问题描述
为了构建 NaiveBayes 多类分类器，我使用 CrossValidator 来选择管道中的最佳参数:
In order to build a NaiveBayes multiclass classifier, I am using a CrossValidator to select the best parameters in my pipeline:
val cv = new CrossValidator()
.setEstimator(pipeline)
.setEstimatorParamMaps(paramGrid)
.setEvaluator(new MulticlassClassificationEvaluator)
.setNumFolds(10)
val cvModel = cv.fit(trainingSet)
管道包含按以下顺序的常用转换器和估计器:Tokenizer、StopWordsRemover、HashingTF、IDF，最后是 NaiveBayes.
The pipeline contains usual transformers and estimators in the following order: Tokenizer, StopWordsRemover, HashingTF, IDF and finally the NaiveBayes.
是否可以访问为最佳模型计算的指标?
Is it possible to access the metrics calculated for best model?
理想情况下，我想访问所有模型的指标，以了解更改参数如何改变分类质量.但就目前而言，最好的模型就足够了.
Ideally, I would like to access the metrics of all models to see how changing the parameters is changing the quality of the classification.But for the moment, the best model is good enough.
仅供参考，我使用的是 Spark 1.6.0
FYI, I am using Spark 1.6.0
推荐答案
我是这样做的:
val pipeline = new Pipeline()
.setStages(Array(tokenizer, stopWordsFilter, tf, idf, word2Vec, featureVectorAssembler, categoryIndexerModel, classifier, categoryReverseIndexer))
...
val paramGrid = new ParamGridBuilder()
.addGrid(tf.numFeatures, Array(10, 100))
.addGrid(idf.minDocFreq, Array(1, 10))
.addGrid(word2Vec.vectorSize, Array(200, 300))
.addGrid(classifier.maxDepth, Array(3, 5))
.build()
paramGrid.size // 16 entries
...
// Print the average metrics per ParamGrid entry
val avgMetricsParamGrid = crossValidatorModel.avgMetrics
// Combine with paramGrid to see how they affect the overall metrics
val combined = paramGrid.zip(avgMetricsParamGrid)
...
val bestModel = crossValidatorModel.bestModel.asInstanceOf[PipelineModel]
// Explain params for each stage
val bestHashingTFNumFeatures = bestModel.stages(2).asInstanceOf[HashingTF].explainParams
val bestIDFMinDocFrequency = bestModel.stages(3).asInstanceOf[IDFModel].explainParams
val bestWord2VecVectorSize = bestModel.stages(4).asInstanceOf[Word2VecModel].explainParams
val bestDecisionTreeDepth = bestModel.stages(7).asInstanceOf[DecisionTreeClassificationModel].explainParams
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