GLRM returns different results for sample weights column with ones vs None
Using a fixed random seed and including constant columns, passing weights_column = None gives different results than weights_column with a sample weight column of only ones
Thank you, Wendy, for your quick response.
instead of using x = traindata.names, specify the columns as x = ["c1","c2",...]
GLRM does not take weights_column. So, if the user add a weight column to the frame and include it in the x specification, GLRM will be performed on the dataset including the weight column. Hence, the user is performing two different GLRM frames, one with weight column and one without the weight column.
Instead of using x = dataset.names when calling glrm, do x = ['c1','c2','c3',…]. If you run GLRM again using the same dataset with and without the weight columns but use x = […] to specify the columns you want to use, you should get the same results when you call predict. Let me know if this helps.