test on multiclass summary

python/pyspark/ml/classification.py

@@ -529,10 +529,9 @@ def summary(self):
         """
         if self.hasSummary:
             java_blrt_summary = self._call_java("summary")
-            java_blrt_interceptVector = self._call_java("interceptVector")
             java_blrt_numClasses = self._call_java("numClasses")
-            java_blrt_binarysummary = self._call_java("binarySummary")
-            if (len(java_blrt_interceptVector) == 1):
+            if (java_blrt_numClasses == 2):
+                java_blrt_binarysummary = self._call_java("binarySummary")
                 return BinaryLogisticRegressionTrainingSummary(java_blrt_binarysummary)
             else:
                 return LogisticRegressionTrainingSummary(java_blrt_summary)

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)
+        # 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
+
     def test_gaussian_mixture_summary(self):
         data = [(Vectors.dense(1.0),), (Vectors.dense(5.0),), (Vectors.dense(10.0),),
                 (Vectors.sparse(1, [], []),)]