[SPARK-22810][ML][PySpark] Expose Python API for LinearRegression with huber loss.

python/pyspark/ml/param/_shared_params_code_gen.py

@@ -154,7 +154,8 @@ def get$Name(self):
         ("aggregationDepth", "suggested depth for treeAggregate (>= 2).", "2",
          "TypeConverters.toInt"),
         ("parallelism", "the number of threads to use when running parallel algorithms (>= 1).",
-         "1", "TypeConverters.toInt")]
+         "1", "TypeConverters.toInt"),
+        ("loss", "the loss function to be optimized.", None, "TypeConverters.toString")]
 
     code = []
     for name, doc, defaultValueStr, typeConverter in shared:

python/pyspark/ml/param/shared.py

@@ -632,6 +632,29 @@ def getParallelism(self):
         return self.getOrDefault(self.parallelism)
 
 
+class HasLoss(Params):
+    """
+    Mixin for param loss: the loss function to be optimized.
+    """
+
+    loss = Param(Params._dummy(), "loss", "the loss function to be optimized.", typeConverter=TypeConverters.toString)
+
+    def __init__(self):
+        super(HasLoss, self).__init__()
+
+    def setLoss(self, value):
+        """
+        Sets the value of :py:attr:`loss`.
+        """
+        return self._set(loss=value)
+
+    def getLoss(self):
+        """
+        Gets the value of loss or its default value.
+        """
+        return self.getOrDefault(self.loss)
+
+
 class DecisionTreeParams(Params):
     """
     Mixin for Decision Tree parameters.

python/pyspark/ml/regression.py

@@ -39,23 +39,26 @@
 @inherit_doc
 class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                        HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
-                       HasStandardization, HasSolver, HasWeightCol, HasAggregationDepth,
+                       HasStandardization, HasSolver, HasWeightCol, HasAggregationDepth, HasLoss,
                        JavaMLWritable, JavaMLReadable):
     """
     Linear regression.
 
-    The learning objective is to minimize the squared error, with regularization.
-    The specific squared error loss function used is: L = 1/2n ||A coefficients - y||^2^
+    The learning objective is to minimize the specified loss function, with regularization.
+    This supports two kinds of loss:
 
-    This supports multiple types of regularization:
-
-     * none (a.k.a. ordinary least squares)
+    * squaredError (a.k.a squared loss)
+    * huber (a hybrid of squared error for relatively small errors and absolute error for \
+    relatively large ones, and we estimate the scale parameter from training data)
 
-     * L2 (ridge regression)
+    This supports multiple types of regularization:
 
-     * L1 (Lasso)
+    * none (a.k.a. ordinary least squares)
+    * L2 (ridge regression)
+    * L1 (Lasso)
+    * L2 + L1 (elastic net)
 
-     * L2 + L1 (elastic net)
+    Note: Fitting with huber loss only supports none and L2 regularization.
 
     >>> from pyspark.ml.linalg import Vectors
     >>> df = spark.createDataFrame([
@@ -98,31 +101,42 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     solver = Param(Params._dummy(), "solver", "The solver algorithm for optimization. Supported " +
                    "options: auto, normal, l-bfgs.", typeConverter=TypeConverters.toString)
 
+    loss = Param(Params._dummy(), "loss", "The loss function to be optimized. Supported " +
+                 "options: squaredError, huber.", typeConverter=TypeConverters.toString)
+
+    epsilon = Param(Params._dummy(), "epsilon", "The shape parameter to control the amount of " +
+                    "robustness. Must be > 1.0. Only valid when loss is huber",
+                    typeConverter=TypeConverters.toFloat)
+
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 standardization=True, solver="auto", weightCol=None, aggregationDepth=2):
+                 standardization=True, solver="auto", weightCol=None, aggregationDepth=2,
+                 loss="squaredError", epsilon=1.35):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 standardization=True, solver="auto", weightCol=None, aggregationDepth=2)
+                 standardization=True, solver="auto", weightCol=None, aggregationDepth=2, \
+                 loss="squaredError", epsilon=1.35)
         """
         super(LinearRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.regression.LinearRegression", self.uid)
-        self._setDefault(maxIter=100, regParam=0.0, tol=1e-6)
+        self._setDefault(maxIter=100, regParam=0.0, tol=1e-6, loss="squaredError", epsilon=1.35)
         kwargs = self._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
     @since("1.4.0")
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  standardization=True, solver="auto", weightCol=None, aggregationDepth=2):
+                  standardization=True, solver="auto", weightCol=None, aggregationDepth=2,
+                  loss="squaredError", epsilon=1.35):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                  standardization=True, solver="auto", weightCol=None, aggregationDepth=2)
+                  standardization=True, solver="auto", weightCol=None, aggregationDepth=2, \
+                  loss="squaredError", epsilon=1.35)
         Sets params for linear regression.
         """
         kwargs = self._input_kwargs
@@ -131,6 +145,20 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
     def _create_model(self, java_model):
         return LinearRegressionModel(java_model)
 
+    @since("2.3.0")
+    def setEpsilon(self, value):
+        """
+        Sets the value of :py:attr:`epsilon`.
+        """
+        return self._set(epsilon=value)
+
+    @since("2.3.0")
+    def getEpsilon(self):
+        """
+        Gets the value of epsilon or its default value.
+        """
+        return self.getOrDefault(self.epsilon)
+
 
 class LinearRegressionModel(JavaModel, JavaPredictionModel, JavaMLWritable, JavaMLReadable):
     """
@@ -155,6 +183,14 @@ def intercept(self):
         """
         return self._call_java("intercept")
 
+    @property
+    @since("2.3.0")
+    def scale(self):
+        """
+        The value by which \|y - X'w\| is scaled down when loss is "huber", otherwise 1.0.
+        """
+        return self._call_java("scale")
+
     @property
     @since("2.0.0")
     def summary(self):

python/pyspark/ml/tests.py

@@ -1725,6 +1725,27 @@ def test_offset(self):
         self.assertTrue(np.isclose(model.intercept, -1.561613, atol=1E-4))
 
 
+class LinearRegressionTest(SparkSessionTestCase):
+
+    def test_linear_regression_with_huber_loss(self):
+
+        data_path = "data/mllib/sample_linear_regression_data.txt"
+        df = self.spark.read.format("libsvm").load(data_path)
+
+        lir = LinearRegression(loss="huber", epsilon=2.0)
+        model = lir.fit(df)
+
+        expectedCoefficients = [0.136, 0.7648, -0.7761, 2.4236, 0.537,
+                                1.2612, -0.333, -0.5694, -0.6311, 0.6053]
+        expectedIntercept = 0.1607
+        expectedScale = 9.758
+
+        self.assertTrue(
+            np.allclose(model.coefficients.toArray(), expectedCoefficients, atol=1E-3))
+        self.assertTrue(np.isclose(model.intercept, expectedIntercept, atol=1E-3))
+        self.assertTrue(np.isclose(model.scale, expectedScale, atol=1E-3))
+
+
 class LogisticRegressionTest(SparkSessionTestCase):
 
     def test_binomial_logistic_regression_with_bound(self):