[SPARK-30378][ML][PYSPARK] Add getter/setter in Python FM

### What changes were proposed in this pull request?
add getter/setter in Python FM

### Why are the changes needed?
to be consistent with other algorithms

### Does this PR introduce any user-facing change?
Yes.
add getter/setter in Python FMRegressor/FMRegressionModel/FMClassifier/FMClassificationModel

### How was this patch tested?
doctest

Closes #27044 from huaxingao/spark-30378.

Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>

python/pyspark/ml/classification.py

@@ -25,7 +25,7 @@ from pyspark.ml.param.shared import *
 from pyspark.ml.tree import _DecisionTreeModel, _DecisionTreeParams, \
     _TreeEnsembleModel, _RandomForestParams, _GBTParams, \
     _HasVarianceImpurity, _TreeClassifierParams, _TreeEnsembleParams
-from pyspark.ml.regression import DecisionTreeRegressionModel
+from pyspark.ml.regression import _FactorizationMachinesParams, DecisionTreeRegressionModel
 from pyspark.ml.util import *
 from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams, \
     JavaPredictor, _JavaPredictorParams, JavaPredictionModel, JavaWrapper
@@ -2765,8 +2765,8 @@ class OneVsRestModel(Model, _OneVsRestParams, JavaMLReadable, JavaMLWritable):
 
 
 @inherit_doc
-class FMClassifier(JavaProbabilisticClassifier, HasMaxIter, HasStepSize, HasTol, HasSolver,
-                   HasSeed, HasFitIntercept, HasRegParam, JavaMLWritable, JavaMLReadable):
+class FMClassifier(JavaProbabilisticClassifier, _FactorizationMachinesParams, JavaMLWritable,
+                   JavaMLReadable):
     """
     Factorization Machines learning algorithm for classification.
 
@@ -2780,8 +2780,12 @@ class FMClassifier(JavaProbabilisticClassifier, HasMaxIter, HasStepSize, HasTol,
     >>> df = spark.createDataFrame([
     ...     (1.0, Vectors.dense(1.0)),
     ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
-    >>> fm = FMClassifier(factorSize=2, seed=11)
+    >>> fm = FMClassifier(factorSize=2)
+    >>> fm.setSeed(11)
+    FMClassifier...
     >>> model = fm.fit(df)
+    >>> model.getMaxIter()
+    100
     >>> test0 = spark.createDataFrame([
     ...     (Vectors.dense(-1.0),),
     ...     (Vectors.dense(0.5),),
@@ -2895,8 +2899,58 @@ class FMClassifier(JavaProbabilisticClassifier, HasMaxIter, HasStepSize, HasTol,
         """
         return self._set(initStd=value)
 
+    @since("3.0.0")
+    def setMaxIter(self, value):
+        """
+        Sets the value of :py:attr:`maxIter`.
+        """
+        return self._set(maxIter=value)
 
-class FMClassificationModel(JavaProbabilisticClassificationModel, JavaMLWritable, JavaMLReadable):
+    @since("3.0.0")
+    def setStepSize(self, value):
+        """
+        Sets the value of :py:attr:`stepSize`.
+        """
+        return self._set(stepSize=value)
+
+    @since("3.0.0")
+    def setTol(self, value):
+        """
+        Sets the value of :py:attr:`tol`.
+        """
+        return self._set(tol=value)
+
+    @since("3.0.0")
+    def setSolver(self, value):
+        """
+        Sets the value of :py:attr:`solver`.
+        """
+        return self._set(solver=value)
+
+    @since("3.0.0")
+    def setSeed(self, value):
+        """
+        Sets the value of :py:attr:`seed`.
+        """
+        return self._set(seed=value)
+
+    @since("3.0.0")
+    def setFitIntercept(self, value):
+        """
+        Sets the value of :py:attr:`fitIntercept`.
+        """
+        return self._set(fitIntercept=value)
+
+    @since("3.0.0")
+    def setRegParam(self, value):
+        """
+        Sets the value of :py:attr:`regParam`.
+        """
+        return self._set(regParam=value)
+
+
+class FMClassificationModel(JavaProbabilisticClassificationModel, _FactorizationMachinesParams,
+                            JavaMLWritable, JavaMLReadable):
     """
     Model fitted by :class:`FMClassifier`.
 

python/pyspark/ml/regression.py

@@ -2297,9 +2297,63 @@ class GeneralizedLinearRegressionTrainingSummary(GeneralizedLinearRegressionSumm
         return self._call_java("toString")
 
 
+class _FactorizationMachinesParams(_JavaPredictorParams, HasMaxIter, HasStepSize, HasTol,
+                                   HasSolver, HasSeed, HasFitIntercept, HasRegParam):
+    """
+    Params for :py:class:`FMRegressor`, :py:class:`FMRegressionModel`, :py:class:`FMClassifier`
+    and :py:class:`FMClassifierModel`.
+
+    .. versionadded:: 3.0.0
+    """
+
+    factorSize = Param(Params._dummy(), "factorSize", "Dimensionality of the factor vectors, " +
+                       "which are used to get pairwise interactions between variables",
+                       typeConverter=TypeConverters.toInt)
+
+    fitLinear = Param(Params._dummy(), "fitLinear", "whether to fit linear term (aka 1-way term)",
+                      typeConverter=TypeConverters.toBoolean)
+
+    miniBatchFraction = Param(Params._dummy(), "miniBatchFraction", "fraction of the input data " +
+                              "set that should be used for one iteration of gradient descent",
+                              typeConverter=TypeConverters.toFloat)
+
+    initStd = Param(Params._dummy(), "initStd", "standard deviation of initial coefficients",
+                    typeConverter=TypeConverters.toFloat)
+
+    solver = Param(Params._dummy(), "solver", "The solver algorithm for optimization. Supported " +
+                   "options: gd, adamW. (Default adamW)", typeConverter=TypeConverters.toString)
+
+    @since("3.0.0")
+    def getFactorSize(self):
+        """
+        Gets the value of factorSize or its default value.
+        """
+        return self.getOrDefault(self.factorSize)
+
+    @since("3.0.0")
+    def getFitLinear(self):
+        """
+        Gets the value of fitLinear or its default value.
+        """
+        return self.getOrDefault(self.fitLinear)
+
+    @since("3.0.0")
+    def getMiniBatchFraction(self):
+        """
+        Gets the value of miniBatchFraction or its default value.
+        """
+        return self.getOrDefault(self.miniBatchFraction)
+
+    @since("3.0.0")
+    def getInitStd(self):
+        """
+        Gets the value of initStd or its default value.
+        """
+        return self.getOrDefault(self.initStd)
+
+
 @inherit_doc
-class FMRegressor(JavaPredictor, HasMaxIter, HasStepSize, HasTol, HasSolver, HasSeed,
-                  HasFitIntercept, HasRegParam, JavaMLWritable, JavaMLReadable):
+class FMRegressor(JavaPredictor, _FactorizationMachinesParams, JavaMLWritable, JavaMLReadable):
     """
     Factorization Machines learning algorithm for regression.
 
@@ -2315,8 +2369,12 @@ class FMRegressor(JavaPredictor, HasMaxIter, HasStepSize, HasTol, HasSolver, Has
     ...     (1.0, Vectors.dense(1.0)),
     ...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
     >>>
-    >>> fm = FMRegressor(factorSize=2, seed=16)
+    >>> fm = FMRegressor(factorSize=2)
+    >>> fm.setSeed(16)
+    FMRegressor...
     >>> model = fm.fit(df)
+    >>> model.getMaxIter()
+    100
     >>> test0 = spark.createDataFrame([
     ...     (Vectors.dense(-2.0),),
     ...     (Vectors.dense(0.5),),
@@ -2426,8 +2484,58 @@ class FMRegressor(JavaPredictor, HasMaxIter, HasStepSize, HasTol, HasSolver, Has
         """
         return self._set(initStd=value)
 
+    @since("3.0.0")
+    def setMaxIter(self, value):
+        """
+        Sets the value of :py:attr:`maxIter`.
+        """
+        return self._set(maxIter=value)
 
-class FMRegressionModel(JavaPredictionModel, JavaMLWritable, JavaMLReadable):
+    @since("3.0.0")
+    def setStepSize(self, value):
+        """
+        Sets the value of :py:attr:`stepSize`.
+        """
+        return self._set(stepSize=value)
+
+    @since("3.0.0")
+    def setTol(self, value):
+        """
+        Sets the value of :py:attr:`tol`.
+        """
+        return self._set(tol=value)
+
+    @since("3.0.0")
+    def setSolver(self, value):
+        """
+        Sets the value of :py:attr:`solver`.
+        """
+        return self._set(solver=value)
+
+    @since("3.0.0")
+    def setSeed(self, value):
+        """
+        Sets the value of :py:attr:`seed`.
+        """
+        return self._set(seed=value)
+
+    @since("3.0.0")
+    def setFitIntercept(self, value):
+        """
+        Sets the value of :py:attr:`fitIntercept`.
+        """
+        return self._set(fitIntercept=value)
+
+    @since("3.0.0")
+    def setRegParam(self, value):
+        """
+        Sets the value of :py:attr:`regParam`.
+        """
+        return self._set(regParam=value)
+
+
+class FMRegressionModel(JavaPredictionModel, _FactorizationMachinesParams, JavaMLWritable,
+                        JavaMLReadable):
     """
     Model fitted by :class:`FMRegressor`.