With pyspark 1.4 I am trying to use RegressionMetrics() for predictions generated by LinearRegressionWithSGD.
All examples for RegressionMetrics() given in pyspark mllib documentations are for "artificial" predictions and observations like
predictionAndObservations = sc.parallelize([ (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)])
For such "artificial" (generated with sc.parallelize) RDD everything works fine. However, when doing the same with another RDD generated in another way, I get
TypeError: DoubleType can not accept object in type <type 'numpy.float64'>
The short reproducible example is below.
What can be the problem?
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.regression import LinearRegressionWithSGD, LinearRegressionModel
from pyspark.mllib.evaluation import RegressionMetrics
dataRDD = sc.parallelize([LabeledPoint(1, [1,1]), LabeledPoint(2, [2,2]), LabeledPoint(3, [3,3])])
lrModel = LinearRegressionWithSGD.train(dataRDD)
prediObserRDD = dataRDD.map(lambda p: (lrModel.predict(p.features), p.label)).cache()
Let us check that RDD is indeed of (prediction, observation) pairs
prediObserRDD.take(4) # looks OK
Now try to calculate metrics
metrics = RegressionMetrics(prediObserRDD)
It gives the following error
TypeError Traceback (most recent call last)
<ipython-input-1-ca9ad8e9faf1> in <module>()
7 prediObserRDD = dataRDD.map(lambda p: (lrModel.predict(p.features), p.label)).cache()
8 prediObserRDD.take(4)
----> 9 metrics = RegressionMetrics(prediObserRDD)
10 #metrics.explainedVariance
11 #metrics.meanAbsoluteError
/usr/local/spark-1.4.0-bin-hadoop2.6/python/pyspark/mllib/evaluation.py in __init__(self, predictionAndObservations)
99 df = sql_ctx.createDataFrame(predictionAndObservations, schema=StructType([
100 StructField("prediction", DoubleType(), nullable=False),
--> 101 StructField("observation", DoubleType(), nullable=False)]))
102 java_class = sc._jvm.org.apache.spark.mllib.evaluation.RegressionMetrics
103 java_model = java_class(df._jdf)
/usr/local/spark-1.4.0-bin-hadoop2.6/python/pyspark/sql/context.py in createDataFrame(self, data, schema, samplingRatio)
337
338 for row in rows:
--> 339 _verify_type(row, schema)
340
341 # convert python objects to sql data
/usr/local/spark-1.4.0-bin-hadoop2.6/python/pyspark/sql/types.py in _verify_type(obj, dataType)
1027 "length of fields (%d)" % (len(obj), len(dataType.fields)))
1028 for v, f in zip(obj, dataType.fields):
-> 1029 _verify_type(v, f.dataType)
1030
1031 _cached_cls = weakref.WeakValueDictionary()
/usr/local/spark-1.4.0-bin-hadoop2.6/python/pyspark/sql/types.py in _verify_type(obj, dataType)
1011 if type(obj) not in _acceptable_types[_type]:
1012 raise TypeError("%s can not accept object in type %s"
-> 1013 % (dataType, type(obj)))
1014
1015 if isinstance(dataType, ArrayType):
TypeError: DoubleType can not accept object in type <type 'numpy.float64'>
The same problem also appears (for another dataset and classification task) with BinaryClassificationMetrics.
Like the error says TypeError: DoubleType can not accept object in type <type 'numpy.float64'>
You are trying to convert a numpy.float64
into a Double which cannot be done.
To solve that TypeError, you'll have to convert your value to a accepted Type.
Example :
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.regression import LinearRegressionWithSGD, LinearRegressionModel
from pyspark.mllib.evaluation import RegressionMetrics
dataRDD = sc.parallelize([LabeledPoint(1, [1,1]), LabeledPoint(2, [2,2]), LabeledPoint(3, [3,3])])
lrModel = LinearRegressionWithSGD.train(dataRDD)
prediObserRDD = dataRDD.map(lambda p: (float(lrModel.predict(p.features)), p.label)).cache()
If you've noticed, I have converted the prediction label into a double using Python built-in float
function.
Now you can compute your metrics :
>>> metrics = RegressionMetrics(prediObserRDD)
>>> metrics.explainedVariance
1.0
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