wbo4958 commented on code in PR #49596:
URL: https://github.com/apache/spark/pull/49596#discussion_r1926309453
##########
python/pyspark/ml/tests/test_pipeline.py:
##########
@@ -58,6 +70,109 @@ def doTransform(pipeline):
# check that failure to set stages param will raise KeyError for
missing param
self.assertRaises(KeyError, lambda: doTransform(Pipeline()))
+ def test_classification_pipeline(self):
+ df = self.spark.createDataFrame([(1, 1, 0, 3), (0, 2, 0, 1)], ["y",
"a", "b", "c"])
+
+ assembler = VectorAssembler(outputCol="features")
+ assembler.setInputCols(["a", "b", "c"])
+
+ scaler = MaxAbsScaler(inputCol="features", outputCol="scaled_features")
+
+ lr = LogisticRegression(featuresCol="scaled_features", labelCol="y")
+ lr.setMaxIter(1)
+
+ pipeline = Pipeline(stages=[assembler, scaler, lr])
+ self.assertEqual(len(pipeline.getStages()), 3)
+
+ # Pipeline save & load
+ with tempfile.TemporaryDirectory(prefix="classification_pipeline") as
d:
+ pipeline.write().overwrite().save(d)
Review Comment:
I made a PR (https://github.com/apache/spark/pull/49610 ) to combine the
read/write for estimator and model in the same directory, which could avoid
re-creating the directory twice, and the "overwrite" could be exactly tested.
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