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Insurance

Insurance Sample SDK Usage with Local Worker

Scenario: Process on the interface, deploy and access the process

  1. Open insurance.csv

  2. Encoding categorical variables

  3. Delete the originals of the columns you encoded

  4. Navigating the Deploy button, choosing Jupyter Notebook Option:

  5. Clicking Advanced and selecting view code and include security token options:

  6. Seeing your connection and worker json created and your code ready:

Connect to your active worker with get local worker

import practicuscore as prt

worker = prt.get_local_worker()
  • To access the dataset you need to work with connection configuration dictionary
  • Also, you can choose a different Engine than Advance in the Deploy phase
# configuration of connection

data_set_conn = {
    "connection_type": "WORKER_FILE",
    "ws_uuid": "d9b92183-8832-4fd1-8187-ac741ff6aab0",
    "ws_name": "insurance",
    "file_path": "/home/ubuntu/samples/insurance.csv"
}

proc = worker.load(data_set_conn)

Data preparation with Practicus AI SDK

proc.categorical_map(column_name='sex', column_suffix='category') 

proc.categorical_map(column_name='smoker', column_suffix='category') 

proc.categorical_map(column_name='region', column_suffix='category') 

proc.delete_columns(['region', 'smoker', 'sex']) 

proc.wait_until_done()
proc.show_logs()

df = proc.get_df_copy()
display(df)

Finish the process

proc.kill()

You can also prepare this code directly in pipeline:

If you make the process functional and do it with with, you don't need to kill the worker when the process is finished. The worker is automatically killed when this process is finished
# Running the below will terminate the worker 
# with prt.get_local_worker() as worker:

# E.g. the below would run the task, and then kill the process first, and then the worker.
# with prt.get_local_worker() as worker:
#     with worker.load(data_set_conn) as proc:
#         proc.categorical_map(column_name='sex', column_suffix='category'), 
#         proc.categorical_map(column_name='smoker', column_suffix='category'),
#         proc.categorical_map(column_name='region', column_suffix='category'),
#         proc.delete_columns(['region', 'smoker', 'sex']) 
#         proc.wait_until_done()
#         proc.show_logs()

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