Extending spark ML for custom models now with python!

Extending Spark ML
Estimators and Transformers
kroszk@
Built with
public APIs*
*For the most part - see developer for details.

Holden:
● My name is Holden Karau
● Prefered pronouns are she/her
● I’m a Principal Software Engineer at IBM’s Spark Technology Center
● Apache Spark committer (as of January!) :)
● previously Alpine, Databricks, Google, Foursquare & Amazon
● co-author of Learning Spark & Fast Data processing with Spark
○ co-author of a new book focused on Spark performance coming this year*
● @holdenkarau
● Slide share http://www.slideshare.net/hkarau
● Linkedin https://www.linkedin.com/in/holdenkarau
● Github https://github.com/holdenk
● Spark Videos http://bit.ly/holdenSparkVideos

Spark Technology
Center
4
IBM
Spark
Technology
Center
Founded in 2015.
Location:
Physical: 505 Howard St., San Francisco CA
Web: http://spark.tc Twitter: @apachespark_tc
Mission:
Contribute intellectual and technical capital to the Apache Spark
community.
Make the core technology enterprise- and cloud-ready.
Build data science skills to drive intelligence into business
applications — http://bigdatauniversity.com
Key statistics:
About 50 developers, co-located with 25 IBM designers.
Major contributions to Apache Spark http://jiras.spark.tc
Apache SystemML is now an Apache Incubator project.
Founding member of UC Berkeley AMPLab and RISE Lab
Member of R Consortium and Scala Center
Spark Technology
Center

Who I think you wonderful humans are?
● Nice enough people
● Don’t mind pictures of cats
● Might know some Apache Spark
● Possibly know some Scala or Python
● Think machine learning is kind of cool
● Don’t overly mind a grab-bag of topics
Lori Erickson

What are we going to talk about?
● What Spark ML pipelines look like
● What Estimators and Transformers are
● How to implement both of them
● What tools can help us
● My newest project - SparklingML - and trying to get y’all to contribute
● Publishing your fancy new Spark model so other’s (like me) can use it!
● Of course try and sell you many copies of my new book if you have an
expense account.

Why should you care about Spark ML?
● Big data something something profit
● sci-kit-learn inspired API makes it nice to work with
● Ability to experiment and try different algorithms without learning new tool kits.
● Meta algorithms

First: get some data
sparkSession.read returns a DataFrameReader
We can specify general properties & data specific options
● option(“key”, “value”)
○ spark-csv ones we will use are header & inferSchema
● format(“formatName”)
○ built in formats include parquet, jdbc, etc.
● load(“path”)
Jess Johnson

Everyones favourite: CSVs! :p
val df = spark.read
.option("inferSchema", "true")
.option("delimiter", ";")
.format("csv")
.load("hdfs:///user/data/admissions.csv")
Jess Johnson

Spark ML Pipelines
Pipeline
Stage ?data
Pipeline
Stage
Pipeline
Stage
Pipeline
Stage
...
Pipeline
Ray Bodden

Spark ML Pipelines
Pipeline
Stage ?data
Pipeline
Stage
Pipeline
Stage
Pipeline
Stage
...
Pipeline
data ?
Also a pipeline stage!
Ray Bodden

Two main types of pipeline stages
Pipeline
Stage ?data
Transformer Estimatordata data data transformer
Reboots
Michael Coghlan

Pipelines are estimators
Pipeline
data model
Also an estimator!
Transformer Transformer Estimator
Michael Coghlan

PipelineModels are transformers
PipelineModel
data data
Also a transformer!
Transformer Transformer Transformer
Reboots

How are transformers made?
Estimator
data
class Estimator extends PipelineStage {
def fit(dataset: Dataset[_]): Transformer = {
// magic happens here
}
}
Transformer

How is new data made?
Transformer ( data )
class Transformer extends PipelineStage {
def transform(df: Dataset[_]): DataFrame
}
new data.transform

Feature transformations
+-----+-----+----+--------+
|admit| gre| gpa|prestige|
+-----+-----+----+--------+
| no|380.0|3.61| 3.0|
| yes|660.0|3.67| 3.0|
| yes|800.0| 4.0| 1.0|
| yes|640.0|3.19| 4.0|
| no|520.0|2.93| 4.0|
+-----+-----+----+--------+
val assembler = new VectorAssembler()
.setInputCols(Array("gre", "gpa", "prestige"))
val df2 = assembler.transform(df)
VectorAssembler
+-----+-----+----+--------+----------------+
|admit| gre| gpa|prestige| features|
+-----+-----+----+--------+----------------+
| no|380.0|3.61| 3.0|[380.0,3.61,3.0]|
| yes|660.0|3.67| 3.0|[660.0,3.67,3.0]|
| yes|800.0| 4.0| 1.0| [800.0,4.0,1.0]|
| yes|640.0|3.19| 4.0|[640.0,3.19,4.0]|
| no|520.0|2.93| 4.0|[520.0,2.93,4.0]|
+-----+-----+----+--------+----------------+

Train a classifier on the transformed data
StringIndexer
StringIndexerModel
val si = new StringIndexer().setInputCol("admit").setOutputCol("label")
val siModel = si.fit(df2)
val df3 = siModel.transform(df2)
+-----+-----+----+--------+----------------+
+-----+-----+----+--------+----------------+
| no|380.0|3.61| 3.0|[380.0,3.61,3.0]|
| yes|660.0|3.67| 3.0|[660.0,3.67,3.0]|
| yes|800.0| 4.0| 1.0| [800.0,4.0,1.0]|
| yes|640.0|3.19| 4.0|[640.0,3.19,4.0]|
| no|520.0|2.93| 4.0|[520.0,2.93,4.0]|
+-----+-----+----+--------+----------------+
+-----+-----+----+--------+----------------+-----+
|admit| gre| gpa|prestige| features|label|
+-----+-----+----+--------+----------------+-----+
| no|380.0|3.61| 3.0|[380.0,3.61,3.0]| 0.0|
| yes|660.0|3.67| 3.0|[660.0,3.67,3.0]| 1.0|
| yes|800.0| 4.0| 1.0| [800.0,4.0,1.0]| 1.0|
| yes|640.0|3.19| 4.0|[640.0,3.19,4.0]| 1.0|
| no|520.0|2.93| 4.0|[520.0,2.93,4.0]| 0.0|
+-----+-----+----+--------+----------------+-----+

Train a classifier on the transformed data
+----------------+-----+
| features|label|
+----------------+-----+
|[380.0,3.61,3.0]| 0.0|
|[660.0,3.67,3.0]| 1.0|
| [800.0,4.0,1.0]| 1.0|
|[640.0,3.19,4.0]| 1.0|
|[520.0,2.93,4.0]| 0.0|
+----------------+-----+
DecisionTreeClassifier
DecisionTree
ClassificationModel
+----------------+-----+----------+
| features|label|prediction|
+----------------+-----+----------+
|[380.0,3.61,3.0]| 0.0| 0.0|
|[660.0,3.67,3.0]| 1.0| 0.0|
| [800.0,4.0,1.0]| 1.0| 1.0|
|[640.0,3.19,4.0]| 1.0| 1.0|
|[520.0,2.93,4.0]| 0.0| 0.0|
+----------------+-----+----------+
val dt = new DecisionTreeClassifier()
val dtModel = dt.fit(df3)
val df4 = dtModel.transform(df3)

Or just throw it all in a pipeline
● Keeping track of intermediate data and calling fit/transform on every stage is
way too much work
● This problem is worse when more stages are used
● Use a pipeline instead!
assembler.setInputCols(Array("gre", "gpa", "prestige"))
val sb = new StringIndexer()
sb.setInputCol("admit").setOutputCol("label")
val pipeline = new Pipeline()
pipeline.setStages(Array(assembler, sb, dt))
val pipelineModel = pipeline.fit(df)
jasonwoodhead23

Yay! You have an ML pipeline!
Photo by Jessica Fiess-Hill

Pipeline API has many models:
● org.apache.spark.ml.classification
○ BinaryLogisticRegressionClassification, DecissionTreeClassification,
GBTClassifier, etc.
● org.apache.spark.ml.regression
○ DecissionTreeRegression, GBTRegressor, IsotonicRegression,
LinearRegression, etc.
● org.apache.spark.ml.recommendation
○ ALS
● You can also check out spark-packages for some more
● But possible not your special AwesomeFooBazinatorML
PROcarterse Follow

& data prep stages...
● org.apache.spark.ml.feature
○ ~30 elements from VectorAssembler to Tokenizer, to PCA, etc.
● Often simpler to understand while getting started with
building our own stages
PROcarterse Follow

What is/why Sparkling ML
● A place for useful Spark ML pipeline stages to live
○ Including both feature transformers and estimators
● The why: Spark ML can’t keep up with every new algorithm
● Lots of cool ML on Spark tools exist, but many don’t play nice with Spark ML
or together
Pretend there is a
cool looking logo
from fiverr here :p

So now begins our adventure to add stages

So what does a pipeline stage look like?
Must provide:
● Scala: transformSchema (used to validate input schema is
reasonable) & copy
● Both: Either a “fit” (for estimator) or transform (for
transformer)
Often have:
● Params for configuration (so we can do meta-algorithms)
Wendy Piersall

Building a simple transformer:
class HardCodedWordCountStage(override val uid: String) extends Transformer {
def this() = this(Identifiable.randomUID("hardcodedwordcount"))
def copy(extra: ParamMap): HardCodedWordCountStage = {
defaultCopy(extra)
}
...
}
Not to be confused with the Transformers franchise from Hasbro and Tomy.

Verify the input schema is reasonable:
override def transformSchema(schema: StructType): StructType = {
// Check that the input type is a string
val idx = schema.fieldIndex("happy_pandas")
val field = schema.fields(idx)
if (field.dataType != StringType) {
throw new Exception(s"Input type ${field.dataType} did not match
input type StringType")
}
// Add the return field
schema.add(StructField("happy_panda_counts", IntegerType, false))
}

How is transformSchema used?
● When you call fit on a pipeline it calls transformSchema
on the pipeline stages in order
● This is used to verify that things should work
● Ideally allows pipelines to fail fast when misconfigured,
instead of at the final stage of a 48-hour process
● Doesn’t always work that way :p
● Not supported in Python (I’m sorry!)
Tricia Hall

Do the “work” (e.g. predict labels or w/e):
def transform(df: Dataset[_]): DataFrame = {
val wordcount = udf { in: String => in.split(" ").size }
df.select(col("*"),
wordcount(df.col("happy_pandas")).as("happy_panda_counts"))
}
vic15

Do the “work” (e.g. call numpy):
class StrLenPlus3Transformer(Model):
@keyword_only
def __init__(self):
super(StrLenPlusKTransformer, self).__init__()
def _transform(self, dataset):
func = lambda x : len(x) + 3
retType = IntegerType()
udf = UserDefinedFunction(func, retType)
return dataset.withColumn(
"magic", udf("input")
)
vic15

What about configuring our stage?
class ConfigurableWordCount(override val uid: String) extends
Transformer {
final val inputCol= new Param[String](this, "inputCol", "The input
column")
final val outputCol = new Param[String](this, "outputCol", "The
output column")
def setInputCol(value: String): this.type = set(inputCol, value)
def setOutputCol(value: String): this.type = set(outputCol, value)
Jason Wesley Upton

class StrLenPlusKTransformer(Model, HasInputCol, HasOutputCol):
# We need a parameter to configure k
k = Param(Params._dummy(),
"k", "amount to add to str len",
typeConverter=TypeConverters.toInt)
@keyword_only
def __init__(self, k=None, inputCol=None, outputCol=None):
super(StrLenPlusKTransformer, self).__init__()
kwargs = self._input_kwargs
self.setParams(**kwargs)
Jason Wesley Upton

@keyword_only
def setParams(self, k=None, inputCol=None, outputCol=None):
kwargs = self._input_kwargs
return self._set(**kwargs)
def setK(self, value):
return self._set(k=value)
def getK(self):
return self.getOrDefault(self.k)
Jason Wesley Upton

So why do we configure it that way?
● Allow meta algorithms to work on it
● Scala:
○ If you look inside of spark you’ll see
“sharedParams.scala” for common params (like input
column)
○ We can’t access those unless we pretend to be inside
of org.apache.spark - so we have to make our own
● Python: Just import pyspark.ml.param.shared
Tricia Hall

So how to make an estimator?
● Very similar, instead of directly providing transform
provide a `fit` which returns a “model” which implements
the estimator interface as shown above
● Also take a look at the algorithms in Spark itself (helpful
traits you can mixin to take care of many common things).
● Let’s look at a simple one now!
sneakerdog

A simple string indexer estimator
class SimpleIndexer(override val uid: String) extends
Estimator[SimpleIndexerModel] with SimpleIndexerParams {
….
override def fit(dataset: Dataset[_]): SimpleIndexerModel = {
import dataset.sparkSession.implicits._
val words = dataset.select(dataset($(inputCol)).as[String]).distinct
.collect()
new SimpleIndexerModel(uid, words)
}
}

Quick aside: What’ts that “$(inputCol)”?
● How you get access to a configuration parameter
● Inside stage only (external use getInputCol just like
Java™ :p)

And our friend the transformer is back:
class SimpleIndexerModel(
override val uid: String, words: Array[String]) extends
Model[SimpleIndexerModel] with SimpleIndexerParams {
...
private val labelToIndex: Map[String, Double] = words.zipWithIndex.
map{case (x, y) => (x, y.toDouble)}.toMap
override def transform(dataset: Dataset[_]): DataFrame = {
val indexer = udf { label: String => labelToIndex(label) }
dataset.select(col("*"),
indexer(dataset($(inputCol)).cast(StringType)).as($(outputCol)))
Still not to be confused with the Transformers franchise from Hasbro and Tomy.

Ok so how do you make the train function?
● Read some papers on the algorithm(s) you care about
● Most likely some iterative approach (pro-tip: RDDs >
Datasets for iterative)
○ Seth has some interesting work around pluggable
optimizers
● Closed form solution? Go have a party!

What else can you add to your models?
● Put in an ML pipeline
● Do hyper-parameter tuning
And if you have some coffee left over:
● Persistence*
○ MLWriter & MLReader give you the basics
○ You’ll have to do a lot of work yourself :(
● Serving*
*With enough coffee. Not guaranteed.

Ok so I put my new fancy thing on GitHub
● Yay thank you!
● Please publish to maven central
● Also consider contributing it to SparklingML
● Also consider listing on spark-packages + user@ list
○ Let me know ( holden@pigscanfly.ca ) :)
● Think of the Python users (and I guess the R users) too?

Custom Estimators/Transformers in the Wild
Classification/Regression
xgboost
Deep Learning!
MXNet
Feature Transformation
FeatureHasher

More resources:
● High Performance Spark Example Repo has some
sample models
○ Of course buy several copies of the book - it is the gift of the season :p
● The models inside of Spark itself (internal APIs though)
● Sparkling ML - So much fun!
● Nick Pentreath’s FeatureHasher
● O’Reilly radar blog post
https://www.oreilly.com/learning/extend-structured-streami
ng-for-spark-ml
Captain Pancakes

Learning Spark
Fast Data
Processing with
Spark
(Out of Date)
Fast Data
Processing with
Spark
(2nd edition)
Advanced
Analytics with
Spark
Spark in Action
High Performance SparkLearning PySpark

High Performance Spark!
Available today! I brought one copy for one lucky person.
The rest of you can buy it from that scrapy Seattle Book
store :p
* Early Release means extra mistakes, but also a chance to help us make a more awesome
book.

And some upcoming talks:
● Kafak Summit (SF, August)
● Data Day Seattle (SEA, Sept)
● Strata New York (NYC, Sept)
● Strange Loop (Sept/Oct)
● Spark Summit EU (Dublin, October)
● November: Possibly Big Data Spain + Bee Scala (TBD)
● Strata Singapore (Singapore, December)
● ScalaX (London, December)
● Know of interesting conferences/webinar things that
should be on my radar? Let me know!

k thnx bye :)
If you care about Spark testing and
don’t hate surveys:
http://bit.ly/holdenTestingSpark
Will tweet results
“eventually” @holdenkarau
Any PySpark Users: Have some
simple UDFs you wish ran faster
you are willing to share?:
http://bit.ly/pySparkUDF
Pssst: Have feedback on the presentation? Give me a
shout (holden@pigscanfly.ca) if you feel comfortable doing
so :)

Cross-validation
because saving a test set is effort
● Automagically* fit your model params
● Because thinking is effort
● org.apache.spark.ml.tuning has the tools
Jonathan Kotta

Cross-validation
because saving a test set is effort & a reason to integrate
// ParamGridBuilder constructs an Array of parameter
combinations.
val paramGrid: Array[ParamMap] = new ParamGridBuilder()
.addGrid(nb.smoothing, Array(0.1, 0.5, 1.0, 2.0))
.build()
val cv = new CrossValidator()
.setEstimator(pipeline)
.setEstimatorParamMaps(paramGrid)
val cvModel = cv.fit(df)
val bestModel = cvModel.bestModel
Jonathan Kotta

So what does a pipeline stage look like?
Are either an:
● Estimator - has a method called “fit” which returns a
Transformer (e.g. NaiveBayes, etc.)
● Transformer - no need to train can directly transform (e.g.
HashingTF, VectorAssembler, etc.) (with transform)
Wendy Piersall

We’ve left out a lot of “transformSchema”...
● It is necessary (but I’m lazy)
● But there are helper classes that can implement some of
the boiler plate we’ve been skipping
● Classifier & Estimator base classes are your friends
● They provide transformSchema

Let’s make a Classifier* :)
// Example only - not for production use.
class SimpleNaiveBayes(val uid: String)
extends Classifier[Vector, SimpleNaiveBayes, SimpleNaiveBayesModel] {
Input type Trained Model

Let’s make a Classifier* :)
override def train(ds: Dataset[_]): SimpleNaiveBayesModel = {
import ds.sparkSession.implicits._
ds.cache()
….
…
….
}

If you reallllly want to see inside the ...s (1/5)
// Get the number of features by peaking at the first row
val numFeatures: Integer = ds.select(col($(featuresCol))).head
.get(0).asInstanceOf[Vector].size
// Determine the number of records for each class
val groupedByLabel = ds.select(col($(labelCol)).as[Double]).groupByKey(x =>
x)
val classCounts = groupedByLabel.agg(count("*").as[Long])
.sort(col("value")).collect().toMap
// Select the labels and features so we can more easily map over them.
// Note: we do this as a DataFrame using the untyped API because the Vector
// UDT is no longer public.
val df = ds.select(col($(labelCol)).cast(DoubleType), col($(featuresCol)))

// Note: you can use getNumClasses & extractLabeledPoints to get an RDD
instead
// Using the RDD approach is common when integrating with legacy machine
learning code
// or iterative algorithms which can create large query plans.
// Here we use `Datasets` since neither of those apply.
// Compute the number of documents
val numDocs = ds.count
// Get the number of classes.
// Note this estimator assumes they start at 0 and go to numClasses
val numClasses = getNumClasses(ds)

// Figure out the non-zero frequency of each feature for each label and
// output label index pairs using a case clas to make it easier to work
with.
val labelCounts: Dataset[LabeledToken] = df.flatMap {
case Row(label: Double, features: Vector) =>
features.toArray.zip(Stream from 1)
.filter{vIdx => vIdx._2 == 1.0}
.map{case (v, idx) => LabeledToken(label, idx)}
}
// Use the typed Dataset aggregation API to count the number of non-zero
// features for each label-feature index.
val aggregatedCounts: Array[((Double, Integer), Long)] = labelCounts
.groupByKey(x => (x.label, x.index))
.agg(count("*").as[Long]).collect()
val theta = Array.fill(numClasses)(new Array[Double](numFeatures))

// Compute the denominator for the general prioirs
val piLogDenom = math.log(numDocs + numClasses)
// Compute the priors for each class
val pi = classCounts.map{case(_, cc) =>
math.log(cc.toDouble) - piLogDenom }.toArray
// For each label/feature update the probabilities
aggregatedCounts.foreach{case ((label, featureIndex), count) =>
// log of number of documents for this label + 2.0 (smoothing)
val thetaLogDenom = math.log(
classCounts.get(label).map(_.toDouble).getOrElse(0.0) + 2.0)
theta(label.toInt)(featureIndex) = math.log(count + 1.0) - thetaLogDenom
}
// Unpersist now that we are done computing everything
ds.unpersist()

// Construct a model
new SimpleNaiveBayesModel(uid, numClasses, numFeatures, Vectors.dense(pi),
new DenseMatrix(numClasses, theta(0).length, theta.flatten, true))
}
override def copy(extra: ParamMap) = {
defaultCopy(extra)
}
}

What is Spark?
● General purpose distributed system
○ With a really nice API including Python :)
● Apache project (one of the most active)
● Much faster than Hadoop Map/Reduce
● Good when data is too big for a single
machine
● Built on top of two abstractions for
distributed data: RDDs & Datasets

DataFrames & Datasets
Totally the future
● Distributed collection
● Recomputed on node failure
● Distributes data & work across the cluster
● Lazily evaluated (transformations & actions)
● Has runtime schema information
● Allows for relational queries & supports SQL
● Declarative - many optimizations applied automagically
● Input for Spark Machine Learning
Helen Olney

What is the performance like?
Andrew Skudder

Spark ML pipelines
Tokenizer HashingTF String Indexer Naive Bayes
Tokenizer HashingTF String Indexer Naive Bayes
fit(df)
Estimator
Transformer
● Consist of different stages (estimators or transformers)
● Themselves are an estimator
We are going to
build a stage
together!

Minimal data prep:
● At a minimum most algorithms in Spark work on feature
vectors of doubles (and if labeled - doubles too)
Imports:
import org.apache.spark.ml._
import org.apache.spark.ml.feature._
import org.apache.spark.ml.classification._
import org.apache.spark.ml.linalg.{Vector => SparkVector}
Huang
Yun
Chung

Minimal prep continued
// Combines a list of double input features into a vector
assembler.setInputCols(Array("age", "education-num"))
// String indexer converts a set of strings into doubles
val sb = new StringIndexer()
sb.setInputCol("category").setOutputCol("category-index")
// Can be used to combine pipeline components together
pipeline.setStages(Array(assembler, sb))
Huang
Yun
Chung

Minimal prep continued
assembler.setInputCols(Array("gre", "gpa", "prestige"))
val si = new StringIndexer()
si.setInputCol("admit").setOutputCol("label")
pipeline.setStages(Array(assembler, si))
Huang
Yun
Chung
+-----+-----+----+--------+----------------+-----+
|admit| gre| gpa|prestige| features|label|
+-----+-----+----+--------+----------------+-----+
| no|380.0|3.61| 3.0|[380.0,3.61,3.0]| 0.0|
| yes|660.0|3.67| 3.0|[660.0,3.67,3.0]| 1.0|
| yes|800.0| 4.0| 1.0| [800.0,4.0,1.0]| 1.0|
| yes|640.0|3.19| 4.0|[640.0,3.19,4.0]| 1.0|
| no|520.0|2.93| 4.0|[520.0,2.93,4.0]| 0.0|
+-----+-----+----+--------+----------------+-----+
+-----+-----+----+--------+----------------+
+-----+-----+----+--------+----------------+
| no|380.0|3.61| 3.0|[380.0,3.61,3.0]|
| yes|660.0|3.67| 3.0|[660.0,3.67,3.0]|
| yes|800.0| 4.0| 1.0| [800.0,4.0,1.0]|
| yes|640.0|3.19| 4.0|[640.0,3.19,4.0]|
| no|520.0|2.93| 4.0|[520.0,2.93,4.0]|
+-----+-----+----+--------+----------------+
+-----+-----+----+--------+
|admit| gre| gpa|prestige|
+-----+-----+----+--------+
| no|380.0|3.61| 3.0|
| yes|660.0|3.67| 3.0|
| yes|800.0| 4.0| 1.0|
| yes|640.0|3.19| 4.0|
| no|520.0|2.93| 4.0|
+-----+-----+----+--------+

So a bit more about that pipeline
● Each of our previous components has “fit” & “transform”
stage
● Constructing the pipeline this way makes it easier to
work with (only need to call one fit & one transform)
● Can re-use the fitted model on future data
val model = pipeline.fit(df)
val prepared = model.transform(df)
Andrey

Let's train a model on our prepared data:
// Specify model
dt.setFeaturesCol("features")
dt.setPredictionCol("prediction")
// Fit it
val dtModel = dt.fit(prepared)

Or wait let's just add it to the pipeline:
// Specify model
dt.setFeaturesCol("features")
dt.setPredictionCol("prediction")
// Add to the pipeline
pipeline.setStages(Array(assembler, si, dt))
pipelineModel = pipeline.fit(df)

And predict the results on the same data:
pipelineModel.transform(df).select("prediction",
"label").take(20)
+----------+-----+
|prediction|label|
+----------+-----+
| 0.0| 0.0|
| 0.0| 1.0|
| 1.0| 1.0|
| 1.0| 1.0|
| 0.0| 0.0|
+----------+-----+

Extending spark ML for custom models now with python!

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Extending spark ML for custom models now with python!