zhengruifeng created SPARK-27867:
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Summary: RegressionEvaluator cache lastest RegressionMetrics to
avoid duplicated computation
Key: SPARK-27867
URL: https://issues.apache.org/jira/browse/SPARK-27867
Project: Spark
Issue Type: Improvement
Components: ML
Affects Versions: 3.0.0
Reporter: zhengruifeng
In most cases, given a model, we have to obtain multi metrics of it.
For examples, a regression model, we may need to obtain the R2, MAE and MSE.
However, current design of `Evaluator` do not support computing multi metrics
at once.
In practice, we usually use RegressionEvaluator like this:
{code:java}
val evaluator = new RegressionEvaluator()
val r2 = evaluator.setMetricName("r2").evaluate(df)
val mae = evaluator.setMetricName("mae").evaluate(df)
val mse = evaluator.setMetricName("mse").evaluate(df){code}
However, current impl of RegressionEvaluator needs one pass of the whole input
dataset to compute one metric. So, above example needs 3 passes.
This can be optimized since in \{RegressionMetrics} all metrics can be
computed at once.
If we cache the lastest inputs, and then if the next evaluate call keep the
inputs (except the metricName), then we can directly obtain the metric from the
internal intermediate summary.
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