Jeremy Freeman created SPARK-5089:
-------------------------------------
Summary: Vector conversion broken for non-float64 arrays
Key: SPARK-5089
URL: https://issues.apache.org/jira/browse/SPARK-5089
Project: Spark
Issue Type: Bug
Components: MLlib, PySpark
Affects Versions: 1.2.0
Reporter: Jeremy Freeman
Prior to performing many MLlib operations in PySpark (e.g. KMeans), data are
automatically converted to `DenseVectors`. If the data are numpy arrays with
dtype `float64` this works. If data are numpy arrays with lower precision (e.g.
`float16` or `float32`), they should be upcast to `float64`, but due to a small
bug in this line this currently doesn't happen (casting is not inplace).
```
if ar.dtype != np.float64:
ar.astype(np.float64)
```
Non-float64 values are in turn mangled during SerDe. This can have significant
consequences. For example, the following yields confusing and erroneous results:
```
from numpy import random
from pyspark.mllib.clustering import KMeans
data = sc.parallelize(random.randn(100,10).astype('float32'))
model = KMeans.train(data, k=3)
len(model.centers[0])
>> 5 # should be 10!
```
But this works fine:
```
data = sc.parallelize(random.randn(100,10).astype('float64'))
model = KMeans.train(data, k=3)
len(model.centers[0])
>> 10 # this is correct
```
The fix is trivial, I'll submit a PR shortly.
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