python/pyspark/ml/tests/typing/test_feature.yml

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- case: featureMethodChaining
  main: |
    from pyspark.ml.feature import NGram

    reveal_type(NGram().setInputCol("foo").setOutputCol("bar"))

  out: |
    main:3: note: Revealed type is "pyspark.ml.feature.NGram"


- case: stringIndexerOverloads
  main: |
    from pyspark.ml.feature import StringIndexer

    # No arguments is OK
    StringIndexer()

    StringIndexer(inputCol="foo")
    StringIndexer(outputCol="bar")
    StringIndexer(inputCol="foo", outputCol="bar")

    StringIndexer(inputCols=["foo"])
    StringIndexer(outputCols=["bar"])
    StringIndexer(inputCols=["foo"], outputCols=["bar"])

    StringIndexer(inputCol="foo", outputCols=["bar"])
    StringIndexer(inputCols=["foo"], outputCol="bar")

  out: |
    main:14: error: No overload variant of "StringIndexer" matches argument types "str", "list[str]"  [call-overload]
    main:14: note: Possible overload variants:
    main:14: note:     def StringIndexer(self, *, inputCol: str | None = ..., outputCol: str | None = ..., handleInvalid: str = ..., stringOrderType: str = ...) -> StringIndexer
    main:14: note:     def StringIndexer(self, *, inputCols: list[str] | None = ..., outputCols: list[str] | None = ..., handleInvalid: str = ..., stringOrderType: str = ...) -> StringIndexer
    main:15: error: No overload variant of "StringIndexer" matches argument types "list[str]", "str"  [call-overload]
    main:15: note: Possible overload variants:
    main:15: note:     def StringIndexer(self, *, inputCol: str | None = ..., outputCol: str | None = ..., handleInvalid: str = ..., stringOrderType: str = ...) -> StringIndexer
    main:15: note:     def StringIndexer(self, *, inputCols: list[str] | None = ..., outputCols: list[str] | None = ..., handleInvalid: str = ..., stringOrderType: str = ...) -> StringIndexer