Mastering Spark [PART 06]: List of Spark Machine Learning Models & Non-overwritten Prediction Columns

2 minute read


I was implementing a paper related to balanced random forest (BRF). Just FYI, a BRF consists of some decision trees where each tree receives instances with a ratio of 1:1 for minority and majority class. A BRF also uses m features selected randomly to determine the best split.

However, implementing a BRF in Spark has a limitation. I’ve mentioned about the limitation here. To deal with such an issue, I made several random forest trees as the replacement to common decision trees. More precisely, I used Spark’s random forest.

A problem occurred when the BRF is doing prediction. Since our BRF’s model is a list of Spark’s random forest classifiers, we need to call transform() method for each classifier. This transform() method will add the following new columns to the dataframe that is being predicted:

  • rawPrediction

For the sake of clarity, here is the code we can use for model’s prediction:

[1] models = [RandomForestClassifier_01, RandomForestClassifier_02, ..., RandomForestClassifier_0N]
[3] for index, model in enumerate(models):
[4]	predicted_df = model.transform(assembled_df)
[6]	col_name = 'POSITIVE_PROBA' + str(index)
[7]	positive_probability_udf = F.udf(lambda probas: float(probas[1]), DoubleType())
[8]	assembled_df = predicted_df.withColumn(col_name, positive_probability_udf('PROBABILITY'))

From the above code, there are several remarks:

  1. RandomForestClassifier is a type of RandomForestClassificationModel
  2. assembled_df is the data test which includes the features column (list of features)
  3. positive_probability_udf is our defined function which casts the value of positive class probability to real type
  4. Line [8] shows that we'd like Spark to add a new column named col_name. The value of this new column is the probability of positive class converted to real type

Alright, I executed the code and got these errors:

pyspark.sql.utils.IllegalArgumentException: 'requirement failed: Column PREDICTION already exists.'
pyspark.sql.utils.IllegalArgumentException: 'requirement failed: Column PROBABILITY already exists.'
pyspark.sql.utils.IllegalArgumentException: 'requirement failed: Column rawPrediction already exists.'

A quick analysis showed that after the first prediction, the dataset has already had those 3 columns. When the 2nd model made a prediction, it couldn’t store the values for those 3 columns since they are already exist. I still don’t know why Spark doesn’t overwrite the values. Anyone knows?

As you might have already known, the solution to this problem is simple. Just add this line after adding the new column (line [8]):

[9]  assembled_df = assembled_df.drop('PROBABILITY', 'PREDICTION', 'rawPrediction')

Problem solved.

Thanks for reading.