Max Moroz created SPARK-16834:
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Summary: TrainValildationSplit and direct evaluation produce
different scores
Key: SPARK-16834
URL: https://issues.apache.org/jira/browse/SPARK-16834
Project: Spark
Issue Type: Bug
Components: ML, PySpark
Affects Versions: 2.0.0
Reporter: Max Moroz
The two segments of code below are supposed to do the same thing: one is using
TrainValidationSplit, the other performs the same evaluation manually. However,
their results are statistically different (in my case, in a loop of 20, I
regularly get ~19 True values).
Unfortunately, I didn't find the bug in the source code.
{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()
# note that test is NEVER used in this code
# I create it only to utilize randomSplit
for i in range(20):
train, test = dataset.randomSplit([0.8, 0.2])
tvs =
pyspark.ml.tuning.TrainValidationSplit(estimator=pyspark.ml.regression.LinearRegression(),
estimatorParamMaps=paramGrid,
evaluator=pyspark.ml.evaluation.RegressionEvaluator(),
trainRatio=0.5)
model = tvs.fit(train)
train, val, test = dataset.randomSplit([0.4, 0.4, 0.2])
lr=pyspark.ml.regression.LinearRegression()
evaluator=pyspark.ml.evaluation.RegressionEvaluator()
lrModel = lr.fit(train)
predicted = lrModel.transform(val)
print(model.validationMetrics[0] < evaluator.evaluate(predicted))
{code}
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