Max Moroz created SPARK-16832:
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Summary: CrossValidator and TrainValidationSplit are not random
without seed
Key: SPARK-16832
URL: https://issues.apache.org/jira/browse/SPARK-16832
Project: Spark
Issue Type: Bug
Components: ML, PySpark
Affects Versions: 2.0.0
Reporter: Max Moroz
Priority: Minor
Repeatedly running CrossValidator or TrainValidationSplit without an explicit
seed parameter does not change results. It is supposed to be seeded with a
random seed, but it seems to be instead seeded with some constant. (If seed is
explicitly provided, the two classes behave as expected.)
{code}
dataset = spark.createDataFrame(
[(Vectors.dense([0.0]), 0.0),
(Vectors.dense([0.4]), 1.0),
(Vectors.dense([0.5]), 0.0),
(Vectors.dense([0.6]), 1.0),
(Vectors.dense([1.0]), 1.0)] * 1000,
["features", "label"]).cache()
paramGrid = pyspark.ml.tuning.ParamGridBuilder().build()
tvs =
pyspark.ml.tuning.TrainValidationSplit(estimator=pyspark.ml.regression.LinearRegression(),
estimatorParamMaps=paramGrid,
evaluator=pyspark.ml.evaluation.RegressionEvaluator(),
trainRatio=0.8)
model = tvs.fit(train)
print(model.validationMetrics)
for folds in (3, 5, 10):
cv =
pyspark.ml.tuning.CrossValidator(estimator=pyspark.ml.regression.LinearRegression(),
estimatorParamMaps=paramGrid,
evaluator=pyspark.ml.evaluation.RegressionEvaluator(),
numFolds=folds
)
cvModel = cv.fit(dataset)
print(folds, cvModel.avgMetrics)
{code}
This code produces identical results upon repeated calls.
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