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.assert_param_maps_equal(
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_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 _run_test_save_load_nested_estimator(self, LogisticRegressionCls):
# 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=LogisticRegressionCls())
lr1 = LogisticRegressionCls().setMaxIter(100)
lr2 = LogisticRegressionCls().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.assert_param_maps_equal(loadedTvs.getEstimatorParamMaps(), grid)
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.assert_param_maps_equal(loadedModel.getEstimatorParamMaps(), grid)
self.assertEqual(loadedModel.bestModel.uid, tvsModel.bestModel.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())
tvsPath = temp_path + "/tvs"
tvs.save(tvsPath)
loadedTvs = TrainValidationSplit.load(tvsPath)
self.assert_param_maps_equal(loadedTvs.getEstimatorParamMaps(), paramGrid)
self.assertEqual(loadedTvs.getEstimator().uid, tvs.getEstimator().uid)
# 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())
tvs2Path = temp_path + "/tvs2"
tvs2.save(tvs2Path)
loadedTvs2 = TrainValidationSplit.load(tvs2Path)
self.assert_param_maps_equal(loadedTvs2.getEstimatorParamMaps(), paramGrid)
self.assertEqual(loadedTvs2.getEstimator().uid, tvs2.getEstimator().uid)
# 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)
