# # 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 tempfile import unittest from pyspark.ml.feature import HashingTF, Tokenizer from pyspark.ml import Estimator, Pipeline, 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 class HasInducedError(Params): def __init__(self): super(HasInducedError, self).__init__() self.inducedError = Param(self, "inducedError", "Uniformly-distributed error added to feature") def getInducedError(self): return self.getOrDefault(self.inducedError) class InducedErrorModel(Model, HasInducedError): def __init__(self): super(InducedErrorModel, self).__init__() def _transform(self, dataset): return dataset.withColumn("prediction", dataset.feature + (rand(0) * self.getInducedError())) class InducedErrorEstimator(Estimator, HasInducedError): def __init__(self, inducedError=1.0): super(InducedErrorEstimator, self).__init__() self._set(inducedError=inducedError) def _fit(self, dataset): model = InducedErrorModel() self._copyValues(model) return model class ParamGridBuilderTests(SparkSessionTestCase): def test_addGrid(self): with self.assertRaises(TypeError): grid = (ParamGridBuilder() .addGrid("must be an instance of Param", ["not", "string"]) .build()) class CrossValidatorTests(SparkSessionTestCase): def test_copy(self): dataset = self.spark.createDataFrame([ (10, 10.0), (50, 50.0), (100, 100.0), (500, 500.0)] * 10, ["feature", "label"]) iee = InducedErrorEstimator() evaluator = RegressionEvaluator(metricName="rmse") grid = (ParamGridBuilder() .addGrid(iee.inducedError, [100.0, 0.0, 10000.0]) .build()) cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator) cvCopied = cv.copy() self.assertEqual(cv.getEstimator().uid, cvCopied.getEstimator().uid) cvModel = cv.fit(dataset) cvModelCopied = cvModel.copy() for index in range(len(cvModel.avgMetrics)): self.assertTrue(abs(cvModel.avgMetrics[index] - cvModelCopied.avgMetrics[index]) < 0.0001) def test_fit_minimize_metric(self): dataset = self.spark.createDataFrame([ (10, 10.0), (50, 50.0), (100, 100.0), (500, 500.0)] * 10, ["feature", "label"]) iee = InducedErrorEstimator() evaluator = RegressionEvaluator(metricName="rmse") grid = (ParamGridBuilder() .addGrid(iee.inducedError, [100.0, 0.0, 10000.0]) .build()) cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator) cvModel = cv.fit(dataset) bestModel = cvModel.bestModel bestModelMetric = evaluator.evaluate(bestModel.transform(dataset)) self.assertEqual(0.0, bestModel.getOrDefault('inducedError'), "Best model should have zero induced error") self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0") def test_fit_maximize_metric(self): dataset = self.spark.createDataFrame([ (10, 10.0), (50, 50.0), (100, 100.0), (500, 500.0)] * 10, ["feature", "label"]) iee = InducedErrorEstimator() evaluator = RegressionEvaluator(metricName="r2") grid = (ParamGridBuilder() .addGrid(iee.inducedError, [100.0, 0.0, 10000.0]) .build()) cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator) cvModel = cv.fit(dataset) bestModel = cvModel.bestModel bestModelMetric = evaluator.evaluate(bestModel.transform(dataset)) self.assertEqual(0.0, bestModel.getOrDefault('inducedError'), "Best model should have zero induced error") self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1") def test_param_grid_type_coercion(self): lr = LogisticRegression(maxIter=10) paramGrid = ParamGridBuilder().addGrid(lr.regParam, [0.5, 1]).build() for param in paramGrid: for v in param.values(): assert(type(v) == float) def test_save_load_trained_model(self): # This tests saving and loading the trained model only. # Save/load for CrossValidator will be added later: SPARK-13786 temp_path = tempfile.mkdtemp() dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() evaluator = BinaryClassificationEvaluator() cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) cvModel = cv.fit(dataset) lrModel = cvModel.bestModel cvModelPath = temp_path + "/cvModel" lrModel.save(cvModelPath) loadedLrModel = LogisticRegressionModel.load(cvModelPath) self.assertEqual(loadedLrModel.uid, lrModel.uid) self.assertEqual(loadedLrModel.intercept, lrModel.intercept) def test_save_load_simple_estimator(self): temp_path = tempfile.mkdtemp() dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() evaluator = BinaryClassificationEvaluator() # test save/load of CrossValidator cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) cvModel = cv.fit(dataset) cvPath = temp_path + "/cv" cv.save(cvPath) loadedCV = CrossValidator.load(cvPath) self.assertEqual(loadedCV.getEstimator().uid, cv.getEstimator().uid) self.assertEqual(loadedCV.getEvaluator().uid, cv.getEvaluator().uid) self.assertEqual(loadedCV.getEstimatorParamMaps(), cv.getEstimatorParamMaps()) # test save/load of CrossValidatorModel cvModelPath = temp_path + "/cvModel" cvModel.save(cvModelPath) loadedModel = CrossValidatorModel.load(cvModelPath) self.assertEqual(loadedModel.bestModel.uid, cvModel.bestModel.uid) def test_parallel_evaluation(self): dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [5, 6]).build() evaluator = BinaryClassificationEvaluator() # test save/load of CrossValidator cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) cv.setParallelism(1) cvSerialModel = cv.fit(dataset) cv.setParallelism(2) cvParallelModel = cv.fit(dataset) self.assertEqual(cvSerialModel.avgMetrics, cvParallelModel.avgMetrics) def test_expose_sub_models(self): temp_path = tempfile.mkdtemp() dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() evaluator = BinaryClassificationEvaluator() numFolds = 3 cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator, numFolds=numFolds, collectSubModels=True) def checkSubModels(subModels): self.assertEqual(len(subModels), numFolds) for i in range(numFolds): self.assertEqual(len(subModels[i]), len(grid)) cvModel = cv.fit(dataset) checkSubModels(cvModel.subModels) # Test the default value for option "persistSubModel" to be "true" testSubPath = temp_path + "/testCrossValidatorSubModels" savingPathWithSubModels = testSubPath + "cvModel3" cvModel.save(savingPathWithSubModels) cvModel3 = CrossValidatorModel.load(savingPathWithSubModels) checkSubModels(cvModel3.subModels) cvModel4 = cvModel3.copy() checkSubModels(cvModel4.subModels) savingPathWithoutSubModels = testSubPath + "cvModel2" cvModel.write().option("persistSubModels", "false").save(savingPathWithoutSubModels) cvModel2 = CrossValidatorModel.load(savingPathWithoutSubModels) self.assertEqual(cvModel2.subModels, None) for i in range(numFolds): for j in range(len(grid)): self.assertEqual(cvModel.subModels[i][j].uid, cvModel3.subModels[i][j].uid) def test_save_load_nested_estimator(self): temp_path = tempfile.mkdtemp() dataset = self.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() # test save/load of CrossValidator cv = CrossValidator(estimator=ova, estimatorParamMaps=grid, evaluator=evaluator) cvModel = cv.fit(dataset) cvPath = temp_path + "/cv" cv.save(cvPath) loadedCV = CrossValidator.load(cvPath) self.assertEqual(loadedCV.getEstimator().uid, cv.getEstimator().uid) self.assertEqual(loadedCV.getEvaluator().uid, cv.getEvaluator().uid) originalParamMap = cv.getEstimatorParamMaps() loadedParamMap = loadedCV.getEstimatorParamMaps() for i, param in enumerate(loadedParamMap): for p in param: if p.name == "classifier": self.assertEqual(param[p].uid, originalParamMap[i][p].uid) else: self.assertEqual(param[p], originalParamMap[i][p]) # test save/load of CrossValidatorModel cvModelPath = temp_path + "/cvModel" cvModel.save(cvModelPath) loadedModel = CrossValidatorModel.load(cvModelPath) self.assertEqual(loadedModel.bestModel.uid, cvModel.bestModel.uid) def test_save_load_pipeline_estimator(self): temp_path = tempfile.mkdtemp() training = self.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") ova = OneVsRest(classifier=LogisticRegression()) lr1 = LogisticRegression().setMaxIter(5) lr2 = LogisticRegression().setMaxIter(10) pipeline = Pipeline(stages=[tokenizer, hashingTF, ova]) paramGrid = ParamGridBuilder() \ .addGrid(hashingTF.numFeatures, [10, 100]) \ .addGrid(ova.classifier, [lr1, lr2]) \ .build() crossval = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid, evaluator=MulticlassClassificationEvaluator(), numFolds=2) # use 3+ folds in practice # Run cross-validation, and choose the best set of parameters. cvModel = crossval.fit(training) # test save/load of CrossValidatorModel cvModelPath = temp_path + "/cvModel" cvModel.save(cvModelPath) loadedModel = CrossValidatorModel.load(cvModelPath) self.assertEqual(loadedModel.bestModel.uid, cvModel.bestModel.uid) self.assertEqual(len(loadedModel.bestModel.stages), len(cvModel.bestModel.stages)) for loadedStage, originalStage in zip(loadedModel.bestModel.stages, cvModel.bestModel.stages): self.assertEqual(loadedStage.uid, originalStage.uid) # Test nested pipeline nested_pipeline = Pipeline(stages=[tokenizer, Pipeline(stages=[hashingTF, ova])]) crossval2 = CrossValidator(estimator=nested_pipeline, estimatorParamMaps=paramGrid, evaluator=MulticlassClassificationEvaluator(), numFolds=2) # use 3+ folds in practice # Run cross-validation, and choose the best set of parameters. cvModel2 = crossval2.fit(training) # test save/load of CrossValidatorModel cvModelPath2 = temp_path + "/cvModel2" cvModel2.save(cvModelPath2) loadedModel2 = CrossValidatorModel.load(cvModelPath2) self.assertEqual(loadedModel2.bestModel.uid, cvModel2.bestModel.uid) loaded_nested_pipeline_model = loadedModel2.bestModel.stages[1] original_nested_pipeline_model = cvModel2.bestModel.stages[1] self.assertEqual(loaded_nested_pipeline_model.uid, original_nested_pipeline_model.uid) self.assertEqual(len(loaded_nested_pipeline_model.stages), len(original_nested_pipeline_model.stages)) for loadedStage, originalStage in zip(loaded_nested_pipeline_model.stages, original_nested_pipeline_model.stages): self.assertEqual(loadedStage.uid, originalStage.uid) class TrainValidationSplitTests(SparkSessionTestCase): def test_fit_minimize_metric(self): dataset = self.spark.createDataFrame([ (10, 10.0), (50, 50.0), (100, 100.0), (500, 500.0)] * 10, ["feature", "label"]) iee = InducedErrorEstimator() evaluator = RegressionEvaluator(metricName="rmse") grid = ParamGridBuilder() \ .addGrid(iee.inducedError, [100.0, 0.0, 10000.0]) \ .build() tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator) tvsModel = tvs.fit(dataset) bestModel = tvsModel.bestModel bestModelMetric = evaluator.evaluate(bestModel.transform(dataset)) validationMetrics = tvsModel.validationMetrics self.assertEqual(0.0, bestModel.getOrDefault('inducedError'), "Best model should have zero induced error") self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0") self.assertEqual(len(grid), len(validationMetrics), "validationMetrics has the same size of grid parameter") self.assertEqual(0.0, min(validationMetrics)) def test_fit_maximize_metric(self): dataset = self.spark.createDataFrame([ (10, 10.0), (50, 50.0), (100, 100.0), (500, 500.0)] * 10, ["feature", "label"]) iee = InducedErrorEstimator() evaluator = RegressionEvaluator(metricName="r2") grid = ParamGridBuilder() \ .addGrid(iee.inducedError, [100.0, 0.0, 10000.0]) \ .build() tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator) tvsModel = tvs.fit(dataset) bestModel = tvsModel.bestModel bestModelMetric = evaluator.evaluate(bestModel.transform(dataset)) validationMetrics = tvsModel.validationMetrics self.assertEqual(0.0, bestModel.getOrDefault('inducedError'), "Best model should have zero induced error") self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1") self.assertEqual(len(grid), len(validationMetrics), "validationMetrics has the same size of grid parameter") self.assertEqual(1.0, max(validationMetrics)) def test_save_load_trained_model(self): # This tests saving and loading the trained model only. # Save/load for TrainValidationSplit will be added later: SPARK-13786 temp_path = tempfile.mkdtemp() dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() evaluator = BinaryClassificationEvaluator() tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) tvsModel = tvs.fit(dataset) lrModel = tvsModel.bestModel tvsModelPath = temp_path + "/tvsModel" lrModel.save(tvsModelPath) loadedLrModel = LogisticRegressionModel.load(tvsModelPath) self.assertEqual(loadedLrModel.uid, lrModel.uid) self.assertEqual(loadedLrModel.intercept, lrModel.intercept) def test_save_load_simple_estimator(self): # This tests saving and loading the trained model only. # Save/load for TrainValidationSplit will be added later: SPARK-13786 temp_path = tempfile.mkdtemp() dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() evaluator = BinaryClassificationEvaluator() tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) tvsModel = tvs.fit(dataset) tvsPath = temp_path + "/tvs" tvs.save(tvsPath) loadedTvs = TrainValidationSplit.load(tvsPath) self.assertEqual(loadedTvs.getEstimator().uid, tvs.getEstimator().uid) self.assertEqual(loadedTvs.getEvaluator().uid, tvs.getEvaluator().uid) self.assertEqual(loadedTvs.getEstimatorParamMaps(), tvs.getEstimatorParamMaps()) tvsModelPath = temp_path + "/tvsModel" tvsModel.save(tvsModelPath) loadedModel = TrainValidationSplitModel.load(tvsModelPath) self.assertEqual(loadedModel.bestModel.uid, tvsModel.bestModel.uid) def test_parallel_evaluation(self): dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [5, 6]).build() evaluator = BinaryClassificationEvaluator() tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) tvs.setParallelism(1) tvsSerialModel = tvs.fit(dataset) tvs.setParallelism(2) tvsParallelModel = tvs.fit(dataset) self.assertEqual(tvsSerialModel.validationMetrics, tvsParallelModel.validationMetrics) def test_expose_sub_models(self): temp_path = tempfile.mkdtemp() dataset = self.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"]) lr = LogisticRegression() grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() evaluator = BinaryClassificationEvaluator() tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator, collectSubModels=True) tvsModel = tvs.fit(dataset) self.assertEqual(len(tvsModel.subModels), len(grid)) # Test the default value for option "persistSubModel" to be "true" testSubPath = temp_path + "/testTrainValidationSplitSubModels" savingPathWithSubModels = testSubPath + "cvModel3" tvsModel.save(savingPathWithSubModels) tvsModel3 = TrainValidationSplitModel.load(savingPathWithSubModels) self.assertEqual(len(tvsModel3.subModels), len(grid)) tvsModel4 = tvsModel3.copy() self.assertEqual(len(tvsModel4.subModels), len(grid)) savingPathWithoutSubModels = testSubPath + "cvModel2" tvsModel.write().option("persistSubModels", "false").save(savingPathWithoutSubModels) tvsModel2 = TrainValidationSplitModel.load(savingPathWithoutSubModels) self.assertEqual(tvsModel2.subModels, None) for i in range(len(grid)): self.assertEqual(tvsModel.subModels[i].uid, tvsModel3.subModels[i].uid) def test_save_load_nested_estimator(self): # This tests saving and loading the trained model only. # Save/load for TrainValidationSplit will be added later: SPARK-13786 temp_path = tempfile.mkdtemp() dataset = self.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() tvs = TrainValidationSplit(estimator=ova, estimatorParamMaps=grid, evaluator=evaluator) tvsModel = tvs.fit(dataset) tvsPath = temp_path + "/tvs" tvs.save(tvsPath) loadedTvs = TrainValidationSplit.load(tvsPath) self.assertEqual(loadedTvs.getEstimator().uid, tvs.getEstimator().uid) self.assertEqual(loadedTvs.getEvaluator().uid, tvs.getEvaluator().uid) originalParamMap = tvs.getEstimatorParamMaps() loadedParamMap = loadedTvs.getEstimatorParamMaps() for i, param in enumerate(loadedParamMap): for p in param: if p.name == "classifier": self.assertEqual(param[p].uid, originalParamMap[i][p].uid) else: self.assertEqual(param[p], originalParamMap[i][p]) tvsModelPath = temp_path + "/tvsModel" tvsModel.save(tvsModelPath) loadedModel = TrainValidationSplitModel.load(tvsModelPath) self.assertEqual(loadedModel.bestModel.uid, tvsModel.bestModel.uid) def test_save_load_pipeline_estimator(self): temp_path = tempfile.mkdtemp() training = self.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") ova = OneVsRest(classifier=LogisticRegression()) lr1 = LogisticRegression().setMaxIter(5) lr2 = LogisticRegression().setMaxIter(10) pipeline = Pipeline(stages=[tokenizer, hashingTF, ova]) paramGrid = ParamGridBuilder() \ .addGrid(hashingTF.numFeatures, [10, 100]) \ .addGrid(ova.classifier, [lr1, lr2]) \ .build() tvs = TrainValidationSplit(estimator=pipeline, estimatorParamMaps=paramGrid, evaluator=MulticlassClassificationEvaluator()) # Run train validation split, and choose the best set of parameters. tvsModel = tvs.fit(training) # test save/load of CrossValidatorModel tvsModelPath = temp_path + "/tvsModel" tvsModel.save(tvsModelPath) loadedModel = TrainValidationSplitModel.load(tvsModelPath) self.assertEqual(loadedModel.bestModel.uid, tvsModel.bestModel.uid) self.assertEqual(len(loadedModel.bestModel.stages), len(tvsModel.bestModel.stages)) for loadedStage, originalStage in zip(loadedModel.bestModel.stages, tvsModel.bestModel.stages): self.assertEqual(loadedStage.uid, originalStage.uid) # Test nested pipeline nested_pipeline = Pipeline(stages=[tokenizer, Pipeline(stages=[hashingTF, ova])]) tvs2 = TrainValidationSplit(estimator=nested_pipeline, estimatorParamMaps=paramGrid, evaluator=MulticlassClassificationEvaluator()) # Run train validation split, and choose the best set of parameters. tvsModel2 = tvs2.fit(training) # test save/load of CrossValidatorModel tvsModelPath2 = temp_path + "/tvsModel2" tvsModel2.save(tvsModelPath2) loadedModel2 = TrainValidationSplitModel.load(tvsModelPath2) self.assertEqual(loadedModel2.bestModel.uid, tvsModel2.bestModel.uid) loaded_nested_pipeline_model = loadedModel2.bestModel.stages[1] original_nested_pipeline_model = tvsModel2.bestModel.stages[1] self.assertEqual(loaded_nested_pipeline_model.uid, original_nested_pipeline_model.uid) self.assertEqual(len(loaded_nested_pipeline_model.stages), len(original_nested_pipeline_model.stages)) for loadedStage, originalStage in zip(loaded_nested_pipeline_model.stages, original_nested_pipeline_model.stages): self.assertEqual(loadedStage.uid, originalStage.uid) def test_copy(self): dataset = self.spark.createDataFrame([ (10, 10.0), (50, 50.0), (100, 100.0), (500, 500.0)] * 10, ["feature", "label"]) iee = InducedErrorEstimator() evaluator = RegressionEvaluator(metricName="r2") grid = ParamGridBuilder() \ .addGrid(iee.inducedError, [100.0, 0.0, 10000.0]) \ .build() tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator) tvsModel = tvs.fit(dataset) tvsCopied = tvs.copy() tvsModelCopied = tvsModel.copy() self.assertEqual(tvs.getEstimator().uid, tvsCopied.getEstimator().uid, "Copied TrainValidationSplit has the same uid of Estimator") self.assertEqual(tvsModel.bestModel.uid, tvsModelCopied.bestModel.uid) self.assertEqual(len(tvsModel.validationMetrics), len(tvsModelCopied.validationMetrics), "Copied validationMetrics has the same size of the original") for index in range(len(tvsModel.validationMetrics)): self.assertEqual(tvsModel.validationMetrics[index], tvsModelCopied.validationMetrics[index]) if __name__ == "__main__": from pyspark.ml.tests.test_tuning import * try: import xmlrunner testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)