spark.yarn.executor.memoryOverhead Is just the max value .The goal is to calculate OVERHEAD as a percentage of real executor memory, as used by RDDs and DataFrames –executor-memory/spark.executor.memory controls the executor heap size, but JVMs can also use some memory off heap, for example for interned Strings and direct byte buffers. The value of the spark.yarn.executor.memoryOverhead property … Read more
As of Spark 1.4, you can use Transformer org.apache.spark.ml.feature.VectorAssembler. Just provide column names you want to be features. val assembler = new VectorAssembler() .setInputCols(Array(“col1”, “col2”, “col3”)) .setOutputCol(“features”) and add it to your pipeline.
Class weight with Spark ML As of this very moment, the class weighting for the Random Forest algorithm is still under development (see here) But If you’re willing to try other classifiers – this functionality has been already added to the Logistic Regression. Consider a case where we have 80% positives (label == 1) in … Read more
Ran into this problem as well. I found out you need to call the java property for some reason I don’t know why. So just do this: from pyspark.ml.tuning import TrainValidationSplit, ParamGridBuilder, CrossValidator from pyspark.ml.regression import LinearRegression from pyspark.ml.evaluation import RegressionEvaluator evaluator = RegressionEvaluator(metricName=”mae”) lr = LinearRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [500]) \ .addGrid(lr.regParam, [0]) \ … Read more
If your problem is the .(dot) in the column name, you could use `(backticks) to enclose the column name. df.select(“`col0.1`”)
So I don’t know anything about joblib or dask, let alone your application specific data format. But it is actually possible to read sparse matrices from disk in chunks while retaining the sparse data structure. While the Wikipedia article for the CSR format does a great job explaining how it works, I’ll give a short … Read more
You can’t map a dataframe, but you can convert the dataframe to an RDD and map that by doing spark_df.rdd.map(). Prior to Spark 2.0, spark_df.map would alias to spark_df.rdd.map(). With Spark 2.0, you must explicitly call .rdd first.
Communication using default Py4J gateway is simply not possible. To understand why we have to take a look at the following diagram from the PySpark Internals document [1]: Since Py4J gateway runs on the driver it is not accessible to Python interpreters which communicate with JVM workers through sockets (See for example PythonRDD / rdd.py). … Read more
From one hand, a machine learning model built with spark can’t be served the way you serve in Azure ML or Amazon ML in a traditional manner. Databricks claims to be able to deploy models using it’s notebook but I haven’t actually tried that yet. On other hand, you can use a model in three … Read more
The janino error is due the number of constant variables created during the optimizer process. The maximum limit of constant variables allowed in the JVM is ((2^16) -1). If this limit is exceeded, then you get the Constant pool for class … has grown past JVM limit of 0xFFFF The JIRA that will fix this … Read more