Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/19185#discussion_r138792851
--- Diff: python/pyspark/ml/tests.py ---
@@ -1464,20 +1464,79 @@ def test_logistic_regression_summary(self):
self.assertEqual(s.probabilityCol, "probability")
self.assertEqual(s.labelCol, "label")
self.assertEqual(s.featuresCol, "features")
+ self.assertEqual(s.predictionCol, "prediction")
objHist = s.objectiveHistory
self.assertTrue(isinstance(objHist, list) and
isinstance(objHist[0], float))
self.assertGreater(s.totalIterations, 0)
+ self.assertTrue(isinstance(s.labels, list))
+ self.assertTrue(isinstance(s.truePositiveRateByLabel, list))
+ self.assertTrue(isinstance(s.falsePositiveRateByLabel, list))
+ self.assertTrue(isinstance(s.precisionByLabel, list))
+ self.assertTrue(isinstance(s.recallByLabel, list))
+ self.assertTrue(isinstance(s.fMeasureByLabel(), list))
+ self.assertTrue(isinstance(s.fMeasureByLabel(1.0), list))
self.assertTrue(isinstance(s.roc, DataFrame))
self.assertAlmostEqual(s.areaUnderROC, 1.0, 2)
self.assertTrue(isinstance(s.pr, DataFrame))
self.assertTrue(isinstance(s.fMeasureByThreshold, DataFrame))
self.assertTrue(isinstance(s.precisionByThreshold, DataFrame))
self.assertTrue(isinstance(s.recallByThreshold, DataFrame))
+ self.assertAlmostEqual(s.accuracy, 1.0, 2)
+ self.assertAlmostEqual(s.weightedTruePositiveRate, 1.0, 2)
+ self.assertAlmostEqual(s.weightedFalsePositiveRate, 0.0, 2)
+ self.assertAlmostEqual(s.weightedRecall, 1.0, 2)
+ self.assertAlmostEqual(s.weightedPrecision, 1.0, 2)
+ self.assertAlmostEqual(s.weightedFMeasure(), 1.0, 2)
+ self.assertAlmostEqual(s.weightedFMeasure(1.0), 1.0, 2)
# 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.assertAlmostEqual(sameSummary.areaUnderROC, s.areaUnderROC)
+ def test_multiclass_logistic_regression_summary(self):
+ df = self.spark.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
+ (0.0, 2.0, Vectors.sparse(1, [],
[])),
+ (2.0, 2.0, Vectors.dense(2.0)),
+ (2.0, 2.0, Vectors.dense(1.9))],
+ ["label", "weight", "features"])
+ lr = LogisticRegression(maxIter=5, regParam=0.01,
weightCol="weight", fitIntercept=False)
+ model = lr.fit(df)
+ self.assertTrue(model.hasSummary)
+ s = model.summary
+ # test that api is callable and returns expected types
+ self.assertTrue(isinstance(s.predictions, DataFrame))
+ self.assertEqual(s.probabilityCol, "probability")
+ self.assertEqual(s.labelCol, "label")
+ self.assertEqual(s.featuresCol, "features")
+ self.assertEqual(s.predictionCol, "prediction")
+ objHist = s.objectiveHistory
+ self.assertTrue(isinstance(objHist, list) and
isinstance(objHist[0], float))
+ self.assertGreater(s.totalIterations, 0)
+ self.assertTrue(isinstance(s.labels, list))
+ self.assertTrue(isinstance(s.truePositiveRateByLabel, list))
+ self.assertTrue(isinstance(s.falsePositiveRateByLabel, list))
+ self.assertTrue(isinstance(s.precisionByLabel, list))
+ self.assertTrue(isinstance(s.recallByLabel, list))
+ self.assertTrue(isinstance(s.fMeasureByLabel(), list))
+ self.assertTrue(isinstance(s.fMeasureByLabel(1.0), list))
+ self.assertAlmostEqual(s.accuracy, 0.75, 2)
+ self.assertAlmostEqual(s.weightedTruePositiveRate, 0.75, 2)
+ self.assertAlmostEqual(s.weightedFalsePositiveRate, 0.25, 2)
+ self.assertAlmostEqual(s.weightedRecall, 0.75, 2)
+ self.assertAlmostEqual(s.weightedPrecision, 0.583, 2)
+ self.assertAlmostEqual(s.weightedFMeasure(), 0.65, 2)
+ self.assertAlmostEqual(s.weightedFMeasure(1.0), 0.65, 2)
+ # 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.assertAlmostEqual(sameSummary.accuracy, s.accuracy)
--- End diff --
Nit: Like mentioned in annotation, one check is enough to verify a summary
is returned, let's remove others to simplify the test. Thanks.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
