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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.classification
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
import org.apache.spark.ml.param.shared.HasRawPredictionCol
import org.apache.spark.ml.util.SchemaUtils
import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{DataType, StructType}
/**
* (private[spark]) Params for classification.
*/
private[spark] trait ClassifierParams
extends PredictorParams with HasRawPredictionCol {
override protected def validateAndTransformSchema(
schema: StructType,
fitting: Boolean,
featuresDataType: DataType): StructType = {
val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType)
SchemaUtils.appendColumn(parentSchema, $(rawPredictionCol), new VectorUDT)
}
}
/**
* :: DeveloperApi ::
*
* Single-label binary or multiclass classification.
* Classes are indexed {0, 1, ..., numClasses - 1}.
*
* @tparam FeaturesType Type of input features. E.g., [[Vector]]
* @tparam E Concrete Estimator type
* @tparam M Concrete Model type
*/
@DeveloperApi
abstract class Classifier[
FeaturesType,
E <: Classifier[FeaturesType, E, M],
M <: ClassificationModel[FeaturesType, M]]
extends Predictor[FeaturesType, E, M] with ClassifierParams {
/** @group setParam */
def setRawPredictionCol(value: String): E = set(rawPredictionCol, value).asInstanceOf[E]
// TODO: defaultEvaluator (follow-up PR)
}
/**
* :: DeveloperApi ::
*
* Model produced by a [[Classifier]].
* Classes are indexed {0, 1, ..., numClasses - 1}.
*
* @tparam FeaturesType Type of input features. E.g., [[Vector]]
* @tparam M Concrete Model type
*/
@DeveloperApi
abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[FeaturesType, M]]
extends PredictionModel[FeaturesType, M] with ClassifierParams {
/** @group setParam */
def setRawPredictionCol(value: String): M = set(rawPredictionCol, value).asInstanceOf[M]
/** Number of classes (values which the label can take). */
def numClasses: Int
/**
* 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]].
*
* @param dataset input dataset
* @return transformed dataset
*/
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
// Output selected columns only.
// This is a bit complicated since it tries to avoid repeated computation.
var outputData = dataset
var numColsOutput = 0
if (getRawPredictionCol != "") {
val predictRawUDF = udf { (features: Any) =>
predictRaw(features.asInstanceOf[FeaturesType])
}
outputData = outputData.withColumn(getRawPredictionCol, predictRawUDF(col(getFeaturesCol)))
numColsOutput += 1
}
if (getPredictionCol != "") {
val predUDF = if (getRawPredictionCol != "") {
udf(raw2prediction _).apply(col(getRawPredictionCol))
} else {
val predictUDF = udf { (features: Any) =>
predict(features.asInstanceOf[FeaturesType])
}
predictUDF(col(getFeaturesCol))
}
outputData = outputData.withColumn(getPredictionCol, predUDF)
numColsOutput += 1
}
if (numColsOutput == 0) {
logWarning(s"$uid: ClassificationModel.transform() was called as NOOP" +
" since no output columns were set.")
}
outputData.toDF
}
/**
* Predict label for the given features.
* This internal method is used to implement [[transform()]] and output [[predictionCol]].
*
* This default implementation for classification predicts the index of the maximum value
* from [[predictRaw()]].
*/
override protected def predict(features: FeaturesType): Double = {
raw2prediction(predictRaw(features))
}
/**
* Raw prediction for each possible label.
* The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives
* a measure of confidence in each possible label (where larger = more confident).
* This internal method is used to implement [[transform()]] and output [[rawPredictionCol]].
*
* @return vector where element i is the raw prediction for label i.
* This raw prediction may be any real number, where a larger value indicates greater
* confidence for that label.
*/
protected def predictRaw(features: FeaturesType): Vector
/**
* Given a vector of raw predictions, select the predicted label.
* This may be overridden to support thresholds which favor particular labels.
* @return predicted label
*/
protected def raw2prediction(rawPrediction: Vector): Double = rawPrediction.argmax
}
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