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From njayaram2 <...@git.apache.org>
Subject [GitHub] madlib pull request #277: DT: Add impurity importance metric
Date Mon, 18 Jun 2018 16:54:21 GMT
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.


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