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