Github user tomerk commented on a diff in the pull request:
https://github.com/apache/spark/pull/3637#discussion_r22752722
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
---
@@ -0,0 +1,143 @@
+/*
+ * 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.classification
+
+import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
+import org.apache.spark.ml.param.{HasProbabilityCol, ParamMap, Params}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.Star
+
+/**
+ * Params for probabilistic classification.
+ */
+private[classification] trait ProbabilisticClassifierParams
+ extends ClassifierParams with HasProbabilityCol {
+
+ override protected def validateAndTransformSchema(
+ schema: StructType,
+ paramMap: ParamMap,
+ fitting: Boolean,
+ featuresDataType: DataType): StructType = {
+ val parentSchema = super.validateAndTransformSchema(schema, paramMap,
fitting, featuresDataType)
+ val map = this.paramMap ++ paramMap
+ addOutputColumn(parentSchema, map(probabilityCol), new VectorUDT)
+ }
+}
+
+
+/**
+ * :: AlphaComponent ::
+ *
+ * Single-label binary or multiclass classifier which can output class
conditional probabilities.
+ *
+ * @tparam FeaturesType Type of input features. E.g., [[Vector]]
+ * @tparam Learner Concrete Estimator type
+ * @tparam M Concrete Model type
+ */
+@AlphaComponent
+abstract class ProbabilisticClassifier[
+ FeaturesType,
+ Learner <: ProbabilisticClassifier[FeaturesType, Learner, M],
+ M <: ProbabilisticClassificationModel[FeaturesType, M]]
+ extends Classifier[FeaturesType, Learner, M] with
ProbabilisticClassifierParams {
+
+ def setProbabilityCol(value: String): Learner = set(probabilityCol,
value).asInstanceOf[Learner]
+}
+
+
+/**
+ * :: AlphaComponent ::
+ *
+ * Model produced by a [[ProbabilisticClassifier]].
+ * Classes are indexed {0, 1, ..., numClasses - 1}.
+ *
+ * @tparam FeaturesType Type of input features. E.g., [[Vector]]
+ * @tparam M Concrete Model type
+ */
+@AlphaComponent
+abstract class ProbabilisticClassificationModel[
+ FeaturesType,
+ M <: ProbabilisticClassificationModel[FeaturesType, M]]
+ extends ClassificationModel[FeaturesType, M] with
ProbabilisticClassifierParams {
+
+ def setProbabilityCol(value: String): M = set(probabilityCol,
value).asInstanceOf[M]
+
+ /**
+ * Transforms dataset by reading from [[featuresCol]], and appending new
columns as specified by
+ * parameters:
+ * - predicted labels as [[predictionCol]] of type [[Double]]
+ * - raw predictions (confidences) as [[rawPredictionCol]] of type
[[Vector]]
+ * - probability of each class as [[probabilityCol]] of type [[Vector]].
+ *
+ * @param dataset input dataset
+ * @param paramMap additional parameters, overwrite embedded params
+ * @return transformed dataset
+ */
+ override def transform(dataset: SchemaRDD, paramMap: ParamMap):
SchemaRDD = {
+ // This default implementation should be overridden as needed.
+ import dataset.sqlContext._
+ import org.apache.spark.sql.catalyst.dsl._
+
+ // Check schema
+ transformSchema(dataset.schema, paramMap, logging = true)
+ val map = this.paramMap ++ paramMap
+
+ // Prepare model
+ val tmpModel = if (paramMap.size != 0) {
+ val tmpModel = this.copy()
+ Params.inheritValues(paramMap, parent, tmpModel)
+ tmpModel
+ } else {
+ this
+ }
+
+ val (numColsOutput, outputData) =
+ ClassificationModel.transformColumnsImpl[FeaturesType](dataset,
tmpModel, map)
+
+ // Output selected columns only.
+ if (map(probabilityCol) != "") {
+ // output probabilities
+ val features2probs: FeaturesType => Vector = (features) => {
--- End diff --
In order for ```features2probs.call(*)``` to do the implicit conversion to
a UDF
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