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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
import scala.annotation.varargs
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.ml.param.{ParamMap, ParamPair}
import org.apache.spark.sql.DataFrame
/**
* :: DeveloperApi ::
* Abstract class for estimators that fit models to data.
*/
@DeveloperApi
abstract class Estimator[M <: Model[M]] extends PipelineStage {
/**
* Fits a single model to the input data with optional parameters.
*
* @param dataset input dataset
* @param firstParamPair the first param pair, overrides embedded params
* @param otherParamPairs other param pairs. These values override any specified in this
* Estimator's embedded ParamMap.
* @return fitted model
*/
@varargs
def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M = {
val map = new ParamMap()
.put(firstParamPair)
.put(otherParamPairs: _*)
fit(dataset, map)
}
/**
* Fits a single model to the input data with provided parameter map.
*
* @param dataset input dataset
* @param paramMap Parameter map.
* These values override any specified in this Estimator's embedded ParamMap.
* @return fitted model
*/
def fit(dataset: DataFrame, paramMap: ParamMap): M = {
copy(paramMap).fit(dataset)
}
/**
* Fits a model to the input data.
*/
def fit(dataset: DataFrame): M
/**
* Fits multiple models to the input data with multiple sets of parameters.
* The default implementation uses a for loop on each parameter map.
* Subclasses could override this to optimize multi-model training.
*
* @param dataset input dataset
* @param paramMaps An array of parameter maps.
* These values override any specified in this Estimator's embedded ParamMap.
* @return fitted models, matching the input parameter maps
*/
def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[M] = {
paramMaps.map(fit(dataset, _))
}
override def copy(extra: ParamMap): Estimator[M]
}
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