[SPARK-29212][ML][PYSPARK] Add common classes without using JVM backend

python/pyspark/ml/__init__.py

@@ -19,13 +19,16 @@
 DataFrame-based machine learning APIs to let users quickly assemble and configure practical
 machine learning pipelines.
 """
-from pyspark.ml.base import Estimator, Model, Transformer, UnaryTransformer
+from pyspark.ml.base import Estimator, Model, Predictor, PredictionModel, \
+    Transformer, UnaryTransformer
 from pyspark.ml.pipeline import Pipeline, PipelineModel
 from pyspark.ml import classification, clustering, evaluation, feature, fpm, \
     image, pipeline, recommendation, regression, stat, tuning, util, linalg, param
 
 __all__ = [
-    "Transformer", "UnaryTransformer", "Estimator", "Model", "Pipeline", "PipelineModel",
+    "Transformer", "UnaryTransformer", "Estimator", "Model",
+    "Predictor", "PredictionModel",
+    "Pipeline", "PipelineModel",
     "classification", "clustering", "evaluation", "feature", "fpm", "image",
     "recommendation", "regression", "stat", "tuning", "util", "linalg", "param",
 ]

python/pyspark/ml/base.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 #
 
-from abc import ABCMeta, abstractmethod
+from abc import ABCMeta, abstractmethod, abstractproperty
 
 import copy
 import threading
@@ -246,3 +246,82 @@ def _transform(self, dataset):
         transformedDataset = dataset.withColumn(self.getOutputCol(),
                                                 transformUDF(dataset[self.getInputCol()]))
         return transformedDataset
+
+
+@inherit_doc
+class _PredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):
+    """
+    Params for :py:class:`Predictor` and :py:class:`PredictorModel`.
+
+    .. versionadded:: 3.0.0
+    """
+    pass
+
+
+@inherit_doc
+class Predictor(Estimator, _PredictorParams):
+    """
+    Estimator for prediction tasks (regression and classification).
+    """
+
+    __metaclass__ = ABCMeta
+
+    @since("3.0.0")
+    def setLabelCol(self, value):
+        """
+        Sets the value of :py:attr:`labelCol`.
+        """
+        return self._set(labelCol=value)
+
+    @since("3.0.0")
+    def setFeaturesCol(self, value):
+        """
+        Sets the value of :py:attr:`featuresCol`.
+        """
+        return self._set(featuresCol=value)
+
+    @since("3.0.0")
+    def setPredictionCol(self, value):
+        """
+        Sets the value of :py:attr:`predictionCol`.
+        """
+        return self._set(predictionCol=value)
+
+
+@inherit_doc
+class PredictionModel(Model, _PredictorParams):
+    """
+    Model for prediction tasks (regression and classification).
+    """
+
+    __metaclass__ = ABCMeta
+
+    @since("3.0.0")
+    def setFeaturesCol(self, value):
+        """
+        Sets the value of :py:attr:`featuresCol`.
+        """
+        return self._set(featuresCol=value)
+
+    @since("3.0.0")
+    def setPredictionCol(self, value):
+        """
+        Sets the value of :py:attr:`predictionCol`.
+        """
+        return self._set(predictionCol=value)
+
+    @abstractproperty
+    @since("2.1.0")
+    def numFeatures(self):
+        """
+        Returns the number of features the model was trained on. If unknown, returns -1
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    @since("3.0.0")
+    def predict(self, value):
+        """
+        Predict label for the given features.
+        """
+        raise NotImplementedError()