问题描述
限时送ChatGPT账号..我知道我可以使用 CrossValidator 调整单个模型.但是,相互评估不同模型的建议方法是什么?例如,假设我想评估一个 LogisticRegression 分类器针对 LinearSVC 分类器使用 CrossValidator.
I know that I can use a CrossValidator to tune a single model. But what is the suggested approach for evaluating different models against each other? For example, say that I wanted to evaluate a LogisticRegression classifier against a LinearSVC classifier using CrossValidator.
推荐答案
在熟悉了 API 之后,我通过实现一个自定义的 Estimator 包装两个或多个它可以委派给的估算器,其中选定的估算器由单个 Param[Int].实际代码如下:
After familiarizing myself a bit with the API, I solved this problem by implementing a custom Estimator that wraps two or more estimators it can delegate to, where the selected estimator is controlled by a single Param[Int]. Here is the actual code:
import org.apache.spark.ml.Estimator
import org.apache.spark.ml.Model
import org.apache.spark.ml.param.Param
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.param.Params
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.types.StructType
trait DelegatingEstimatorModelParams extends Params {
final val selectedEstimator = new Param[Int](this, "selectedEstimator", "The selected estimator")
}
class DelegatingEstimator private (override val uid: String, delegates: Array[Estimator[_]]) extends Estimator[DelegatingEstimatorModel] with DelegatingEstimatorModelParams {
private def this(estimators: Array[Estimator[_]]) = this(Identifiable.randomUID("delegating-estimator"), estimators)
def this(estimator1: Estimator[_], estimator2: Estimator[_], estimators: Estimator[_]*) = {
this((Seq(estimator1, estimator2) ++ estimators).toArray)
}
setDefault(selectedEstimator -> 0)
override def fit(dataset: Dataset[_]): DelegatingEstimatorModel = {
val estimator = delegates(getOrDefault(selectedEstimator))
val model = estimator.fit(dataset).asInstanceOf[Model[_]]
new DelegatingEstimatorModel(uid, model)
}
override def copy(extra: ParamMap): Estimator[DelegatingEstimatorModel] = {
val that = new DelegatingEstimator(uid, delegates)
copyValues(that, extra)
}
override def transformSchema(schema: StructType): StructType = {
// All delegates are assumed to perform the same schema transformation,
// so we can simply select the first one:
delegates(0).transformSchema(schema)
}
}
class DelegatingEstimatorModel(override val uid: String, val delegate: Model[_]) extends Model[DelegatingEstimatorModel] with DelegatingEstimatorModelParams {
def copy(extra: ParamMap): DelegatingEstimatorModel = new DelegatingEstimatorModel(uid, delegate.copy(extra).asInstanceOf[Model[_]])
def transform(dataset: Dataset[_]): DataFrame = delegate.transform(dataset)
def transformSchema(schema: StructType): StructType = delegate.transformSchema(schema)
}
评估一个 LogistcRegression 针对 LinearSVC 上面的类可以这样使用:
The evaluate a LogistcRegression against a LinearSVC the classes from above can be employed like this:
val logRegression = new LogisticRegression()
.setFeaturesCol(columnNames.features)
.setPredictionCol(columnNames.prediction)
.setRawPredictionCol(columnNames.rawPrediciton)
.setLabelCol(columnNames.label)
val svmEstimator = new LinearSVC()
.setFeaturesCol(columnNames.features)
.setPredictionCol(columnNames.prediction)
.setRawPredictionCol(columnNames.rawPrediciton)
.setLabelCol(columnNames.label)
val delegatingEstimator = new DelegatingEstimator(logRegression, svmEstimator)
val paramGrid = new ParamGridBuilder()
.addGrid(delegatingEstimator.selectedEstimator, Array(0, 1))
.build()
val model = crossValidator.fit(data)
val bestModel = model.bestModel.asInstanceOf[DelegatingEstimatorModel].delegate
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