Problem applying CategoricalImputer to a large number of missing values

User 3255 | 3/3/2016, 11:01:09 PM

Hi there,

I think there is a bug in applying feature engineering categoricalimputer when a large number of missing values is found. well I have SFrame with 11 categorical columns. when I apply categoricalimputer it works on all columns except two of them when I inspected both columns I noticed that one of them have 60110 missing value and the other one have 55304 missing value the error displayed is

**RuntimeError: Runtime Exception. Column "internalfixed_label" has different size than current columns!**

appreciate your help


User 1359 | 3/4/2016, 7:50:49 PM

Thanks for reporting your issue. I'm looking into it now. If possible, I will report back on a workaround!

User 3330 | 3/20/2016, 3:36:29 PM

I am getting the same error. See Examples Below

To make things easier to debug Here are some other specifications about the machine. AWS EC2 - graphlab-create-1.8.3-coursera (ami-b1f1f6db) m4.10xlarge (40 vCPU, 160GB Memory, 1TB storage)

There are 8 million total rows in the data.


train1_imputed = train1

from graphlab.toolkits.feature_engineering import *



train1imputed = imputer.fittransform(train1_imputed)`

`Choosing initial cluster centers with Kmeans++.

| Center number | Row index |

| 0 | 259765 |

| 1 | 406111 |

| 2 | 76726 |

HTTP/1.1 200 OK Transfer-Encoding: chunked Date: Thu, 21 Jul 2016 23:13:36 GMT Server: Warp/3.2.6 Content-Type: application/json

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