Repository: spark Updated Branches: refs/heads/master 2ca563cc4 -> 2099e05f9
[SPARK-15617][ML][DOC] Clarify that fMeasure in MulticlassMetrics is "micro" f1_score ## What changes were proposed in this pull request? 1, del precision,recall in `ml.MulticlassClassificationEvaluator` 2, update user guide for `mlllib.weightedFMeasure` ## How was this patch tested? local build Author: Ruifeng Zheng <ruife...@foxmail.com> Closes #13390 from zhengruifeng/clarify_f1. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2099e05f Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2099e05f Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2099e05f Branch: refs/heads/master Commit: 2099e05f93067937cdf6cedcf493afd66e212abe Parents: 2ca563c Author: Ruifeng Zheng <ruife...@foxmail.com> Authored: Sat Jun 4 13:56:04 2016 +0100 Committer: Sean Owen <so...@cloudera.com> Committed: Sat Jun 4 13:56:04 2016 +0100 ---------------------------------------------------------------------- docs/mllib-evaluation-metrics.md | 16 +++------------- .../MulticlassClassificationEvaluator.scala | 12 +++++------- .../MulticlassClassificationEvaluatorSuite.scala | 2 +- python/pyspark/ml/evaluation.py | 4 +--- 4 files changed, 10 insertions(+), 24 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/2099e05f/docs/mllib-evaluation-metrics.md ---------------------------------------------------------------------- diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md index a269dbf..c49bc4f 100644 --- a/docs/mllib-evaluation-metrics.md +++ b/docs/mllib-evaluation-metrics.md @@ -140,7 +140,7 @@ definitions of positive and negative labels is straightforward. #### Label based metrics Opposed to binary classification where there are only two possible labels, multiclass classification problems have many -possible labels and so the concept of label-based metrics is introduced. Overall precision measures precision across all +possible labels and so the concept of label-based metrics is introduced. Accuracy measures precision across all labels - the number of times any class was predicted correctly (true positives) normalized by the number of data points. Precision by label considers only one class, and measures the number of time a specific label was predicted correctly normalized by the number of times that label appears in the output. @@ -182,21 +182,11 @@ $$\hat{\delta}(x) = \begin{cases}1 & \text{if $x = 0$}, \\ 0 & \text{otherwise}. </td> </tr> <tr> - <td>Overall Precision</td> - <td>$PPV = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - - \mathbf{y}_i\right)$</td> - </tr> - <tr> - <td>Overall Recall</td> - <td>$TPR = \frac{TP}{TP + FN} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - + <td>Accuracy</td> + <td>$ACC = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i - \mathbf{y}_i\right)$</td> </tr> <tr> - <td>Overall F1-measure</td> - <td>$F1 = 2 \cdot \left(\frac{PPV \cdot TPR} - {PPV + TPR}\right)$</td> - </tr> - <tr> <td>Precision by label</td> <td>$PPV(\ell) = \frac{TP}{TP + FP} = \frac{\sum_{i=0}^{N-1} \hat{\delta}(\hat{\mathbf{y}}_i - \ell) \cdot \hat{\delta}(\mathbf{y}_i - \ell)} http://git-wip-us.apache.org/repos/asf/spark/blob/2099e05f/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala index 0b84e0a..794b1e7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala @@ -39,16 +39,16 @@ class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") overrid def this() = this(Identifiable.randomUID("mcEval")) /** - * param for metric name in evaluation (supports `"f1"` (default), `"precision"`, `"recall"`, - * `"weightedPrecision"`, `"weightedRecall"`, `"accuracy"`) + * param for metric name in evaluation (supports `"f1"` (default), `"weightedPrecision"`, + * `"weightedRecall"`, `"accuracy"`) * @group param */ @Since("1.5.0") val metricName: Param[String] = { - val allowedParams = ParamValidators.inArray(Array("f1", "precision", - "recall", "weightedPrecision", "weightedRecall", "accuracy")) + val allowedParams = ParamValidators.inArray(Array("f1", "weightedPrecision", + "weightedRecall", "accuracy")) new Param(this, "metricName", "metric name in evaluation " + - "(f1|precision|recall|weightedPrecision|weightedRecall|accuracy)", allowedParams) + "(f1|weightedPrecision|weightedRecall|accuracy)", allowedParams) } /** @group getParam */ @@ -82,8 +82,6 @@ class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") overrid val metrics = new MulticlassMetrics(predictionAndLabels) val metric = $(metricName) match { case "f1" => metrics.weightedFMeasure - case "precision" => metrics.accuracy - case "recall" => metrics.accuracy case "weightedPrecision" => metrics.weightedPrecision case "weightedRecall" => metrics.weightedRecall case "accuracy" => metrics.accuracy http://git-wip-us.apache.org/repos/asf/spark/blob/2099e05f/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala ---------------------------------------------------------------------- diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala index 522f667..1a3a8a1 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala @@ -33,7 +33,7 @@ class MulticlassClassificationEvaluatorSuite val evaluator = new MulticlassClassificationEvaluator() .setPredictionCol("myPrediction") .setLabelCol("myLabel") - .setMetricName("recall") + .setMetricName("accuracy") testDefaultReadWrite(evaluator) } http://git-wip-us.apache.org/repos/asf/spark/blob/2099e05f/python/pyspark/ml/evaluation.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py index b8b2b37..c480525 100644 --- a/python/pyspark/ml/evaluation.py +++ b/python/pyspark/ml/evaluation.py @@ -258,9 +258,7 @@ class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio >>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction") >>> evaluator.evaluate(dataset) 0.66... - >>> evaluator.evaluate(dataset, {evaluator.metricName: "precision"}) - 0.66... - >>> evaluator.evaluate(dataset, {evaluator.metricName: "recall"}) + >>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"}) 0.66... .. versionadded:: 1.5.0 --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
