Github user njayaram2 commented on a diff in the pull request:
https://github.com/apache/madlib/pull/277#discussion_r196150565
--- Diff:
src/ports/postgres/modules/recursive_partitioning/decision_tree.py_in ---
@@ -1097,28 +1121,21 @@ def _one_step(schema_madlib, training_table_name,
cat_features,
"$3", "$2",
null_proxy)
-# The arguments of the aggregate (in the same order):
-# 1. current tree state, madlib.bytea8
-# 2. categorical features (integer format) in a single array
-# 3. continuous features in a single array
-# 4. weight value
-# 5. categorical sorted levels (integer format) in a combined array
-# 6. continuous splits
-# 7. number of dependent levels
train_sql = """
SELECT (result).* from (
SELECT
-{schema_madlib}._dt_apply($1,
+{schema_madlib}._dt_apply(
+$1,
{schema_madlib}._compute_leaf_stats(
-$1,
-{cat_features_str},
-{con_features_str},
+$1, -- current tree state,
madlib.bytea8
+{cat_features_str}, -- categorical features in an
array
+{con_features_str}, -- continuous features in an
array
{dep_var},
-{weights},
-$2,
-$4,
-{dep_n_levels}::smallint,
-{subsample}::boolean
+{weights}, -- weight value
+$2, -- categorical sorted levels
in a combined array
+$4, -- continuous splits
+{dep_n_levels}::smallint, -- number of dependent
levels
+{subsample}::boolean -- should we use a subsample
of data
--- End diff --
Oh okay, thank you. I think a comment will be useful.
---
