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[SPARK-14900][ML][PYSPARK] Add accuracy and deprecate precison,recall,f1

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## What changes were proposed in this pull request?
1, add accuracy for MulticlassMetrics
2, deprecate overall precision,recall,f1 and recommend accuracy usage

## How was this patch tested?
manual tests in pyspark shell

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13511 from zhengruifeng/deprecate_py_precisonrecall.
This commit is contained in:
Zheng RuiFeng 2016-06-06 15:19:22 +01:00 committed by Sean Owen
parent a95252823e
commit 00ad4f054c
1 changed files with 18 additions and 0 deletions
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18
python/pyspark/mllib/evaluation.py
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@ -15,6 +15,8 @@
# limitations under the License.
#
import warnings
from pyspark import since
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
from pyspark.sql import SQLContext
@ -181,6 +183,8 @@ class MulticlassMetrics(JavaModelWrapper):
0.66...
>>> metrics.recall()
0.66...
>>> metrics.accuracy()
0.66...
>>> metrics.weightedFalsePositiveRate
0.19...
>>> metrics.weightedPrecision
@ -233,6 +237,8 @@ class MulticlassMetrics(JavaModelWrapper):
Returns precision or precision for a given label (category) if specified.
"""
if label is None:
# note:: Deprecated in 2.0.0. Use accuracy.
warnings.warn("Deprecated in 2.0.0. Use accuracy.")
return self.call("precision")
else:
return self.call("precision", float(label))
@ -243,6 +249,8 @@ class MulticlassMetrics(JavaModelWrapper):
Returns recall or recall for a given label (category) if specified.
"""
if label is None:
# note:: Deprecated in 2.0.0. Use accuracy.
warnings.warn("Deprecated in 2.0.0. Use accuracy.")
return self.call("recall")
else:
return self.call("recall", float(label))
@ -254,6 +262,8 @@ class MulticlassMetrics(JavaModelWrapper):
"""
if beta is None:
if label is None:
# note:: Deprecated in 2.0.0. Use accuracy.
warnings.warn("Deprecated in 2.0.0. Use accuracy.")
return self.call("fMeasure")
else:
return self.call("fMeasure", label)
@ -263,6 +273,14 @@ class MulticlassMetrics(JavaModelWrapper):
else:
return self.call("fMeasure", label, beta)
@since('2.0.0')
def accuracy(self):
"""
Returns accuracy (equals to the total number of correctly classified instances
out of the total number of instances).
"""
return self.call("accuracy")
@property
@since('1.4.0')
def weightedTruePositiveRate(self):