From 2099e05f93067937cdf6cedcf493afd66e212abe Mon Sep 17 00:00:00 2001
From: Ruifeng Zheng <ruifengz@foxmail.com>
Date: Sat, 4 Jun 2016 13:56:04 +0100
Subject: [PATCH] [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 <ruifengz@foxmail.com>
Closes #13390 from zhengruifeng/clarify_f1.
---
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(-)
diff --git a/docs/mllib-evaluation-metrics.md b/docs/mllib-evaluation-metrics.md
index a269dbf030..c49bc4ff12 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.
@@ -182,20 +182,10 @@ $$\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} =
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 0b84e0a3fa..794b1e7d9d 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
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 522f6675d7..1a3a8a13a2 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)
}
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index b8b2b37af5..c480525e9b 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
--
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