[SPARK-21310][ML][PYSPARK] Expose offset in PySpark

## What changes were proposed in this pull request?
Add offset to PySpark in GLM as in #16699.

## How was this patch tested?
Python test

Author: actuaryzhang <[email protected]>

Closes #18534 from actuaryzhang/pythonOffset.

Author: actuaryzhang

python/pyspark/ml/regression.py

@@ -1376,17 +1376,20 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
                       typeConverter=TypeConverters.toFloat)
     solver = Param(Params._dummy(), "solver", "The solver algorithm for optimization. Supported " +
                    "options: irls.", typeConverter=TypeConverters.toString)
+    offsetCol = Param(Params._dummy(), "offsetCol", "The offset column name. If this is not set " +
+                      "or empty, we treat all instance offsets as 0.0",
+                      typeConverter=TypeConverters.toString)
 
     @keyword_only
     def __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction",
                  family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,
                  regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None,
-                 variancePower=0.0, linkPower=None):
+                 variancePower=0.0, linkPower=None, offsetCol=None):
         """
         __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction", \
                  family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, \
                  regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, \
-                 variancePower=0.0, linkPower=None)
+                 variancePower=0.0, linkPower=None, offsetCol=None)
         """
         super(GeneralizedLinearRegression, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -1402,12 +1405,12 @@ def __init__(self, labelCol="label", featuresCol="features", predictionCol="pred
     def setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction",
                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,
                   regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None,
-                  variancePower=0.0, linkPower=None):
+                  variancePower=0.0, linkPower=None, offsetCol=None):
         """
         setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction", \
                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, \
                   regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None, \
-                  variancePower=0.0, linkPower=None)
+                  variancePower=0.0, linkPower=None, offsetCol=None)
         Sets params for generalized linear regression.
         """
         kwargs = self._input_kwargs
@@ -1486,6 +1489,20 @@ def getLinkPower(self):
         """
         return self.getOrDefault(self.linkPower)
 
+    @since("2.3.0")
+    def setOffsetCol(self, value):
+        """
+        Sets the value of :py:attr:`offsetCol`.
+        """
+        return self._set(offsetCol=value)
+
+    @since("2.3.0")
+    def getOffsetCol(self):
+        """
+        Gets the value of offsetCol or its default value.
+        """
+        return self.getOrDefault(self.offsetCol)
+
 
 class GeneralizedLinearRegressionModel(JavaModel, JavaPredictionModel, JavaMLWritable,
                                        JavaMLReadable):

python/pyspark/ml/tests.py

@@ -1291,6 +1291,20 @@ def test_tweedie_distribution(self):
         self.assertTrue(np.allclose(model2.coefficients.toArray(), [-0.6667, 0.5], atol=1E-4))
         self.assertTrue(np.isclose(model2.intercept, 0.6667, atol=1E-4))
 
+    def test_offset(self):
+
+        df = self.spark.createDataFrame(
+            [(0.2, 1.0, 2.0, Vectors.dense(0.0, 5.0)),
+             (0.5, 2.1, 0.5, Vectors.dense(1.0, 2.0)),
+             (0.9, 0.4, 1.0, Vectors.dense(2.0, 1.0)),
+             (0.7, 0.7, 0.0, Vectors.dense(3.0, 3.0))], ["label", "weight", "offset", "features"])
+
+        glr = GeneralizedLinearRegression(family="poisson", weightCol="weight", offsetCol="offset")
+        model = glr.fit(df)
+        self.assertTrue(np.allclose(model.coefficients.toArray(), [0.664647, -0.3192581],
+                                    atol=1E-4))
+        self.assertTrue(np.isclose(model.intercept, -1.561613, atol=1E-4))
+
 
 class FPGrowthTests(SparkSessionTestCase):
     def setUp(self):