diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 4e645519c47c7bceb652616ea852619e4156a9d6..7abbde8b260eb78b7b75ced72ab118df78a320b7 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -292,7 +292,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
>>> si_model = stringIndexer.fit(df)
>>> td = si_model.transform(df)
- >>> rf = RandomForestClassifier(numTrees=2, maxDepth=2, labelCol="indexed")
+ >>> rf = RandomForestClassifier(numTrees=2, maxDepth=2, labelCol="indexed", seed=42)
>>> model = rf.fit(td)
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.transform(test0).head().prediction
@@ -319,12 +319,12 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
def __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):
+ numTrees=20, featureSubsetStrategy="auto", seed=None):
"""
__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)
+ numTrees=20, featureSubsetStrategy="auto", seed=None)
"""
super(RandomForestClassifier, self).__init__()
self._java_obj = self._new_java_obj(
@@ -347,7 +347,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
"The number of features to consider for splits at each tree node. Supported " +
"options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
- maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
+ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
impurity="gini", numTrees=20, featureSubsetStrategy="auto")
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@@ -355,12 +355,12 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
@keyword_only
def 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,
+ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
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, \
+ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \
impurity="gini", numTrees=20, featureSubsetStrategy="auto")
Sets params for linear classification.
"""
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index c8115cb5bcf63d97fcdb02332e99485276ecd830..5511dceb7041936b97edffd3a743ae8674f64b84 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -876,10 +876,10 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
@keyword_only
def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
- seed=42, inputCol=None, outputCol=None):
+ seed=None, inputCol=None, outputCol=None):
"""
__init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, \
- seed=42, inputCol=None, outputCol=None)
+ seed=None, inputCol=None, outputCol=None)
"""
super(Word2Vec, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Word2Vec", self.uid)
@@ -891,15 +891,15 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
"the minimum number of times a token must appear to be included " +
"in the word2vec model's vocabulary")
self._setDefault(vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
- seed=42)
+ seed=None)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
def setParams(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
- seed=42, inputCol=None, outputCol=None):
+ seed=None, 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=None, \
inputCol=None, outputCol=None)
Sets params for this Word2Vec.
"""
diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py
index 67fb6e3dc74fbae3e7c73ed6d2fdcdd287c64df9..7845536161e072ec49466a165b943de242b4d2ed 100644
--- a/python/pyspark/ml/param/__init__.py
+++ b/python/pyspark/ml/param/__init__.py
@@ -147,7 +147,7 @@ class Params(Identifiable):
def getOrDefault(self, param):
"""
Gets the value of a param in the user-supplied param map or its
- default value. Raises an error if either is set.
+ default value. Raises an error if neither is set.
"""
param = self._resolveParam(param)
if param in self._paramMap:
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 91e45ec3735188b7df136fdaf7098ffc760137d0..ccb929af184b815548b621969a0a21f60baf164c 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -56,9 +56,10 @@ def _gen_param_header(name, doc, defaultValueStr):
def __init__(self):
super(Has$Name, self).__init__()
#: param for $doc
- self.$name = Param(self, "$name", "$doc")
- if $defaultValueStr is not None:
- self._setDefault($name=$defaultValueStr)'''
+ self.$name = Param(self, "$name", "$doc")'''
+ if defaultValueStr is not None:
+ template += '''
+ self._setDefault($name=$defaultValueStr)'''
Name = name[0].upper() + name[1:]
return template \
@@ -118,7 +119,7 @@ if __name__ == "__main__":
("outputCol", "output column name", None),
("numFeatures", "number of features", None),
("checkpointInterval", "checkpoint interval (>= 1)", None),
- ("seed", "random seed", None),
+ ("seed", "random seed", "hash(type(self).__name__)"),
("tol", "the convergence tolerance for iterative algorithms", None),
("stepSize", "Step size to be used for each iteration of optimization.", None)]
code = []
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index a5dc9b7ef29ed25f204b14926998724dccdf365b..0b93788899124e3bd33977f92092485b3326d88c 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -32,8 +32,6 @@ class HasMaxIter(Params):
super(HasMaxIter, self).__init__()
#: param for max number of iterations (>= 0)
self.maxIter = Param(self, "maxIter", "max number of iterations (>= 0)")
- if None is not None:
- self._setDefault(maxIter=None)
def setMaxIter(self, value):
"""
@@ -61,8 +59,6 @@ class HasRegParam(Params):
super(HasRegParam, self).__init__()
#: param for regularization parameter (>= 0)
self.regParam = Param(self, "regParam", "regularization parameter (>= 0)")
- if None is not None:
- self._setDefault(regParam=None)
def setRegParam(self, value):
"""
@@ -90,8 +86,7 @@ class HasFeaturesCol(Params):
super(HasFeaturesCol, self).__init__()
#: param for features column name
self.featuresCol = Param(self, "featuresCol", "features column name")
- if 'features' is not None:
- self._setDefault(featuresCol='features')
+ self._setDefault(featuresCol='features')
def setFeaturesCol(self, value):
"""
@@ -119,8 +114,7 @@ class HasLabelCol(Params):
super(HasLabelCol, self).__init__()
#: param for label column name
self.labelCol = Param(self, "labelCol", "label column name")
- if 'label' is not None:
- self._setDefault(labelCol='label')
+ self._setDefault(labelCol='label')
def setLabelCol(self, value):
"""
@@ -148,8 +142,7 @@ class HasPredictionCol(Params):
super(HasPredictionCol, self).__init__()
#: param for prediction column name
self.predictionCol = Param(self, "predictionCol", "prediction column name")
- if 'prediction' is not None:
- self._setDefault(predictionCol='prediction')
+ self._setDefault(predictionCol='prediction')
def setPredictionCol(self, value):
"""
@@ -177,8 +170,7 @@ class HasProbabilityCol(Params):
super(HasProbabilityCol, self).__init__()
#: param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.
self.probabilityCol = Param(self, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.")
- if 'probability' is not None:
- self._setDefault(probabilityCol='probability')
+ self._setDefault(probabilityCol='probability')
def setProbabilityCol(self, value):
"""
@@ -206,8 +198,7 @@ class HasRawPredictionCol(Params):
super(HasRawPredictionCol, self).__init__()
#: param for raw prediction (a.k.a. confidence) column name
self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name")
- if 'rawPrediction' is not None:
- self._setDefault(rawPredictionCol='rawPrediction')
+ self._setDefault(rawPredictionCol='rawPrediction')
def setRawPredictionCol(self, value):
"""
@@ -235,8 +226,6 @@ class HasInputCol(Params):
super(HasInputCol, self).__init__()
#: param for input column name
self.inputCol = Param(self, "inputCol", "input column name")
- if None is not None:
- self._setDefault(inputCol=None)
def setInputCol(self, value):
"""
@@ -264,8 +253,6 @@ class HasInputCols(Params):
super(HasInputCols, self).__init__()
#: param for input column names
self.inputCols = Param(self, "inputCols", "input column names")
- if None is not None:
- self._setDefault(inputCols=None)
def setInputCols(self, value):
"""
@@ -293,8 +280,6 @@ class HasOutputCol(Params):
super(HasOutputCol, self).__init__()
#: param for output column name
self.outputCol = Param(self, "outputCol", "output column name")
- if None is not None:
- self._setDefault(outputCol=None)
def setOutputCol(self, value):
"""
@@ -322,8 +307,6 @@ class HasNumFeatures(Params):
super(HasNumFeatures, self).__init__()
#: param for number of features
self.numFeatures = Param(self, "numFeatures", "number of features")
- if None is not None:
- self._setDefault(numFeatures=None)
def setNumFeatures(self, value):
"""
@@ -351,8 +334,6 @@ class HasCheckpointInterval(Params):
super(HasCheckpointInterval, self).__init__()
#: param for checkpoint interval (>= 1)
self.checkpointInterval = Param(self, "checkpointInterval", "checkpoint interval (>= 1)")
- if None is not None:
- self._setDefault(checkpointInterval=None)
def setCheckpointInterval(self, value):
"""
@@ -380,8 +361,7 @@ class HasSeed(Params):
super(HasSeed, self).__init__()
#: param for random seed
self.seed = Param(self, "seed", "random seed")
- if None is not None:
- self._setDefault(seed=None)
+ self._setDefault(seed=hash(type(self).__name__))
def setSeed(self, value):
"""
@@ -409,8 +389,6 @@ class HasTol(Params):
super(HasTol, self).__init__()
#: param for the convergence tolerance for iterative algorithms
self.tol = Param(self, "tol", "the convergence tolerance for iterative algorithms")
- if None is not None:
- self._setDefault(tol=None)
def setTol(self, value):
"""
@@ -438,8 +416,6 @@ class HasStepSize(Params):
super(HasStepSize, self).__init__()
#: param for Step size to be used for each iteration of optimization.
self.stepSize = Param(self, "stepSize", "Step size to be used for each iteration of optimization.")
- if None is not None:
- self._setDefault(stepSize=None)
def setStepSize(self, value):
"""
@@ -467,6 +443,7 @@ class DecisionTreeParams(Params):
minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.")
maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
+
def __init__(self):
super(DecisionTreeParams, self).__init__()
diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py
index 39c25275437741aa0b9dceaad322a41106189f76..b3e0dd7abf68155d8ee6630837213a3389dd5dab 100644
--- a/python/pyspark/ml/recommendation.py
+++ b/python/pyspark/ml/recommendation.py
@@ -89,11 +89,11 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
@keyword_only
def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
- implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0,
+ implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
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, \
+ implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=None, \
ratingCol="rating", nonnegative=false, checkpointInterval=10)
"""
super(ALS, self).__init__()
@@ -109,18 +109,18 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
self.nonnegative = Param(self, "nonnegative",
"whether to use nonnegative constraint for least squares")
self._setDefault(rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
- implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0,
+ implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
ratingCol="rating", nonnegative=False, checkpointInterval=10)
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@keyword_only
def setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
- implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=0,
+ implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
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, \
+ implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, \
ratingCol="rating", nonnegative=False, checkpointInterval=10)
Sets params for ALS.
"""
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index ff809cdafdf51b5c5f62313404340e894f03abc0..b139e27372d800909d1b00ddce51ef55eb2fce0e 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -257,7 +257,7 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
>>> df = sqlContext.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
- >>> rf = RandomForestRegressor(numTrees=2, maxDepth=2)
+ >>> rf = RandomForestRegressor(numTrees=2, maxDepth=2, seed=42)
>>> model = rf.fit(df)
>>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.transform(test0).head().prediction
@@ -284,12 +284,13 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
def __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):
+ numTrees=20, featureSubsetStrategy="auto", seed=None):
"""
__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)
+ impurity="variance", numTrees=20, \
+ featureSubsetStrategy="auto", seed=None)
"""
super(RandomForestRegressor, self).__init__()
self._java_obj = self._new_java_obj(
@@ -312,7 +313,7 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
"The number of features to consider for splits at each tree node. Supported " +
"options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
- maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=42,
+ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
impurity="variance", numTrees=20, featureSubsetStrategy="auto")
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@@ -320,12 +321,12 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
@keyword_only
def 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,
+ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
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, \
+ maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \
impurity="variance", numTrees=20, featureSubsetStrategy="auto")
Sets params for linear regression.
"""
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 10fe0ef8db38ff2cc25e43dd82341fec912e7f9f..6adbf166f34a8b3963785a4c44f428104f72f922 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -33,7 +33,8 @@ else:
from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
from pyspark.sql import DataFrame, SQLContext
from pyspark.ml.param import Param, Params
-from pyspark.ml.param.shared import HasMaxIter, HasInputCol
+from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
+from pyspark.ml.util import keyword_only
from pyspark.ml import Estimator, Model, Pipeline, Transformer
from pyspark.ml.feature import *
from pyspark.mllib.linalg import DenseVector
@@ -111,14 +112,46 @@ class PipelineTests(PySparkTestCase):
self.assertEqual(6, dataset.index)
-class TestParams(HasMaxIter, HasInputCol):
+class TestParams(HasMaxIter, HasInputCol, HasSeed):
"""
- A subclass of Params mixed with HasMaxIter and HasInputCol.
+ A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed.
"""
-
- def __init__(self):
+ @keyword_only
+ def __init__(self, seed=None):
super(TestParams, self).__init__()
self._setDefault(maxIter=10)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, seed=None):
+ """
+ setParams(self, seed=None)
+ Sets params for this test.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+
+class OtherTestParams(HasMaxIter, HasInputCol, HasSeed):
+ """
+ A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed.
+ """
+ @keyword_only
+ def __init__(self, seed=None):
+ super(OtherTestParams, self).__init__()
+ self._setDefault(maxIter=10)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, seed=None):
+ """
+ setParams(self, seed=None)
+ Sets params for this test.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
class ParamTests(PySparkTestCase):
@@ -134,9 +167,10 @@ class ParamTests(PySparkTestCase):
testParams = TestParams()
maxIter = testParams.maxIter
inputCol = testParams.inputCol
+ seed = testParams.seed
params = testParams.params
- self.assertEqual(params, [inputCol, maxIter])
+ self.assertEqual(params, [inputCol, maxIter, seed])
self.assertTrue(testParams.hasParam(maxIter))
self.assertTrue(testParams.hasDefault(maxIter))
@@ -154,10 +188,29 @@ class ParamTests(PySparkTestCase):
with self.assertRaises(KeyError):
testParams.getInputCol()
+ # Since the default is normally random, set it to a known number for debug str
+ testParams._setDefault(seed=41)
+ testParams.setSeed(43)
+
self.assertEquals(
testParams.explainParams(),
"\n".join(["inputCol: input column name (undefined)",
- "maxIter: max number of iterations (>= 0) (default: 10, current: 100)"]))
+ "maxIter: max number of iterations (>= 0) (default: 10, current: 100)",
+ "seed: random seed (default: 41, current: 43)"]))
+
+ def test_hasseed(self):
+ noSeedSpecd = TestParams()
+ withSeedSpecd = TestParams(seed=42)
+ other = OtherTestParams()
+ # Check that we no longer use 42 as the magic number
+ self.assertNotEqual(noSeedSpecd.getSeed(), 42)
+ origSeed = noSeedSpecd.getSeed()
+ # Check that we only compute the seed once
+ self.assertEqual(noSeedSpecd.getSeed(), origSeed)
+ # Check that a specified seed is honored
+ self.assertEqual(withSeedSpecd.getSeed(), 42)
+ # Check that a different class has a different seed
+ self.assertNotEqual(other.getSeed(), noSeedSpecd.getSeed())
class FeatureTests(PySparkTestCase):