I am using Spark's MultilayerPerceptronClassifier. This generates a column 'predicted' in 'predictions'. When I try to show it I get the error:
SparkException: Failed to execute user defined function($anonfun$1: (vector) => double) ... Caused by: java.lang.IllegalArgumentException: requirement failed: A & B Dimension mismatch! Other columns, for example, vector display OK. Part of predictions schema:
|-- vector: vector (nullable = true) |-- prediction: double (nullable = true) My code is:
//racist is boolean, needs to be string: val train2 = train.withColumn("racist", 'racist.cast("String")) val test2 = test.withColumn("racist", 'racist.cast("String")) val indexer = new StringIndexer().setInputCol("racist").setOutputCol("indexracist") val word2Vec = new Word2Vec().setInputCol("lemma").setOutputCol("vector") //.setVectorSize(3).setMinCount(0) val layers = Array[Int](4,5, 2) val mpc = new MultilayerPerceptronClassifier().setLayers(layers).setBlockSize(128).setSeed(1234L).setMaxIter(100).setFeaturesCol("vector").setLabelCol("indexracist") val pipeline = new Pipeline().setStages(Array(indexer, word2Vec, mpc)) val model = pipeline.fit(train2) val predictions = model.transform(test2) predictions.select("prediction").show() EDIT the proposed similar question's problem was
val layers = Array[Int](0, 0, 0, 0) which is not the case here, nor is it the same error.
EDIT AGAIN: part0 of train and test are saved in PARQUET format here.
1 Answers
Answers 1
The addition of .setVectorSize(3).setMinCount(0) and changnig val layers = Array[Int](3,5, 2) made it work:
val word2Vec = new Word2Vec().setInputCol("lemma").setOutputCol("vector").setVectorSize(3).setMinCount(0) // specify layers for the neural network: // input layer of size 4 (features), two intermediate of size 5 and 4 // and output of size 3 (classes) val layers = Array[Int](3,5, 2) 
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