[SPARK-7619] [PYTHON] fix docstring signature

Just realized that we need `\` at the end of the docstring. brkyvz

Author: Xiangrui Meng <[email protected]>

Closes apache#6161 from mengxr/SPARK-7619 and squashes the following commits:

e44495f [Xiangrui Meng] fix docstring signature

Author: mengxr

python/docs/pyspark.ml.rst

@@ -1,16 +1,16 @@
 pyspark.ml package
-=====================
+==================
 
 ML Pipeline APIs
---------------
+----------------
 
 .. automodule:: pyspark.ml
     :members:
     :undoc-members:
     :inherited-members:
 
 pyspark.ml.param module
--------------------------
+-----------------------
 
 .. automodule:: pyspark.ml.param
     :members:
@@ -34,31 +34,31 @@ pyspark.ml.classification module
     :inherited-members:
 
 pyspark.ml.recommendation module
--------------------------
+--------------------------------
 
 .. automodule:: pyspark.ml.recommendation
     :members:
     :undoc-members:
     :inherited-members:
 
 pyspark.ml.regression module
--------------------------
+----------------------------
 
 .. automodule:: pyspark.ml.regression
     :members:
     :undoc-members:
     :inherited-members:
 
 pyspark.ml.tuning module
---------------------------------
+------------------------
 
 .. automodule:: pyspark.ml.tuning
     :members:
     :undoc-members:
     :inherited-members:
 
 pyspark.ml.evaluation module
---------------------------------
+----------------------------
 
 .. automodule:: pyspark.ml.evaluation
     :members:

python/pyspark/ml/classification.py

@@ -71,7 +71,7 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  threshold=0.5, probabilityCol="probability"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                 maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+                 maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                  threshold=0.5, probabilityCol="probability")
         """
         super(LogisticRegression, self).__init__()
@@ -96,8 +96,8 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                   threshold=0.5, probabilityCol="probability"):
         """
-        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                  threshold=0.5, probabilityCol="probability")
         Sets params for logistic regression.
         """
@@ -220,7 +220,7 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
+                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
         """
         super(DecisionTreeClassifier, self).__init__()
@@ -242,9 +242,8 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   impurity="gini"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
-                  impurity="gini")
+                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini")
         Sets params for the DecisionTreeClassifier.
         """
         kwargs = self.setParams._input_kwargs
@@ -320,9 +319,9 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini",
                  numTrees=20, featureSubsetStrategy="auto", seed=42):
         """
-        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini",
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini", \
                  numTrees=20, featureSubsetStrategy="auto", seed=42)
         """
         super(RandomForestClassifier, self).__init__()
@@ -355,9 +354,9 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
                   impurity="gini", numTrees=20, featureSubsetStrategy="auto"):
         """
-        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42, \
                   impurity="gini", numTrees=20, featureSubsetStrategy="auto")
         Sets params for linear classification.
         """
@@ -471,10 +470,10 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="logistic",
                  maxIter=20, stepSize=0.1):
         """
-        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="logistic",
-                 maxIter=20, stepSize=0.1)
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
+                 lossType="logistic", maxIter=20, stepSize=0.1)
         """
         super(GBTClassifier, self).__init__()
         #: param for Loss function which GBT tries to minimize (case-insensitive).
@@ -502,9 +501,9 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                   lossType="logistic", maxIter=20, stepSize=0.1):
         """
-        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
                   lossType="logistic", maxIter=20, stepSize=0.1)
         Sets params for Gradient Boosted Tree Classification.
         """

python/pyspark/ml/feature.py

@@ -481,7 +481,7 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
     def __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
                  inputCol=None, outputCol=None):
         """
-        __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
+        __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", \
                  inputCol=None, outputCol=None)
         """
         super(RegexTokenizer, self).__init__()
@@ -496,7 +496,7 @@ def __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+"
     def setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
                   inputCol=None, outputCol=None):
         """
-        setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
+        setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+", \
                   inputCol="input", outputCol="output")
         Sets params for this RegexTokenizer.
         """
@@ -869,7 +869,7 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
     def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
                  seed=42, inputCol=None, outputCol=None):
         """
-        __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
+        __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, \
                  seed=42, inputCol=None, outputCol=None)
         """
         super(Word2Vec, self).__init__()
@@ -889,7 +889,7 @@ def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025,
     def setParams(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
                   seed=42, inputCol=None, outputCol=None):
         """
-        setParams(self, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=42,
+        setParams(self, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=42, \
                  inputCol=None, outputCol=None)
         Sets params for this Word2Vec.
         """

python/pyspark/ml/recommendation.py

@@ -92,8 +92,8 @@ def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemB
                  implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0,
                  ratingCol="rating", nonnegative=False, checkpointInterval=10):
         """
-        __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
-                 implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=0,
+        __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
+                 implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=0, \
                  ratingCol="rating", nonnegative=false, checkpointInterval=10)
         """
         super(ALS, self).__init__()
@@ -118,8 +118,8 @@ def setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItem
                   implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0,
                   ratingCol="rating", nonnegative=False, checkpointInterval=10):
         """
-        setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
-                 implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0,
+        setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
+                 implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0, \
                  ratingCol="rating", nonnegative=False, checkpointInterval=10)
         Sets params for ALS.
         """

python/pyspark/ml/regression.py

@@ -33,8 +33,7 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     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^
+    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)
@@ -191,7 +190,7 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
+                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
         """
         super(DecisionTreeRegressor, self).__init__()
@@ -213,9 +212,8 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   impurity="variance"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
-                  impurity="variance")
+                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")
         Sets params for the DecisionTreeRegressor.
         """
         kwargs = self.setParams._input_kwargs
@@ -286,10 +284,10 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance",
                  numTrees=20, featureSubsetStrategy="auto", seed=42):
         """
-        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance",
-                 numTrees=20, featureSubsetStrategy="auto", seed=42)
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
+                 impurity="variance", numTrees=20, featureSubsetStrategy="auto", seed=42)
         """
         super(RandomForestRegressor, self).__init__()
         #: param for Criterion used for information gain calculation (case-insensitive).
@@ -321,9 +319,9 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
                   impurity="variance", numTrees=20, featureSubsetStrategy="auto"):
         """
-        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42, \
                   impurity="variance", numTrees=20, featureSubsetStrategy="auto")
         Sets params for linear regression.
         """
@@ -432,10 +430,10 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared",
                  maxIter=20, stepSize=0.1):
         """
-        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared",
-                 maxIter=20, stepSize=0.1)
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
+                 lossType="squared", maxIter=20, stepSize=0.1)
         """
         super(GBTRegressor, self).__init__()
         #: param for Loss function which GBT tries to minimize (case-insensitive).
@@ -463,9 +461,9 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                   lossType="squared", maxIter=20, stepSize=0.1):
         """
-        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
                   lossType="squared", maxIter=20, stepSize=0.1)
         Sets params for Gradient Boosted Tree Regression.
         """