Toolkit for Apache Spark ML for Feature clean-up, feature Importance calculation suite, Information Gain selection, Distributed SMOTE, Model selection and training, Hyper parameter optimization and selection, Model interprability.
License: Other
Hi
I got an Error : java.lang.ArrayIndexOutOfBoundsException: 1 when i execute the FamilyRunner
or AutomationRunner
I used the practice example in README.md.
How can i solve this problem?
val data = spark.table("DF")
val overrides = Map("labelCol" -> "class")
val randomForestConfig = ConfigurationGenerator.generateConfigFromMap("RandomForest", "classifier", overrides)
val gbtConfig = ConfigurationGenerator.generateConfigFromMap("GBT", "classifier", overrides)
val logConfig = ConfigurationGenerator.generateConfigFromMap("LogisticRegression", "classifier", overrides)
val runner = FamilyRunner(data, Array(randomForestConfig, gbtConfig, logConfig))..execute()
And Error code is below :
ava.lang.ArrayIndexOutOfBoundsException: 1
at com.databricks.labs.automl.utils.WorkspaceDirectoryValidation.validate(WorkspaceDirectoryValidation.scala:96)
at com.databricks.labs.automl.utils.WorkspaceDirectoryValidation$.apply(WorkspaceDirectoryValidation.scala:123)
at com.databricks.labs.automl.executor.DataPrep.prepData(DataPrep.scala:275)
at com.databricks.labs.automl.executor.FamilyRunner$$anonfun$execute$1.apply(FamilyRunner.scala:125)
at com.databricks.labs.automl.executor.FamilyRunner$$anonfun$execute$1.apply(FamilyRunner.scala:119)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at com.databricks.labs.automl.executor.FamilyRunner.execute(FamilyRunner.scala:119)
at line3fa913e91f964622bbee0641bf7664fb138.$read$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-983:1)
at line3fa913e91f964622bbee0641bf7664fb138.$read$$iw$$iw$$iw$$iw$$iw.<init>(command-983:48)
at line3fa913e91f964622bbee0641bf7664fb138.$read$$iw$$iw$$iw$$iw.<init>(command-983:50)
at line3fa913e91f964622bbee0641bf7664fb138.$read$$iw$$iw$$iw.<init>(command-983:52)
at line3fa913e91f964622bbee0641bf7664fb138.$read$$iw$$iw.<init>(command-983:54)
at line3fa913e91f964622bbee0641bf7664fb138.$read$$iw.<init>(command-983:56)
at line3fa913e91f964622bbee0641bf7664fb138.$read.<init>(command-983:58)
at line3fa913e91f964622bbee0641bf7664fb138.$read$.<init>(command-983:62)
at line3fa913e91f964622bbee0641bf7664fb138.$read$.<clinit>(command-983)
at line3fa913e91f964622bbee0641bf7664fb138.$eval$.$print$lzycompute(<notebook>:7)
at line3fa913e91f964622bbee0641bf7664fb138.$eval$.$print(<notebook>:6)
at line3fa913e91f964622bbee0641bf7664fb138.$eval.$print(<notebook>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:793)
at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1054)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:645)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:644)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:644)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:576)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:572)
at com.databricks.backend.daemon.driver.DriverILoop.execute(DriverILoop.scala:215)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply$mcV$sp(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExitInternal$.trapExit(DriverLocal.scala:679)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExit$.apply(DriverLocal.scala:632)
at com.databricks.backend.daemon.driver.ScalaDriverLocal.repl(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$8.apply(DriverLocal.scala:368)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$8.apply(DriverLocal.scala:345)
at com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:238)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:233)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:48)
at com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:271)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:48)
at com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:345)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at scala.util.Try$.apply(Try.scala:192)
at com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:639)
at com.databricks.backend.daemon.driver.DriverWrapper.getCommandOutputAndError(DriverWrapper.scala:485)
at com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:597)
at com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:390)
at com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:337)
at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:219)
at java.lang.Thread.run(Thread.java:748)
Hi, After complete the FamilyRunner, I got an error code with belw
java.lang.NoSuchMethodError: org.mlflow.api.proto.Service$CreateRun$Builder.setRunName(Ljava/lang/String;)Lorg/mlflow/api/proto/Service$CreateRun$Builder;
at com.databricks.labs.automl.tracking.MLFlowTracker.com$databricks$labs$automl$tracking$MLFlowTracker$$generateMlFlowRun(MLFlowTracker.scala:148)
at com.databricks.labs.automl.tracking.MLFlowTracker.logBest(MLFlowTracker.scala:401)
at com.databricks.labs.automl.tracking.MLFlowTracker.logMlFlowDataAndModels(MLFlowTracker.scala:352)
at com.databricks.labs.automl.AutomationRunner.logResultsToMlFlow(AutomationRunner.scala:1291)
at com.databricks.labs.automl.AutomationRunner.liftedTree1$1(AutomationRunner.scala:1439)
at com.databricks.labs.automl.AutomationRunner.executeTuning(AutomationRunner.scala:1438)
at com.databricks.labs.automl.executor.FamilyRunner$$anonfun$execute$1.apply(FamilyRunner.scala:129)
at com.databricks.labs.automl.executor.FamilyRunner$$anonfun$execute$1.apply(FamilyRunner.scala:119)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at com.databricks.labs.automl.executor.FamilyRunner.execute(FamilyRunner.scala:119)
at linea339e92b41aa489e83cc214c9c04f05540.$read$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-1020:5)
at linea339e92b41aa489e83cc214c9c04f05540.$read$$iw$$iw$$iw$$iw$$iw.<init>(command-1020:53)
at linea339e92b41aa489e83cc214c9c04f05540.$read$$iw$$iw$$iw$$iw.<init>(command-1020:55)
at linea339e92b41aa489e83cc214c9c04f05540.$read$$iw$$iw$$iw.<init>(command-1020:57)
at linea339e92b41aa489e83cc214c9c04f05540.$read$$iw$$iw.<init>(command-1020:59)
at linea339e92b41aa489e83cc214c9c04f05540.$read$$iw.<init>(command-1020:61)
at linea339e92b41aa489e83cc214c9c04f05540.$read.<init>(command-1020:63)
at linea339e92b41aa489e83cc214c9c04f05540.$read$.<init>(command-1020:67)
at linea339e92b41aa489e83cc214c9c04f05540.$read$.<clinit>(command-1020)
at linea339e92b41aa489e83cc214c9c04f05540.$eval$.$print$lzycompute(<notebook>:7)
at linea339e92b41aa489e83cc214c9c04f05540.$eval$.$print(<notebook>:6)
at linea339e92b41aa489e83cc214c9c04f05540.$eval.$print(<notebook>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:793)
at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1054)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:645)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:644)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:644)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:576)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:572)
at com.databricks.backend.daemon.driver.DriverILoop.execute(DriverILoop.scala:215)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply$mcV$sp(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExitInternal$.trapExit(DriverLocal.scala:679)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExit$.apply(DriverLocal.scala:632)
at com.databricks.backend.daemon.driver.ScalaDriverLocal.repl(ScalaDriverLocal.scala:197)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$8.apply(DriverLocal.scala:368)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$8.apply(DriverLocal.scala:345)
at com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:238)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:233)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:48)
at com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:271)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:48)
at com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:345)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at scala.util.Try$.apply(Try.scala:192)
at com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:639)
at com.databricks.backend.daemon.driver.DriverWrapper.getCommandOutputAndError(DriverWrapper.scala:485)
at com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:597)
at com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:390)
at com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:337)
at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:219)
at java.lang.Thread.run(Thread.java:748)
And my code is
import com.databricks.labs.automl.executor.config.ConfigurationGenerator
import com.databricks.labs.automl.executor.FamilyRunner
val sourceData = spark.read("<DATA>")
val overrides = Map("labelCol" -> "is_attributed",
"mlFlowExperimentName" -> "<User-Defined-Name>",
"mlFlowTrackingURI" -> "<Databricks Host URI>",
"mlFlowAPIToken" -> dbutils.notebook.getContext().apiToken.get,
"mlFlowModelSaveDirectory" -> "<User-Defined-Directory>",
"inferenceConfigSaveLocation" -> "<User-Defined-Directory>",
"tunerParallelism" -> 30
)
val randomForestConfig = ConfigurationGenerator.generateConfigFromMap("RandomForest", "classifier", overrides)
val gbtConfig = ConfigurationGenerator.generateConfigFromMap("GBT", "classifier", overrides)
val logConfig = ConfigurationGenerator.generateConfigFromMap("LogisticRegression", "classifier", overrides)
val runner = FamilyRunner(sourceData, Array(logConfig)).execute()
Additionaly I installed library on my cluster, items are in below :
automatedml_2_11_0_5_1.jar
JAR
Installed
dbfs:/FileStore/jars/0391c7b8_92d3_4a41_92e4_1456ab5d4d54-automatedml_2_11_0_5_1-3990a.jar
azureml
PyPI
Uninstall pending restart
Hyperopt
PyPI
Installed
keras
PyPI
Installed
koalas
PyPI
Installed
ml.combust.mleap:mleap-spark_2.11:0.14.0
Maven
Installed
mleap
PyPI
Installed
mlflow
PyPI
Installed
org.mlflow:mlflow-client:1.2.0
Maven
Installed
org.mlflow:mlflow-scoring:1.2.0
Maven
Installed
seaborn
PyPI
Installed
sklearn
PyPI
Installed
xgboost
PyPI
Installed
xgboost4j_spark_0_90.jar
JAR
Installed
dbfs:/FileStore/jars/2afc2977_6cc0_4511_8b70_555882caa8af-xgboost4j_spark_0_90-b50ca.jar
It seems that the current version only supports scala APIs to build automation flow. Will Python APIs be provided?
I am trying to run the python example on databricks.
When I get to this line I get the error in the subject.
fi_importances = FI.run_feature_importances("XGBoost", "classifier", dataframe,20.0,"count",generic_overrides)
I have attached the wheel file pyAutoML-0.2.0-py3-none-any.whl to my cluster.
Would it be possible to install this using Maven so I don't have to build it myself. Thanks!
This module it very promising.
I want to use it but I dont know how can I get .whl file.
As in python installation document,
Currently, this library exists as a .whl file in the /dist directory.
Where can find the .whl file and add in Databricks?
Many thanks in advance.
When following this tutorial, I encounter the following error during feature selection thrown by DataSplitUtility:
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
The thing I do differently from the tutorial is setting the trainTestSplitMethod to "chronological" as in:
Map(
...
"tunerTrainSplitMethod" -> "chronological",
"tunerTrainSplitChronologicalColumn" -> "id",
"tunerTrainSplitChronologicalRandomPercentage" -> 0.25,
...
)
Any ideas on how to fix the issue?
I am using:
When the dbutils namespace was highjacked and compiled the apiToken.get doesn't work, with the maven compiled version of the code following the build instructions. It seems like it should work as it's excluded but for some reason it does not (it could be the maven repo version only, not sure).
Line 237 in 5c2e530
Since yesterday, I tried using FamilyRunner and it works past DropColumnsTransformer stage as long as I don't use "chronological" split method -- but fails in DataSplitUtility.split as reported here
The error I get with FamilyRunner is different from the above. In my understanding, DropColumnsTransformer drops tunerTrainSplitChronologicalColumn despite the fact that I add it to fieldsToIgnoreInVector.
In my understanding, columns in fieldsToIgnoreInVector should be left untouched by all transformers, but it doesn't seem to be the case. It is possible to spot the problem with the debug flag. In my experiment, tunerTrainSplitChronologicalColumn -> "id_col", but it is not present in the step output dataset:
...
Output dataset schema: root
|-- label_col: integer (nullable = true)
|-- automl_internal_id: long (nullable = false)
|-- features: vector (nullable = true)
=== End of class com.databricks.labs.automl.pipeline.DropColumnsTransformer Pipeline Stage log <==
I will look deeper into this and open a PR to fix it.
hey guys, i'm reading the source code and i would like to sincerely thank all those works you've done there, and public all of code too. But i noticed that some of code in "FeatureInteraction" is running too slow, for example:
`val nominalScores = nominalFields.map { x =>
x -> ColumnScoreData(
scoreColumn(
df,
modelType,
x,
getFieldType("nominal"),
totalRecordCount
),
"nominal"
)
}.toMap
val continuousScores = continuousFields.map { x =>
x -> ColumnScoreData(
scoreColumn(
df,
modelType,
x,
getFieldType("continuous"),
totalRecordCount
),
"continuous"
)
}.toMap`
is there any suggestions for paralisim? looking forward your reply!
Hi,
I guess this has to do with how I installed the package. Since I couln't install it via pip, I just git clone the thing to site-packages in my databricks compute's web terminal.
Hello, I followed the instructions in this repo and was able to build using SBT. I installed it in my cluster using the GUI, but still, have an error when importing. If you could provide guidance, that would be great.
Thanks!
Hi,
What is the plan with the license for this project? Will it become Apache 2.0 like DeltaLake?
Also will there be a Spark 3.0/Scala 2.12 release?
Thanks
Current example does not work.
https://github.com/databrickslabs/automl-toolkit/blob/master/demos/AutoMLPresentationDemo.dbc
Issues:
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:1102)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:1100)
at scala.collection.mutable.HashSet.foreach(HashSet.scala:78)
at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:1100)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:2592)
at org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)
at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:2503)
at org.apache.spark.SparkContext$$anonfun$stop$6.apply$mcV$sp(SparkContext.scala:2107)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1506)
at org.apache.spark.SparkContext.stop(SparkContext.scala:2106)
at org.apache.spark.TaskFailedListener$$anon$1$$anonfun$run$1.apply$mcV$sp(SparkParallelismTracker.scala:131)
at org.apache.spark.TaskFailedListener$$anon$1$$anonfun$run$1.apply(SparkParallelismTracker.scala:131)
at org.apache.spark.TaskFailedListener$$anon$1$$anonfun$run$1.apply(SparkParallelismTracker.scala:131)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.TaskFailedListener$$anon$1.run(SparkParallelismTracker.scala:130)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:893)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2243)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2265)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2284)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2309)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:961)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:379)
at org.apache.spark.rdd.RDD.collect(RDD.scala:960)
at org.apache.spark.RangePartitioner$.sketch(Partitioner.scala:309)
at org.apache.spark.RangePartitioner.<init>(Partitioner.scala:171)
at org.apache.spark.RangePartitioner.<init>(Partitioner.scala:151)
at org.apache.spark.rdd.OrderedRDDFunctions$$anonfun$sortByKey$1.apply(OrderedRDDFunctions.scala:62)
at org.apache.spark.rdd.OrderedRDDFunctions$$anonfun$sortByKey$1.apply(OrderedRDDFunctions.scala:61)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:379)
at org.apache.spark.rdd.OrderedRDDFunctions.sortByKey(OrderedRDDFunctions.scala:61)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.x$4$lzycompute(BinaryClassificationMetrics.scala:155)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.x$4(BinaryClassificationMetrics.scala:146)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.confusions$lzycompute(BinaryClassificationMetrics.scala:148)
Current pom.xml use MLflow 0.9.1 (pre-GA). Should be using latest MLflow 1.2.0.
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