80161238fe
### What changes were proposed in this pull request?
Fix: Pyspark ML Validator params in estimatorParamMaps may be lost after saving and reloading
When saving validator estimatorParamMaps, will check all nested stages in tuned estimator to get correct param parent.
Two typical cases to manually test:
~~~python
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression()
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
paramGrid = ParamGridBuilder() \
.addGrid(hashingTF.numFeatures, [10, 100]) \
.addGrid(lr.maxIter, [100, 200]) \
.build()
tvs = TrainValidationSplit(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator())
tvs.save(tvsPath)
loadedTvs = TrainValidationSplit.load(tvsPath)
# check `loadedTvs.getEstimatorParamMaps()` restored correctly.
~~~
~~~python
lr = LogisticRegression()
ova = OneVsRest(classifier=lr)
grid = ParamGridBuilder().addGrid(lr.maxIter, [100, 200]).build()
evaluator = MulticlassClassificationEvaluator()
tvs = TrainValidationSplit(estimator=ova, estimatorParamMaps=grid, evaluator=evaluator)
tvs.save(tvsPath)
loadedTvs = TrainValidationSplit.load(tvsPath)
# check `loadedTvs.getEstimatorParamMaps()` restored correctly.
~~~
### Why are the changes needed?
Bug fix.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes #30539 from WeichenXu123/fix_tuning_param_maps_io.
Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
#
|
|
# Licensed to the Apache Software Foundation (ASF) under one or more
|
|
# contributor license agreements. See the NOTICE file distributed with
|
|
# this work for additional information regarding copyright ownership.
|
|
# The ASF licenses this file to You under the Apache License, Version 2.0
|
|
# (the "License"); you may not use this file except in compliance with
|
|
# the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import unittest
|
|
|
|
from pyspark.ml import Pipeline
|
|
from pyspark.ml.classification import LogisticRegression, OneVsRest
|
|
from pyspark.ml.feature import VectorAssembler
|
|
from pyspark.ml.linalg import Vectors
|
|
from pyspark.ml.util import MetaAlgorithmReadWrite
|
|
from pyspark.testing.mlutils import SparkSessionTestCase
|
|
|
|
|
|
class MetaAlgorithmReadWriteTests(SparkSessionTestCase):
|
|
|
|
def test_getAllNestedStages(self):
|
|
def _check_uid_set_equal(stages, expected_stages):
|
|
uids = set(map(lambda x: x.uid, stages))
|
|
expected_uids = set(map(lambda x: x.uid, expected_stages))
|
|
self.assertEqual(uids, expected_uids)
|
|
|
|
df1 = self.spark.createDataFrame([
|
|
(Vectors.dense([1., 2.]), 1.0),
|
|
(Vectors.dense([-1., -2.]), 0.0),
|
|
], ['features', 'label'])
|
|
df2 = self.spark.createDataFrame([
|
|
(1., 2., 1.0),
|
|
(1., 2., 0.0),
|
|
], ['a', 'b', 'label'])
|
|
vs = VectorAssembler(inputCols=['a', 'b'], outputCol='features')
|
|
lr = LogisticRegression()
|
|
pipeline = Pipeline(stages=[vs, lr])
|
|
pipelineModel = pipeline.fit(df2)
|
|
ova = OneVsRest(classifier=lr)
|
|
ovaModel = ova.fit(df1)
|
|
|
|
ova_pipeline = Pipeline(stages=[vs, ova])
|
|
nested_pipeline = Pipeline(stages=[ova_pipeline])
|
|
|
|
_check_uid_set_equal(
|
|
MetaAlgorithmReadWrite.getAllNestedStages(pipeline),
|
|
[pipeline, vs, lr]
|
|
)
|
|
_check_uid_set_equal(
|
|
MetaAlgorithmReadWrite.getAllNestedStages(pipelineModel),
|
|
[pipelineModel] + pipelineModel.stages
|
|
)
|
|
_check_uid_set_equal(
|
|
MetaAlgorithmReadWrite.getAllNestedStages(ova),
|
|
[ova, lr]
|
|
)
|
|
_check_uid_set_equal(
|
|
MetaAlgorithmReadWrite.getAllNestedStages(ovaModel),
|
|
[ovaModel, lr] + ovaModel.models
|
|
)
|
|
_check_uid_set_equal(
|
|
MetaAlgorithmReadWrite.getAllNestedStages(nested_pipeline),
|
|
[nested_pipeline, ova_pipeline, vs, ova, lr]
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.ml.tests.test_util import * # noqa: F401
|
|
|
|
try:
|
|
import xmlrunner # type: ignore[import]
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|