Obtain SHAP values from MOJO model
----------------------------------

Obtaining SHAP values is possible only from H2OGBM and H2OXGBoost pipeline wrappers and for
regression or binomial problems.

To get SHAP values(=contributions) from H2OXGBoost model, please do:

.. content-tabs::

    .. tab-container:: Scala
        :title: Scala

        First, let's start Sparkling Shell as

        .. code:: shell

            ./bin/sparkling-shell

        Start H2O cluster inside the Spark environment

        .. code:: scala

            import org.apache.spark.h2o._
            import java.net.URI
            val hc = H2OContext.getOrCreate(spark)

        Parse the data using H2O and convert them to Spark Frame

        .. code:: scala

            val frame = new H2OFrame(new URI("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv"))
            val sparkDF = hc.asDataFrame(frame).withColumn("CAPSULE", $"CAPSULE" cast "string")
            val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2))

        Train the model. You can configure all the available XGBoost arguments using provided setters, such as the label column.

        .. code:: scala

            import ai.h2o.sparkling.ml.algos.H2OXGBoost
            val estimator = new H2OXGBoost().setLabelCol("CAPSULE").setWithDetailedPredictionCol(true)
            val model = estimator.fit(trainingDF)

        The call ``setWithDetailedPredictionCol(true)`` tells Sparkling Water to create additional prediction column with
        additional prediction details, such as the contributions. The name of this column is by default "detailed_prediction"
        and can be modified via ``setDetailedPredictionCol`` setter.

        Run Predictions

        .. code:: scala

            val predictions = model.transform(testingDF).show(false)

        Show contributions

        .. code:: scala

            predictions.select("detailed_prediction.contribution").show()



    .. tab-container:: Python
        :title: Python

        First, let's start PySparkling Shell as

        .. code:: shell

            ./bin/pysparkling

        Start H2O cluster inside the Spark environment

        .. code:: python

            from pysparkling import *
            hc = H2OContext.getOrCreate(spark)

        Parse the data using H2O and convert them to Spark Frame

        .. code:: python

            import h2o
            frame = h2o.import_file("https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv")
            sparkDF = hc.as_spark_frame(frame)
            sparkDF = sparkDF.withColumn("CAPSULE", sparkDF.CAPSULE.cast("string"))
            [trainingDF, testingDF] = sparkDF.randomSplit([0.8, 0.2])

        Train the model. You can configure all the available XGBoost arguments using provided setters or constructor parameters, such as the label column.

        .. code:: python

            from pysparkling.ml import H2OXGBoost
            estimator = H2OXGBoost(labelCol = "CAPSULE", withDetailedPredictionCol = True)
            model = estimator.fit(trainingDF)

        The parameter ``withDetailedPredictionCol = True`` tells Sparkling Water to create additional prediction column with
        additional prediction details, such as the contributions. The name of this column is by default "detailed_prediction"
        and can be modified via ``detailedPredictionCol`` parameter.

        Run Predictions

        .. code:: python

            model.transform(testingDF).show(truncate = False)

        Show contributions

        .. code:: python

            predictions.select("detailed_prediction.contribution").show()