[SPARK-20764][ML][PYSPARK][FOLLOWUP] Fix visibility discrepancy with numInstances and degreesOfFreedom in LR and GLR - Python version

## What changes were proposed in this pull request?
Add test cases for PR-18062

## How was this patch tested?
The existing UT

Author: Peng <[email protected]>

Closes #18068 from mpjlu/moreTest.

Author: Peng

python/pyspark/ml/tests.py

@@ -1066,6 +1066,7 @@ def test_linear_regression_summary(self):
         self.assertAlmostEqual(s.r2, 1.0, 2)
         self.assertTrue(isinstance(s.residuals, DataFrame))
         self.assertEqual(s.numInstances, 2)
+        self.assertEqual(s.degreesOfFreedom, 1)
         devResiduals = s.devianceResiduals
         self.assertTrue(isinstance(devResiduals, list) and isinstance(devResiduals[0], float))
         coefStdErr = s.coefficientStandardErrors
@@ -1075,7 +1076,8 @@ def test_linear_regression_summary(self):
         pValues = s.pValues
         self.assertTrue(isinstance(pValues, list) and isinstance(pValues[0], float))
         # 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
+        # one check is enough to verify a summary is returned
+        # The child class LinearRegressionTrainingSummary runs full test
         sameSummary = model.evaluate(df)
         self.assertAlmostEqual(sameSummary.explainedVariance, s.explainedVariance)
 
@@ -1093,6 +1095,7 @@ def test_glr_summary(self):
         self.assertEqual(s.numIterations, 1)  # this should default to a single iteration of WLS
         self.assertTrue(isinstance(s.predictions, DataFrame))
         self.assertEqual(s.predictionCol, "prediction")
+        self.assertEqual(s.numInstances, 2)
         self.assertTrue(isinstance(s.residuals(), DataFrame))
         self.assertTrue(isinstance(s.residuals("pearson"), DataFrame))
         coefStdErr = s.coefficientStandardErrors
@@ -1111,7 +1114,8 @@ def test_glr_summary(self):
         self.assertTrue(isinstance(s.nullDeviance, float))
         self.assertTrue(isinstance(s.dispersion, float))
         # 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
+        # one check is enough to verify a summary is returned
+        # The child class GeneralizedLinearRegressionTrainingSummary runs full test
         sameSummary = model.evaluate(df)
         self.assertAlmostEqual(sameSummary.deviance, s.deviance)