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author | Zheng RuiFeng <ruifengz@foxmail.com> | 2016-06-06 15:19:22 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-06-06 15:19:22 +0100 |
commit | 00ad4f054cd044e17d29b7c2c62efd8616462619 (patch) | |
tree | 119be6ea5c41a4535809381b71710be82e348cac /python/pyspark/mllib | |
parent | a95252823e09939b654dd425db38dadc4100bc87 (diff) | |
download | spark-00ad4f054cd044e17d29b7c2c62efd8616462619.tar.gz spark-00ad4f054cd044e17d29b7c2c62efd8616462619.tar.bz2 spark-00ad4f054cd044e17d29b7c2c62efd8616462619.zip |
[SPARK-14900][ML][PYSPARK] Add accuracy and deprecate precison,recall,f1
## 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.
Diffstat (limited to 'python/pyspark/mllib')
-rw-r--r-- | python/pyspark/mllib/evaluation.py | 18 |
1 files changed, 18 insertions, 0 deletions
diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py index 5f32f092c7..2eaac87b67 100644 --- a/python/pyspark/mllib/evaluation.py +++ b/python/pyspark/mllib/evaluation.py @@ -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): |