Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/17384#discussion_r109074112
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
---
@@ -189,6 +191,47 @@ class IsotonicRegressionSuite
assert(expected.predictions === actual.predictions)
}
}
+
+ test("Besides VectorUDT, should support all NumericType features, and
not support other types") {
+ val df = generateIsotonicInput(Seq(1, 2, 3))
+ val ir = new IsotonicRegression()
+
+ val expected = ir.fit(df)
+ val expectedPrediction =
+
expected.transform(df).select("prediction").map(_.getDouble(0)).collect()
+
+ val types =
+ Seq(ShortType, LongType, IntegerType, FloatType, ByteType,
DoubleType, DecimalType(10, 0))
+
+ types.foreach { t =>
+ val df2 = df.select(col("label"), col("features").cast(t),
col("weight"))
+
+ val actual = ir.fit(df2)
+ assert(expected.boundaries === actual.boundaries)
+ assert(expected.predictions === actual.predictions)
+
+ val actualPrediction =
+
actual.transform(df2).select("prediction").map(_.getDouble(0)).collect()
+ assert(expectedPrediction === actualPrediction)
+ }
+
+ val dfWithStringFeatures =
+ df.select(col("label"), col("features").cast(StringType),
col("weight"))
+
+ val thrown = intercept[IllegalArgumentException] {
+ ir.fit(dfWithStringFeatures)
+ }
+ assert(thrown.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+
+ val thrown2 = intercept[IllegalArgumentException] {
+ expected.transform(dfWithStringFeatures)
+ }
+ assert(thrown2.getMessage.contains(
+ "Column features must be of type NumericType or VectorUDT," +
+ " but was actually of type StringType"))
+ }
--- End diff --
`checkNumericTypes` only test `fit` method on `labelCol` and `weightCol`.
`IsotonicRegressor` is the only algorithm which need to test both `fit` and
`transform` on `featureCol`. So I prefer not to use `checkNumericTypes`. Thanks
for your review
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