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-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): |