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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.r
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NominalAttribute}
import org.apache.spark.ml.classification.{NaiveBayes, NaiveBayesModel}
import org.apache.spark.ml.feature.{IndexToString, RFormula}
import org.apache.spark.sql.{DataFrame, Dataset}
private[r] class NaiveBayesWrapper private (
pipeline: PipelineModel,
val labels: Array[String],
val features: Array[String]) {
import NaiveBayesWrapper._
private val naiveBayesModel: NaiveBayesModel = pipeline.stages(1).asInstanceOf[NaiveBayesModel]
lazy val apriori: Array[Double] = naiveBayesModel.pi.toArray.map(math.exp)
lazy val tables: Array[Double] = naiveBayesModel.theta.toArray.map(math.exp)
def transform(dataset: Dataset[_]): DataFrame = {
pipeline.transform(dataset)
.drop(PREDICTED_LABEL_INDEX_COL)
.drop(naiveBayesModel.getFeaturesCol)
}
}
private[r] object NaiveBayesWrapper {
val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
val PREDICTED_LABEL_COL = "prediction"
def fit(formula: String, data: DataFrame, laplace: Double): NaiveBayesWrapper = {
val rFormula = new RFormula()
.setFormula(formula)
.fit(data)
// get labels and feature names from output schema
val schema = rFormula.transform(data).schema
val labelAttr = Attribute.fromStructField(schema(rFormula.getLabelCol))
.asInstanceOf[NominalAttribute]
val labels = labelAttr.values.get
val featureAttrs = AttributeGroup.fromStructField(schema(rFormula.getFeaturesCol))
.attributes.get
val features = featureAttrs.map(_.name.get)
// assemble and fit the pipeline
val naiveBayes = new NaiveBayes()
.setSmoothing(laplace)
.setModelType("bernoulli")
.setPredictionCol(PREDICTED_LABEL_INDEX_COL)
val idxToStr = new IndexToString()
.setInputCol(PREDICTED_LABEL_INDEX_COL)
.setOutputCol(PREDICTED_LABEL_COL)
.setLabels(labels)
val pipeline = new Pipeline()
.setStages(Array(rFormula, naiveBayes, idxToStr))
.fit(data)
new NaiveBayesWrapper(pipeline, labels, features)
}
}
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