A growing post which gathers short pieces of code

Let’s suppose spark to be an opened spark session.

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# Open a spark session
spark = SparkSession \
    .builder.config(conf=conf) \
    .appName("code-test").getOrCreate()
How to merge 2 spark DataFrame
  • Python:
# Id is the column on which we want to merge 2 DataFrame
dataset1 = spark.read.csv("../input/data1.csv", header="true",inferSchema="true") 
dataset2 = spark.read.csv("../input/data2.csv", header="true",inferSchema="true")
dataset= dataset1.join(dataset2, dataset1.Id ==dataset2.Id).drop(dataset1.Id)
  • Scala:
//Id is the column on which we want to merge 2 DataFrame
val dataset1 = spark.read.option("header", "true").option("inferSchema", true).csv(inputTestPath + "/input/dataset1.csv")
val dataset2 = spark.read.option("header", "true").option("inferSchema", true).csv(inputTestPath + "/input/dataset2.csv")
val dataset = datset1.join(dataset1, dataset1("Id") === dataset2("Id")).drop(dataset1.col("Id"))

How to perform Cross Validation using spark ML in python
gbt = GBTClassifier(labelCol="label", featuresCol="features", maxIter=100, seed=77)
paramGrid = ParamGridBuilder().addGrid(gbt.maxDepth, [9,10,11]).addGrid(gbt.stepSize, [0.1,0.01,0.3]).build()
# Chain indexers and GBT in a Pipeline
pipeline = Pipeline(stages=[gbt])
evaluator = MulticlassClassificationEvaluator(
    labelCol="label", predictionCol="prediction", metricName="accuracy")
# Set up 3-fold cross validation
crossval = CrossValidator(estimator=pipeline,
                          estimatorParamMaps=paramGrid,
                          evaluator=evaluator,
                          numFolds=3)

model = crossval.fit(trainingData)

# Fetch best model
best_model = model.bestModel

 

 

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