Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6016#discussion_r30112918
--- Diff: python/pyspark/ml/regression.py ---
@@ -0,0 +1,509 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark.ml.util import keyword_only
+from pyspark.ml.wrapper import JavaEstimator, JavaModel
+from pyspark.ml.param.shared import *
+from pyspark.mllib.common import inherit_doc
+
+
+__all__ = ['DecisionTreeRegressor', 'DecisionTreeRegressionModel',
'GBTRegressor', 'GBTModel',
+ 'LinearRegression', 'LinearRegressionModel',
'RandomForestRegressor',
+ 'RandomForestRegressionModel']
+
+
+@inherit_doc
+class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol, HasMaxIter,
+ HasRegParam, HasTol):
+ """
+ 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 weights - y||^2^
+
+ This support multiple types of regularization:
+ - none (a.k.a. ordinary least squares)
+ - L2 (ridge regression)
+ - L1 (Lasso)
+ - L2 + L1 (elastic net)
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sqlContext.createDataFrame([
+ ... (1.0, Vectors.dense(1.0)),
+ ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
+ >>> lr = LinearRegression(maxIter=5, regParam=0.0)
+ >>> model = lr.fit(df)
+ >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)],
["features"])
+ >>> model.transform(test0).head().prediction
+ -1.0
+ >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0],
[1.0]),)], ["features"])
+ >>> model.transform(test1).head().prediction
+ 1.0
+ >>> lr.setParams("vector")
+ Traceback (most recent call last):
+ ...
+ TypeError: Method setParams forces keyword arguments.
+ """
+ _java_class = "org.apache.spark.ml.regression.LinearRegression"
+ # a placeholder to make it appear in the generated doc
+ elasticNetParam = Param(Params._dummy(), "elasticNetParam",
+ "the ElasticNet mixing parameter, in range [0,
1]. For alpha = 0, " +
+ "the penalty is an L2 penalty. For alpha = 1,
it is an L1 penalty.")
+
+ @keyword_only
+ def __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
+ """
+ __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
+ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+ """
+ super(LinearRegression, self).__init__()
+ self.elasticNetParam = Param(self, "elasticNetParam",
+ "the ElasticNet mixing parameter, in
range [0, 1]. For " +
+ "alpha = 0, the penalty is an L2
penalty. For alpha = 1, " +
+ "it is an L1 penalty.")
+ self._setDefault(maxIter=100, regParam=0.0, elasticNetParam=0.0,
tol=1e-6)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ maxIter=100, regParam=0.0, elasticNetParam=0.0,
tol=1e-6):
+ """
+ setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
+ maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+ Sets params for linear regression.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def _create_model(self, java_model):
+ return LinearRegressionModel(java_model)
+
+ def setElasticNetParam(self, value):
+ """
+ Sets the value of :py:attr:`elasticNetParam`.
+ """
+ self.paramMap[self.elasticNetParam] = value
+ return self
+
+ def getElasticNetParam(self):
+ """
+ Gets the value of elasticNetParam or its default value.
+ """
+ return self.getOrDefault(self.elasticNetParam)
+
+
+class LinearRegressionModel(JavaModel):
+ """
+ Model fitted by LinearRegression.
+ """
+
+
+class TreeRegressorParams():
--- End diff --
`()` -> `(object)`
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