Migration Guide¶

Migration guide between Sparkling Water versions.

From 3.38 to 3.40¶

The parameter namedMojoOutputColumns and methods getNamedMojoOutputColumns, setNamedMojoOutputColumns on H2OAlgorithmCommonParams have been removed without replacement. The behaviour will stay the same as it was for “true” value which was the default value in the past.

From 3.36 to 3.38¶

org.apache.spark.h2o.H2OConf has been replaced by ai.h2o.sparkling.H2OConf
org.apache.spark.h2o.H2OContext has been replaced by ai.h2o.sparkling.H2OContext
The support for Apache Spark 2.2.x has been removed.
The parameter variableImportances of H2ODeepLearning has been replaced with calculateFeatureImportances as well as the methods getVariableImportances and setVariableImportances on H2ODeepLearning have been replaced with getCalculateFeatureImportances and setCalculateFeatureImportances.
The method getVariableImportances of H2ODeepLearningMOJOModel has been replaced with getCalculateFeatureImportances.
The parameter autoencoder and methods getAutoencoder, setAutoencoder on H2ODeepLearning have been removed without replacement.
The method getAutoencoder of H2ODeepLearningMOJOModel has been removed without replacement.

From 3.34 to 3.36¶

The methods getWithDetailedPredictionCol and setWithDetailedPredictionCol on all SW Algorithms and MOJO models were removed without replacement.
The withDetailedPredictionCol field on H2OMOJOSettings was removed without a replacement.
Boolean type mapping from Spark’s DataFrame to H20Frame was changed from numerical 0, 1 to “False”, “True” categorical values.

From 3.32.1 to 3.34¶

On H2OConf, the setters setClientIcedDir and setNodeIcedDir are replaced by setIcedDir and getters clientIcedDir and nodeIcedDir are replaced by icedDir. Also the spark options spark.ext.h2o.client.iced.dir and spark.ext.h2o.node.iced.dir are replaced by spark.ext.h2o.iced.dir.
On H2OConf, the setters setH2OClientLogLevel and setH2ONodeLogLevel are replaced by setLogLevel and getters h2oClientLogLevel and h2oNodeLogLevel are replaced by logLevel. Also the spark options spark.ext.h2o.client.log.level and spark.ext.h2o.node.log.level are replaced by spark.ext.h2o.log.level.
Spark option spark.ext.h2o.client.flow.dir is replaced by spark.ext.h2o.flow.dir.
On H2OConf, the setters setClientBasePort and setNodeBasePort are replaced by setBasePort and getters clientBasePort and nodeBasePort are replaced by basePort. Also the spark options spark.ext.h2o.client.port.base and spark.ext.h2o.node.port.base are replaced by spark.ext.h2o.base.port.
On H2OConf, the setters setH2OClientLogDir and setH2ONodeLogDir are replaced by setLogDir and getters h2oClientLogDir and h2oNodeLogDir are replaced by logDir. Also the spark options spark.ext.h2o.client.log.dir and spark.ext.h2o.node.log.dir are replaced by spark.ext.h2o.log.dir.
On H2OConf, the setters setClientExtraProperties and setNodeExtraProperties are replaced by setExtraProperties and getters clientExtraProperties and nodeExtraProperties are replaced by extraProperties. Also the spark options spark.ext.h2o.client.extra and spark.ext.h2o.node.extra are replaced by spark.ext.h2o.extra.properties.
On H2OConf, the setter setMapperXmx is replaced by setExternalMemory and the getter mapperXmx is replaced by externalMemory. Also the Spark option spark.ext.h2o.hadoop.memory is replaced by spark.ext.h2o.external.memory.
The weightCol parameter on H2OKmeans was removed without a replacement.
The distribution parameter on H2OGLM was removed without a replacement.
The support for Apache Spark 2.1.x has been removed.
Binary models could be downloaded only if the algorithm parameter keepBinaryModels was set to true.

From 3.32 to 3.32.1¶

The data type of H2OTargetEncoder output columns has been changed from DoubleType to ml.linalg.VectorUDT.
The sub-columns of the prediction column produced by H2OMOJOPipelineModel could be of the type float instead of double if MOJOModelSettings.namedMojoOutputColumns is set to true.

From 3.30.1 to 3.32¶

We have created two new classes - ai.h2o.sparkling.H2OContext and ai.h2o.sparkling.H2OConf. The behaviour of the context and configuration is the same as in the original org.apache.spark.h2o package except that in this case we no longer use H2O client on Spark driver. This means that H2O is running only on worker nodes and not on Spark driver. This change affects only Scala API as PySparkling and RSparkling are already using the new API internally. We have also changed all documentation materials to point to the new classes.

We are keeping the original context and conf available in the code in case the user needs to use some of the H2O client features directly on the Spark driver, but please note that once the API in the new package ai.h2o.sparkling is complete, the context and conf classes will get removed. We therefore encourage users to migrate to new context class and report missing features to us via Github issues.

For example, if the user is training XGboost model using H2O Java api but is not using H2OXGBoost from Sparkling Water, this feature requires the H2O client to be available.
In PySparkling, the H2OConf no longer accepts any arguments. To create new H2OConf, please just call conf = H2OConf(). Also the H2OContext.getOrCreate method no longer accepts the spark argument. You can start H2OContext as H2OContext.getOrCreate() or H2OContext.getOrCreate(conf)
In RSparkling, the H2OConf no longer accepts any arguments. To create new H2OConf, please just call conf <- H2OConf(). Also the H2OContext.getOrCreate method no longer accepts the spark argument. You can start H2OContext as H2OContext.getOrCreate() or H2OContext.getOrCreate(conf)
In Scala, H2OConf can be created as new H2OConf() or new H2OConf(sparkConf). Other constructor variants have been removed. Also, H2OContext can be created as H2OContext.getOrCreate() or H2OContext.getOrCreate(conf). The other variants of this method have been removed.
The setH2OCluster(ip, port) method on H2OConf in all APIs doesn’t implicitly set the external backend anymore. The method setExternalClusterMode() must be called explicitly.
The method classify in the hex.ModelUtils object is removed. Please use Sparkling Water algorithm API to train and score H2O models. This removal affects only Scala API as other APIs don’t have such functionality.
The method DLModel in water.support.DeepLearningSupport is removed. Please use H2ODeepLearning instead. The same holds for method GBMModel in water.support.GBMSupport. Please use H2OGBM instead. The classes wrapping these methods are removed as well. This removal affects only Scala API as other APIs don’t have such functionality.
The method splitFrame and split in water.support.H2OFrameSupport is removed. Please use ai.h2o.sparkling.H2OFrame(frameKeyString).split(ratios) instead.
The method withLockAndUpdate in water.support.H2OFrameSupport is removed. Please use ai.h2o.sparkling.backend.utils.H2OClientUtils.withLockAndUpdate instead.
The methods columnsToCategorical with both indices and column names argument in water.support.H2OFrameSupport are removed. Please use ai.h2o.sparkling.H2OFrame(frameKeyString).convertColumnsToCategorical instead.
Method modelMetrics in water.support.ModelMetricsSupport is removed. Please use methods getTrainingMetrics, getValidationMetrics or getCrossValidationMetrics on the H2OMOJOModel. You can also use method getCurrentMetrics, which returns cross validation metrics if nfolds was specified and higher than 0, validation metrics if validation frame has been specified ( splitRatio was set and lower than 1 ) and nfolds was 0 and training metrics otherwise ( splitRatio is 1 and nfolds is 0).
The whole trait ModelSerializationSupport in Scala is removed. The MOJO is a first class citizen in Sparkling Water and most code works with our Spark MOJO wrapper. Please use the following approaches to migrate from previous methods in the model serialization support:
To create Spark MOJO wrapper in Sparkling Water, you can load it from H2O-3 as:
val mojoModel = H2OMOJOModel.createFromMojo(path)
or train model using Sparkling Water API, such as
val gbm = H2OGBM().setLabelCol("label") val mojoModel = gbm.fit(data)
In this case the mojoModel is Spark wrapper around the H2O’s mojo providing Spark friendly API. This also means that the such model can be embedded into Spark pipelines without any additional work.

To export it as, please call:
mojoModel.write.save("path")
The advantage is that this variant is H2O-version independent and when such model is loaded, H2O run-time is not required.

You can load the exported model from Sparkling Water as:
val mojoModel = H2OMOJOModel.read.load("path")
For additional information about how to load MOJO into Sparkling Water, please see Loading MOJOs into Sparkling Water.
The methods join, innerJoin, outerJoin, leftJoin and rightJoin in water.support.JoinSupport are removed together with their encapsulating class. The enum water.support.munging.JoinMethod is also removed. In order to perform joins, please use the following methods:
- Inner join: ai.h2o.sparkling.H2OFrame(idOfLeftFrame).innerJoin(rightFrame)
- Outer join: ai.h2o.sparkling.H2OFrame(idOfLeftFrame).outerJoin(rightFrame)
- Left join: ai.h2o.sparkling.H2OFrame(idOfLeftFrame).leftJoin(rightFrame)
- Right join: ai.h2o.sparkling.H2OFrame(idOfLeftFrame).rightJoin(rightFrame)
The JoinMethod enum is removed as it is no longer required.
Since the method asH2OFrame of H2OContext converts strings to categorical columns automatically according to the heuristic from H2O parsers, the methods getAllStringColumnsToCategorical and setAllStringColumnsToCategorical have been removed from all SW API algorithms in Python and Scala API.
Methods setH2ONodeLogLevel and setH2OClientLogLevel are removed on H2OContext. Please use setH2OLogLevel instead.
Methods asDataFrame on Scala H2OContext has been replaced by methods asSparkFrame with same arguments. This was done to ensure full consistency between Scala, Python and R APIs.
JavaH2OContext is removed. Please use org.apache.spark.h2o.H2OContext instead.
When using H2O as Spark data source, the approach val df = spark.read.h2o(key) has been removed. Please use val df = spark.read.format("h2o").load(key) instead. The same holds for spark.write.h2o(key). Please use df.write.format("h2o").save("new_key") instead.
Starting from the version 3.32, H2OGridSearch hyper-parameters now correspond to parameter names in Sparkling Water. Previously, the hyper-parameters were specified using internal H2O names such as _ntrees or _max_depth. At this version, the parameter names follow the naming convention of getters and setters of the corresponding parameter, such as ntrees or maxDepth.

Also the output of getGridModelsParams now contains column names which correspond to Sparkling Water parameter names instead of H2O internal ones. When updating to version 3.32, please make sure to update your hyper parameter names.
On H2OConf, the methods setHiveSupportEnabled, setHiveSupportDisabled and isHiveSupportEnabled are replaced by setKerberizedHiveEnabled, setKerberizedHiveDisabled and isKerberizedHiveEnabled to reflect their actual meaning. Also the option spark.ext.h2o.hive.enabled is replaced by spark.ext.h2o.kerberized.hive.enabled.
The below list of Grid Search parameters with their getters and setters were replaced by the same parameters on the algorithm the grid search is applied to.

Parameter Name	Getter	Setter
`labelCol`	`getLabelCol`	`setLabelCol`
`offsetCol`	`getOffsetCol`	`setOffsetCol`
`foldCol`	`getFoldCol`	`setFoldCol`
`weightCol`	`getWeightCol`	`setWeightCol`
`splitRatio`	`getSplitRatio`	`setSplitRatio`
`nfolds`	`getNfolds`	`setNfolds`
`columnsToCategorical`	`getColumnsToCategorical`	`setColumnsToCategorical`
`predictionCol`	`getPredictionCol`	`setPredictionCol`
`detailedPredictionCol`	`getDetailedPredictionCol`	`setDetailedPredictionCol`
`withDetailedPredictionCol`	`getWithDetailedPredictionCol`	`setWithDetailedPredictionCol`
`featuresCols`	`getFeaturesCols`	`setFeaturesCols`
`convertUnknownCategoricalLevelsToNa`	`getConvertUnknownCategoricalLevelsToNa`	`setConvertUnknownCategoricalLevelsToNa`
`convertInvalidNumbersToNa`	`getConvertInvalidNumbersToNa`	`setConvertInvalidNumbersToNa`
`namedMojoOutputColumns`	`getNamedMojoOutputColumns`	`setNamedMojoOutputColumns`

Schema of detailed predictions produced by H2OMOJOModel and thus by all Sparkling Water algorithms has been changed a bit. The MapType sub-columns probabilities, calibratedProbabilities and contributions have been changed to StructType columns.
On H2OXGBoost, the options minSumHessianInLeaf and minDataInLeaf have been removed as well as the corresponding getters and setters. The methods are removed without replacement as these parameters weren’t valid XGBoost parameters.

From 3.30 to 3.30.1¶

The detailed prediction columns is always enabled for all types of MOJO predictions.

From 3.28.1 to 3.30¶

It is now required to explicitly create H2OContext before you run any of our exposed algorithms. Previously, the algorithm would create the H2OContext on demand.
It is no longer possible to disable web (REST API endpoints) on the worker nodes in the internal client as we require the endpoints to be available. In particular, the methods setH2ONodeWebEnabled, setH2ONodeWebDisabled and h2oNodeWebEnabled are removed without replacement. Also the option spark.ext.h2o.node.enable.web does not have any effect anymore.
It is no longer possible to disable web (REST API endpoints) on the client node as we require the Rest API to be available. In particular, the methods setClientWebEnabled, setClientWebDisabled and clientWebEnabled are removed without replacement. Also the option spark.ext.h2o.client.enable.web does not have any effect anymore.
The property spark.ext.h2o.node.iced.dir and the setter method setNodeIcedDir on H2OConf has no effect in all 3.30.x.y-z versions. If users need to set a custom iced directory for executors, they can set the property spark.ext.h2o.node.extra to -ice_root dir, where dir is a user-specified directory.

Removal of Deprecated Methods and Classes¶

On PySparkling, passing authentication on H2OContext via auth param is removed in favor of methods setUserName and setPassword ond the H2OConf or via the Spark options spark.ext.h2o.user.name and spark.ext.h2o.password directly.
On Pysparkling, passing verify_ssl_certificates parameter as H2OContext argument is removed in favor of method setVerifySslCertificates on H2OConf or via the spark option spark.ext.h2o.verify_ssl_certificates.
On RSparkling, the method h2o_context is removed. To create H2OContext, please call hc <- H2OContext.getOrCreate(). Also the methods h2o_flow, as_h2o_frame and as_spark_dataframe are removed. Please use the methods available on the H2OContext instance created via hc <- H2OContext.getOrCreate(). Instead of h2o_flow, use hc$openFlow, instead of as_h2o_frame, use asH2OFrame and instead of as_spark_dataframe use asSparkFrame.

Also the H2OContext.getOrCreate() does not have username and password arguments anymore. The correct way how to pass authentication details to H2OContext is via H2OConf class, such as:
```
conf <- H2OConf()
conf$setUserName(username)
conf$setPassword(password)
hc <- H2OContext.getOrCreate(conf)
```
The Spark options spark.ext.h2o.user.name and spark.ext.h2o.password correspond to these setters and can be also used directly.
In H2OContext Python API, the method as_spark_frame is replaced by the method asSparkFrame and the method as_h2o_frame is replaced by asH2OFrame.
In H2OXGBoost Scala And Python API, the methods getNEstimators and setNEstimators are removed. Please use getNtrees and setNtrees instead.
In Scala and Python API for tree-based algorithms, the method getR2Stopping is removed in favor of getStoppingRounds, getStoppingMetric, getStoppingTolerance methods and the method setR2Stopping is removed in favor of setStoppingRounds, setStoppingMetric, setStoppingTolerance methods.
Method download_h2o_logs on PySparkling H2OContext is removed in favor of the downloadH2OLogs method.
Method get_conf on PySparkling H2OContext is removed in favor of the getConf method.
On Python and Scala H2OGLM API, the methods setExactLambdas and getExactLambdas are removed without replacement.
On H2OConf Python API, the following methods have been renamed to be consistent with the Scala counterparts:
- h2o_cluster -> h2oCluster
- h2o_cluster_host -> h2oClusterHost
- h2o_cluster_port -> h2oClusterPort
- cluster_size -> clusterSize
- cluster_start_timeout -> clusterStartTimeout
- cluster_config_file -> clusterInfoFile
- mapper_xmx -> mapperXmx
- hdfs_output_dir -> HDFSOutputDir
- cluster_start_mode -> clusterStartMode
- is_auto_cluster_start_used -> isAutoClusterStartUsed
- is_manual_cluster_start_used -> isManualClusterStartUsed
- h2o_driver_path -> h2oDriverPath
- yarn_queue -> YARNQueue
- is_kill_on_unhealthy_cluster_enabled -> isKillOnUnhealthyClusterEnabled
- kerberos_principal -> kerberosPrincipal
- kerberos_keytab -> kerberosKeytab
- run_as_user -> runAsUser
- set_h2o_cluster -> setH2OCluster
- set_cluster_size -> setClusterSize
- set_cluster_start_timeout -> setClusterStartTimeout
- set_cluster_config_file -> setClusterInfoFile
- set_mapper_xmx -> setMapperXmx
- set_hdfs_output_dir -> setHDFSOutputDir
- use_auto_cluster_start -> useAutoClusterStart
- use_manual_cluster_start -> useManualClusterStart
- set_h2o_driver_path -> setH2ODriverPath
- set_yarn_queue -> setYARNQueue
- set_kill_on_unhealthy_cluster_enabled -> setKillOnUnhealthyClusterEnabled
- set_kill_on_unhealthy_cluster_disabled -> setKillOnUnhealthyClusterDisabled
- set_kerberos_principal -> setKerberosPrincipal
- set_kerberos_keytab -> setKerberosKeytab
- set_run_as_user -> setRunAsUser
- num_h2o_workers -> numH2OWorkers
- drdd_mul_factor -> drddMulFactor
- num_rdd_retries -> numRddRetries
- default_cloud_size -> defaultCloudSize
- subseq_tries -> subseqTries
- h2o_node_web_enabled -> h2oNodeWebEnabled
- node_iced_dir -> nodeIcedDir
- set_num_h2o_workers -> setNumH2OWorkers
- set_drdd_mul_factor -> setDrddMulFactor
- set_num_rdd_retries -> setNumRddRetries
- set_default_cloud_size -> setDefaultCloudSize
- set_subseq_tries -> setSubseqTries
- set_h2o_node_web_enabled -> setH2ONodeWebEnabled
- set_h2o_node_web_disabled -> setH2ONodeWebDisabled
- set_node_iced_dir -> setNodeIcedDir
- backend_cluster_mode -> backendClusterMode
- cloud_name -> cloudName
- is_h2o_repl_enabled -> isH2OReplEnabled
- scala_int_default_num -> scalaIntDefaultNum
- is_cluster_topology_listener_enabled -> isClusterTopologyListenerEnabled
- is_spark_version_check_enabled -> isSparkVersionCheckEnabled
- is_fail_on_unsupported_spark_param_enabled -> isFailOnUnsupportedSparkParamEnabled
- jks_pass -> jksPass
- jks_alias -> jksAlias
- hash_login -> hashLogin
- ldap_login -> ldapLogin
- kerberos_login -> kerberosLogin
- login_conf -> loginConf
- ssl_conf -> sslConf
- auto_flow_ssl -> autoFlowSsl
- h2o_node_log_level -> h2oNodeLogLevel
- h2o_node_log_dir -> h2oNodeLogDir
- cloud_timeout -> cloudTimeout
- node_network_mask -> nodeNetworkMask
- stacktrace_collector_interval -> stacktraceCollectorInterval
- context_path -> contextPath
- flow_scala_cell_async -> flowScalaCellAsync
- max_parallel_scala_cell_jobs -> maxParallelScalaCellJobs
- internal_port_offset -> internalPortOffset
- mojo_destroy_timeout -> mojoDestroyTimeout
- node_base_port -> nodeBasePort
- node_extra_properties -> nodeExtraProperties
- flow_extra_http_headers -> flowExtraHttpHeaders
- is_internal_secure_connections_enabled -> isInternalSecureConnectionsEnabled
- flow_dir -> flowDir
- client_ip -> clientIp
- client_iced_dir -> clientIcedDir
- h2o_client_log_level -> h2oClientLogLevel
- h2o_client_log_dir -> h2oClientLogDir
- client_base_port -> clientBasePort
- client_web_port -> clientWebPort
- client_verbose_output -> clientVerboseOutput
- client_network_mask -> clientNetworkMask
- ignore_spark_public_dns -> ignoreSparkPublicDNS
- client_web_enabled -> clientWebEnabled
- client_flow_baseurl_override -> clientFlowBaseurlOverride
- client_extra_properties -> clientExtraProperties
- runs_in_external_cluster_mode -> runsInExternalClusterMode
- runs_in_internal_cluster_mode -> runsInInternalClusterMode
- client_check_retry_timeout -> clientCheckRetryTimeout
- set_internal_cluster_mode -> setInternalClusterMode
- set_external_cluster_mode -> setExternalClusterMode
- set_cloud_name -> setCloudName
- set_nthreads -> setNthreads
- set_repl_enabled -> setReplEnabled
- set_repl_disabled -> setReplDisabled
- set_default_num_repl_sessions -> setDefaultNumReplSessions
- set_cluster_topology_listener_enabled -> setClusterTopologyListenerEnabled
- set_cluster_topology_listener_disabled -> setClusterTopologyListenerDisabled
- set_spark_version_check_disabled -> setSparkVersionCheckDisabled
- set_fail_on_unsupported_spark_param_enabled -> setFailOnUnsupportedSparkParamEnabled
- set_fail_on_unsupported_spark_param_disabled -> setFailOnUnsupportedSparkParamDisabled
- set_jks -> setJks
- set_jks_pass -> setJksPass
- set_jks_alias -> setJksAlias
- set_hash_login_enabled -> setHashLoginEnabled
- set_hash_login_disabled -> setHashLoginDisabled
- set_ldap_login_enabled -> setLdapLoginEnabled
- set_ldap_login_disabled -> setLdapLoginDisabled
- set_kerberos_login_enabled -> setKerberosLoginEnabled
- set_kerberos_login_disabled -> setKerberosLoginDisabled
- set_login_conf -> setLoginConf
- set_ssl_conf -> setSslConf
- set_auto_flow_ssl_enabled -> setAutoFlowSslEnabled
- set_auto_flow_ssl_disabled -> setAutoFlowSslDisabled
- set_h2o_node_log_level -> setH2ONodeLogLevel
- set_h2o_node_log_dir -> setH2ONodeLogDir
- set_cloud_timeout -> setCloudTimeout
- set_node_network_mask -> setNodeNetworkMask
- set_stacktrace_collector_interval -> setStacktraceCollectorInterval
- set_context_path -> setContextPath
- set_flow_scala_cell_async_enabled -> setFlowScalaCellAsyncEnabled
- set_flow_scala_cell_async_disabled -> setFlowScalaCellAsyncDisabled
- set_max_parallel_scala_cell_jobs -> setMaxParallelScalaCellJobs
- set_internal_port_offset -> setInternalPortOffset
- set_node_base_port -> setNodeBasePort
- set_mojo_destroy_timeout -> setMojoDestroyTimeout
- set_node_extra_properties -> setNodeExtraProperties
- set_flow_extra_http_headers -> setFlowExtraHttpHeaders
- set_internal_secure_connections_enabled -> setInternalSecureConnectionsEnabled
- set_internal_secure_connections_disabled -> setInternalSecureConnectionsDisabled
- set_flow_dir -> setFlowDir
- set_client_ip -> setClientIp
- set_client_iced_dir -> setClientIcedDir
- set_h2o_client_log_level -> setH2OClientLogLevel
- set_h2o_client_log_dir -> setH2OClientLogDir
- set_client_port_base -> setClientBasePort
- set_client_web_port -> setClientWebPort
- set_client_verbose_enabled -> setClientVerboseEnabled
- set_client_verbose_disabled -> setClientVerboseDisabled
- set_client_network_mask -> setClientNetworkMask
- set_ignore_spark_public_dns_enabled -> setIgnoreSparkPublicDNSEnabled
- set_ignore_spark_public_dns_disabled -> setIgnoreSparkPublicDNSDisabled
- set_client_web_enabled -> setClientWebEnabled
- set_client_web_disabled -> setClientWebDisabled
- set_client_flow_baseurl_override -> setClientFlowBaseurlOverride
- set_client_check_retry_timeout -> setClientCheckRetryTimeout
- set_client_extra_properties -> setClientExtraProperties
In H2OAutoML Python and Scala API, the member leaderboard()/leaderboard is replaced by the method getLeaderboard().
The method setClusterConfigFile was removed from H2OConf in Scala API. The replacement method is setClusterInfoFile on H2OConf.
The method setClientPortBase was removed from H2OConf in Scala API. The replacement method is setClientBasePort on H2OConf.
In H2OGridSearch Python API, the methods: get_grid_models, get_grid_models_params and `` get_grid_models_metrics`` are removed and replaced by getGridModels, getGridModelsParams and `` getGridModelsMetrics``.
On H2OXGboost Scala and Python API, the methods getInitialScoreIntervals, setInitialScoreIntervals, getScoreInterval and setScoreInterval are removed without replacement. They correspond to an internal H2O argument which should not be exposed.
On H2OXGboost Scala and Python API, the methods getLearnRateAnnealing and setLearnRateAnnealing are removed without replacement as this parameter is currently not exposed in H2O.
The methods ignoreSparkPublicDNS, setIgnoreSparkPublicDNSEnabled and setIgnoreSparkPublicDNSDisabled are removed without replacement as they are no longer required. Also the option spark.ext.h2o.client.ignore.SPARK_PUBLIC_DNS does not have any effect anymore.

From 3.28.0 to 3.28.1¶

On H2OConf Python API, the methods external_write_confirmation_timeout and set_external_write_confirmation_timeout are removed without replacement. On H2OConf Scala API, the methods externalWriteConfirmationTimeout and setExternalWriteConfirmationTimeout are removed without replacement. Also the option spark.ext.h2o.external.write.confirmation.timeout does not have any effect anymore.
The environment variable H2O_EXTENDED_JAR specifying path to an extended driver jar was entirely replaced with H2O_DRIVER_JAR. The H2O_DRIVER_JAR should contain a path to a plain H2O driver jar without any extensions. For more details, see External Backend.
The location of Sparkling Water assembly JAR has changed inside the Sparkling Water distribution archive which you can download from our download page. It has been moved from assembly/build/libs to just jars.
H2OSVM has been removed from the Scala API. We have removed this API as it was just wrapping Spark SVM and complicated the future development. If you still need to use SVM, please use Spark SVM directly. All the parameters remain the same. We are planning to expose proper H2O’s SVM implementation in Sparkling Water in the following major releases.
In case of binomial predictions on H2O MOJOs, the fields p0 and p1 in the detailed prediction column are replaced by a single field probabilities which is a map from label to predicted probability. The same is done for the fields p0_calibrated and p1_calibrated. These fields are replaced by a single field calibratedProbabilities which is a map from label to predicted calibrated probability.
In case of multinomial predictions on H2O MOJOs, the type of field probabilities in the detailed prediction column is changed from array of probabilities to a map from label to predicted probability.
In case of ordinal predictions on H2O MOJOs, the type of field probabilities in the detailed prediction column is changed from array of probabilities to a map from label to predicted probability.
On H2OConf in all clients, the methods externalCommunicationBlockSizeAsBytes, externalCommunicationBlockSize and setExternalCommunicationBlockSize have been removed as they are no longer needed.
Method Security.enableSSL in Scala API has been removed. Please use setInternalSecureConnectionsEnabled on H2OConf to secure your cluster. This setter is available on Scala, Python and R clients.
For the users of the manual backend we have simplified the configuration and there is no need to specify a cluster size anymore in advance. Sparkling Water automatically discovers the cluster size. In particular spark.ext.h2o.external.cluster.size does not have any effect anymore.

From 3.26 To 3.28.0¶

Passing Authentication in Scala¶

The users of Scala who set up any form of authentication on the backend side are now required to specify credentials on the H2OConf object via setUserName and setPassword. It is also possible to specify these directly as Spark options spark.ext.h2o.user.name and spark.ext.h2o.password. Note: Actually only users of external backend need to specify these options at this moment as the external backend is using communication via REST api but all our documentation is using these options already as the internal backend will start using the REST api soon as well.

String instead of enums in Sparkling Water Algo API¶

In scala, setters of the pipeline wrappers for H2O algorithms now accepts strings in places where they accepted enum values before. Before, we called, for example:

import hex.genmodel.utils.DistributionFamily
val gbm = H2OGBM()
gbm.setDistribution(DistributionFamily.multinomial)

Now, the correct code is:

val gbm = H2OGBM()
gbm.setDistribution("multinomial")

which makes the Python and Scala APIs consistent. Both upper case and lower case values are valid and if a wrong input is entered, warning is printed out with correct possible values.

Switch to Java 1.8 on Spark 2.1¶

Sparkling Water for Spark 2.1 now requires Java 1.8 and higher.

DRF exposed into Sparkling Water Algorithm API¶

DRF is now exposed in the Sparkling Water. Please see our documentation to learn how to use it Train DRF Model in Sparkling Water.

Also we can run our Grid Search API on DRF.

Change Default Name of Prediction Column¶

The default name of the prediction column has been changed from prediction_output to prediction.

Single value in prediction column¶

The prediction column contains directly the predicted value. For example, before this change, the prediction column contained another struct field called value (in case of regression issue), which contained the value. From now on, the predicted value is always stored directly in the prediction column. In case of regression issue, the predicted numeric value and in case of classification, the predicted label. If you are interested in more details created during the prediction, please make sure to set withDetailedPredictionCol to true via the setters on both PySparkling and Sparkling Water. When enabled, additional column named detailed_prediction is created which contains additional prediction details, such as probabilities, contributions and so on.

In manual mode of external backend always require a specification of cluster location¶

In previous versions, H2O client was able to discover nodes using the multicast search. That is now removed and IP:Port of any node of external cluster to which we need to connect is required. This also means that in the users of multicast cloud up in case of external H2O backend in manual standalone (no Hadoop) mode now need to pass the flatfile argument external H2O. For more information, please see Manual Mode of External Backend without Hadoop (standalone).

Removal of Deprecated Methods and Classes¶

getColsampleBytree and setColsampleBytree methods are removed from the XGBoost API. Please use the new getColSampleByTree and setColSampleByTree.
Removal of deprecated option spark.ext.h2o.external.cluster.num.h2o.nodes and corresponding setters. Please use spark.ext.h2o.external.cluster.size or the corresponding setter setClusterSize.
Removal of deprecated algorithm classes in package org.apache.spark.h2o.ml.algos. Please use the classes from the package ai.h2o.sparkling.ml.algos. Their API remains the same as before. This is the beginning of moving Sparkling Water classes to our distinct package ai.h2o.sparkling
Removal of deprecated option spark.ext.h2o.external.read.confirmation.timeout and related setters. This option is removed without a replacement as it is no longer needed.
Removal of deprecated parameter SelectBestModelDecreasing on the Grid Search API. Related getters and setters have been also removed. This method is removed without replacement as we now internally sort the models with the ordering meaningful to the specified sort metric.
TargetEncoder transformer now accepts the outputCols parameter which can be used to override the default output column names.
On PySparkling H2OGLM API, we removed deprecated parameter alpha in favor of alphaValue and lambda_ in favor of lambdaValue. On Both PySparkling and Sparkling Water H2OGLM API, we removed methods getAlpha in favor of getAlphaValue, getLambda in favor of getLambdaValue, setAlpha in favor of setAlphaValue and setLambda in favor of setLambdaValue. These changes ensure the consistency across Python and Scala APIs.
In Sparkling Water H2OConf API, we removed method h2oDriverIf in favor of externalH2ODriverIf and setH2ODriverIf in favor of setExternalH2ODriverIf. In PySparkling H2OConf API, we removed method h2o_driver_if in favor of externalH2ODriverIf and set_h2o_driver_if in favor of setExternalH2ODriverIf.
On PySparkling H2OConf API, the method user_name has been removed in favor of the userName method and method set_user_name had been removed in favor of the setUserName method.
The configurations spark.ext.h2o.external.kill.on.unhealthy.interval, spark.ext.h2o.external.health.check.interval and spark.ext.h2o.ui.update.interval have been removed and were replaced by a single option spark.ext.h2o.backend.heartbeat.interval. On H2OConf Scala API, the methods backendHeartbeatInterval and setBackendHeartbeatInterval were added and the following methods were removed: uiUpdateInterval, setUiUpdateInterval, killOnUnhealthyClusterInterval, setKillOnUnhealthyClusterInterval, healthCheckInterval and setHealthCheckInterval. On H2OConf Python API, the methods backendHeartbeatInterval and setBackendHeartbeatInterval were added and the following methods were removed: ui_update_interval, set_ui_update_interval, kill_on_unhealthy_cluster_interval, set_kill_on_unhealthy_cluster_interval, get_health_check_interval and set_health_check_interval. The added methods are used to configure single interval which was previously specified by these 3 different methods.
The configuration spark.ext.h2o.cluster.client.connect.timeout is removed without replacement as it is no longer needed. on H2OConf Scala API, the methods clientConnectionTimeout and setClientConnectionTimeout were removed and on H2OConf Python API, the methods set_client_connection_timeout and set_client_connection_timeout were removed.

Change of Versioning Scheme¶

Version of Sparkling Water is changed to the following pattern: H2OVersion-SWPatchVersion-SparkVersion, where: H2OVersion is full H2O Version which is integrated to Sparkling Water. SWPatchVersion is used to specify a patch version and SparkVersion is a Spark version. This change of scheme allows us to do releases of Sparkling Water without the need of releasing H2O if there is only change on the Sparkling Water side. In that case, we just increment the SWPatchVersion. The new version therefore looks, for example, like 3.26.0.9-2-2.4. This version tells us this Sparkling Water is integrating H2O 3.26.0.9, it is the second release with 3.26.0.9 version and is for Spark 2.4.

Renamed Property for Passing Extra HTTP Headers for Flow UI¶

The configuration property spark.ext.h2o.client.flow.extra.http.headers was renamed to to spark.ext.h2o.flow.extra.http.headers since Flow UI can also run on H2O nodes and the value of the property is also propagated to H2O nodes since the major version 3.28.0.1-1.

External Backend now keeps H2O Flow accessible on worker nodes¶

The option spark.ext.h2o.node.enable.web does not have any effect anymore for automatic mode of external backend as we required H2O Flow to be accessible on the worker nodes. The associated getters and setters do also not have any effect in this case.

It is also required that the users of manual mode of external backend keep REST api available on all worker nodes. In particular, the H2O option -disable_web can’t be specified when starting H2O.

Default Values of Some AutoML Parameters Have Changed¶

The default values of the following AutoML parameters have changed across all APIs.

Parameter Name	Old Value	New Value
`maxRuntimeSecs`	`3600.0`	`0.0` (unlimited)
`keepCrossValidationPredictions`	`true`	`false`
`keepCrossValidationModels`	`true`	`false`

From any previous version to 3.26.11¶

Users of Sparkling Water external cluster in manual mode on Hadoop need to update the command the external cluster is launched with. A new parameter -sw_ext_backend needs to be added to the h2odriver invocation.