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[SPARK-7619] [PYTHON] fix docstring signature

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

Author: Xiangrui Meng <meng@databricks.com>

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

e44495f [Xiangrui Meng] fix docstring signature
This commit is contained in:
Xiangrui Meng 2015-05-14 18:16:22 -07:00
parent 723853edab
commit 48fc38f584
5 changed files with 52 additions and 55 deletions
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14
python/docs/pyspark.ml.rst
View file

@ -1,8 +1,8 @@
pyspark.ml package
=====================
==================
ML Pipeline APIs
--------------
----------------
.. automodule:: pyspark.ml
:members:
@ -10,7 +10,7 @@ ML Pipeline APIs
:inherited-members:
pyspark.ml.param module
-------------------------
-----------------------
.. automodule:: pyspark.ml.param
:members:
@ -34,7 +34,7 @@ pyspark.ml.classification module
:inherited-members:
pyspark.ml.recommendation module
-------------------------
--------------------------------
.. automodule:: pyspark.ml.recommendation
:members:
@ -42,7 +42,7 @@ pyspark.ml.recommendation module
:inherited-members:
pyspark.ml.regression module
-------------------------
----------------------------
.. automodule:: pyspark.ml.regression
:members:
@ -50,7 +50,7 @@ pyspark.ml.regression module
:inherited-members:
pyspark.ml.tuning module
--------------------------------
------------------------
.. automodule:: pyspark.ml.tuning
:members:
@ -58,7 +58,7 @@ pyspark.ml.tuning module
:inherited-members:
pyspark.ml.evaluation module
--------------------------------
----------------------------
.. automodule:: pyspark.ml.evaluation
:members:

39
python/pyspark/ml/classification.py
View file

@ -71,7 +71,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
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 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
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 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
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 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
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.
"""

8
python/pyspark/ml/feature.py
View file

@ -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 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
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 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
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.
"""

8
python/pyspark/ml/recommendation.py
View file

@ -92,8 +92,8 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
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 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
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.
"""

38
python/pyspark/ml/regression.py
View file

@ -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 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
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 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
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 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
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 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
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 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
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 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
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.
"""