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[SPARK-31681][ML][PySpark] Python multiclass logistic regression evaluate should return LogisticRegressionSummary #28503

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[SPARK-31681][ML][PySpark] Python multiclass logistic regression evaluate should return LogisticRegressionSummary #28503

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[SPARK-31681][ML][PySpark] Python multiclass logistic regression eval…
huaxingao May 11, 2020
5f7c160
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huaxingao May 11, 2020
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5 changes: 4 additions & 1 deletion python/pyspark/ml/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -932,7 +932,10 @@ def evaluate(self, dataset):
if not isinstance(dataset, DataFrame):
raise ValueError("dataset must be a DataFrame but got %s." % type(dataset))
java_blr_summary = self._call_java("evaluate", dataset)
return BinaryLogisticRegressionSummary(java_blr_summary)
if self.numClasses <= 2:
return BinaryLogisticRegressionSummary(java_blr_summary)
else:
return LogisticRegressionSummary(java_blr_summary)


class LogisticRegressionSummary(JavaWrapper):
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6 changes: 5 additions & 1 deletion python/pyspark/ml/tests/test_training_summary.py
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Expand Up @@ -21,7 +21,8 @@
if sys.version > '3':
basestring = str

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.classification import BinaryLogisticRegressionSummary, LogisticRegression, \
LogisticRegressionSummary
from pyspark.ml.clustering import BisectingKMeans, GaussianMixture, KMeans
from pyspark.ml.linalg import Vectors
from pyspark.ml.regression import GeneralizedLinearRegression, LinearRegression
Expand Down Expand Up @@ -149,6 +150,7 @@ def test_binary_logistic_regression_summary(self):
# test evaluation (with training dataset) produces a summary with same values
# one check is enough to verify a summary is returned, Scala version runs full test
sameSummary = model.evaluate(df)
self.assertTrue(isinstance(sameSummary, BinaryLogisticRegressionSummary))
self.assertAlmostEqual(sameSummary.areaUnderROC, s.areaUnderROC)

def test_multiclass_logistic_regression_summary(self):
Expand Down Expand Up @@ -187,6 +189,8 @@ def test_multiclass_logistic_regression_summary(self):
# test evaluation (with training dataset) produces a summary with same values
# one check is enough to verify a summary is returned, Scala version runs full test
sameSummary = model.evaluate(df)
self.assertTrue(isinstance(sameSummary, LogisticRegressionSummary))
self.assertFalse(isinstance(sameSummary, BinaryLogisticRegressionSummary))
self.assertAlmostEqual(sameSummary.accuracy, s.accuracy)

def test_gaussian_mixture_summary(self):
Expand Down