wbo4958 opened a new pull request, #48791:
URL: https://github.com/apache/spark/pull/48791
### What changes were proposed in this pull request?
This PR supports running spark.ml on connect
### Why are the changes needed?
It's a new feature that makes spark.ml run on connect environment.
### Does this PR introduce _any_ user-facing change?
Yes, new feature.
### How was this patch tested?
The below manual test can work without any exception.
``` python
(pyspark) user@bobby:~ $ pyspark --remote sc://localhost
Python 3.11.10 (main, Oct 3 2024, 07:29:13) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 4.0.0.dev0
/_/
Using Python version 3.11.10 (main, Oct 3 2024 07:29:13)
Client connected to the Spark Connect server at localhost
SparkSession available as 'spark'.
>>> from pyspark.ml.classification import (LogisticRegression,
... LogisticRegressionModel)
>>> from pyspark.ml.linalg import Vectors
>>>
>>> df = spark.createDataFrame([
... (Vectors.dense([1.0, 2.0]), 1),
... (Vectors.dense([2.0, -1.0]), 1),
... (Vectors.dense([-3.0, -2.0]), 0),
... (Vectors.dense([-1.0, -2.0]), 0),
... ], schema=['features', 'label'])
>>> lr = LogisticRegression()
>>> lr.setMaxIter(30)
LogisticRegression_a842693fc5e7
>>> model: LogisticRegressionModel = lr.fit(df)
>>> model.predictRaw(Vectors.dense([1.0, 2.0]))
DenseVector([-21.1048, 21.1048])
>>> assert model.getMaxIter() == 30
>>> model.summary.roc.show()
+---+---+
|FPR|TPR|
+---+---+
|0.0|0.0|
|0.0|0.5|
|0.0|1.0|
|0.5|1.0|
|1.0|1.0|
|1.0|1.0|
+---+---+
>>> model.summary.weightedRecall
1.0
>>> model.summary.recallByLabel
[1.0, 1.0]
>>> model.coefficients
DenseVector([10.3964, 4.513])
>>> model.intercept
1.6823489096339976
>>> model.transform(df).show()
+-----------+-----+--------------------+--------------------+----------+
| features|label| rawPrediction| probability|prediction|
+-----------+-----+--------------------+--------------------+----------+
| [1.0,2.0]| 1|[-21.104818251026...|[6.82800596288997...| 1.0|
| [2.0,-1.0]| 1|[-17.962094978515...|[1.58183529116629...| 1.0|
|[-3.0,-2.0]| 0|[38.5329050234205...| [1.0,0.0]| 0.0|
|[-1.0,-2.0]| 0|[17.7401204317582...|[0.99999998025016...| 0.0|
+-----------+-----+--------------------+--------------------+----------+
>>> model.write().overwrite().save("/tmp/connect-ml-demo")
>>> loaded_model = LogisticRegressionModel.load("/tmp/connect-ml-demo")
>>> assert loaded_model.getMaxIter() == 30
>>> loaded_model.transform(df).show()
+-----------+-----+--------------------+--------------------+----------+
| features|label| rawPrediction| probability|prediction|
+-----------+-----+--------------------+--------------------+----------+
| [1.0,2.0]| 1|[-21.104818251026...|[6.82800596288997...| 1.0|
| [2.0,-1.0]| 1|[-17.962094978515...|[1.58183529116629...| 1.0|
|[-3.0,-2.0]| 0|[38.5329050234205...| [1.0,0.0]| 0.0|
|[-1.0,-2.0]| 0|[17.7401204317582...|[0.99999998025016...| 0.0|
+-----------+-----+--------------------+--------------------+----------+
```
### Was this patch authored or co-authored using generative AI tooling?
No
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