Adding getBestModel and getBestModelInfo to TuneHyperparameters and f…

…ixed autogenerated code for model name (#355)

notebooks/samples/203 - Breast Cancer - Tune Hyperparameters.ipynb

@@ -125,6 +125,23 @@
     "              paramSpace=randomSpace.space(), seed=0).fit(tune)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can view the best model's parameters and retrieve the underlying best model pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(bestModel.getBestModelInfo())\n",
+    "print(bestModel.getBestModel())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

src/codegen/src/main/scala/WrapperGenerator.scala

@@ -23,6 +23,8 @@ abstract class WrapperGenerator {
 
   def wrapperName(myClass: Class[_]): String
 
+  def modelWrapperName(myClass: Class[_], modelName: String): String
+
   def generateEstimatorWrapper(entryPoint: Estimator[_],
                                entryPointName: String,
                                entryPointQualifiedName: String,
@@ -59,16 +61,18 @@ abstract class WrapperGenerator {
              generateTransformerTestWrapper(t, className, qualifiedClassName))
           case e: Estimator[_] =>
             val sc = iterate[Class[_]](myClass)(_.getSuperclass)
-                     .find(c => Seq("Estimator", "Predictor").contains(c.getSuperclass.getSimpleName))
+                     .find(c => Seq("Estimator", "ProbabilisticClassifier", "Predictor")
+                       .contains(c.getSuperclass.getSimpleName))
                      .get
             val typeArgs = sc.getGenericSuperclass.asInstanceOf[ParameterizedType]
               .getActualTypeArguments
             val getModelFromGenericType = (modelType: Type) => {
               val modelClass = modelType.getTypeName.split("<").head
-              (modelClass.split("\\.").last, modelClass)
+              (modelWrapperName(myClass, modelClass.split("\\.").last), modelClass)
             }
             val (modelClass, modelQualifiedClass) = sc.getSuperclass.getSimpleName match {
               case "Estimator" => getModelFromGenericType(typeArgs.head)
+              case "ProbabilisticClassifier" => getModelFromGenericType(typeArgs(2))
               case "Predictor" => getModelFromGenericType(typeArgs(2))
             }
 
@@ -149,6 +153,11 @@ class PySparkWrapperGenerator extends WrapperGenerator {
     prefix + myClass.getSimpleName
   }
 
+  def modelWrapperName(myClass: Class[_], modelName: String): String = {
+    val prefix = if (needsInternalWrapper(myClass)) internalPrefix else ""
+    prefix + modelName
+  }
+
   def generateEstimatorWrapper(entryPoint: Estimator[_],
                                entryPointName: String,
                                entryPointQualifiedName: String,
@@ -228,15 +237,19 @@ class SparklyRWrapperGenerator extends WrapperGenerator {
                 |export(sdf_transform)
                 |""".stripMargin)
 
-  def wrapperName(myClass: Class[_]): String =
-    myClass.getSimpleName.foldLeft((true, ""))((base, c) => {
+  def formatWrapperName(name: String): String =
+    name.foldLeft((true, ""))((base, c) => {
       val ignoreCaps = base._1
       val partialStr = base._2
       if (!c.isUpper)      (false, partialStr + c)
       else if (ignoreCaps) (true,  partialStr + c.toLower)
       else                 (true,  partialStr + "_" + c.toLower)
     })._2
 
+  def wrapperName(myClass: Class[_]): String = formatWrapperName(myClass.getSimpleName)
+
+  def modelWrapperName(myClass: Class[_], modelName: String): String = formatWrapperName(modelName)
+
   def generateEstimatorWrapper(entryPoint: Estimator[_],
                                entryPointName: String,
                                entryPointQualifiedName: String,

src/lightgbm/src/main/python/LightGBMClassifier.py

@@ -9,7 +9,7 @@
     basestring = str
 
 from mmlspark._LightGBMClassifier import _LightGBMClassifier
-from mmlspark._LightGBMClassifier import M
+from mmlspark._LightGBMClassifier import _LightGBMClassificationModel
 from pyspark.ml.common import inherit_doc
 
 @inherit_doc
@@ -21,7 +21,7 @@ def _create_model(self, java_model):
         return model
 
 @inherit_doc
-class LightGBMClassificationModel(M):
+class LightGBMClassificationModel(_LightGBMClassificationModel):
     def saveNativeModel(self, sparkSession, filename):
         """
         Save the booster as string format to a local or WASB remote location.

src/lightgbm/src/main/python/LightGBMRegressor.py

@@ -9,7 +9,7 @@
     basestring = str
 
 from mmlspark._LightGBMRegressor import _LightGBMRegressor
-from mmlspark._LightGBMRegressor import M
+from mmlspark._LightGBMRegressor import _LightGBMRegressionModel
 from pyspark.ml.common import inherit_doc
 
 @inherit_doc
@@ -21,7 +21,7 @@ def _create_model(self, java_model):
         return model
 
 @inherit_doc
-class LightGBMRegressionModel(M):
+class LightGBMRegressionModel(_LightGBMRegressionModel):
     def saveNativeModel(self, sparkSession, filename):
         """
         Save the booster as string format to a local or WASB remote location.

src/tune-hyperparameters/src/main/python/TuneHyperparameters.py

@@ -0,0 +1,36 @@
+# Copyright (C) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See LICENSE in project root for information.
+
+import sys
+from pyspark import SQLContext
+from pyspark import SparkContext
+
+if sys.version >= '3':
+    basestring = str
+
+from mmlspark._TuneHyperparameters import _TuneHyperparameters
+from mmlspark._TuneHyperparameters import _TuneHyperparametersModel
+from pyspark.ml.wrapper import JavaParams
+from pyspark.ml.common import inherit_doc
+
+@inherit_doc
+class TuneHyperparameters(_TuneHyperparameters):
+    def _create_model(self, java_model):
+        model = TuneHyperparametersModel()
+        model._java_obj = java_model
+        model._transfer_params_from_java()
+        return model
+
+@inherit_doc
+class TuneHyperparametersModel(_TuneHyperparametersModel):
+    def getBestModel(self):
+        """
+        Returns the best model.
+        """
+        return JavaParams._from_java(self._java_obj.getBestModel())
+
+    def getBestModelInfo(self):
+        """
+        Returns the best model parameter info.
+        """
+        return self._java_obj.getBestModelInfo()

src/tune-hyperparameters/src/main/scala/TuneHyperparameters.scala

@@ -29,6 +29,7 @@ import scala.util.control.NonFatal
   * Allows user to specify multiple untrained models to tune using various search strategies.
   * Currently supports cross validation with random grid search.
   */
+@InternalWrapper
 class TuneHyperparameters(override val uid: String) extends Estimator[TuneHyperparametersModel]
   with Wrappable with ComplexParamsWritable with HasEvaluationMetric {
 
@@ -191,6 +192,7 @@ class TuneHyperparameters(override val uid: String) extends Estimator[TuneHyperp
 object TuneHyperparameters extends ComplexParamsReadable[TuneHyperparameters]
 
 /** Model produced by [[TuneHyperparameters]]. */
+@InternalWrapper
 class TuneHyperparametersModel(val uid: String,
                                val model: Transformer,
                                val bestMetric: Double)