[SPARK-7380][MLLIB] pipeline stages should be copyable in Python

mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala

@@ -61,7 +61,7 @@ class RegexTokenizer(override val uid: String)
    * Default: 1, to avoid returning empty strings
    * @group param
    */
-  val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length (>= 0)",
+  val minTokenLength: IntParam = new IntParam(this, "minTokenLength", "minimum token length (>= 0)",
     ParamValidators.gtEq(0))
 
   /** @group setParam */

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

@@ -483,16 +483,15 @@ trait Params extends Identifiable with Serializable {
   def copy(extra: ParamMap): Params = {
     val that = this.getClass.getConstructor(classOf[String]).newInstance(uid)
     copyValues(that, extra)
-    that
   }
 
   /**
    * Extracts the embedded default param values and user-supplied values, and then merges them with
    * extra values from input into a flat param map, where the latter value is used if there exist
-   * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap.
+   * conflicts, i.e., with ordering: default param values < user-supplied values < extra.
    */
-  final def extractParamMap(extraParamMap: ParamMap): ParamMap = {
-    defaultParamMap ++ paramMap ++ extraParamMap
+  final def extractParamMap(extra: ParamMap): ParamMap = {
+    defaultParamMap ++ paramMap ++ extra
   }
 
   /**

mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala

@@ -23,15 +23,17 @@ import java.util.UUID
 /**
  * Trait for an object with an immutable unique ID that identifies itself and its derivatives.
  */
-trait Identifiable {
+private[spark] trait Identifiable {
 
   /**
    * An immutable unique ID for the object and its derivatives.
    */
   val uid: String
+
+  override def toString: String = uid
 }
 
-object Identifiable {
+private[spark] object Identifiable {
 
   /**
    * Returns a random UID that concatenates the given prefix, "_", and 12 random hex chars.

python/pyspark/ml/classification.py

@@ -55,7 +55,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
         ...
     TypeError: Method setParams forces keyword arguments.
     """
-    _java_class = "org.apache.spark.ml.classification.LogisticRegression"
+
     # a placeholder to make it appear in the generated doc
     elasticNetParam = \
         Param(Params._dummy(), "elasticNetParam",
@@ -75,6 +75,8 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  threshold=0.5, probabilityCol="probability")
         """
         super(LogisticRegression, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.LogisticRegression", self.uid)
         #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty
         #  is an L2 penalty. For alpha = 1, it is an L1 penalty.
         self.elasticNetParam = \
@@ -111,7 +113,7 @@ def setElasticNetParam(self, value):
         """
         Sets the value of :py:attr:`elasticNetParam`.
         """
-        self.paramMap[self.elasticNetParam] = value
+        self._paramMap[self.elasticNetParam] = value
         return self
 
     def getElasticNetParam(self):
@@ -124,7 +126,7 @@ def setFitIntercept(self, value):
         """
         Sets the value of :py:attr:`fitIntercept`.
         """
-        self.paramMap[self.fitIntercept] = value
+        self._paramMap[self.fitIntercept] = value
         return self
 
     def getFitIntercept(self):
@@ -137,7 +139,7 @@ def setThreshold(self, value):
         """
         Sets the value of :py:attr:`threshold`.
         """
-        self.paramMap[self.threshold] = value
+        self._paramMap[self.threshold] = value
         return self
 
     def getThreshold(self):
@@ -208,7 +210,6 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     1.0
     """
 
-    _java_class = "org.apache.spark.ml.classification.DecisionTreeClassifier"
     # a placeholder to make it appear in the generated doc
     impurity = Param(Params._dummy(), "impurity",
                      "Criterion used for information gain calculation (case-insensitive). " +
@@ -224,6 +225,8 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
         """
         super(DecisionTreeClassifier, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.DecisionTreeClassifier", self.uid)
         #: param for Criterion used for information gain calculation (case-insensitive).
         self.impurity = \
             Param(self, "impurity",
@@ -256,7 +259,7 @@ def setImpurity(self, value):
         """
         Sets the value of :py:attr:`impurity`.
         """
-        self.paramMap[self.impurity] = value
+        self._paramMap[self.impurity] = value
         return self
 
     def getImpurity(self):
@@ -299,7 +302,6 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     1.0
     """
 
-    _java_class = "org.apache.spark.ml.classification.RandomForestClassifier"
     # a placeholder to make it appear in the generated doc
     impurity = Param(Params._dummy(), "impurity",
                      "Criterion used for information gain calculation (case-insensitive). " +
@@ -325,6 +327,8 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  numTrees=20, featureSubsetStrategy="auto", seed=42)
         """
         super(RandomForestClassifier, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.RandomForestClassifier", self.uid)
         #: param for Criterion used for information gain calculation (case-insensitive).
         self.impurity = \
             Param(self, "impurity",
@@ -370,7 +374,7 @@ def setImpurity(self, value):
         """
         Sets the value of :py:attr:`impurity`.
         """
-        self.paramMap[self.impurity] = value
+        self._paramMap[self.impurity] = value
         return self
 
     def getImpurity(self):
@@ -383,7 +387,7 @@ def setSubsamplingRate(self, value):
         """
         Sets the value of :py:attr:`subsamplingRate`.
         """
-        self.paramMap[self.subsamplingRate] = value
+        self._paramMap[self.subsamplingRate] = value
         return self
 
     def getSubsamplingRate(self):
@@ -396,7 +400,7 @@ def setNumTrees(self, value):
         """
         Sets the value of :py:attr:`numTrees`.
         """
-        self.paramMap[self.numTrees] = value
+        self._paramMap[self.numTrees] = value
         return self
 
     def getNumTrees(self):
@@ -409,7 +413,7 @@ def setFeatureSubsetStrategy(self, value):
         """
         Sets the value of :py:attr:`featureSubsetStrategy`.
         """
-        self.paramMap[self.featureSubsetStrategy] = value
+        self._paramMap[self.featureSubsetStrategy] = value
         return self
 
     def getFeatureSubsetStrategy(self):
@@ -452,7 +456,6 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
     1.0
     """
 
-    _java_class = "org.apache.spark.ml.classification.GBTClassifier"
     # a placeholder to make it appear in the generated doc
     lossType = Param(Params._dummy(), "lossType",
                      "Loss function which GBT tries to minimize (case-insensitive). " +
@@ -476,6 +479,8 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  lossType="logistic", maxIter=20, stepSize=0.1)
         """
         super(GBTClassifier, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.GBTClassifier", self.uid)
         #: param for Loss function which GBT tries to minimize (case-insensitive).
         self.lossType = Param(self, "lossType",
                               "Loss function which GBT tries to minimize (case-insensitive). " +
@@ -517,7 +522,7 @@ def setLossType(self, value):
         """
         Sets the value of :py:attr:`lossType`.
         """
-        self.paramMap[self.lossType] = value
+        self._paramMap[self.lossType] = value
         return self
 
     def getLossType(self):
@@ -530,7 +535,7 @@ def setSubsamplingRate(self, value):
         """
         Sets the value of :py:attr:`subsamplingRate`.
         """
-        self.paramMap[self.subsamplingRate] = value
+        self._paramMap[self.subsamplingRate] = value
         return self
 
     def getSubsamplingRate(self):
@@ -543,7 +548,7 @@ def setStepSize(self, value):
         """
         Sets the value of :py:attr:`stepSize`.
         """
-        self.paramMap[self.stepSize] = value
+        self._paramMap[self.stepSize] = value
         return self
 
     def getStepSize(self):

python/pyspark/ml/evaluation.py

@@ -42,8 +42,6 @@ class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPrediction
     0.83...
     """
 
-    _java_class = "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator"
-
     # a placeholder to make it appear in the generated doc
     metricName = Param(Params._dummy(), "metricName",
                        "metric name in evaluation (areaUnderROC|areaUnderPR)")
@@ -56,6 +54,8 @@ def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
                  metricName="areaUnderROC")
         """
         super(BinaryClassificationEvaluator, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid)
         #: param for metric name in evaluation (areaUnderROC|areaUnderPR)
         self.metricName = Param(self, "metricName",
                                 "metric name in evaluation (areaUnderROC|areaUnderPR)")
@@ -68,7 +68,7 @@ def setMetricName(self, value):
         """
         Sets the value of :py:attr:`metricName`.
         """
-        self.paramMap[self.metricName] = value
+        self._paramMap[self.metricName] = value
         return self
 
     def getMetricName(self):