWrapper(mojo)

// Get Mojo Details
var features = mojo.getNames.toBuffer

// Creating the row
val r = new RowData
r.put("AGE", "68")
r.put("RACE", "2")
r.put("DCAPS", "2")
r.put("VOL", "0")
r.put("GLEASON", "6")

// Performing the Prediction
val prediction = easyModel.predictBinomial(r).classProbabilities

Above the MOJO model is stored into local file system as gbm_prostate_model.zip and it is loaded as resources inside the Scala code.  The full execution of above code is available here.

Following is the simple Java code which shows how you could use the same code to write a Java application to perform scoring based on H2O MOJO Model:

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;

public class main {
  public static void main(String[] args) throws Exception {
    EasyPredictModelWrapper model = new EasyPredictModelWrapper(MojoModel.load("gbm_prostate_model.zip"));

    hex.genmodel.GenModel mojo = MojoModel.load("gbm_prostate_model.zip");

    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());

    for (int i = 0; i < mojo.getNumCols(); i++) {
      String[] domainValues = mojo.getDomainValues(i);
      System.out.println(Arrays.toString(domainValues));
    }

    RowData row = new RowData();
    row.put("AGE", "68");
    row.put("RACE", "2");
    row.put("DCAPS", "2");
    row.put("VOL", "0");
    row.put("GLEASON", "6");

    BinomialModelPrediction p = model.predictBinomial(row);
    System.out.println("Has penetrated the prostatic capsule (1=yes; 0=no): " + p.label);
    System.out.print("Class probabilities: ");
    for (int i = 0; i < p.classProbabilities.length; i++) {
      if (i > 0) {
    System.out.print(",");
      }
      System.out.print(p.classProbabilities[i]);
    }
    System.out.println("");
  }
}

Thats it, enjoy!!

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Calculating AUC and GINI model metrics for logistic classification

For logistics classification problem we use AUC metrics to check the model performance. The higher is better however any value above 80% is considered good and over 90% means the model is behaving great.

AUC is an abbreviation for Area Under the Curve. It is used in classification analysis in order to determine which of the used models predicts the classes best. An example of its application are ROC curves. Here, the true positive rates are plotted against false positive rates. You can learn more about AUC in this QUORA discussion.

We will also look for GINI metric which you can learn from wiki.  In this example we will learn how AUC and GINI model metric is calculated using True Positive Results (TPR) and False Positive Results (FPR) values from a given test dataset.

You can get the full working Jupyter Notebook here from my Github.

Lets build a logistic classification model in H2O using the prostate data set:

Preparation of H2O environment and dataset:

## Importing required libraries
import h2o
import sys
import pandas as pd
from h2o.estimators.gbm import H2OGradientBoostingEstimator

## Starting H2O machine learning cluster
h2o.init()

## Importing dataset
local_url = "https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv"
df = h2o.import_file(local_url)

## defining feaures and response column
y = "CAPSULE"
feature_names = df.col_names
feature_names.remove(y)

## setting our response column to catagorical so our model classify the problem
df[y] = df[y].asfactor()

Now we will be splitting the dataset into 3 sets for training, validation and test:

df_train, df_valid, df_test = df.split_frame(ratios=[0.8,0.1])
print(df_train.shape)
print(df_valid.shape)
print(df_test.shape)

Setting  H2O GBM Estimator and building GBM Model:

prostate_gbm = H2OGradientBoostingEstimator(model_id = "prostate_gbm",
 ntrees=500,
 learn_rate=0.001,
 max_depth=10,
 score_each_iteration=True)

## Building H2O GBM Model:
prostate_gbm.train(x = feature_names, y = y, training_frame=df_train, validation_frame=df_valid)

## Understand the H2O GBM Model
prostate_gbm

Generating model performance with training, validation & test datasets:

train_performance = prostate_gbm.model_performance(df_train)
valid_performance = prostate_gbm.model_performance(df_valid)
test_performance = prostate_gbm.model_performance(df_test)

Let’s take a look at the AUC metrics provided by Model performance:

print(train_performance.auc())
print(valid_performance.auc())
print(test_performance.auc())
print(prostate_gbm.auc())

Let’s take a look at the GINI metrics provided by Model performance:

print(train_performance.gini())
print(valid_performance.gini())
print(test_performance.gini())
print(prostate_gbm.gini())

Let generate the predictions using test dataset:

predictions = prostate_gbm.predict(df_test)
## Here we will get the probability for the 'p1' values from the prediction frame:
predict_probability = predictions['p1']

Now we will import required scikit-learn libraries to generate AUC manually:

from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import random

Lets get the real response results from the test data frame:

actual = df_test[y].as_data_frame()
actual_list = actual['CAPSULE'].tolist()
print(actual_list)

Now lets get the results probabilities from the prediction frame:

predictions_temp = predict_probability_x['p1'].as_data_frame()
predictions_list = predictions_temp['p1'].tolist()
print(predictions_list)

Calculating False Positive Rate and True Positive Rate:

Lets calculate TPR, FPR and Threshold metrics from the predictions and original data frame
– False Positive Rate (fpr)
– True Positive Rate (tpr)
– Threashold

fpr, tpr, thresholds = roc_curve(actual_list, predictions_list)
roc_auc = auc(fpr, tpr)
print(roc_auc)
print(test_performance.auc())

Note: Above you will see that our calculated ROC values is exactly same as given by model performance for test dataset. 

Lets plot the AUC Curve using matplotlib:

plt.title('ROC (Receiver Operating Characteristic)')
plt.plot(fpr, tpr, 'b',
label='AUC = %0.4f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate (TPR)')
plt.xlabel('False Positive Rate (FPR)')
plt.show()

Screen Shot 2017-10-19 at 10.30.21 PM

This is how GINI metric is calculated from AUC:

GINI = (2 * roc_auc) - 1
print(GINI)
print(test_performance.gini())

Note: Above you will see that our calculated GINI values is exactly same as given by model performance for test dataset.

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")

Note:

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 " "
 line.split("""\W+""")
 .map(_.toLowerCase)

//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 = "172.16.181.131" 
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 ;
connection_string='jdbc:netezza://'+dsn_hostname+':'+dsn_port+'/'+dsn_database
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)))
print(result[0])

Step 5: Printing all records

for i in range(len(result)):
    print(result[i])

Step 6: Closing all connections

curs.close()
conn.close()

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 {
            Class.forName("org.netezza.Driver");
            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);
                i++;
            }
            if (i==0){
                System.out.println(" No data found");
            }
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            try {
                if( rs != null) 
                    rs.close();
                if( st!= null)
                    st.close();
                if( conn != null)
                    conn.close();
            } catch (SQLException e1) {
                    e1.printStackTrace();
                }
        }
    }
}

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:

h2o.init()
df = h2o.import_file('https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate.csv')
y = 'CAPSULE'
x = df.col_names
x.remove(y)
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-3.14.0.3/h2o.jar'
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:
 gbm_cv3.download_mojo(mojo_file_name)

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.")
    else: 
        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 + "\')")
    else:
        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
Image(filename='/Users/avkashchauhan/Downloads/my_gbm_tree_0.png')

tree-0

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

# Just pass the Tree Image file name depending on your tree
Image(filename='/Users/avkashchauhan/Downloads/my_gbm_tree_1.png')

tree-1

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

# Just pass the Tree Image file name depending on your tree
Image(filename='/Users/avkashchauhan/Downloads/my_gbm_tree_2.png')

tree-2

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

Just pass the Tree Image file name depending on your tree

Image(filename=’/Users/avkashchauhan/Downloads/my_gbm_tree_3.png’)

tree-3

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:

stackedEnsembleModel

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:

dlModel

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!!