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[GSProcessing] Add saving and re-applying for numerical transforms. #1085

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6 changes: 4 additions & 2 deletions docs/source/cli/graph-construction/distributed/example.rst
Original file line number Diff line number Diff line change
Expand Up @@ -259,7 +259,9 @@ the graph structure, features, and labels. In more detail:
GSProcessing will use the transformation values listed here
instead of creating new ones, ensuring that models trained with the original
data can still be used in the newly transformed data. Currently only
categorical transformations can be re-applied.
categorical and numerical transformations can be re-applied. Note that
the Rank-Gauss transformation cannot support re-application, it can
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only work for transductive tasks.
* ``updated_row_counts_metadata.json``:
This file is meant to be used as the input configuration for the
distributed partitioning pipeline. ``gs-repartition`` produces
Expand Down Expand Up @@ -313,7 +315,7 @@ you can use the following command to run the partition job locally:
--num-parts 2 \
--dgl-tool-path ./dgl/tools \
--partition-algorithm random \
--ip-config ip_list.txt
--ip-config ip_list.txt

The command above will first do graph partitioning to determine the ownership for each partition and save the results.
Then it will do data dispatching to physically assign the partitions to graph data and dispatch them to each machine.
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ def get_transformation_name() -> str:
return "DistBucketNumericalTransformation"

def apply(self, input_df: DataFrame) -> DataFrame:
imputed_df = apply_imputation(self.cols, self.shared_imputation, input_df)
imputed_df = apply_imputation(self.cols, self.shared_imputation, input_df).imputed_df
# TODO: Make range optional by getting min/max from data.
min_val, max_val = self.range

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