Importing H2O MOJOs from H2O-3

When training algorithm using Sparkling Water API, Sparkling Water always produces H2OMOJOModel. It is however also possible to import existing MOJO into the Sparkling Water ecosystem from H2O-3. After importing the H2O-3 MOJO the API is unified for the loaded MOJO and the one created in Sparkling Water, for example, using H2OXGBoost.

H2O MOJOs can be imported to Sparkling Water from all data sources supported by Apache Spark such as a local file, S3 or HDFS and the semantics of the import is the same as in the Spark API.

When creating a MOJO specified by a relative path and HDFS is enabled, the method attempts to load the MOJO from the HDFS home directory of the current user. In case we are not running on a HDFS-enabled system, we create the mojo from a current working directory.

Scala

import ai.h2o.sparkling.ml.models._
val model = H2OMOJOModel.createFromMojo("prostate_mojo.zip")

Python

from pysparkling.ml import *
model = H2OMOJOModel.createFromMojo("prostate_mojo.zip")

R

library(rsparkling)
sc <- spark_connect(master = "local")
model <- H2OMOJOModel.createFromMojo("prostate_mojo.zip")

An absolute local path can also be used. To create a MOJO model from a locally available MOJO, call:

Scala

import ai.h2o.sparkling.ml.models._
val model = H2OMOJOModel.createFromMojo("/Users/peter/prostate_mojo.zip")

Python

from pysparkling.ml import *
model = H2OMOJOModel.createFromMojo("/Users/peter/prostate_mojo.zip")

R

library(rsparkling)
sc <- spark_connect(master = "local")
model <- H2OMOJOModel.createFromMojo("/Users/peter/prostate_mojo.zip")

Absolute paths on Hadoop can also be used. To create a MOJO model from a MOJO stored on HDFS, call:

Scala

import ai.h2o.sparkling.ml.models._
val model = H2OMOJOModel.createFromMojo("/user/peter/prostate_mojo.zip")

Python

from pysparkling.ml import *
model = H2OMOJOModel.createFromMojo("/user/peter/prostate_mojo.zip")

R

library(rsparkling)
sc <- spark_connect(master = "local")
model <- H2OMOJOModel.createFromMojo("/user/peter/prostate_mojo.zip")

The call loads the mojo file from the following location hdfs://{server}:{port}/user/peter/prostate_mojo.zip, where {server} and {port} is automatically filled in by Spark.

We can also manually specify the type of data source we need to use, in that case, we need to provide the schema:

Scala

import ai.h2o.sparkling.ml.models._
// HDFS
val modelHDFS = H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate_mojo.zip")
// Local file
val modelLocal = H2OMOJOModel.createFromMojo("file:///Users/peter/prostate_mojo.zip")

Python

from pysparkling.ml import *
# HDFS
modelHDFS = H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate_mojo.zip")
# Local file
modelLocal = H2OMOJOModel.createFromMojo("file:///Users/peter/prostate_mojo.zip")

R

library(rsparkling)
sc <- spark_connect(master = "local")
 # HDFS
modelHDFS <- H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate_mojo.zip")
# Local file
modelLocal <- H2OMOJOModel.createFromMojo("file:///Users/peter/prostate_mojo.zip")

The loaded model is an immutable instance, so it’s not possible to change the configuration of the model during its existence. On the other hand, the model can be configured during its creation via H2OMOJOSettings:

Scala

import ai.h2o.sparkling.ml.models._
val settings = H2OMOJOSettings(convertUnknownCategoricalLevelsToNa = true, convertInvalidNumbersToNa = true)
val model = H2OMOJOModel.createFromMojo("prostate_mojo.zip", settings)

Python

from pysparkling.ml import *
settings = H2OMOJOSettings(convertUnknownCategoricalLevelsToNa = True, convertInvalidNumbersToNa = True)
model = H2OMOJOModel.createFromMojo("prostate_mojo.zip", settings)

R

library(rsparkling)
sc <- spark_connect(master = "local")
settings <- H2OMOJOSettings(convertUnknownCategoricalLevelsToNa = TRUE, convertInvalidNumbersToNa = TRUE)
model <- H2OMOJOModel.createFromMojo("prostate_mojo.zip", settings)

To score the dataset using the loaded mojo, call:

Scala

model.transform(dataset)

Python

model.transform(dataset)

R

model$transform(dataset)

In Scala, the createFromMojo method returns a mojo model instance cast as a base class H2OMOJOModel. This class holds only properties that are shared across all MOJO model types from the following type hierarchy:

  • H2OMOJOModel

    • H2OUnsupervisedMOJOModel

    • H2OSupervisedMOJOModel

      • H2OTreeBasedSupervisedMOJOModel

If a Scala user wants to get a property specific for a given MOJO model type, he/she must utilize casting or call the createFromMojo method on the specific MOJO model type.

import ai.h2o.sparkling.ml.models._
val specificModel = H2OTreeBasedSupervisedMOJOModel.createFromMojo("prostate_mojo.zip")
println(s"Ntrees: ${specificModel.getNTrees()}") // Relevant only to GBM, DRF and XGBoost

Exporting the loaded MOJO model using Sparkling Water

To export the MOJO model, call model.write.save(path). In case of Hadoop enabled system, the command by default uses HDFS.

Importing the previously exported MOJO model from Sparkling Water

To import the MOJO model, call H2OMOJOModel.read.load(path). In case of Hadoop enabled system, the command by default uses HDFS.

Accessing additional prediction details

After computing predictions, the prediction column contains in case of classification problem the predicted label and in case regression problem the predicted number. If we need to access more details for each prediction, see the content of a detailed prediction column. By default, the column is named named detailed_prediction. It could contain, for example, predicted probabilities for each predicted label in case of classification problem, Shapley values, and other information.

Customizing the MOJO Settings

We can configure the output and format of predictions via the H2OMOJOSettings. The available options are

  • predictionCol - Specifies the name of the generated prediction column. The default value is prediction.

  • detailedPredictionCol - Specifies the name of the generated detailed prediction column. The detailed prediction column, if enabled, contains additional details, such as probabilities, Shapley values etc. The default value is detailed_prediction.

  • convertUnknownCategoricalLevelsToNa - Enables or disables conversion of unseen categoricals to NAs. By default, it is disabled.

  • convertInvalidNumbersToNa - Enables or disables conversion of invalid numbers to NAs. By default, it is disabled.

  • withContributions - Enables or disables computing Shapley values. Shapley values are generated as a sub-column for the detailed prediction column. By default, it is disabled.

  • withLeafNodeAssignments - When enabled, a user can obtain the leaf node assignments after the model traininig has finished. By default, it is disabled.

Methods available on MOJO Model

Obtaining Domain Values

To obtain domain values of the trained model, we can run getDomainValues() on the model. This call returns a mapping from a column name to its domain in a form of an array.

Obtaining Model Category

The method getModelCategory can be used to get the model category (such as binomial, multinomial etc).

Obtaining Training Params

The method getTrainingParams can be used to get a map containing all training parameters used in the H2O. It is a map from the parameter name to the value. The parameters name use the H2O’s naming structure.

Obtaining Metrics

There are several methods to obtain metrics from the MOJO model. All return a map from the metric name to its double value.

  • getTrainingMetrics - obtain training metrics

  • getValidationMetrics - obtain validation metrics

  • getCrossValidationMetrics - obtain cross validation metrics

We also have method getCurrentMetrics which gets one of the metrics above based on the following algorithm:

If cross-validation was used, ie, setNfolds was called and the value was higher than zero, this method returns cross-validation metrics. If cross-validation was not used, but the validation frame was used, the method returns validation metrics. The validation frame is used if setSplitRatio was called with the value lower than one. If neither cross-validation nor validation frame was used, this method returns the training metrics.

Obtaining Leaf Node Assignments

To obtain the leaf node assignments, please first make sure to set withLeafNodeAssignments to true on your MOJO settings object. The leaf node assignments are now stored in the ${detailedPredictionCol}.leafNodeAssignments column on the dataset obtained from the prediction. Please replace ${detailedPredictionCol} with the actual value of your detailed prediction col. By default, it is detailed_prediction.