RegressionEvaluator — Evaluator of Regression Models-spark技术分享

RegressionEvaluator — Evaluator of Regression Models

RegressionEvaluator is an Evaluator of regression models (e.g. ALS, DecisionTreeRegressor, DecisionTreeClassifier, GBTRegressor, GBTClassifier, RandomForestRegressor, RandomForestClassifier, LinearRegression, RFormula, NaiveBayes, LogisticRegression, MultilayerPerceptronClassifier, LinearSVC, GeneralizedLinearRegression).

Table 1. RegressionEvaluator’s Metrics and isLargerBetter Flag
Metric	Description	isLargerBetter
`rmse`	Root mean squared error	`false`
`mse`	Mean squared error	`false`
`r2`	(default) Unadjusted coefficient of determination Regression through the origin	`true`
`mae`	Mean absolute error	`false`



import org.apache.spark.ml.evaluation.RegressionEvaluator
val regEval = new RegressionEvaluator().
  setMetricName("r2").
  setPredictionCol("prediction").
  setLabelCol("label")

scala> regEval.isLargerBetter
res0: Boolean = true

scala> println(regEval.explainParams)
labelCol: label column name (default: label, current: label)
metricName: metric name in evaluation (mse|rmse|r2|mae) (default: rmse, current: r2)
predictionCol: prediction column name (default: prediction, current: prediction)

import org.apache.spark.ml.evaluation.RegressionEvaluator

val regEval = new RegressionEvaluator().

setMetricName("r2").

setPredictionCol("prediction").

setLabelCol("label")

scala> regEval.isLargerBetter

res0: Boolean = true

scala> println(regEval.explainParams)

labelCol: label column name (default: label, current: label)

metricName: metric name in evaluation (mse|rmse|r2|mae) (default: rmse, current: r2)

predictionCol: prediction column name (default: prediction, current: prediction)

Table 2. RegressionEvaluator’ Parameters
Parameter	Default Value	Description
`metricName`	`areaUnderROC`	Name of the classification metric for evaluation Can be one of the following: `mae`, `mse`, `rmse` (default), `r2`
`predictionCol`	`prediction`	Name of the column with predictions
`labelCol`	`label`	Name of the column with indexed labels



// prepare a fake input dataset using transformers
import org.apache.spark.ml.feature.Tokenizer
val tok = new Tokenizer().setInputCol("text")

import org.apache.spark.ml.feature.HashingTF
val hashTF = new HashingTF()
  .setInputCol(tok.getOutputCol)  // it reads the output of tok
  .setOutputCol("features")

// Scala trick to chain transform methods
// It's of little to no use since we've got Pipelines
// Just to have it as an alternative
val transform = (tok.transform _).andThen(hashTF.transform _)

val dataset = Seq((0, "hello world", 0.0)).toDF("id", "text", "label")

// we're using Linear Regression algorithm
import org.apache.spark.ml.regression.LinearRegression
val lr = new LinearRegression

import org.apache.spark.ml.Pipeline
val pipeline = new Pipeline().setStages(Array(tok, hashTF, lr))

val model = pipeline.fit(dataset)

// Let's do prediction
// Note that we're using the same dataset as for fitting the model
// Something you'd definitely not be doing in prod
val predictions = model.transform(dataset)

// Now we're ready to evaluate the model
// Evaluator works on datasets with predictions

import org.apache.spark.ml.evaluation.RegressionEvaluator
val regEval = new RegressionEvaluator

scala> regEval.evaluate(predictions)
res0: Double = 0.0

// prepare a fake input dataset using transformers

import org.apache.spark.ml.feature.Tokenizer

val tok = new Tokenizer().setInputCol("text")

import org.apache.spark.ml.feature.HashingTF

val hashTF = new HashingTF()

.setInputCol(tok.getOutputCol) // it reads the output of tok

.setOutputCol("features")

// Scala trick to chain transform methods

// It's of little to no use since we've got Pipelines

// Just to have it as an alternative

val transform = (tok.transform _).andThen(hashTF.transform _)

val dataset = Seq((0, "hello world", 0.0)).toDF("id", "text", "label")

// we're using Linear Regression algorithm

import org.apache.spark.ml.regression.LinearRegression

val lr = new LinearRegression

import org.apache.spark.ml.Pipeline

val pipeline = new Pipeline().setStages(Array(tok, hashTF, lr))

val model = pipeline.fit(dataset)

// Let's do prediction

// Note that we're using the same dataset as for fitting the model

// Something you'd definitely not be doing in prod

val predictions = model.transform(dataset)

// Now we're ready to evaluate the model

// Evaluator works on datasets with predictions

import org.apache.spark.ml.evaluation.RegressionEvaluator

val regEval = new RegressionEvaluator

scala> regEval.evaluate(predictions)

res0: Double = 0.0

Evaluating Model Output — `evaluate` Method



evaluate(dataset: Dataset[_]): Double

evaluate(dataset: Dataset[_]): Double

Note	`evaluate` is part of Evaluator Contract.

evaluate…FIXME

RegressionEvaluator — Evaluator of Regression Models