How to fit two numpy matrices with Pyspark's SVM? - numpy

Question

I have two numpy matrices like this:
Features:
(878049, 6)
<type 'numpy.ndarray'>
Labels:
(878049,)
<type 'numpy.ndarray'>
I was curious about if I can use Pyspark's random forests to fit the previous mentioned matrices. From the documentation, we have that RF algorithm can be used as follows:
model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},
numTrees=3, featureSubsetStrategy="auto",
impurity='gini', maxDepth=4, maxBins=32)
# Evaluate model on test instances and compute test error
predictions = model.predict(testData.map(lambda x: x.features))
Thus, my questions are: do I need to transform the numpy arrays to an rdd or in which format should I need to convert the features and labels matrices in order to fit them with the RF implementation of MLlib?.
Update
Then from #CafeFeed answer I tried the following:
In [24]:
#CV
(trainingData, testData) = data.randomSplit([0.7, 0.3])
In [26]:
from pyspark.mllib.tree import DecisionTree, DecisionTreeModel
from pyspark.mllib.util import MLUtils
import numpy as np
​
# Train a DecisionTree model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
​
model = DecisionTree.trainClassifier(trainingData, numClasses=np.unique(y))
​
# Evaluate model on test instances and compute test error
predictions = model.predict(testData.map(lambda x: x.features))
labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count())
print('Test Error = ' + str(testErr))
print('Learned classification tree model:')
print(model.toDebugString())
​
However, I got this exception:
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-27-ded4b074521b> in <module>()
6 # Empty categoricalFeaturesInfo indicates all features are continuous.
7
----> 8 model = DecisionTree.trainClassifier(trainingData, numClasses=np.unique(y), categoricalFeaturesInfo={},impurity='gini', maxDepth=5, maxBins=32)
9
10 # Evaluate model on test instances and compute test error
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/mllib/tree.pyc in trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
183 """
184 return cls._train(data, "classification", numClasses, categoricalFeaturesInfo,
--> 185 impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
186
187 #classmethod
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/mllib/tree.pyc in _train(cls, data, type, numClasses, features, impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
124 assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint"
125 model = callMLlibFunc("trainDecisionTreeModel", data, type, numClasses, features,
--> 126 impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
127 return DecisionTreeModel(model)
128
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/mllib/common.pyc in callMLlibFunc(name, *args)
128 sc = SparkContext._active_spark_context
129 api = getattr(sc._jvm.PythonMLLibAPI(), name)
--> 130 return callJavaFunc(sc, api, *args)
131
132
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/mllib/common.pyc in callJavaFunc(sc, func, *args)
120 def callJavaFunc(sc, func, *args):
121 """ Call Java Function """
--> 122 args = [_py2java(sc, a) for a in args]
123 return _java2py(sc, func(*args))
124
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/mllib/common.pyc in _py2java(sc, obj)
86 else:
87 data = bytearray(PickleSerializer().dumps(obj))
---> 88 obj = sc._jvm.SerDe.loads(data)
89 return obj
90
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in __call__(self, *args)
536 answer = self.gateway_client.send_command(command)
537 return_value = get_return_value(answer, self.gateway_client,
--> 538 self.target_id, self.name)
539
540 for temp_arg in temp_args:
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/pyspark/sql/utils.pyc in deco(*a, **kw)
34 def deco(*a, **kw):
35 try:
---> 36 return f(*a, **kw)
37 except py4j.protocol.Py4JJavaError as e:
38 s = e.java_exception.toString()
/usr/local/Cellar/apache-spark/1.5.1/libexec/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
298 raise Py4JJavaError(
299 'An error occurred while calling {0}{1}{2}.\n'.
--> 300 format(target_id, '.', name), value)
301 else:
302 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.
: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.core.multiarray._reconstruct)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:85)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)
at org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)
at org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)
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:379)
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:207)
at java.lang.Thread.run(Thread.java:745)

Answer 1

Docs are clear. You need RDD:
>>> from pyspark.mllib.regression import LabeledPoint
>>> from pyspark.mllib.tree import RandomForest
>>> import numpy as np
>>>
>>> np.random.seed(1)
>>> features = np.random.random((100, 10))
>>> labels = np.random.choice([0, 1], 100)
>>> data = sc.parallelize(zip(labels, features)).map(lambda x: LabeledPoint(x[0], x[1]))
>>> RandomForest.trainClassifier(data, numClasses=2, categoricalFeaturesInfo={}, numTrees=2)
TreeEnsembleModel classifier with 2 trees