Importing H2O MOJOs
-------------------

H2O MOJOs can be imported to Sparkling Water from all data sources supported by Apache Spark such as 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 HDFS-enabled system, we create
the mojo from a current working directory.

.. content-tabs::

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

        .. code:: scala

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

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

        .. code:: python

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

    .. tab-container:: R
        :title: R

        .. code:: r

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


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

.. content-tabs::

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

        .. code:: scala

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

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

        .. code:: python

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

    .. tab-container:: R
        :title: R

        .. code:: 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:


.. content-tabs::

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

        .. code:: scala

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

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

        .. code:: python

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

    .. tab-container:: R
        :title: R

        .. code:: 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.


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


.. content-tabs::

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

        .. code:: 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")

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

        .. code:: 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")


    .. tab-container:: R
        :title: R

        .. code:: 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``, in Scala:


.. content-tabs::

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

        .. code:: scala

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

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

        .. code:: python

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

    .. tab-container:: R
        :title: R

        .. code:: 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:

.. content-tabs::

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

        .. code:: scala

            model.transform(dataset)

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

        .. code:: python

            model.transform(dataset)

    .. tab-container:: R
        :title: R

        .. code:: r

            model$transform(dataset)


In Scala, the ``createFromMojo`` method returns a mojo model instance casted as a base class ``H2OMOJOModel``. This class holds
only properties that are shared accross 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.

.. code:: scala

    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