I think you have this flipped around - you want to one-hot encode, then compute interactions. As it is you are treating the product of {0,1,2,3,4} x {0,1,2,3,4} as if it's a categorical index. That doesn't have nearly 25 possible values and probably is not what you intend.

On Mon, Nov 9, 2020 at 7:53 AM Du, Yi <YDu@archcapservices.com> wrote:


How are you doing?


Please first introduce myself to you. I am Yi Du, working in a mortgage insurance company called ‘Arch Capital Group’ based in Washington DC office in US. I find your profile under the repo Spark of Github and would like to ask you one particular coding issue under Spark ML. I tried to read the documentation of Spark and also asked in Stackoverflow but still have no clue.


I am using Pyspark and using ML to build models. I have categorical variables as predictors and would like to have interactions between two categorical variables in the model as well.


I was trying to follow the example here: https://spark.apache.org/docs/latest/ml-features#interaction to create the interaction between two categorical variables.


Here is my snippet of code:



stringIndexer = StringIndexer(inputCols=['fico_group','ltv_group'], outputCols=['fico_groupIndex1','ltv_groupIndex1'], stringOrderType='frequencyAsc')

trs_data_index = stringIndexer.fit(trs_data).transform(trs_data)


interaction = Interaction(inputCols=['fico_groupIndex1','ltv_groupIndex1'], outputCol="interactedCol")

trs_data_interacted_temp = interaction.transform(trs_data_index)


encoder = OneHotEncoder(inputCols=['interactedCol'], outputCols=['interactedColVec'])

trs_data_interacted = encoder.fit(trs_data_interacted_temp).transform(trs_data_interacted_temp)



I basically index ‘fico_group’ and ‘ltv_group’ first and interact them together and use onehotencoder to create the final column ‘interactedColVec’ for use.


However, the final results didn’t come as expected. My ‘fico_group’ has 5 levels and so does ‘ltv_group’. So there are 5*5 = 25 combinations. But in the model estimates, one level should be treated as base so I expected to see 25-1 = 24 interactions in the final estimates. However, by using the above code, I have 25 interactions in the model estimates.


This is my post under Stackoverflow. https://stackoverflow.com/questions/64602060/add-interaction-term-to-ml


I don’t know if I articulated my question/issues clearly to you. But I do really appreciate your help if possible or if you can direct me to the person who knows this.


Again, thank you very much for your help.





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