simplify java models

mllib/src/main/scala/org/apache/spark/ml/param/params.scala

@@ -293,7 +293,6 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten
     new ParamMap(this.map ++ other.map)
   }
 
-
   /**
    * Adds all parameters from the input param map into this param map.
    */

python/pyspark/ml/__init__.py

@@ -101,6 +101,18 @@ def transform(self, dataset, params={}):
         raise NotImplementedError()
 
 
+@inherit_doc
+class Model(Transformer):
+    """
+    Abstract class for models fitted by :py:class:`Estimator`s.
+    """
+
+    ___metaclass__ = ABCMeta
+
+    def __init__(self):
+        super(Model, self).__init__()
+
+
 @inherit_doc
 class Pipeline(Estimator):
     """
@@ -169,7 +181,7 @@ def fit(self, dataset, params={}):
 
 
 @inherit_doc
-class PipelineModel(Transformer):
+class PipelineModel(Model):
     """
     Represents a compiled pipeline with transformers and fitted models.
     """
@@ -204,9 +216,9 @@ def _java_class(self):
         """
         raise NotImplementedError
 
-    def _create_java_obj(self):
+    def _java_obj(self):
         """
-        Creates a new Java object and returns its reference.
+        Returns or creates a Java object.
         """
         java_obj = _jvm()
         for name in self._java_class.split("."):
@@ -231,6 +243,13 @@ def _empty_java_param_map(self):
         """
         return _jvm().org.apache.spark.ml.param.ParamMap()
 
+    def _create_java_param_map(self, params, java_obj):
+        paramMap = self._empty_java_param_map()
+        for param, value in params.items():
+            if param.parent is self:
+                paramMap.put(java_obj.getParam(param.name), value)
+        return paramMap
+
 
 @inherit_doc
 class JavaEstimator(Estimator, JavaWrapper):
@@ -259,7 +278,7 @@ def _fit_java(self, dataset, params={}):
         :param params: additional params (overwriting embedded values)
         :return: fitted Java model
         """
-        java_obj = self._create_java_obj()
+        java_obj = self._java_obj()
         self._transfer_params_to_java(params, java_obj)
         return java_obj.fit(dataset._jschema_rdd, self._empty_java_param_map())
 
@@ -281,7 +300,24 @@ def __init__(self):
         super(JavaTransformer, self).__init__()
 
     def transform(self, dataset, params={}):
-        java_obj = self._create_java_obj()
-        self._transfer_params_to_java(params, java_obj)
-        return SchemaRDD(java_obj.transform(dataset._jschema_rdd, self._empty_java_param_map()),
+        java_obj = self._java_obj()
+        self._transfer_params_to_java({}, java_obj)
+        java_param_map = self._create_java_param_map(params, java_obj)
+        return SchemaRDD(java_obj.transform(dataset._jschema_rdd, java_param_map),
                          dataset.sql_ctx)
+
+
+@inherit_doc
+class JavaModel(JavaTransformer):
+    """
+    Base class for :py:class:`Model`s that wrap Java/Scala
+    implementations.
+    """
+
+    __metaclass__ = ABCMeta
+
+    def __init__(self):
+        super(JavaTransformer, self).__init__()
+
+    def _java_obj(self):
+        return self._java_model

python/pyspark/ml/classification.py

@@ -15,8 +15,8 @@
 # limitations under the License.
 #
 
-from pyspark.sql import SchemaRDD, inherit_doc
-from pyspark.ml import JavaEstimator, Transformer, _jvm
+from pyspark.sql import inherit_doc
+from pyspark.ml import JavaEstimator, JavaModel
 from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\
     HasRegParam
 
@@ -40,7 +40,7 @@ def _create_model(self, java_model):
 
 
 @inherit_doc
-class LogisticRegressionModel(Transformer):
+class LogisticRegressionModel(JavaModel):
     """
     Model fitted by LogisticRegression.
     """
@@ -49,8 +49,6 @@ def __init__(self, java_model):
         super(LogisticRegressionModel, self).__init__()
         self._java_model = java_model
 
-    def transform(self, dataset, params={}):
-        # TODO: handle params here.
-        return SchemaRDD(self._java_model.transform(
-            dataset._jschema_rdd,
-            _jvm().org.apache.spark.ml.param.ParamMap()), dataset.sql_ctx)
+    @property
+    def _java_class(self):
+        return "org.apache.spark.ml.classification.LogisticRegressionModel"