đź“”
Data Science with Apache Spark
  • Preface
  • Contents
  • Basic Prerequisite Skills
  • Computer needed for this course
  • Spark Environment Setup
  • Dev environment setup, task list
  • JDK setup
  • Download and install Anaconda Python and create virtual environment with Python 3.6
  • Download and install Spark
  • Eclipse, the Scala IDE
  • Install findspark, add spylon-kernel for scala
  • ssh and scp client
  • Summary
  • Development environment on MacOS
  • Production Spark Environment Setup
  • VirtualBox VM
  • VirtualBox only shows 32bit on AMD CPU
  • Configure VirtualBox NAT as Network Adapter on Guest VM and Allow putty ssh Through Port Forwarding
  • Docker deployment of Spark Cluster
  • Create customized Apache Spark Docker container
  • Dockerfile
  • docker-compose and docker-compose.yml
  • Launch custom built Docker container with docker-compose
  • Entering Docker Container
  • Setup Hadoop, Hive and Spark on Linux without docker
  • Hadoop Preparation
  • Hadoop setup
  • Configure $HADOOP_HOME/etc/hadoop
  • HDFS
  • Start and stop Hadoop
  • Work with Hadoop and HDFS file system
  • Connect to Hadoop web interface port 50070 and 8088
  • Install Hive
  • hive home
  • Initialize hive schema
  • Start hive metastore service.
  • hive-site.xml
  • Hive client
  • Setup Apache Spark
  • Spark Home
  • Jupyter-notebook server
  • Python 3 Warm Up
  • Basics
  • Iterables/Collections
  • Strings
  • List
  • Tuple
  • Dictionary
  • Set
  • Conditional statement
  • for loop
  • while loop
  • Functions and methods
  • map and filter
  • map and filter takes function as input
  • lambda
  • Python Class
  • Input and if statement
  • Input from a file
  • Output to a file
  • try except
  • Python coding exercise
  • Scala Warm Up
  • Start Spylon-kernel on Jupyter-notebook
  • Type of Variable: Mutable or immutable
  • Block statement
  • Scala Data Type
  • Array in Scala
  • Methods
  • Functions
  • Anonymous function
  • Scala map and filter methods
  • Class
  • Objects
  • Trait
  • Tuple in Scala
  • List/Seq
  • Set in Scala
  • Scala Map
  • Scala if statement
  • Scala for loop
  • Scala While Loop
  • Scala Exceptions + try catch finally
  • Scala coding exercise
  • Run a program to estimate pi
  • Common Spark command line
  • Run Scala code with spark-submit
  • Python with Apache Spark using Jupyter notebook
  • Spark Core Introduction
  • Spark and Scala Version
  • Basic Spark Package
  • Resilient Distributed Datasets (RDDs)
  • RDD Operations
  • Passing Function to Spark
  • Printing elements of an RDD
  • Working with key value pair
  • RDD Transformation Functions
  • RDD Action Functions
  • SPARK SQL
  • SQL
  • Datasets and DataFrames
  • SparkSession
  • Creating DataFrames
  • Running SQL Queries Programmatically
  • Issue from running Cartesian Join Query
  • Creating Datasets
  • Interoperating with RDD
  • Untyped User-Defined Aggregate Functions
  • Generic Load/Save Functions
  • Manually specify file option
  • Run SQL on files directly
  • Save Mode
  • Saving to Persistent Tables
  • Bucketing, Sorting and Partitioning
  • Apache Arrow
  • Install Python Arrow Module PyArrow
  • Issue might happen import PyArrow
  • Enabling for Conversion to/from Pandas in Python
  • Connect to any data source the same consistent way
  • Spark SQL Implementation Example in Scala
  • Run scala code in Eclipse IDE
  • Hive Integration, run SQL or HiveQL queries on existing warehouses.
  • Example: Enrich JSON
  • Integrate Tableau Data Visualization with Hive Data Warehouse and Apache Spark SQL
  • Connect Tableau to Spark SQL running in VM with VirtualBox with NAT
  • Issues with connecting from Tableau to Spark SQL
  • SPARK Streaming
  • Discretized Streams (DStreams)
  • Transformations on DStreams
  • map(func)
  • filter(func)
  • repartition(numPartitions)
  • union(otherStream)
  • reduce(func)
  • count()
  • countByValue()
  • reduceByKey(func, [numTasks])
  • join(otherStream, [numTasks])
  • cogroup(otherStream, [numTasks])
  • transform(func)
  • updateStateByKey(func)
  • Scala Tips for updateStateByKey
  • repartition(numPartitions)
  • DStream Window Operations
  • DStream Window Transformation
  • countByWindow(windowLength, slideInterval)
  • reduceByWindow(func, windowLength, slideInterval)
  • reduceByKeyAndWindow(func, windowLength, slideInterval, [numTasks])
  • reduceByKeyAndWindow(func, invFunc, windowLength, slideInterval, [numTasks])
  • countByValueAndWindow(windowLength, slideInterval, [numTasks])
  • window(windowLength, slideInterval)
  • Window DStream print(n)
  • saveAsTextFiles(prefix, [suffix])
  • saveAsObjectFiles(prefix, [suffix])
  • saveAsHadoopFiles(prefix, [suffix])
  • foreachRDD(func)
  • Build Twitter Scala API Library for Spark Streaming using sbt
  • Spark Streaming with Twitter, you can get public tweets by using Twitter API.
  • Spark streaming use case with Python
  • Spark Graph Computing
  • Spark Graph Computing Continue
  • Graphx
  • Package org.apache.spark.graphx
  • Edge Class
  • EdgeContext Class
  • EdgeDirection Class
  • EdgeRDD Class
  • EdgeTriplet Class
  • Graph Class
  • GraphLoader Object
  • GraphOps Class
  • GraphXUtils Object
  • PartitionStrategy Trait
  • Pregel Object
  • TripletFields Class
  • VertexRDD Class
  • Package org.apache.spark.graphx.impl
  • AggregatingEdgeContext Class
  • EdgeRDDImpl Class
  • Class GraphImpl<VD,ED>
  • Class VertexRDDImpl<VD>
  • Package org.apache.spark.graphx.lib
  • Class ConnectedComponents
  • Class LabelPropagation
  • Class PageRank
  • Class ShortestPaths
  • Class StronglyConnectedComponents
  • Class SVDPlusPlus
  • Class SVDPlusPlus.Conf
  • Class TriangleCount
  • Package org.apache.spark.graphx.util
  • Class BytecodeUtils
  • Class GraphGenerators
  • Graphx Example 1
  • Graphx Example 2
  • Graphx Example 3
  • Spark Graphx Describes Organization Chart Easy and Fast
  • Page Rank with Apache Spark Graphx
  • bulk synchronous parallel with Google Pregel Graphx Implementation Use Cases
  • Tree and Graph Traversal with and without Spark Graphx
  • Graphx Graph Traversal with Pregel Explained
  • Spark Machine Learning
  • Binary Classification
  • Multiclass Classification
  • Regression
  • Correlation
  • Image Data Source
  • ML DataFrame is SQL DataFrame
  • ML Transformer
  • ML Estimator
  • ML Pipeline
  • Transformer/Estimator Parameters
  • Extracting, transforming and selecting features
  • TF-IDF
  • Word2Vec
  • FeatureHasher
  • Tokenizer
  • CountVectorizer
  • StopWordRemover
  • n-gram
  • Binarizer
  • PCA
  • PolynomialExpansion
  • StringIndexer
  • Discrete Cosine Transform (DCT)
  • One-hot encoding
  • StandardScaler
  • IndexToString
  • VectorIndexer
  • Interaction
  • Normalizer
  • MinMaxScaler
  • MaxAbScaler
  • Bucketizer
  • ElementwiseProduct
  • SQLTransformer
  • VectorAssembler
  • VectorSizeHint
  • QuantileDiscretizer
  • Imputer
  • VectorSlicer
  • RFormula
  • ChiSqSelector
  • Locality Sensitive Hashing
  • MinHash for Jaccard Distance
  • Classification and Regression
  • LogisticRegression
  • OneVsRest
  • Naive Bayes classifiers
  • Decision trees
  • Random forests
  • Gradient-boosted trees (GBTs)
  • Multilayer perceptron classifier
  • Linear Support Vector Machine
  • Linear Regression
  • Generalized linear regression
  • Isotonic regression
  • Decision Tree Regression
  • Random Forest Regression
  • Gradient-boosted tree regression
  • Survival regression
  • Clustering
  • k-means
  • Latent Dirichlet allocation or LDA
  • Bisecting k-means
  • A Gaussian Mixture Model
  • Collaborative filtering
  • Frequent Pattern Mining
  • FP-Growth
  • PrefixSpan
  • ML Tuning: model selection and hyperparameter tuning
  • Model selection (a.k.a. hyperparameter tuning)
  • Cross-Validation
  • Train-Validation Split
  • Spark Machine Learning Applications
  • Apache Spark SQL & Machine Learning on Genetic Variant Classifications
  • Data Visualization with Vegas Viz and Scala with Spark ML
  • Apache Spark Machine Learning with Dremio Data Lake Engine
  • Dremio Data Lake Engine Apache Arrow Flight Connector with Spark Machine Learning
  • Neural Network with Apache Spark Machine Learning Multilayer Perceptron Classifier
  • Setup TensorFlow, Keras, Theano, Pytorch/torchvision on the CentOS VM
  • Virus Xray Image Classification with Tensorflow Keras Python and Apache Spark Scala
  • Appendix -- Video Presentations
  • References
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Data Visualization with Vegas Viz and Scala with Spark ML

PreviousApache Spark SQL & Machine Learning on Genetic Variant ClassificationsNextApache Spark Machine Learning with Dremio Data Lake Engine

Last updated 5 years ago

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If you are python programmer working on data science, you are certainly very familiar with Matplotlib to visualize your classification or regression result, especially when you are using jupyter notebook. Matplotlib.pyplot is standard tool for data visualization, but there is one problem, it is only for Python.

If you code in Scala, you will need to use alternative, one of the great tools for data visualization with Scala is Vegas.

Vegas works with Jupyter-scala kernel, which is not necessarily tightly integrated with Apache Spark like for example, Spylon-kernel does. jupyter-scala kernel is light weight, great for pure Scala coding without Apache Spark, I have done pure coding with Scala with Jupyter-scala kernel when I do not need to use Apache Spark.

Currently, as I am typing this text, Vegas-viz is only available on Maven repository, up to Scala version 2.11, if you are running Scala 2.12, it is not yet available from Maven repository, meaning you can not download needed jar files from Maven.

The use case I would like to demonstrate is to integrate light weight Jupyter-scala kernel, now it is called Almond

and Apache Spark.

To make Vegas-viz work, you need up to Scala 2.11, you can setup Apache Spark 2.4.4 with Hadoop 2.7 that includes Scala 2.11.

This assume you have Jupyter-notebook already. Then you just follow the install Almond instructions available on Almond website to install Jupyter-scala kernel.

Once installed, you are set to play with Jupyter-scala on Jupyter-notebook and Vegas-viz data visualization tool.

Here is a simple Scala code on Linear Regression from Apache Spark ML library to run under Almond/Jupyter-scala kernel on Jupyter-notebook.

//start with pulling Vegas-viz library jars from Maven repository, your machines needs to connect to the
// internet
import $ivy.`org.vegas-viz::vegas:0.3.11`
import $ivy.`org.vegas-viz::vegas-spark:0.3.11`
//You will see lots of downloads from running the above 2 lines
//Once done, you can import Vegas library for plotting
import vegas._
import vegas.render.WindowRenderer._
//Almond/Jupyter-scala does not integrated with Spark, so you will need to integrate it manually
//by download necessary jars from Maven, for the Spark libraries need to accomplish this demo
import $ivy.`org.jupyter-scala::spark:0.4.2`
import $ivy.`org.apache.spark::spark-sql:2.4.4`
//Again lots of downloads from above 2 lines, once download is done, you are ready to import Spark libs
import org.apache.spark.SparkContext._
import org.apache.spark.rdd._
import org.apache.spark.util.LongAccumulator
import org.apache.log4j._
import org.apache.spark.sql._
//Create Spark session
Logger.getLogger(“org”).setLevel(Level.ERROR)
val spark = SparkSession
.builder
.appName(“Vegas”)
.master(“local[*]”)
.config(“spark.sql.warehouse.dir”, “file:///tmp”)
.getOrCreate()
//and SparkContext sc
val sc = spark.sparkContext
//Even a simple LinearRegression, it is from Spark ML library, and Jupyter-Scala does not have it.
//Then download from Maven for Spark 2.4.4
import $ivy.`org.apache.spark::spark-mllib:2.4.4`
//now import Vegas-viz library
import vegas._
import vegas.data.External._
//Generate random dataset
import scala.math._
var X=Array[Double]()
var Y=Array[Double]()
//Create 100 random pairs of data points in X and Y Array with Double datatype, ranging from 0 to 10
for (i<-0 until 100)
{
var x=random*10
var b=random*10
X:+=x
Y:+=3*x+b
}
//You will notice the dataset is random, how Y=3X+b, it will fit nicely with LinearRegression
//That is not the point, the point is to play with Vegas-viz data visualization
//Create features, label Spark dataframe that sorted by features
import spark.implicits._
val df = sc.parallelize(X zip Y).toDF(“features”,”label”).sort(“features”)
df.show(3,false)
+ — — — — — — — — — -+ — — — — — — — — — +
|features |label |
+ — — — — — — — — — -+ — — — — — — — — — +
|0.12428231703539572|2.6330542403970045|
|0.12875124825893702|9.111634435646987 |
|0.21021408668144725|10.037147132043941|
+ — — — — — — — — — -+ — — — — — — — — — +
only showing top 3 rows
//Import Vegas plotting libraries
import vegas._
import vegas.render.WindowRenderer._
import vegas.sparkExt._
//Plot original Spark dataframe to see how these 100 data points visually
val plot = Vegas(“linear regression”,width=800, height=600).
withDataFrame(df).
mark(Point).
encodeX(“features”, Quantitative).
encodeY(“label”, Quantitative).
mark(Point).
show
//LinearRegression is to find the best fit straight line in the form of
// y_pred=coeffient*X+Intercept
//In simple term, coeffient is weight, intercept is bias
// Below is to call Apache Spark ML LinearRegression API to finish the job
import org.apache.spark.ml.feature.VectorAssembler
//Vectorize feature columns into “feature_new”
val vectorAssembler = new VectorAssembler()
.setInputCols(Array(“features”))
.setOutputCol(“features_new”)
//Create a dataframe that feature column in vector and sorted, that is required by Spark ML API
var vector_df = vectorAssembler.transform(df)
vector_df = vector_df.select(“features_new”, “label”).sort(“features_new”)
vector_df.show(3,false)

/*
output:
+---------------------+------------------+
|features_new         |label             |
+---------------------+------------------+
|[0.12428231703539572]|2.6330542403970045|
|[0.12875124825893702]|9.111634435646987 |
|[0.21021408668144725]|10.037147132043941|
+---------------------+------------------+
only showing top 3 rows


*/
//Split 100 datapoints to 70 for training and 30 for testing
val splits = vector_df.randomSplit(Array(0.7,0.3))
val train_df = splits(0)
val test_df = splits(1)
print(test_df.count())
//Create LinearRegression model with train_df
import org.apache.spark.ml.regression.LinearRegression
val lr = new LinearRegression()
.setFeaturesCol(“features_new”)
.setLabelCol(“label”)
.setRegParam(0.3)
.setElasticNetParam(0.8)
.setMaxIter(10)
val lr_model = lr.fit(train_df)
// Then test the trained model with test_df
val lr_predictions = lr_model.transform(test_df)
//Then display testing results
lr_predictions.select(“prediction”,”label”,”features_new”).sort(“features_new”).show(3,false)

/*
Output:
+-----------------+------------------+---------------------+
|prediction       |label             |features_new         |
+-----------------+------------------+---------------------+
|6.184323007474771|9.111634435646987 |[0.12875124825893702]|
|6.877166443408621|6.914045632096169 |[0.3713831792261324] |
|8.485151210258538|7.8911343006901555|[0.9344951735265017] |
+-----------------+------------------+---------------------+
only showing top 3 rows

*/

//evaluate the training quality, show R square score
import org.apache.spark.ml.evaluation.RegressionEvaluator
val lr_evaluator = new RegressionEvaluator().setPredictionCol(“prediction”).
setLabelCol(“label”).setMetricName(“r2”)
print(“R Squared (R2) on test data =” + lr_evaluator.evaluate(lr_predictions))

/*
Output:
R Squared (R2) on test data =0.9248598537955983
*/

//Now, based on 100 original data points, to generate 100 predicted value from weight and bias
// that comes from trained model.
// weight is coeffient, which is lr_model.coefficients(0)
// bias is intercept, which is lr_model.intercept
// Best fit linear equation is
// y_pred= weight*x+bias
// which is
//y_pred= lr_model.coefficients(0)*x+ lr_model.intercept
// now generate 100 y_pred from 100 original X based on
// y_pred= lr_model.coefficients(0)*x+ lr_model.intercept
var y_pred=Array[Double]()
for (i<-0 until X.length)
{
y_pred:+=(lr_model.coefficients(0))*X(i)+lr_model.intercept
}
//Create df_pref dataframe, sorted from Array X zip y_pred
val df_pred = sc.parallelize(X zip y_pred).toDF(“features”,”prediction”).sort(“features”)
// Plot it out together with original 100 datapoints
Vegas.layered(“linear regression”,width=800, height=600).
withLayers(
Layer().
withDataFrame(df).
mark(Point).
encodeX(“features”, Quantitative).
encodeY(“label”, Quantitative),
Layer().
withDataFrame(df_pred).
mark(Line).
encodeX(“features”, Quantitative).
encodeY(“prediction”, Quantitative)
).show

With Vegas-viz, you can visualize anything that you see fit from dataset you have on Scala.

As always, code used in this writing is in my github site:

https://github.com/geyungjen/jentekllc/blob/master/Spark/Scala/Enrich_Json/ScalaJsonChallengeNoSparkNew.ipynb
Vegas-viz.org
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