Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/19185#discussion_r138048004
--- Diff: python/pyspark/ml/tests.py ---
@@ -1478,6 +1478,40 @@ def test_logistic_regression_summary(self):
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.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)
--- End diff --
We need to add these check for the above
```test_logistic_regression_summary``` and rename it to
```test_binary_logistic_regression_summary```, since binary logistic regression
summary has these variables as well.
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