alva | Create living prototypes with code components | Frontend Utils library

Data frame one.

I installed a Kubernetes cluster of three nodes, the control node looked ok, when I tried to join the other two nodes the status for both of is: Not Ready

On control node:

I have a dataset for credit card transaction.

I split this dataset by group using below code

I am trying to parse a string with parentheses inside parentheses. As long as the string to parse is pretty small and don't have to many nested parentheses everything is working fine.

But, when the string to parse get big I keep getting errors like FATAL ERROR: CALL_AND_RETRY_LAST Allocation failed - JavaScript heap out of memory and RangeError: Maximum call stack size exceeded.

Can anyone tell me how I can optimize/fix the code below so it works on strings of bigger size without memory and stack size errors? The big string I am trying to parse can be found here

The goal is to turn a string looking like this

Any help appreciated. I have a JSON data with format like this

{ state1: [ member1, member2], state2: [ member,member4...], ... }

And there is a dropdown which shows the list of states on the JSON data. Based on the selected state I need to display the corresponding list of member on the table.

I'm wondering what causes difference in output when it should be the same. It's like my program is ignoring the sorted function and feature_names. The sorting of the coef_ is quite crucial for me to find out which features are actually helping the predictions the most. I get the individual words from vectorizer.get_feature_names but not when it is in a loop or function definition. Does anybody have any idea what could be happening, or if anybody has other methods of extracting ngram feature weights and their names for an SVC with kernel='linear'.

My code: