[pyspark] support a list of feature column names #8117

wbo4958 · 2022-07-26T09:14:15Z

This PR introduces a new Param storing a list of feature column names, so with it, users do not need to vectorize or create an array feature column beforehand, instead, they need to specify feature_col with a list of column names.

Eg,

feature_col_names = [x.name for x in raw_df.schema if x.name != label]
xgb = SparkXGBClassifier(features_col=feature_col_names, label_col=label)

why introduce this new parameter?

The internal of XGBoost is wrapping the feature columns into a vector or an array column on the JVM side, but it will unwrap the vector/array column into each feature column on the python side. So if we just pass each feature column, which can improve the performance.

But for now, there are some limitations that users must enable use_gpu when using this way and it may break some spark ml pipelines eg, OneVsRest

wbo4958 · 2022-07-26T09:14:40Z

@trivialfis @WeichenXu123 Could you help to review this PR? Thx

trivialfis

Thank you for the PR. Could you please:

add tests
Add a demo or test that shows how to use it with other spark ml utilities.

wbo4958 · 2022-07-27T06:43:55Z

Thank you for the PR. Could you please:

add tests

Add a demo or test that shows how to use it with other spark ml utilities.

Done @trivialfis @WeichenXu123

WeichenXu123 · 2022-07-27T12:52:54Z

python-package/xgboost/spark/core.py

+    for c in features_col_name:
+        if isinstance(dataset.schema[c].dataType, DoubleType):
+            feature_cols.append(col(c).cast(FloatType()).alias(c))
+        elif isinstance(dataset.schema[c].dataType, (FloatType, IntegralType)):


What's IntegralType type ? Do you mean IntegerType ?

@trivialfis, could you correct me if xgboost supports IntegralType (Byte/Integer/Long/Short) ?

Yes, sort of. XGBoost will convert the data to float internally.

BTW, XGBoost can convert it on the fly without creating a copy of data.

Thx @trivialfis, Yeah, if the type is short/byte, it will reduce the size of the shuffle write.

python-package/xgboost/spark/core.py

WeichenXu123 · 2022-07-27T12:55:56Z

python-package/xgboost/spark/core.py

@@ -341,6 +363,22 @@ def _validate_and_convert_feature_col_as_array_col(dataset, features_col_name):
    return features_array_col


+def _validate_and_convert_feature_col(


Is the function being used ?

Thx, removed.

python-package/xgboost/spark/core.py

WeichenXu123 · 2022-07-27T13:02:11Z

python-package/xgboost/spark/core.py

-                    pandas_df_iter,
-                    None,
-                    dmatrix_kwargs,
+                    pandas_df_iter, features_cols_names, dmatrix_kwargs


What if features_cols_names conflicts with label / weight / base_margin column name ?

Thx, I tried, it worked, no exception happened.

trivialfis · 2022-07-28T01:59:16Z

~~Could you please add a test for cross validation fro sparkml as well?~~
NVM. I don't think it's needed

the gpu dask tests may corrupt the whole gpu env

wbo4958 · 2022-08-02T09:45:59Z

@trivialfis could you help to review again. Thx

trivialfis

Thank you for the PR! Some questions in the comments.

python-package/xgboost/spark/core.py

trivialfis · 2022-08-03T05:37:37Z

python-package/xgboost/spark/core.py

+        feature_col_names = self.getOrDefault(self.features_cols)
+        features_col = []
+        if (
+            len(feature_col_names)


This condition looks weird?

len(feature_col_names) > 0 >= len(...)

is it correct?

yes. the pylint required this

all(c in dataset.columns for c in feature_col_names)

Done, I use another set way to check.

trivialfis · 2022-08-03T05:41:20Z

python-package/xgboost/spark/core.py

-        select_cols = [features_array_col, label_col]
+        select_cols = [label_col]
+        features_cols_names = None
+        if len(self.getOrDefault(self.features_cols)):


Suggested change

if len(self.getOrDefault(self.features_cols)):

if self.getOrDefault(self.features_cols):

trivialfis · 2022-08-03T10:48:14Z

note to myself: need to update the linter script.

WeichenXu123

LGTM, leave one question though.

WeichenXu123 · 2022-08-08T08:58:14Z

@trivialfis @wbo4958
I will file PR for #8108 once this PR merged.
And hope my next PR will go into xgboost 2.0 .
Thanks!

wbo4958 · 2022-08-08T09:07:02Z

@trivialfis @WeichenXu123 Thx for reviewing.

[pyspark] support a list of feature column names

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wbo4958 added 9 commits July 28, 2022 13:06

resolve comments

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fix bug

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update

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fix gpu id

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fix

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format

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format

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add max_bin support

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move the gpu dask tests running behind spark

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the gpu dask tests may corrupt the whole gpu env

wbo4958 force-pushed the features_cols branch from c0b1a99 to 0f388d9 Compare August 2, 2022 03:15

wbo4958 added 5 commits August 2, 2022 11:27

pylint

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update cv parameters

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pylint issue

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format

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Merge remote-tracking branch 'upstream/master' into features_cols

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wbo4958 requested review from WeichenXu123 and trivialfis and removed request for WeichenXu123 August 3, 2022 01:17

trivialfis mentioned this pull request Aug 3, 2022

Conflicts between dask and pyspark GPU tests. #8134

Closed

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resolve comments

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trivialfis approved these changes Aug 8, 2022

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trivialfis merged commit 03cc3b3 into dmlc:master Aug 8, 2022

wbo4958 deleted the features_cols branch August 8, 2022 09:07

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[pyspark] support a list of feature column names #8117

[pyspark] support a list of feature column names #8117

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WeichenXu123 commented Aug 8, 2022

wbo4958 commented Aug 8, 2022

		@@ -341,6 +363,22 @@ def _validate_and_convert_feature_col_as_array_col(dataset, features_col_name):
		return features_array_col


		def _validate_and_convert_feature_col(

	if len(self.getOrDefault(self.features_cols)):
	if self.getOrDefault(self.features_cols):

[pyspark] support a list of feature column names #8117

[pyspark] support a list of feature column names #8117

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