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From "Jeremy Freeman (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-5089) Vector conversion broken for non-float64 arrays
Date Mon, 05 Jan 2015 17:02:34 GMT

     [ https://issues.apache.org/jira/browse/SPARK-5089?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Jeremy Freeman updated SPARK-5089:
----------------------------------
    Description: 
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.

  was:
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.


> 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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