Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/3099#discussion_r19927481
--- Diff: mllib/src/main/scala/org/apache/spark/ml/Estimator.scala ---
@@ -0,0 +1,70 @@
+/*
+ * 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.ml.param.{ParamMap, ParamPair, Params}
+import org.apache.spark.sql.SchemaRDD
+
+/**
+ * Abstract class for estimators that fits models to data.
+ */
+abstract class Estimator[M <: Model] extends PipelineStage with Params {
+
+ /**
+ * Fits a single model to the input data with optional parameters.
+ *
+ * @param dataset input dataset
+ * @param paramPairs optional list of param pairs, overwrite embedded
params
+ * @return fitted model
+ */
+ @varargs
+ def fit(dataset: SchemaRDD, paramPairs: ParamPair[_]*): M = {
+ val map = new ParamMap()
+ paramPairs.foreach(map.put(_))
+ fit(dataset, map)
+ }
+
+ /**
+ * Fits a single model to the input data with provided parameter map.
+ *
+ * @param dataset input dataset
+ * @param paramMap parameters
+ * @return fitted model
+ */
+ def fit(dataset: SchemaRDD, paramMap: ParamMap): 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 overwrite this to optimize multi-model training.
+ *
+ * @param dataset input dataset
+ * @param paramMaps an array of parameter maps
+ * @return fitted models, matching the input parameter maps
+ */
+ def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[M] = { //
how to return an array?
+ paramMaps.map(fit(dataset, _))
--- End diff --
.toArray ?
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