📔
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
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  • Class
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  • Set in Scala
  • Scala Map
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  • 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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On this page
  • Introduction
  • Context
  • Objective
  • Class Label
  • ML Project Steps
  • Data Acquisition
  • Load the csv file into Spark SQL DataFrame
  • Data Exploration:
  • Evaluate schema:
  • Data Preprocessing
  • NULL replacement
  • Encode the String value to integer using StringIndexer
  • Make all integer column to double
  • Feature Vectorization
  • Normalize feature values in vector column features, using MinMax Scaler
  • Train/Test Split, randomly split 70% of rows for training, 30% for test
  • Training and Prediction
  • Algorithm Selection
  • Multilayer Perceptron Classifier, there are 45 feature columns, and binary classification, I construct a neural network of 4 layer as below:
  • Train and test the neural network
  • Logistic Regression Classifier
  • Take away:

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Apache Spark SQL & Machine Learning on Genetic Variant Classifications

PreviousSpark Machine Learning ApplicationsNextData Visualization with Vegas Viz and Scala with Spark ML

Last updated 4 years ago

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Introduction

Covid-19 has driven up the interest of the bioinformatics, the data science of collecting and analyzing complex biological data such as genetic codes and sequences, a combination of mathematics and biology.

Context

ClinVar is a public resource containing annotations about human genetic variants. Detail can be found on the National Institute of Health website.

These variants are usually manually classified by clinical laboratories on a categorical spectrum ranging from benign, likely benign, uncertain significance, likely pathogenic, and pathogenic. Variants that have conflicting classifications (from laboratory to laboratory) can cause confusion when clinicians or researchers try to interpret whether the variant has an impact on the disease of a given patient.

Objective

The objective is to predict whether a ClinVar variant will have conflicting classifications. This is presented as a binary classification problem, each record in the dataset is a genetic variant.

Conflicting classifications are when two of any of the following three categories are present for one variant, two submissions of one category are not considered conflicting.

· Likely Benign or Benign

· VUS (Variance of Uncertain Significance. A variation in a genetic sequence for which the association with disease risk is unclear)

· Likely Pathogenic or Pathogenic

Class Label

Conflicting classification has been assigned to the CLASS column. It is a binary representation of whether or not a variant has conflicting classifications, where 0 represents consistent classifications and 1 represents conflicting classifications.

The genetic variant dataset is public domain available from Kaggle

ML Project Steps

Data Acquisition

Download the csv file from Kaggle, place into a folder on Apache Spark driver node:

Load the csv file into Spark SQL DataFrame

import org.apache.spark._
import org.apache.spark.SparkContext._
import org.apache.spark.rdd._
import org.apache.spark.util.LongAccumulator
import org.apache.log4j._
import scala.collection.mutable.ArrayBuffer
import org.apache.spark.sql._
Logger.getLogger("org").setLevel(Level.ERROR)
 
val spark = SparkSession
    .builder
    .appName("genetic classifier")
    .master("local[*]")
    .config("spark.sql.warehouse.dir", "file:///tmp")
  .getOrCreate()
/*
Load the csv file into SparkSQL DataFrame, with options:
inferSchema=true, set the datatype of the feature columns based upon value data types
header=true, set the DataFrame column names from the headers of the csv file.
*/ 
val ds = spark.read.format("csv").option("inferSchema", "true").option("header", "true")
  .option("quote", "\"")
    .load("file:///home/bigdata2/dataset/clinvar_conflicting.csv")
val df: DataFrame = ds.toDF()

Data Exploration:

df.show(3)
/*
|CHROM|    POS|REF|ALT|AF_ESP|AF_EXAC|AF_TGP|            CLNDISDB|CLNDISDBINCL|               CLNDN|CLNDNINCL|             CLNHGVS|CLNSIGINCL|               CLNVC|               CLNVI|                  MC|ORIGIN|SSR|CLASS|Allele|     Consequence|  IMPACT| SYMBOL|Feature_type|       Feature|       BIOTYPE|EXON|INTRON|cDNA_position|CDS_position|Protein_position|Amino_acids| Codons|DISTANCE|STRAND|BAM_EDIT|                SIFT|         PolyPhen|MOTIF_NAME|MOTIF_POS|HIGH_INF_POS|MOTIF_SCORE_CHANGE|LoFtool|CADD_PHRED| CADD_RAW|BLOSUM62|
|    1|1168180|  G|  C|0.0771| 0.1002|0.1066|     MedGen:CN169374|         nan|       not_specified|      nan|NC_000001.10:g.11...|       nan|single_nucleotide...|UniProtKB_(protei...|SO:0001583|missen...|     1|nan|    0|     C|missense_variant|MODERATE|B3GALT6|  Transcript|   NM_080605.3|protein_coding| 1/1|  null|          552|         522|             174|        E/D|gaG/gaC|    null|     1|    null|           tolerated|           benign|      null|     null|        null|              null|   null|     1.053|-0.208682|       2|
|    1|1470752|  G|  A|   0.0|    0.0|   0.0|MedGen:C1843891,O...|         nan|Spinocerebellar_a...|      nan|NC_000001.10:g.14...|       nan|single_nucleotide...|OMIM_Allelic_Vari...|SO:0001583|missen...|     1|nan|    0|     A|missense_variant|MODERATE|TMEM240|  Transcript|NM_001114748.1|protein_coding| 4/4|  null|          523|         509|             170|        P/L|cCg/cTg|    null|    -1|      OK|deleterious_low_c...|           benign|      null|     null|        null|              null|   null|      31.0| 6.517838|      -3|
|    1|1737942|  A|  G|   0.0| 1.0E-5|   0.0|Human_Phenotype_O...|         nan|Strabismus|Nystag...|      nan|NC_000001.10:g.17...|       nan|single_nucleotide...|OMIM_Allelic_Vari...|SO:0001583|missen...|    35|nan|    1|     G|missense_variant|MODERATE|   GNB1|  Transcript|   NM_002074.4|protein_coding|6/12|  null|          632|         239|              80|        I/T|aTc/aCc|    null|    -1|      OK|         deleterious|probably_damaging|      null|     null|        null|              null|   null|      28.1| 6.061752|      -1|
 
only showing top 3 rows
*/

Notice there are lots missing values, i.e., null values in the dataset. Dataset having significant missing values usually affect training and prediction accuracy.

Some column values are large, like 6 digits, some column values are smaller than 1. Without addressing, this can confuse ML algorithm, which may regard some small values as statistically insignificant and disregard as result. This can be addressed in the data preprocessing using scaler. I plan to use MinMaxScaler, which will make all feature column values between 0 and 1

Evaluate schema:

df.printSchema

/*
root
 |-- CHROM: string (nullable = true)
 |-- POS: integer (nullable = true)
 |-- REF: string (nullable = true)
 |-- ALT: string (nullable = true)
 |-- AF_ESP: double (nullable = true)
 |-- AF_EXAC: double (nullable = true)
 |-- AF_TGP: double (nullable = true)
 |-- CLNDISDB: string (nullable = true)
 |-- CLNDISDBINCL: string (nullable = true)
 |-- CLNDN: string (nullable = true)
 |-- CLNDNINCL: string (nullable = true)
 |-- CLNHGVS: string (nullable = true)
 |-- CLNSIGINCL: string (nullable = true)
 |-- CLNVC: string (nullable = true)
 |-- CLNVI: string (nullable = true)
 |-- MC: string (nullable = true)
 |-- ORIGIN: integer (nullable = true)
 |-- SSR: string (nullable = true)
 |-- CLASS: integer (nullable = true)
 |-- Allele: string (nullable = true)
 |-- Consequence: string (nullable = true)
 |-- IMPACT: string (nullable = true)
 |-- SYMBOL: string (nullable = true)
 |-- Feature_type: string (nullable = true)
 |-- Feature: string (nullable = true)
 |-- BIOTYPE: string (nullable = true)
 |-- EXON: string (nullable = true)
 |-- INTRON: string (nullable = true)
 |-- cDNA_position: string (nullable = true)
 |-- CDS_position: string (nullable = true)
 |-- Protein_position: string (nullable = true)
 |-- Amino_acids: string (nullable = true)
 |-- Codons: string (nullable = true)
 |-- DISTANCE: integer (nullable = true)
 |-- STRAND: integer (nullable = true)
 |-- BAM_EDIT: string (nullable = true)
 |-- SIFT: string (nullable = true)
 |-- PolyPhen: string (nullable = true)
 |-- MOTIF_NAME: string (nullable = true)
 |-- MOTIF_POS: integer (nullable = true)
 |-- HIGH_INF_POS: string (nullable = true)
 |-- MOTIF_SCORE_CHANGE: double (nullable = true)
 |-- LoFtool: double (nullable = true)
 |-- CADD_PHRED: double (nullable = true)
 |-- CADD_RAW: double (nullable = true)
 |-- BLOSUM62: integer (nullable = true)
 
 */

The DataFrame has 46 columns, with one column CLASS as target/label and 45 columns as features.

Some of them are String data types, which need to be encoded into numeric datatype for ML algorithm to crunching them.

Distribution of row count of CLASS 1 (genetically conflicting) and 0 (genetically consistent) as following:

df.groupBy("CLASS").count().show()

/*
+-----+-----+
|CLASS|count|
+-----+-----+
|    1|16434|
|    0|48754|
+-----+-----+
*/

Data Preprocessing

NULL replacement

Replace null values in the String columns as “ABCD” and 0 in numeric columns

Encode the String value to integer using StringIndexer

Rearrange the DataFrame, after encoding, the first column of the resultant DataFrame is CLASS, the label.

import org.apache.spark.ml.feature.StringIndexer
var df1=df.select("CLASS")
val featureDF=df.drop("CLASS")
val typeArray=featureDF.dtypes
for (i<-0 until featureDF.columns.size)
  {
    if (typeArray(i)._2=="StringType")
      {
          var indexer = new StringIndexer().setInputCol(typeArray(i)._1).setOutputCol(typeArray(i)._1+"Index")
          var indexed = indexer.fit(featureDF.na.fill("ABCD").select(typeArray(i)._1)).transform(featureDF.na.fill("ABCD").select(typeArray(i)._1)) 
          var temp=indexed.select(typeArray(i)._1+"index")
          df1=df1.withColumn("id", monotonically_increasing_id())
          .join(temp.withColumn("id", monotonically_increasing_id()), Seq("id"))
      }
      else
      {
          var temp=featureDF.na.fill(0).select(typeArray(i)._1)
          df1=df1.withColumn("id", monotonically_increasing_id()).join(temp.withColumn("id", monotonically_increasing_id()), Seq("id"))
      }
  }
df1=df1.drop("id")
df1.printSchema
/*

root
 |-- CLASS: integer (nullable = true)
 |-- CHROMindex: double (nullable = false)
 |-- POS: integer (nullable = false)
 |-- REFindex: double (nullable = false)
 |-- ALTindex: double (nullable = false)
 |-- AF_ESP: double (nullable = false)
 |-- AF_EXAC: double (nullable = false)
 |-- AF_TGP: double (nullable = false)
 |-- CLNDISDBindex: double (nullable = false)
 |-- CLNDISDBINCLindex: double (nullable = false)
 |-- CLNDNindex: double (nullable = false)
 |-- CLNDNINCLindex: double (nullable = false)
 |-- CLNHGVSindex: double (nullable = false)
 |-- CLNSIGINCLindex: double (nullable = false)
 |-- CLNVCindex: double (nullable = false)
 |-- CLNVIindex: double (nullable = false)
 |-- MCindex: double (nullable = false)
 |-- ORIGIN: integer (nullable = false)
 |-- SSRindex: double (nullable = false)
 |-- Alleleindex: double (nullable = false)
 |-- Consequenceindex: double (nullable = false)
 |-- IMPACTindex: double (nullable = false)
 |-- SYMBOLindex: double (nullable = false)
 |-- Feature_typeindex: double (nullable = false)
 |-- Featureindex: double (nullable = false)
 |-- BIOTYPEindex: double (nullable = false)
 |-- EXONindex: double (nullable = false)
 |-- INTRONindex: double (nullable = false)
 |-- cDNA_positionindex: double (nullable = false)
 |-- CDS_positionindex: double (nullable = false)
 |-- Protein_positionindex: double (nullable = false)
 |-- Amino_acidsindex: double (nullable = false)
 |-- Codonsindex: double (nullable = false)
 |-- DISTANCE: integer (nullable = false)
 |-- STRAND: integer (nullable = false)
 |-- BAM_EDITindex: double (nullable = false)
 |-- SIFTindex: double (nullable = false)
 |-- PolyPhenindex: double (nullable = false)
 |-- MOTIF_NAMEindex: double (nullable = false)
 |-- MOTIF_POS: integer (nullable = false)
 |-- HIGH_INF_POSindex: double (nullable = false)
 |-- MOTIF_SCORE_CHANGE: double (nullable = false)
 |-- LoFtool: double (nullable = false)
 |-- CADD_PHRED: double (nullable = false)
 |-- CADD_RAW: double (nullable = false)
 |-- BLOSUM62: integer (nullable = false)
 
 */

Make all integer column to double

import org.apache.spark.sql.types._
val newDf = df1.select(df1.columns.map(c => col(c).cast(DoubleType)) : _*)
newDf.printSchema
/*
root
 |-- CLASS: double (nullable = true)
 |-- CHROMindex: double (nullable = false)
 |-- POS: double (nullable = false)
 |-- REFindex: double (nullable = false)
 |-- ALTindex: double (nullable = false)
 |-- AF_ESP: double (nullable = false)
 |-- AF_EXAC: double (nullable = false)
 |-- AF_TGP: double (nullable = false)
 |-- CLNDISDBindex: double (nullable = false)
 |-- CLNDISDBINCLindex: double (nullable = false)
 |-- CLNDNindex: double (nullable = false)
 |-- CLNDNINCLindex: double (nullable = false)
 |-- CLNHGVSindex: double (nullable = false)
 |-- CLNSIGINCLindex: double (nullable = false)
 |-- CLNVCindex: double (nullable = false)
 |-- CLNVIindex: double (nullable = false)
 |-- MCindex: double (nullable = false)
 |-- ORIGIN: double (nullable = false)
 |-- SSRindex: double (nullable = false)
 |-- Alleleindex: double (nullable = false)
 |-- Consequenceindex: double (nullable = false)
 |-- IMPACTindex: double (nullable = false)
 |-- SYMBOLindex: double (nullable = false)
 |-- Feature_typeindex: double (nullable = false)
 |-- Featureindex: double (nullable = false)
 |-- BIOTYPEindex: double (nullable = false)
 |-- EXONindex: double (nullable = false)
 |-- INTRONindex: double (nullable = false)
 |-- cDNA_positionindex: double (nullable = false)
 |-- CDS_positionindex: double (nullable = false)
 |-- Protein_positionindex: double (nullable = false)
 |-- Amino_acidsindex: double (nullable = false)
 |-- Codonsindex: double (nullable = false)
 |-- DISTANCE: double (nullable = false)
 |-- STRAND: double (nullable = false)
 |-- BAM_EDITindex: double (nullable = false)
 |-- SIFTindex: double (nullable = false)
 |-- PolyPhenindex: double (nullable = false)
 |-- MOTIF_NAMEindex: double (nullable = false)
 |-- MOTIF_POS: double (nullable = false)
 |-- HIGH_INF_POSindex: double (nullable = false)
 |-- MOTIF_SCORE_CHANGE: double (nullable = false)
 |-- LoFtool: double (nullable = false)
 |-- CADD_PHRED: double (nullable = false)
 |-- CADD_RAW: double (nullable = false)
 |-- BLOSUM62: double (nullable = false)
 
 */

Feature Vectorization

Apache Spark ML requires to place feature data to be in form of vector, i.e., place all feature columns into one vector, following code is to generate a column that is a vector that contains all feature columns

import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Vectors
val assembler = new VectorAssembler()
.setInputCols(Array("CHROMindex", "POS", "REFindex", "ALTindex", "AF_ESP", "AF_EXAC", "AF_TGP", "CLNDISDBindex"
        , "CLNDISDBINCLindex", "CLNDNindex", "CLNDNINCLindex", "CLNHGVSindex", "CLNSIGINCLindex", "CLNVCindex"
        , "CLNVIindex", "MCindex", "ORIGIN", "SSRindex", "Alleleindex", "Consequenceindex", "IMPACTindex"
        , "SYMBOLindex", "Feature_typeindex", "Featureindex", "BIOTYPEindex", "EXONindex", "INTRONindex"
        , "cDNA_positionindex", "CDS_positionindex", "Protein_positionindex", "Amino_acidsindex", "Codonsindex"
        , "DISTANCE", "STRAND", "BAM_EDITindex", "SIFTindex", "PolyPhenindex", "MOTIF_NAMEindex", "MOTIF_POS"
        , "HIGH_INF_POSindex", "MOTIF_SCORE_CHANGE", "LoFtool", "CADD_PHRED", "CADD_RAW", "BLOSUM62"))
.setOutputCol("features")
val output = assembler.transform(newDf)
Resultant output DataFrame now has a new column features that is the vector containing all feature columns.
output.select("features").show(2,false)
/*
 
|features                                                                                                                                                                                                                     
|(45,[0,1,2,3,4,5,6,11,14,16,18,21,23,25,27,28,29,30,31,33,35,36,42,43,44],[3.0,1168180.0,1.0,3.0,0.0771,0.1002,0.1066,326.0,27451.0,1.0,3.0,1614.0,1728.0,15.0,618.0,471.0,304.0,56.0,129.0,1.0,2.0,1.0,1.053,-0.208682,2.0])|
|(45,[0,1,2,3,7,9,11,14,16,18,21,23,25,27,28,29,30,31,33,34,35,36,42,43,44],[3.0,1470752.0,1.0,1.0,7922.0,8822.0,467.0,25430.0,1.0,1.0,2293.0,2054.0,9.0,1534.0,1465.0,116.0,13.0,17.0,-1.0,1.0,4.0,1.0,31.0,6.517838,-3.0])  |

*/

Extract from output DataFrame into a new DataFrame, that only contains vector column features and label column CLASS

val whole=output.select("features","CLASS")
whole.printSchema
/*
root
 |-- features: vector (nullable = true)
 |-- CLASS: double (nullable = true)
 
 */

Normalize feature values in vector column features, using MinMax Scaler

import org.apache.spark.ml.feature.MinMaxScaler
import org.apache.spark.ml.linalg.Vectors
val scaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
// Compute summary statistics and generate MinMaxScalerModel
val scalerModel = scaler.fit(whole)
// rescale each feature to range [min, max].
val scaledData = scalerModel.transform(whole)
println(s"Features scaled to range: [${scaler.getMin}, ${scaler.getMax}]")
scaledData.select("features", "scaledFeatures").show(2,false)
/*
Features scaled to range: [0.0, 1.0]
|features                                                                                                                                                                                                                     |scaledFeatures                                                                                                                                                                                                               
|(45,[0,1,2,3,4,5,6,11,14,16,18,21,23,25,27,28,29,30,31,33,35,36,42,43,44],[3.0,1168180.0,1.0,3.0,0.0771,0.1002,0.1066,326.0,27451.0,1.0,3.0,1614.0,1728.0,15.0,618.0,471.0,304.0,56.0,129.0,1.0,2.0,1.0,1.053,-0.208682,2.0])|(45,[0,1,2,3,4,5,6,11,14,16,18,21,23,25,27,28,29,30,31,33,35,36,40,42,43,44],[0.13043478260869565,0.004713998164155383,0.0011560693641618498,0.006564551422319475,0.15450901803607214,0.2004440977014943,0.21328531412565024,0.005000997131329867,0.9926592897953279,0.001949317738791423,0.00804289544235925,0.6932989690721649,0.729421696918531,0.004595588235294118,0.044237652111667865,0.03447266339749689,0.041422537130399235,0.044374009508716325,0.05810810810810811,1.0,0.5,0.25,0.9999999999999999,0.010636363636363637,0.10125579884341004,0.8333333333333333])|
|(45,[0,1,2,3,7,9,11,14,16,18,21,23,25,27,28,29,30,31,33,34,35,36,42,43,44],[3.0,1470752.0,1.0,1.0,7922.0,8822.0,467.0,25430.0,1.0,1.0,2293.0,2054.0,9.0,1534.0,1465.0,116.0,13.0,17.0,-1.0,1.0,4.0,1.0,31.0,6.517838,-3.0])  |(45,[0,1,2,3,7,9,11,14,16,18,21,23,25,27,28,29,30,31,34,35,36,40,42,43],[0.13043478260869565,0.0059359829438109775,0.0011560693641618498,0.002188183807439825,0.8580093144156828,0.9528026784749973,0.007164005093040024,0.9195776379547261,0.001949317738791423,0.002680965147453083,0.9849656357388316,0.8670325031658928,0.0027573529411764703,0.1098067287043665,0.10722388933616336,0.015805968115547075,0.010301109350237718,0.0076576576576576575,0.5,1.0,0.25,0.9999999999999999,0.31313131313131315,0.2305282934974466])   

*/

Train/Test Split, randomly split 70% of rows for training, 30% for test

// Prepare training and test data.
val wholeScaledData=scaledData.select("scaledFeatures","CLASS")
val Array(training, test) = wholeScaledData.randomSplit(Array(0.7, 0.3), seed = 12345)

Training and Prediction

Algorithm Selection

Since this is a binary classification, I plan to use Multilayer Perceptron Classifier and Logistic Regression.

Multilayer Perceptron Classifier, there are 45 feature columns, and binary classification, I construct a neural network of 4 layer as below:

val layers = Array[Int](45, 45, 45, 2)
import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
val trainer = new MultilayerPerceptronClassifier()
  .setLayers(layers)
  .setBlockSize(128)
  .setSeed(1234L)
  .setMaxIter(100)
  .setFeaturesCol("scaledFeatures")
  .setLabelCol("CLASS")

Train and test the neural network

val model=trainer.fit(training.select("scaledFeatures","CLASS"))
//and Test
val result = model.transform(test)
//Evaluate accuracy metrics
val predictionAndLabels = result.select("prediction", "CLASS")
val evaluator = new MulticlassClassificationEvaluator().setMetricName("accuracy").setLabelCol("CLASS")
println(s"Test set accuracy = ${evaluator.evaluate(predictionAndLabels)}")
//Test set accuracy = 0.7482470955524848

Logistic Regression Classifier

import org.apache.spark.ml.{Pipeline, PipelineStage}
import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
import scala.collection.mutable
val maxIter=100
val lr = new LogisticRegression()
      .setFeaturesCol("scaledFeatures")
      .setLabelCol("CLASS")
      .setMaxIter(maxIter)
val model_lr = lr.fit(training)
val prediction_lr = model_lr.transform(test)
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
val my_mc_accu = new MulticlassClassificationEvaluator().setPredictionCol("prediction").setLabelCol("CLASS").setMetricName("accuracy")
my_mc_accu.evaluate(prediction_lr)
my_mc_accu: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = mcEval_74ebac4de5a0
//res29: Double = 0.7463534469522494

Take away:

Both Mutilayer Perceptron Classifier and Logistic Regression produce same prediction accuracy about 75% given the dataset having significant amount missing values (null values). To improve machine learning performance, reduce missing values and possible adding additional feature columns may help.

As always, code used in this writing is in my GitHub Repo:

Thank you for your time viewing this writing.

https://www.kaggle.com/kevinarvai/clinvar-conflicting
https://www.kaggle.com/kevinarvai/clinvar-conflicting?select=clinvar_conflicting.csv
https://github.com/geyungjen/jentekllc
https://www.ncbi.nlm.nih.gov/clinvar/