Skip to content

Commit

Permalink
[SW-1985] Reference mojo as prostate_mojo.zip instead of prostate.moj…
Browse files Browse the repository at this point in the history 
…o in doc (#1865)
  • Loading branch information
@jakubhava
jakubhava committed Feb 23, 2020
1 parent c70c3a4 commit 4813466
Show file tree
Hide file tree
Showing 2 changed files with 22 additions and 22 deletions.
40 changes: 20 additions & 20 deletions doc/src/site/sphinx/deployment/load_mojo.rst
View file
Expand Up @@ -17,15 +17,15 @@ the mojo from a current working directory.
.. code:: scala
import ai.h2o.sparkling.ml.models._
val model = H2OMOJOModel.createFromMojo("prostate.mojo")
val model = H2OMOJOModel.createFromMojo("prostate_mojo.zip")
.. tab-container:: Python
:title: Python

.. code:: python
from pysparkling.ml import *
model = H2OMOJOModel.createFromMojo("prostate.mojo")
model = H2OMOJOModel.createFromMojo("prostate_mojo.zip")
.. tab-container:: R
:title: R
Expand All @@ -34,7 +34,7 @@ the mojo from a current working directory.
library(rsparkling)
sc <- spark_connect(master = "local")
model <- H2OMOJOModel.createFromMojo("prostate.mojo")
model <- H2OMOJOModel.createFromMojo("prostate_mojo.zip")
Absolute local path can also be used. To create a MOJO model from a locally available MOJO, call:
Expand All @@ -47,15 +47,15 @@ Absolute local path can also be used. To create a MOJO model from a locally avai
.. code:: scala
import ai.h2o.sparkling.ml.models._
val model = H2OMOJOModel.createFromMojo("/Users/peter/prostate.mojo")
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")
model = H2OMOJOModel.createFromMojo("/Users/peter/prostate_mojo.zip")
.. tab-container:: R
:title: R
Expand All @@ -64,7 +64,7 @@ Absolute local path can also be used. To create a MOJO model from a locally avai
library(rsparkling)
sc <- spark_connect(master = "local")
model <- H2OMOJOModel.createFromMojo("/Users/peter/prostate.mojo")
model <- H2OMOJOModel.createFromMojo("/Users/peter/prostate_mojo.zip")
Expand All @@ -79,15 +79,15 @@ Absolute paths on Hadoop can also be used. To create a MOJO model from a MOJO st
.. code:: scala
import ai.h2o.sparkling.ml.models._
val model = H2OMOJOModel.createFromMojo("/user/peter/prostate.mojo")
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")
model = H2OMOJOModel.createFromMojo("/user/peter/prostate_mojo.zip")
.. tab-container:: R
:title: R
Expand All @@ -96,11 +96,11 @@ Absolute paths on Hadoop can also be used. To create a MOJO model from a MOJO st
library(rsparkling)
sc <- spark_connect(master = "local")
model <- H2OMOJOModel.createFromMojo("/user/peter/prostate.mojo")
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``, where ``{server}`` and ``{port}`` is automatically filled in by Spark.
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:
Expand All @@ -115,9 +115,9 @@ You can also manually specify the type of data source you need to use, in that c
import ai.h2o.sparkling.ml.models._
// HDFS
val modelHDFS = H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate.mojo")
val modelHDFS = H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate_mojo.zip")
// Local file
val modelLocal = H2OMOJOModel.createFromMojo("file:///Users/peter/prostate.mojo")
val modelLocal = H2OMOJOModel.createFromMojo("file:///Users/peter/prostate_mojo.zip")
.. tab-container:: Python
:title: Python
Expand All @@ -126,9 +126,9 @@ You can also manually specify the type of data source you need to use, in that c
from pysparkling.ml import *
# HDFS
modelHDFS = H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate.mojo")
modelHDFS = H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate_mojo.zip")
# Local file
modelLocal = H2OMOJOModel.createFromMojo("file:///Users/peter/prostate.mojo")
modelLocal = H2OMOJOModel.createFromMojo("file:///Users/peter/prostate_mojo.zip")
.. tab-container:: R
Expand All @@ -139,9 +139,9 @@ You can also manually specify the type of data source you need to use, in that c
library(rsparkling)
sc <- spark_connect(master = "local")
# HDFS
modelHDFS <- H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate.mojo")
modelHDFS <- H2OMOJOModel.createFromMojo("hdfs:///user/peter/prostate_mojo.zip")
# Local file
modelLocal <- H2OMOJOModel.createFromMojo("file:///Users/peter/prostate.mojo")
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.
Expand All @@ -157,7 +157,7 @@ On the other hand, the model can be configured during its creation via ``H2OMOJO
import ai.h2o.sparkling.ml.models._
val settings = H2OMOJOSettings(convertUnknownCategoricalLevelsToNa = true, convertInvalidNumbersToNa = true)
val model = H2OMOJOModel.createFromMojo("prostate.mojo", settings)
val model = H2OMOJOModel.createFromMojo("prostate_mojo.zip", settings)
.. tab-container:: Python
:title: Python
Expand All @@ -166,7 +166,7 @@ On the other hand, the model can be configured during its creation via ``H2OMOJO
from pysparkling.ml import *
settings = H2OMOJOSettings(convertUnknownCategoricalLevelsToNa = True, convertInvalidNumbersToNa = True)
model = H2OMOJOModel.createFromMojo("prostate.mojo", settings)
model = H2OMOJOModel.createFromMojo("prostate_mojo.zip", settings)
.. tab-container:: R
:title: R
Expand All @@ -176,7 +176,7 @@ On the other hand, the model can be configured during its creation via ``H2OMOJO
library(rsparkling)
sc <- spark_connect(master = "local")
settings <- H2OMOJOSettings(convertUnknownCategoricalLevelsToNa = TRUE, convertInvalidNumbersToNa = TRUE)
model <- H2OMOJOModel.createFromMojo("prostate.mojo", settings)
model <- H2OMOJOModel.createFromMojo("prostate_mojo.zip", settings)
To score the dataset using the loaded mojo, call:
Expand Down Expand Up @@ -220,5 +220,5 @@ call the ``createFromMojo`` method on the specific MOJO model type.
.. code:: scala
import ai.h2o.sparkling.ml.models._
val specificModel = H2OTreeBasedSupervisedMOJOModel.createFromMojo("prostate.mojo")
val specificModel = H2OTreeBasedSupervisedMOJOModel.createFromMojo("prostate_mojo.zip")
println(s"Ntrees: ${specificModel.getNTrees()}") // Relevant only to GBM, DRF and XGBoost
4 changes: 2 additions & 2 deletions doc/src/site/sphinx/deployment/scoring_mojo_pipeline.rst
View file
Expand Up @@ -55,7 +55,7 @@ At this point, you should have available a PySpark interactive terminal where yo
settings = H2OMOJOSettings(namedMojoOutputColumns = True)
# Load the pipeline. 'settings' is an optional argument. If it's not specified, the default values are used.
mojo = H2OMOJOPipelineModel.createFromMojo("file:///path/to/the/pipeline.mojo", settings)
mojo = H2OMOJOPipelineModel.createFromMojo("file:///path/to/the/pipeline_mojo.zip", settings)
.. code:: python
Expand Down Expand Up @@ -103,7 +103,7 @@ At this point, you should have available a Sparkling Water interactive terminal
val settings = H2OMOJOSettings(namedMojoOutputColumns = true)
// Load the pipeline. 'settings' is an optional argument. If it's not specified, the default values are used.
val mojo = H2OMOJOPipelineModel.createFromMojo("file:///path/to/the/pipeline.mojo", settings)
val mojo = H2OMOJOPipelineModel.createFromMojo("file:///path/to/the/pipeline_mojo.zip", settings)
.. code:: scala
Expand Down

0 comments on commit 4813466

Please sign in to comment.