Hi All,
I noticed that MADlib provides a mini-batch preprocessor (
https://madlib.apache.org/docs/latest/group__grp__minibatch__preprocessing.html)
for Neural Networks.
I'm wondering if this mini-batch processor can work with the linear models
such as SVM and LR (i.e., mini-batch SGD).
I just used this mini-batch preprocessor on a dataset and got the batched
table as follows. When I performed the SVM on it, I encountered an error as
'SVM error: dependent_varname cannot be of array type!'. It seems that SVM
does not work on this batched table.
------------------------------------------------------------
db=# \d susy_b128
Table "public.susy_b128"
Column | Type | Collation | Nullable | Default
---------------------+--------------------+-----------+----------+---------
__id__ | bigint | | |
dependent_varname | double precision[] | | |
independent_varname | double precision[] | | |
db=# SELECT madlib.svm_classification('susy_b128', 'susy_b128_out',
'dependent_varname', 'independent_varname', 'linear', '', '',
'init_stepsize=0.1, decay_factor=0.95, max_iter=3, tolerance=0, lambda=0');
ERROR: plpy.Error: SVM error: dependent_varname cannot be of array type!
------------------------------------------------------------
Any suggestions are welcome! Thanks!
Best,
Lijie
