ai.h2o.sparkling.ml.features.H2OAutoEncoderBase.scala Maven / Gradle / Ivy

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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 ai.h2o.sparkling.ml.features

import ai.h2o.sparkling.H2OFrame
import ai.h2o.sparkling.ml.models.H2OAutoEncoderMOJOModel
import ai.h2o.sparkling.ml.params.{H2OAutoEncoderExtraParams, HasInputCols}
import hex.Model
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.Dataset

import scala.reflect.ClassTag

abstract class H2OAutoEncoderBase[P <: Model.Parameters: ClassTag]
  extends H2OFeatureEstimator[P]
  with H2OAutoEncoderExtraParams
  with HasInputCols {

  override private[sparkling] def getH2OAlgorithmParams(trainingFrame: H2OFrame): Map[String, Any] = {
    super.getH2OAlgorithmParams(trainingFrame) ++ Map("autoencoder" -> true)
  }

  override def fit(dataset: Dataset[_]): H2OAutoEncoderMOJOModel = {
    val model = super.fit(dataset).asInstanceOf[H2OAutoEncoderMOJOModel]
    copyExtraParams(model)
    model
  }

  private[sparkling] def getWeightCol(): String

  override private[sparkling] def getExcludedCols(): Seq[String] = {
    super.getExcludedCols() ++ Seq(getWeightCol())
      .flatMap(Option(_)) // Remove nulls
  }

  override protected def createMOJOUID(): String = Identifiable.randomUID("AutoEncoder")
}