CrossValidator doesn't accept a Pipeline as an estimator

from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.tuning import ParamGridBuilder
from pyspark.ml.tuning import CrossValidator

# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and nb.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
nb = NaiveBayes()
pipeline = Pipeline(stages=[tokenizer, hashingTF, nb])


paramGrid = ParamGridBuilder().addGrid(nb.smoothing, [0, 1]).build()

cv = CrossValidator(estimator=pipeline, 
                    estimatorParamMaps=paramGrid, 
                    evaluator=MulticlassClassificationEvaluator(), 
                    numFolds=4)

cvModel = cv.fit(training_df)

# Load precleaned training set
training_rdd = sc.textFile("data/clean_training.txt")
parts_rdd = training_rdd.map(lambda l: l.split("\t"))
# Filter bad rows out
garantee_col_rdd = parts_rdd.filter(lambda l: len(l) == 3)
typed_rdd = garantee_col_rdd.map(lambda p: (p[0], p[1], float(p[2])))
# Create DataFrame
training_df = sqlContext.createDataFrame(typed_rdd, ["id", "text", "label"])

---------------------------------------------------------------------------Py4JJavaError                             Traceback (most recent call last)<ipython-input-3-34e9e27acada> in <module>()
     17                     numFolds=4)
     18 
---> 19 cvModel = cv.fit(training_df)
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/pipeline.py in fit(self, dataset, params)
     67                 return self.copy(params)._fit(dataset)
     68             else:
---> 69                 return self._fit(dataset)
     70         else:
     71             raise ValueError("Params must be either a param map or a list/tuple of param maps, "
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/tuning.py in _fit(self, dataset)
    237             train = df.filter(~condition)
    238             for j in range(numModels):
--> 239                 model = est.fit(train, epm[j])
    240                 # TODO: duplicate evaluator to take extra params from input
    241                 metric = eva.evaluate(model.transform(validation, epm[j]))
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/pipeline.py in fit(self, dataset, params)
     65         elif isinstance(params, dict):
     66             if params:
---> 67                 return self.copy(params)._fit(dataset)
     68             else:
     69                 return self._fit(dataset)
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/pipeline.py in _fit(self, dataset)
    211                     dataset = stage.transform(dataset)
    212                 else:  # must be an Estimator
--> 213                     model = stage.fit(dataset)
    214                     transformers.append(model)
    215                     if i < indexOfLastEstimator:
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/pipeline.py in fit(self, dataset, params)
     67                 return self.copy(params)._fit(dataset)
     68             else:
---> 69                 return self._fit(dataset)
     70         else:
     71             raise ValueError("Params must be either a param map or a list/tuple of param maps, "
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/wrapper.py in _fit(self, dataset)
    130 
    131     def _fit(self, dataset):
--> 132         java_model = self._fit_java(dataset)
    133         return self._create_model(java_model)
    134 
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/wrapper.py in _fit_java(self, dataset)
    126         :return: fitted Java model
    127         """
--> 128         self._transfer_params_to_java()
    129         return self._java_obj.fit(dataset._jdf)
    130 
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/wrapper.py in _transfer_params_to_java(self)
     80         for param in self.params:
     81             if param in paramMap:
---> 82                 pair = self._make_java_param_pair(param, paramMap[param])
     83                 self._java_obj.set(pair)
     84 
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/ml/wrapper.py in _make_java_param_pair(self, param, value)
     71         java_param = self._java_obj.getParam(param.name)
     72         java_value = _py2java(sc, value)
---> 73         return java_param.w(java_value)
     74 
     75     def _transfer_params_to_java(self):
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
    811         answer = self.gateway_client.send_command(command)
    812         return_value = get_return_value(
--> 813             answer, self.gateway_client, self.target_id, self.name)
    814 
    815         for temp_arg in temp_args:
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/pyspark/sql/utils.py in deco(*a, **kw)
     43     def deco(*a, **kw):
     44         try:
---> 45             return f(*a, **kw)
     46         except py4j.protocol.Py4JJavaError as e:
     47             s = e.java_exception.toString()
/Users/dreyco676/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    306                 raise Py4JJavaError(
    307                     "An error occurred while calling {0}{1}{2}.\n".
--> 308                     format(target_id, ".", name), value)
    309             else:
    310                 raise Py4JError(
Py4JJavaError: An error occurred while calling o113.w.
: java.lang.ClassCastException: java.lang.Integer cannot be cast to java.lang.Double
	at scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:119)
	at org.apache.spark.ml.param.DoubleParam.w(params.scala:223)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:497)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
	at py4j.Gateway.invoke(Gateway.java:259)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:209)
	at java.lang.Thread.run(Thread.java:745)