[
https://issues.apache.org/jira/browse/SPARK-17906?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
zhengruifeng updated SPARK-17906:
---------------------------------
Description:
In practice, I sometime only focus on metric of one special label.
For example, in CTR prediction, I usually only mind F1 of positive class.
In sklearn, this is supported:
{code}
>>> from sklearn.metrics import classification_report
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred, target_names=target_names))
precision recall f1-score support
class 0 0.50 1.00 0.67 1
class 1 0.00 0.00 0.00 1
class 2 1.00 0.67 0.80 3
avg / total 0.70 0.60 0.61 5
{code}
Now, ml only support `weightedXXX`. So I think there may be a point to improve.
The API may be designed like this:
{code}
val dataset = ...
val evaluator = new MulticlassClassificationEvaluator
evaluator.setMetricName("f1")
evaluator.evaluate(dataset) // weightedF1 of all classes
evaluator.setTarget(0.0).setMetricName("f1")
evaluator.evaluate(dataset) // F1 of class "0"
{code}
what's your opinion? [~yanboliang][~josephkb][~sethah][~srowen]
If this is useful and acceptable, I'm happy to work on this.
was:
In practice, I sometime only focus metric of one special label.
For example, in CTR prediction, I usually only mind F1 of positive class.
In sklearn, this is supported:
{code}
>>> from sklearn.metrics import classification_report
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred, target_names=target_names))
precision recall f1-score support
class 0 0.50 1.00 0.67 1
class 1 0.00 0.00 0.00 1
class 2 1.00 0.67 0.80 3
avg / total 0.70 0.60 0.61 5
{code}
Now, ml only support `weightedXXX`. So I think there may be a point to improve.
The API may be designed like this:
{code}
val dataset = ...
val evaluator = new MulticlassClassificationEvaluator
evaluator.setMetricName("f1")
evaluator.evaluate(dataset) // weightedF1 of all classes
evaluator.setTarget(0.0).setMetricName("f1")
evaluator.evaluate(dataset) // F1 of class "0"
{code}
what's your opinion? [~yanboliang][~josephkb][~sethah][~srowen]
If this is useful and acceptable, I'm happy to work on this.
> MulticlassClassificationEvaluator support target label
> ------------------------------------------------------
>
> Key: SPARK-17906
> URL: https://issues.apache.org/jira/browse/SPARK-17906
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Reporter: zhengruifeng
> Priority: Minor
>
> In practice, I sometime only focus on metric of one special label.
> For example, in CTR prediction, I usually only mind F1 of positive class.
> In sklearn, this is supported:
> {code}
> >>> from sklearn.metrics import classification_report
> >>> y_true = [0, 1, 2, 2, 2]
> >>> y_pred = [0, 0, 2, 2, 1]
> >>> target_names = ['class 0', 'class 1', 'class 2']
> >>> print(classification_report(y_true, y_pred, target_names=target_names))
> precision recall f1-score support
> class 0 0.50 1.00 0.67 1
> class 1 0.00 0.00 0.00 1
> class 2 1.00 0.67 0.80 3
> avg / total 0.70 0.60 0.61 5
> {code}
> Now, ml only support `weightedXXX`. So I think there may be a point to
> improve.
> The API may be designed like this:
> {code}
> val dataset = ...
> val evaluator = new MulticlassClassificationEvaluator
> evaluator.setMetricName("f1")
> evaluator.evaluate(dataset) // weightedF1 of all classes
> evaluator.setTarget(0.0).setMetricName("f1")
> evaluator.evaluate(dataset) // F1 of class "0"
> {code}
> what's your opinion? [~yanboliang][~josephkb][~sethah][~srowen]
> If this is useful and acceptable, I'm happy to work on this.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
