Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8067#discussion_r39142512
--- Diff: python/pyspark/ml/classification.py ---
@@ -808,6 +809,118 @@ def theta(self):
return self._call_java("theta")
+@inherit_doc
+class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol,
HasLabelCol, HasPredictionCol,
+ HasMaxIter, HasTol, HasSeed):
+ """
+ Classifier trainer based on the Multilayer Perceptron.
+ Each layer has sigmoid activation function, output layer has softmax.
+ Number of inputs has to be equal to the size of feature vectors.
+ Number of outputs has to be equal to the total number of labels.
+
+ >>> from pyspark.sql import Row
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sc.parallelize([
+ ... Row(label=0.0, features=Vectors.dense([0.0, 0.0])),
+ ... Row(label=1.0, features=Vectors.dense([0.0, 1.0])),
+ ... Row(label=1.0, features=Vectors.dense([1.0, 0.0])),
+ ... Row(label=0.0, features=Vectors.dense([1.0, 1.0]))]).toDF()
+ >>> myLayers = [2, 5, 2]
+ >>> mlp = MultilayerPerceptronClassifier(maxIter=100, layers=myLayers,
blockSize=1, seed=11)
+ >>> model = mlp.fit(df)
+ >>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0,
0.0]))]).toDF()
+ >>> model.transform(test0).head().prediction
+ 1.0
+ >>> test1 = sc.parallelize([Row(features=Vectors.dense([0.0,
0.0]))]).toDF()
+ >>> model.transform(test1).head().prediction
+ 0.0
+ """
+
+ # a placeholder to make it appear in the generated doc
+ layers = Param(Params._dummy(), "layers", "Sizes of layers from input
layer to output layer " +
+ "E.g., Array(780, 100, 10) means 780 inputs, one hidden
layer with 100 " +
+ "neurons and output layer of 10 neurons, default is [1,
1].")
+ blockSize = Param(Params._dummy(), "blockSize", "Block size for
stacking input data in " +
+ "matrices. Data is stacked within partitions. If
block size is more than " +
+ "remaining data in a partition then it is adjusted
to the size of this " +
+ "data. Recommended size is between 10 and 1000,
default is 128.")
+
+ @keyword_only
+ def __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ maxIter=100, tol=1e-4, seed=None, layers=[1, 1],
blockSize=128):
+ """
+ __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ maxIter=100, tol=1e-4, seed=None, layers=[1, 1],
blockSize=128)
+ """
+ super(MultilayerPerceptronClassifier, self).__init__()
+ self._java_obj = self._new_java_obj(
+
"org.apache.spark.ml.classification.MultilayerPerceptronClassifier", self.uid)
+ self.layers = Param(self, "layers", "Sizes of layers from input
layer to output layer " +
+ "E.g., Array(780, 100, 10) means 780 inputs,
one hidden layer with " +
+ "100 neurons and output layer of 10 neurons,
default is [1, 1].")
+ self.blockSize = Param(self, "blockSize", "Block size for stacking
input data in " +
+ "matrices. Data is stacked within
partitions. If block size is " +
+ "more than remaining data in a partition
then it is adjusted to " +
+ "the size of this data. Recommended size is
between 10 and 1000, " +
+ "default is 128.")
+ self._setDefault(maxIter=100, tol=1E-4, layers=[1, 1],
blockSize=128)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ maxIter=100, tol=1e-4, seed=None, layers=[1, 1],
blockSize=128):
+ """
+ setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ maxIter=100, tol=1e-4, seed=None, layers=[1, 1],
blockSize=128)
+ Sets params for MultilayerPerceptronClassifier.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def _create_model(self, java_model):
+ return MultilayerPerceptronClassificationModel(java_model)
+
+ def setLayers(self, value):
+ """
+ Sets the value of :py:attr:`layers`.
+ """
+ self._paramMap[self.layers] = value
+ return self
+
+ def getLayers(self):
+ """
+ Gets the value of layers or its default value.
+ """
+ return self.getOrDefault(self.layers)
+
+ def setBlockSize(self, value):
+ """
+ Sets the value of :py:attr:`blockSize`.
+ """
+ self._paramMap[self.blockSize] = value
+ return self
+
+ def getBlockSize(self):
+ """
+ Gets the value of blockSize or its default value.
+ """
+ return self.getOrDefault(self.blockSize)
+
+
+class MultilayerPerceptronClassificationModel(JavaModel):
+ """
+ Model fitted by MultilayerPerceptronClassifier.
+ """
+
--- End diff --
Agree, done.
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