Add docs for passThroughArgs

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/params/LightGBMParams.scala

@@ -15,7 +15,8 @@ import org.apache.spark.ml.util.DefaultParamsWritable
 trait LightGBMExecutionParams extends Wrappable {
   val passThroughArgs = new Param[String](this, "passThroughArgs",
     "Direct string to pass through to LightGBM library (appended with other explicitly set params). " +
-      "Will override any parameters given with explicit setters. Can include multiple parameters in one string.")
+      "Will override any parameters given with explicit setters. Can include multiple parameters in one string. " +
+      "e.g., force_row_wise=true")
   setDefault(passThroughArgs->"")
   def getPassThroughArgs: String = $(passThroughArgs)
   def setPassThroughArgs(value: String): this.type = set(passThroughArgs, value)

website/docs/features/lightgbm/about.md

@@ -57,6 +57,48 @@ model = LightGBMRegressor(application='quantile',
 For an end to end application, check out the LightGBM [notebook
 example](../LightGBM%20-%20Overview).
 
+### Arguments
+
+SynapseML exposes getters/setters for many common LightGBM parameters.
+In python, you can use the properties as shown above, or in Scala use the
+fluent setters.
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                       .setLearningRate(0.2)
+                       .setNumLeaves(50)
+```
+
+LightGBM has far more parameters than SynapseML exposes. For cases where you
+need to set some parameters that SyanpseML does not expose a setter for, use
+passThroughArgs. This is just a free string that you can use to add extra parameters
+to the command SynapseML sends to configure LightGBM.
+
+```python
+from synapse.ml.lightgbm import LightGBMClassifier
+model = LightGBMClassifier(passThroughArgs="force_row_wise=true",
+                           numIterations=100,
+                           numLeaves=31).fit(train)
+```
+
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                      .setPassThroughArgs("force_row_wise=true min_sum_hessian_in_leaf=2e-3")
+                      .setLearningRate(0.2)
+                      .setNumLeaves(50)
+```
+
+For formatting options and specific argument documentation, see
+[LightGBM docs](https://lightgbm.readthedocs.io/en/v3.3.2/Parameters.html). Some
+parameters SynapseML will set specifically for the Spark distributed environment and
+should not be changed. Some parameters are for cli mode only, and will not work within
+Spark.
+
+Note that you can mix passThroughArgs and explicit args, as shown above. SynapseML will
+merge them to create one argument string to send to LightGBM. If you set a parameter in
+both places, the passThroughArgs will take precedence.
+
 ### Architecture
 
 LightGBM on Spark uses the Simple Wrapper and Interface Generator (SWIG)

website/versioned_docs/version-0.10.0/features/lightgbm/about.md

@@ -57,6 +57,48 @@ model = LightGBMRegressor(application='quantile',
 For an end to end application, check out the LightGBM [notebook
 example](../LightGBM%20-%20Overview).
 
+### Arguments
+
+SynapseML exposes getters/setters for many common LightGBM parameters.
+In python, you can use the properties as shown above, or in Scala use the
+fluent setters.
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                       .setLearningRate(0.2)
+                       .setNumLeaves(50)
+```
+
+LightGBM has far more parameters than SynapseML exposes. For cases where you
+need to set some parameters that SyanpseML does not expose a setter for, use
+passThroughArgs. This is just a free string that you can use to add extra parameters
+to the command SynapseML sends to configure LightGBM.
+
+```python
+from synapse.ml.lightgbm import LightGBMClassifier
+model = LightGBMClassifier(passThroughArgs="force_row_wise=true",
+                           numIterations=100,
+                           numLeaves=31).fit(train)
+```
+
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                      .setPassThroughArgs("force_row_wise=true min_sum_hessian_in_leaf=2e-3")
+                      .setLearningRate(0.2)
+                      .setNumLeaves(50)
+```
+
+For formatting options and specific argument documentation, see
+[LightGBM docs](https://lightgbm.readthedocs.io/en/v3.3.2/Parameters.html). Some
+parameters SynapseML will set specifically for the Spark distributed environment and
+should not be changed. Some parameters are for cli mode only, and will not work within
+Spark.
+
+Note that you can mix passThroughArgs and explicit args, as shown above. SynapseML will
+merge them to create one argument string to send to LightGBM. If you set a parameter in
+both places, the passThroughArgs will take precedence.
+
 ### Architecture
 
 LightGBM on Spark uses the Simple Wrapper and Interface Generator (SWIG)

website/versioned_docs/version-0.10.1/features/lightgbm/about.md

@@ -57,6 +57,48 @@ model = LightGBMRegressor(application='quantile',
 For an end to end application, check out the LightGBM [notebook
 example](../LightGBM%20-%20Overview).
 
+### Arguments
+
+SynapseML exposes getters/setters for many common LightGBM parameters.
+In python, you can use the properties as shown above, or in Scala use the
+fluent setters.
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                       .setLearningRate(0.2)
+                       .setNumLeaves(50)
+```
+
+LightGBM has far more parameters than SynapseML exposes. For cases where you
+need to set some parameters that SyanpseML does not expose a setter for, use
+passThroughArgs. This is just a free string that you can use to add extra parameters
+to the command SynapseML sends to configure LightGBM.
+
+```python
+from synapse.ml.lightgbm import LightGBMClassifier
+model = LightGBMClassifier(passThroughArgs="force_row_wise=true",
+                           numIterations=100,
+                           numLeaves=31).fit(train)
+```
+
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                      .setPassThroughArgs("force_row_wise=true min_sum_hessian_in_leaf=2e-3")
+                      .setLearningRate(0.2)
+                      .setNumLeaves(50)
+```
+
+For formatting options and specific argument documentation, see
+[LightGBM docs](https://lightgbm.readthedocs.io/en/v3.3.2/Parameters.html). Some
+parameters SynapseML will set specifically for the Spark distributed environment and
+should not be changed. Some parameters are for cli mode only, and will not work within
+Spark.
+
+Note that you can mix passThroughArgs and explicit args, as shown above. SynapseML will
+merge them to create one argument string to send to LightGBM. If you set a parameter in
+both places, the passThroughArgs will take precedence.
+
 ### Architecture
 
 LightGBM on Spark uses the Simple Wrapper and Interface Generator (SWIG)

website/versioned_docs/version-0.10.2/features/lightgbm/about.md

@@ -57,6 +57,48 @@ model = LightGBMRegressor(application='quantile',
 For an end to end application, check out the LightGBM [notebook
 example](../LightGBM%20-%20Overview).
 
+### Arguments
+
+SynapseML exposes getters/setters for many common LightGBM parameters.
+In python, you can use the properties as shown above, or in Scala use the
+fluent setters.
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                       .setLearningRate(0.2)
+                       .setNumLeaves(50)
+```
+
+LightGBM has far more parameters than SynapseML exposes. For cases where you
+need to set some parameters that SyanpseML does not expose a setter for, use
+passThroughArgs. This is just a free string that you can use to add extra parameters
+to the command SynapseML sends to configure LightGBM.
+
+```python
+from synapse.ml.lightgbm import LightGBMClassifier
+model = LightGBMClassifier(passThroughArgs="force_row_wise=true",
+                           numIterations=100,
+                           numLeaves=31).fit(train)
+```
+
+```scala
+import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier
+val classifier = new LightGBMClassifier()
+                      .setPassThroughArgs("force_row_wise=true min_sum_hessian_in_leaf=2e-3")
+                      .setLearningRate(0.2)
+                      .setNumLeaves(50)
+```
+
+For formatting options and specific argument documentation, see
+[LightGBM docs](https://lightgbm.readthedocs.io/en/v3.3.2/Parameters.html). Some
+parameters SynapseML will set specifically for the Spark distributed environment and
+should not be changed. Some parameters are for cli mode only, and will not work within
+Spark.
+
+Note that you can mix passThroughArgs and explicit args, as shown above. SynapseML will
+merge them to create one argument string to send to LightGBM. If you set a parameter in
+both places, the passThroughArgs will take precedence.
+
 ### Architecture
 
 LightGBM on Spark uses the Simple Wrapper and Interface Generator (SWIG)