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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.ml.evaluation
import org.apache.spark.annotation.{DeveloperApi, Since}
import org.apache.spark.ml.param.{ParamMap, Params}
import org.apache.spark.sql.DataFrame
/**
* :: DeveloperApi ::
* Abstract class for evaluators that compute metrics from predictions.
*/
@Since("1.5.0")
@DeveloperApi
abstract class Evaluator extends Params {
/**
* Evaluates model output and returns a scalar metric (larger is better).
*
* @param dataset a dataset that contains labels/observations and predictions.
* @param paramMap parameter map that specifies the input columns and output metrics
* @return metric
*/
@Since("1.5.0")
def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = {
this.copy(paramMap).evaluate(dataset)
}
/**
* Evaluates the output.
* @param dataset a dataset that contains labels/observations and predictions.
* @return metric
*/
@Since("1.5.0")
def evaluate(dataset: DataFrame): Double
/**
* Indicates whether the metric returned by [[evaluate()]] should be maximized (true, default)
* or minimized (false).
* A given evaluator may support multiple metrics which may be maximized or minimized.
*/
@Since("1.5.0")
def isLargerBetter: Boolean = true
@Since("1.5.0")
override def copy(extra: ParamMap): Evaluator
}
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