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[SPARK-29093][PYTHON][ML] Remove automatically generated param setters in _shared_params_code_gen.py #26232

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[SPARK-29093][PYTHON][ML] Remove automatically generated param setters in _shared_params_code_gen.py #26232

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a67163e
[SPARK-29093][PYTHON][ML] Remove automatically generated param setter…
huaxingao Oct 23, 2019
d076f9e
add more doc tests
huaxingao Oct 23, 2019
bff5a70
not using UnaryTransformer for now
huaxingao Oct 24, 2019
03c2e4a
address comments
huaxingao Oct 24, 2019
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add more doc tests
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@huaxingao
huaxingao committed Oct 23, 2019
commit d076f9e4515ea4c52e760ceb8a41c645dd2cc65d
14 changes: 14 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/feature/MinHashLSH.scala
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Expand Up @@ -48,6 +48,14 @@ class MinHashLSHModel private[ml](
private[ml] val randCoefficients: Array[(Int, Int)])
extends LSHModel[MinHashLSHModel] {

/** @group setParam */
@Since("2.4.0")
override def setInputCol(value: String): this.type = super.set(inputCol, value)

/** @group setParam */
@Since("2.4.0")
override def setOutputCol(value: String): this.type = super.set(outputCol, value)

@Since("2.1.0")
override protected[ml] def hashFunction(elems: Vector): Array[Vector] = {
require(elems.numNonzeros > 0, "Must have at least 1 non zero entry.")
Expand Down Expand Up @@ -105,6 +113,12 @@ class MinHashLSHModel private[ml](
@Since("2.1.0")
class MinHashLSH(override val uid: String) extends LSH[MinHashLSHModel] with HasSeed {

@Since("2.1.0")
override def setInputCol(value: String): this.type = super.setInputCol(value)

@Since("2.1.0")
override def setOutputCol(value: String): this.type = super.setOutputCol(value)

@Since("2.1.0")
override def setNumHashTables(value: Int): this.type = super.setNumHashTables(value)

Expand Down
24 changes: 18 additions & 6 deletions python/pyspark/ml/evaluation.py
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Expand Up @@ -119,7 +119,9 @@ class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPrediction
... [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)])
>>> dataset = spark.createDataFrame(scoreAndLabels, ["raw", "label"])
...
>>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw")
>>> evaluator = BinaryClassificationEvaluator()
>>> evaluator.setRawPredictionCol("raw")
BinaryClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.70...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
Expand Down Expand Up @@ -239,7 +241,9 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, HasWeigh
... (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)]
>>> dataset = spark.createDataFrame(scoreAndLabels, ["raw", "label"])
...
>>> evaluator = RegressionEvaluator(predictionCol="raw")
>>> evaluator = RegressionEvaluator()
>>> evaluator.setPredictionCol("raw")
RegressionEvaluator...
>>> evaluator.evaluate(dataset)
2.842...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"})
Expand Down Expand Up @@ -360,7 +364,9 @@ class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio
>>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
>>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"])
>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction")
>>> evaluator = MulticlassClassificationEvaluator()
>>> evaluator.setPredictionCol("prediction")
MulticlassClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
Expand Down Expand Up @@ -546,7 +552,9 @@ class MultilabelClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio
... ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])]
>>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"])
...
>>> evaluator = MultilabelClassificationEvaluator(predictionCol="prediction")
>>> evaluator = MultilabelClassificationEvaluator()
>>> evaluator.setPredictionCol("prediction")
MultilabelClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.63...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
Expand Down Expand Up @@ -659,7 +667,9 @@ class ClusteringEvaluator(JavaEvaluator, HasPredictionCol, HasFeaturesCol,
... ([10.5, 11.5], 1.0), ([1.0, 1.0], 0.0), ([8.0, 6.0], 1.0)])
>>> dataset = spark.createDataFrame(featureAndPredictions, ["features", "prediction"])
...
>>> evaluator = ClusteringEvaluator(predictionCol="prediction")
>>> evaluator = ClusteringEvaluator()
>>> evaluator.setPredictionCol("prediction")
ClusteringEvaluator...
>>> evaluator.evaluate(dataset)
0.9079...
>>> ce_path = temp_path + "/ce"
Expand Down Expand Up @@ -759,7 +769,9 @@ class RankingEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol,
... ([1.0, 2.0, 3.0, 4.0, 5.0], [])]
>>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"])
...
>>> evaluator = RankingEvaluator(predictionCol="prediction")
>>> evaluator = RankingEvaluator()
>>> evaluator.setPredictionCol("prediction")
RankingEvaluator...
>>> evaluator.evaluate(dataset)
0.35...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "precisionAtK", evaluator.k: 2})
Expand Down
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