diff options
| -rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 20 | ||||
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala | 77 |
2 files changed, 84 insertions, 13 deletions
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R index e48df038df..5f742d9045 100644 --- a/R/pkg/inst/tests/testthat/test_mllib.R +++ b/R/pkg/inst/tests/testthat/test_mllib.R @@ -64,6 +64,16 @@ test_that("spark.glm and predict", { rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris) expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals) + # binomial family + binomialTraining <- training[training$Species %in% c("versicolor", "virginica"), ] + model <- spark.glm(binomialTraining, Species ~ Sepal_Length + Sepal_Width, + family = binomial(link = "logit")) + prediction <- predict(model, binomialTraining) + expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character") + expected <- c("virginica", "virginica", "virginica", "versicolor", "virginica", + "versicolor", "virginica", "versicolor", "virginica", "versicolor") + expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected) + # poisson family model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species, family = poisson(link = identity)) @@ -128,10 +138,10 @@ test_that("spark.glm summary", { expect_equal(stats$aic, rStats$aic) # Test spark.glm works with weighted dataset - a1 <- c(0, 1, 2, 3) - a2 <- c(5, 2, 1, 3) - w <- c(1, 2, 3, 4) - b <- c(1, 0, 1, 0) + a1 <- c(0, 1, 2, 3, 4) + a2 <- c(5, 2, 1, 3, 2) + w <- c(1, 2, 3, 4, 5) + b <- c(1, 0, 1, 0, 0) data <- as.data.frame(cbind(a1, a2, w, b)) df <- createDataFrame(data) @@ -158,7 +168,7 @@ test_that("spark.glm summary", { data <- as.data.frame(cbind(a1, a2, b)) df <- suppressWarnings(createDataFrame(data)) regStats <- summary(spark.glm(df, b ~ a1 + a2, regParam = 1.0)) - expect_equal(regStats$aic, 13.32836, tolerance = 1e-4) # 13.32836 is from summary() result + expect_equal(regStats$aic, 14.00976, tolerance = 1e-4) # 14.00976 is from summary() result }) test_that("spark.glm save/load", { diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala index b1bb577e1f..995b1ef03b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala @@ -23,11 +23,16 @@ import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ import org.apache.spark.ml.{Pipeline, PipelineModel} -import org.apache.spark.ml.attribute.AttributeGroup -import org.apache.spark.ml.feature.RFormula +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NominalAttribute} +import org.apache.spark.ml.feature.{IndexToString, RFormula} import org.apache.spark.ml.regression._ +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util._ import org.apache.spark.sql._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ private[r] class GeneralizedLinearRegressionWrapper private ( val pipeline: PipelineModel, @@ -42,6 +47,8 @@ private[r] class GeneralizedLinearRegressionWrapper private ( val rNumIterations: Int, val isLoaded: Boolean = false) extends MLWritable { + import GeneralizedLinearRegressionWrapper._ + private val glm: GeneralizedLinearRegressionModel = pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel] @@ -52,7 +59,15 @@ private[r] class GeneralizedLinearRegressionWrapper private ( def residuals(residualsType: String): DataFrame = glm.summary.residuals(residualsType) def transform(dataset: Dataset[_]): DataFrame = { - pipeline.transform(dataset).drop(glm.getFeaturesCol) + if (rFamily == "binomial") { + pipeline.transform(dataset) + .drop(PREDICTED_LABEL_PROB_COL) + .drop(PREDICTED_LABEL_INDEX_COL) + .drop(glm.getFeaturesCol) + } else { + pipeline.transform(dataset) + .drop(glm.getFeaturesCol) + } } override def write: MLWriter = @@ -62,6 +77,10 @@ private[r] class GeneralizedLinearRegressionWrapper private ( private[r] object GeneralizedLinearRegressionWrapper extends MLReadable[GeneralizedLinearRegressionWrapper] { + val PREDICTED_LABEL_PROB_COL = "pred_label_prob" + val PREDICTED_LABEL_INDEX_COL = "pred_label_idx" + val PREDICTED_LABEL_COL = "prediction" + def fit( formula: String, data: DataFrame, @@ -71,8 +90,8 @@ private[r] object GeneralizedLinearRegressionWrapper maxIter: Int, weightCol: String, regParam: Double): GeneralizedLinearRegressionWrapper = { - val rFormula = new RFormula() - .setFormula(formula) + val rFormula = new RFormula().setFormula(formula) + if (family == "binomial") rFormula.setForceIndexLabel(true) RWrapperUtils.checkDataColumns(rFormula, data) val rFormulaModel = rFormula.fit(data) // get labels and feature names from output schema @@ -90,9 +109,27 @@ private[r] object GeneralizedLinearRegressionWrapper .setWeightCol(weightCol) .setRegParam(regParam) .setFeaturesCol(rFormula.getFeaturesCol) - val pipeline = new Pipeline() - .setStages(Array(rFormulaModel, glr)) - .fit(data) + val pipeline = if (family == "binomial") { + // Convert prediction from probability to label index. + val probToPred = new ProbabilityToPrediction() + .setInputCol(PREDICTED_LABEL_PROB_COL) + .setOutputCol(PREDICTED_LABEL_INDEX_COL) + // Convert prediction from label index to original label. + val labelAttr = Attribute.fromStructField(schema(rFormulaModel.getLabelCol)) + .asInstanceOf[NominalAttribute] + val labels = labelAttr.values.get + val idxToStr = new IndexToString() + .setInputCol(PREDICTED_LABEL_INDEX_COL) + .setOutputCol(PREDICTED_LABEL_COL) + .setLabels(labels) + + new Pipeline() + .setStages(Array(rFormulaModel, glr.setPredictionCol(PREDICTED_LABEL_PROB_COL), + probToPred, idxToStr)) + .fit(data) + } else { + new Pipeline().setStages(Array(rFormulaModel, glr)).fit(data) + } val glm: GeneralizedLinearRegressionModel = pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel] @@ -200,3 +237,27 @@ private[r] object GeneralizedLinearRegressionWrapper } } } + +/** + * This utility transformer converts the predicted value of GeneralizedLinearRegressionModel + * with "binomial" family from probability to prediction according to threshold 0.5. + */ +private[r] class ProbabilityToPrediction private[r] (override val uid: String) + extends Transformer with HasInputCol with HasOutputCol with DefaultParamsWritable { + + def this() = this(Identifiable.randomUID("probToPred")) + + def setInputCol(value: String): this.type = set(inputCol, value) + + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transformSchema(schema: StructType): StructType = { + StructType(schema.fields :+ StructField($(outputCol), DoubleType)) + } + + override def transform(dataset: Dataset[_]): DataFrame = { + dataset.withColumn($(outputCol), round(col($(inputCol)))) + } + + override def copy(extra: ParamMap): ProbabilityToPrediction = defaultCopy(extra) +} |