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[SPARK-15617][ML][DOC] Clarify that fMeasure in MulticlassMetrics is "micro" f1_score

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## 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.
This commit is contained in:
Ruifeng Zheng 2016-06-04 13:56:04 +01:00 committed by Sean Owen
parent 2ca563cc45
commit 2099e05f93
4 changed files with 10 additions and 24 deletions
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16
docs/mllib-evaluation-metrics.md
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@ -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>$PPV = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i -
<td>Accuracy</td>
<td>$ACC = \frac{TP}{TP + FP} = \frac{1}{N}\sum_{i=0}^{N-1} \hat{\delta}\left(\hat{\mathbf{y}}_i -
\mathbf{y}_i\right)$</td>
</tr>
<tr>
<td>Overall Recall</td>
<td>$TPR = \frac{TP}{TP + FN} = \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} =

12
mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
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@ -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

2
mllib/src/test/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluatorSuite.scala
View file

@ -33,7 +33,7 @@ class MulticlassClassificationEvaluatorSuite
val evaluator = new MulticlassClassificationEvaluator()
.setPredictionCol("myPrediction")
.setLabelCol("myLabel")
.setMetricName("recall")
.setMetricName("accuracy")
testDefaultReadWrite(evaluator)
}

4
python/pyspark/ml/evaluation.py
View file

@ -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