4a21c4cc92
### What changes were proposed in this pull request? Fix Pyspark CrossValidator/TrainValidationSplit with pipeline estimator cannot save and load model. Most pyspark estimators/transformers inherit `JavaParams`, but some estimators are special (in order to support pure python implemented nested estimators/transformers): * Pipeline * OneVsRest * CrossValidator * TrainValidationSplit But note that, currently, in pyspark, estimators listed above, their model reader/writer do NOT support pure python implemented nested estimators/transformers. Because they use java reader/writer wrapper as python side reader/writer. Pyspark CrossValidator/TrainValidationSplit model reader/writer require all estimators define the `_transfer_param_map_to_java` and `_transfer_param_map_from_java` (used in model read/write). OneVsRest class already defines the two methods, but Pipeline do not, so it lead to this bug. In this PR I add `_transfer_param_map_to_java` and `_transfer_param_map_from_java` into Pipeline class. ### Why are the changes needed? Bug fix. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Unit test. Manually test in pyspark shell: 1) CrossValidator with Simple Pipeline estimator ``` from pyspark.ml import Pipeline from pyspark.ml.classification import LogisticRegression from pyspark.ml.evaluation import BinaryClassificationEvaluator from pyspark.ml.feature import HashingTF, Tokenizer from pyspark.ml.tuning import CrossValidator, CrossValidatorModel, ParamGridBuilder training = spark.createDataFrame([ (0, "a b c d e spark", 1.0), (1, "b d", 0.0), (2, "spark f g h", 1.0), (3, "hadoop mapreduce", 0.0), (4, "b spark who", 1.0), (5, "g d a y", 0.0), (6, "spark fly", 1.0), (7, "was mapreduce", 0.0), ], ["id", "text", "label"]) # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. tokenizer = Tokenizer(inputCol="text", outputCol="words") hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") lr = LogisticRegression(maxIter=10) pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) paramGrid = ParamGridBuilder() \ .addGrid(hashingTF.numFeatures, [10, 100, 1000]) \ .addGrid(lr.regParam, [0.1, 0.01]) \ .build() crossval = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid, evaluator=BinaryClassificationEvaluator(), numFolds=2) # use 3+ folds in practice # Run cross-validation, and choose the best set of parameters. cvModel = crossval.fit(training) cvModel.save('/tmp/cv_model001') CrossValidatorModel.load('/tmp/cv_model001') ``` 2) CrossValidator with Pipeline estimator which include a OneVsRest estimator stage, and OneVsRest estimator nest a LogisticRegression estimator. ``` from pyspark.ml.linalg import Vectors from pyspark.ml import Estimator, Model from pyspark.ml.classification import LogisticRegression, LogisticRegressionModel, OneVsRest from pyspark.ml.evaluation import BinaryClassificationEvaluator, \ MulticlassClassificationEvaluator, RegressionEvaluator from pyspark.ml.linalg import Vectors from pyspark.ml.param import Param, Params from pyspark.ml.tuning import CrossValidator, CrossValidatorModel, ParamGridBuilder, \ TrainValidationSplit, TrainValidationSplitModel from pyspark.sql.functions import rand from pyspark.testing.mlutils import SparkSessionTestCase 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)] * 10, ["features", "label"]) ova = OneVsRest(classifier=LogisticRegression()) lr1 = LogisticRegression().setMaxIter(100) lr2 = LogisticRegression().setMaxIter(150) grid = ParamGridBuilder().addGrid(ova.classifier, [lr1, lr2]).build() evaluator = MulticlassClassificationEvaluator() pipeline = Pipeline(stages=[ova]) cv = CrossValidator(estimator=pipeline, estimatorParamMaps=grid, evaluator=evaluator) cvModel = cv.fit(dataset) cvModel.save('/tmp/model002') cvModel2 = CrossValidatorModel.load('/tmp/model002') ``` TrainValidationSplit testing code are similar so I do not paste them. Closes #28279 from WeichenXu123/fix_pipeline_tuning. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Xiangrui Meng <meng@databricks.com> |
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.. | ||
linalg | ||
param | ||
tests | ||
__init__.py | ||
base.py | ||
classification.py | ||
clustering.py | ||
common.py | ||
evaluation.py | ||
feature.py | ||
fpm.py | ||
functions.py | ||
image.py | ||
pipeline.py | ||
recommendation.py | ||
regression.py | ||
stat.py | ||
tree.py | ||
tuning.py | ||
util.py | ||
wrapper.py |