PySpark MultiLayerPercepTronClassifier seems to not work wirh OneHotEncoding - pyspark

Question

I'm running a duummy example to perform classification with PySpark.
I created an ETL pipeline in which labels are transformed to OneHotEncoding, but
PySpark throws:
IllegalArgumentException: 'requirement failed: Column label must be of type numeric but was actually of type struct<type:tinyint,size:int,indices:array<int>,values:array<double>>.'
Code for Sparse One-hot
from pyspark.ml.feature import StringIndexer, StandardScaler, OneHotEncoderEstimator, StandardScaler
from pyspark.ml import Pipeline
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql.functions import rand
df = spark.createDataFrame([
("Music", 3.45,1245),
("Sports", 4.49,3456),
("Music", 1.22, 323),
("Animals", 2.45,24)], ["category", "rating", "views"])
"""ETL Pipeline over
the whole dataset
"""
indexer = StringIndexer(inputCol="category", outputCol="class",handleInvalid="skip")
encoder = OneHotEncoderEstimator(inputCols=["class"],
outputCols=["label"])
encoder.setDropLast(False)
vectorizer = VectorAssembler(inputCols=["rating","views"],
outputCol="unscaled_features")
etl_pipeline = Pipeline(stages=[indexer,encoder,vectorizer])
etlModel = etl_pipeline.fit(df)
tr_df = etlModel.transform(df)
tr_df.show()
"""Training Pipeline
"""
train_data, test_data = tr_df.randomSplit([.8, .2],seed=23487)
scaler = StandardScaler(inputCol="unscaled_features", outputCol="features",
withStd=True, withMean=True)
# specify layers for the neural network:
layers = [4, 5, 4, 3]
# create the trainer and set its parameters
trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=1, seed=1234)
ml_pipeline = Pipeline(stages=[scaler, trainer])
mlModel = ml_pipeline.fit(train_data)
result = mlModel.transform(test_data)
predictionAndLabels = result.select("prediction", "label")
evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
print("Test set accuracy = " + str(evaluator.evaluate(predictionAndLabels)))
Out
+--------+------+-----+-----+-------------+-----------------+
|category|rating|views|class| label|unscaled_features|
+--------+------+-----+-----+-------------+-----------------+
| Music| 3.45| 1245| 0.0|(3,[0],[1.0])| [3.45,1245.0]|
| Sports| 4.49| 3456| 2.0|(3,[2],[1.0])| [4.49,3456.0]|
| Music| 1.22| 323| 0.0|(3,[0],[1.0])| [1.22,323.0]|
| Animals| 2.45| 24| 1.0|(3,[1],[1.0])| [2.45,24.0]|
+--------+------+-----+-----+-------------+-----------------+
IllegalArgumentException: 'requirement failed: Column label must be of type numeric but was actually of type struct<type:tinyint,size:int,indices:array<int>,values:array<double>>.'
The weird thing is that although I convert the SparseVecotr of one-hot-labels to a DenseVector, the error still remains. It seems that MultilayerPerceptronClassifier converts dense labels to sparse but it is not working properly...
Code for ETL with dense one-hot
from pyspark.ml.feature import StringIndexer, StandardScaler, OneHotEncoderEstimator, StandardScaler
from pyspark.ml import Pipeline
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql.functions import rand
df = spark.createDataFrame([
("Music", 3.45,1245),
("Sports", 4.49,3456),
("Music", 1.22, 323),
("Animals", 2.45,24)], ["category", "rating", "views"])
"""ETL Pipeline over
the whole dataset
"""
indexer = StringIndexer(inputCol="category", outputCol="class",handleInvalid="skip")
encoder = OneHotEncoderEstimator(inputCols=["class"],
outputCols=["label"])
encoder.setDropLast(False)
vectorizer = VectorAssembler(inputCols=["rating","views"],
outputCol="unscaled_features")
etl_pipeline = Pipeline(stages=[indexer,encoder,vectorizer])
etlModel = etl_pipeline.fit(df)
tr_df = etlModel.transform(df)
tr_df = tr_df.select("label", "unscaled_features")
rdd = tr_df.rdd.map(lambda x: Row(label=DenseVector(x[0].toArray()),unscaled_features=x[1])
if (len(x)>1 and hasattr(x[0], "toArray"))
else Row(label=None, unscaled_features=DenseVector([])))
tr_df = rdd.toDF()
tr_df.show()
"""Training Pipeline
"""
train_data, test_data = tr_df.randomSplit([.8, .2],seed=23487)
scaler = StandardScaler(inputCol="unscaled_features", outputCol="features",
withStd=True, withMean=True)
# specify layers for the neural network:
layers = [4, 5, 4, 3]
# create the trainer and set its parameters
trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=1, seed=1234)
ml_pipeline = Pipeline(stages=[scaler, trainer])
mlModel = ml_pipeline.fit(train_data)
result = mlModel.transform(test_data)
predictionAndLabels = result.select("prediction", "label")
evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
print("Test set accuracy = " + str(evaluator.evaluate(predictionAndLabels)))
Out
+-------------+-----------------+
| label|unscaled_features|
+-------------+-----------------+
|[1.0,0.0,0.0]| [3.45,1245.0]|
|[0.0,0.0,1.0]| [4.49,3456.0]|
|[1.0,0.0,0.0]| [1.22,323.0]|
|[0.0,1.0,0.0]| [2.45,24.0]|
+-------------+-----------------+
IllegalArgumentException: 'requirement failed: Column label must be of type numeric but was actually of type struct<type:tinyint,size:int,indices:array<int>,values:array<double>>.'
UPDATE 1: REMOVING ONE-HOT ENCODING FROM PIPELINE
Code
from pyspark.ml.feature import StringIndexer, StandardScaler, OneHotEncoderEstimator, StandardScaler
from pyspark.ml import Pipeline
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql.functions import rand
df = spark.createDataFrame([
("Music", 3.45,1245),
("Sports", 4.49,3456),
("Music", 1.22, 323),
("Animals", 2.45,24)], ["category", "rating", "views"])
"""ETL Pipeline over
the whole dataset
"""
indexer = StringIndexer(inputCol="category", outputCol="label",handleInvalid="skip")
# encoder = OneHotEncoderEstimator(inputCols=["class"],
# outputCols=["label"])
# encoder.setDropLast(False)
vectorizer = VectorAssembler(inputCols=["rating","views"],
outputCol="unscaled_features")
etl_pipeline = Pipeline(stages=[indexer,vectorizer])
etlModel = etl_pipeline.fit(df)
tr_df = etlModel.transform(df)
tr_df.show()
"""Training Pipeline
"""
train_data, test_data = tr_df.randomSplit([.8, .2],seed=23487)
scaler = StandardScaler(inputCol="unscaled_features", outputCol="features",
withStd=True, withMean=True)
# specify layers for the neural network:
layers = [4, 5, 4, 3]
# create the trainer and set its parameters
trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)
ml_pipeline = Pipeline(stages=[scaler, trainer])
mlModel = ml_pipeline.fit(train_data)
result = mlModel.transform(test_data)
predictionAndLabels = result.select("prediction", "label")
evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
print("Test set accuracy = " + str(evaluator.evaluate(predictionAndLabels)))
OUT
+--------+------+-----+-----+-----------------+
|category|rating|views|label|unscaled_features|
+--------+------+-----+-----+-----------------+
| Music| 3.45| 1245| 0.0| [3.45,1245.0]|
| Sports| 4.49| 3456| 2.0| [4.49,3456.0]|
| Music| 1.22| 323| 0.0| [1.22,323.0]|
| Animals| 2.45| 24| 1.0| [2.45,24.0]|
+--------+------+-----+-----+-----------------+
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-9-58967f1d5bce> in <module>
60
61 ml_pipeline = Pipeline(stages=[scaler, trainer])
---> 62 mlModel = ml_pipeline.fit(train_data)
63 result = mlModel.transform(test_data)
64 predictionAndLabels = result.select("prediction", "label")
~/.local/lib/python3.5/site-packages/pyspark/ml/base.py in fit(self, dataset, params)
130 return self.copy(params)._fit(dataset)
131 else:
--> 132 return self._fit(dataset)
133 else:
134 raise ValueError("Params must be either a param map or a list/tuple of param maps, "
~/.local/lib/python3.5/site-packages/pyspark/ml/pipeline.py in _fit(self, dataset)
107 dataset = stage.transform(dataset)
108 else: # must be an Estimator
--> 109 model = stage.fit(dataset)
110 transformers.append(model)
111 if i < indexOfLastEstimator:
~/.local/lib/python3.5/site-packages/pyspark/ml/base.py in fit(self, dataset, params)
130 return self.copy(params)._fit(dataset)
131 else:
--> 132 return self._fit(dataset)
133 else:
134 raise ValueError("Params must be either a param map or a list/tuple of param maps, "
~/.local/lib/python3.5/site-packages/pyspark/ml/wrapper.py in _fit(self, dataset)
293
294 def _fit(self, dataset):
--> 295 java_model = self._fit_java(dataset)
296 model = self._create_model(java_model)
297 return self._copyValues(model)
~/.local/lib/python3.5/site-packages/pyspark/ml/wrapper.py in _fit_java(self, dataset)
290 """
291 self._transfer_params_to_java()
--> 292 return self._java_obj.fit(dataset._jdf)
293
294 def _fit(self, dataset):
~/.local/lib/python3.5/site-packages/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
~/.local/lib/python3.5/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
~/.local/lib/python3.5/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling o870.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 26.0 failed 1 times, most recent failure: Lost task 0.0 in stage 26.0 (TID 26, localhost, executor driver): java.lang.ArrayIndexOutOfBoundsException
at java.lang.System.arraycopy(Native Method)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3$$anonfun$apply$4.apply(Layer.scala:665)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3$$anonfun$apply$4.apply(Layer.scala:664)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3.apply(Layer.scala:664)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3.apply(Layer.scala:660)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
at org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:222)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:299)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1165)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1156)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD.count(RDD.scala:1168)
at org.apache.spark.mllib.optimization.LBFGS$.runLBFGS(LBFGS.scala:195)
at org.apache.spark.mllib.optimization.LBFGS.optimize(LBFGS.scala:142)
at org.apache.spark.ml.ann.FeedForwardTrainer.train(Layer.scala:854)
at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$train$1.apply(MultilayerPerceptronClassifier.scala:249)
at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$train$1.apply(MultilayerPerceptronClassifier.scala:205)
at org.apache.spark.ml.util.Instrumentation$$anonfun$11.apply(Instrumentation.scala:185)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:185)
at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:205)
at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:114)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:118)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ArrayIndexOutOfBoundsException
at java.lang.System.arraycopy(Native Method)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3$$anonfun$apply$4.apply(Layer.scala:665)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3$$anonfun$apply$4.apply(Layer.scala:664)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3.apply(Layer.scala:664)
at org.apache.spark.ml.ann.DataStacker$$anonfun$5$$anonfun$apply$3.apply(Layer.scala:660)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:410)
at org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:222)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:299)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1165)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1156)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
PySPark version
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.4
/_/
Using Scala version 2.11.12, OpenJDK 64-Bit Server VM, 1.8.0_222
Java Version
openjdk version "1.8.0_222"
OpenJDK Runtime Environment (build 1.8.0_222-8u222-b10-1ubuntu1~16.04.1-b10)
OpenJDK 64-Bit Server VM (build 25.222-b10, mixed mode)

Answer 1

You should run the features via a VectorAssembler but you don't need to do one-hot-encoding on the label column. You should just pass the labels column as numeric classes as they are:
+------+-----------------+
| label|unscaled_features|
+------+-----------------+
| 0| [3.45,1245.0]|
| 2| [4.49,3456.0]|
| 0| [1.22,323.0]|
| 1| [2.45,24.0]|
+------+-----------------+
This should solve your error.