njayaram2 commented on a change in pull request #397: DL: Update content of
the model summary table
URL: https://github.com/apache/madlib/pull/397#discussion_r288352504
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File path: src/ports/postgres/modules/deep_learning/madlib_keras.py_in
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@@ -319,6 +308,38 @@ def fit(schema_madlib, source_table,
model,model_arch_table,
#TODO add a unit test for this in a future PR
reset_cuda_env(original_cuda_env)
+def get_source_summary_table_dict(fit_validator):
+ source_summary = plpy.execute("""
+ SELECT
+ {class_values} AS class_values,
+ {norm_const} AS norm_const,
+ {dep_vartype} AS dep_vartype,
+ {dep_varname} AS dependent_varname_in_source_table,
+ {indep_varname} AS independent_varname_in_source_table
+ FROM {tbl}
+ """.format(class_values=CLASS_VALUES_COLNAME,
+ norm_const=NORMALIZING_CONST_COLNAME,
+ dep_vartype=DEPENDENT_VARTYPE_COLNAME,
+ dep_varname='dependent_varname',
Review comment:
I think the `dependent_varname` variable is equal to
`MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL` (the column name of the dependent
variable in the minibatched table), whereas the string `'dependent_varname'`
here refers to the column name in the minibatched summary table that stores the
value of the dependent variable column name (say `y`) in the original
non-minibatched table.
Confusing, but this is another refactor JIRA by itself I guess!
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