Wayne Zhang created SPARK-20604:
-----------------------------------
Summary: Allow Imputer to handle all numeric types
Key: SPARK-20604
URL: https://issues.apache.org/jira/browse/SPARK-20604
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 2.1.0
Reporter: Wayne Zhang
Imputer currently requires input column to be Double or Float, but the logic
should work on any numeric data types. Many practical problems have integer
data types, and it could get very tedious to manually cast them into Double
before calling imputer. This transformer could be extended to handle all
numeric types.
The example below shows failure of Bucketizer on integer data.
{code}
val df = spark.createDataFrame( Seq(
(0, 1.0, 1.0, 1.0),
(1, 11.0, 11.0, 11.0),
(2, 1.5, 1.5, 1.5),
(3, Double.NaN, 4.5, 1.5)
)).toDF("id", "value1", "expected_mean_value1", "expected_median_value1")
val imputer = new Imputer()
.setInputCols(Array("value1"))
.setOutputCols(Array("out1"))
imputer.fit(df.withColumn("value1", col("value1").cast(IntegerType)))
java.lang.IllegalArgumentException: requirement failed: Column value1 must be
of type equal to one of the following types: [DoubleType, FloatType] but was
actually of type IntegerType.
{code}
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
