Playground: Credit Cards¶

The notebook creates a fresh catalog using the credit card dataset. You can use it to practice feature engineering.

Load the featurebyte library and connect to the local instance of featurebyte¶

In [1]:

# library imports
import pandas as pd
import numpy as np

# load the featurebyte SDK
import featurebyte as fb

# start the local server, then wait for it to be healthy before proceeding
fb.playground()

# library imports import pandas as pd import numpy as np # load the featurebyte SDK import featurebyte as fb # start the local server, then wait for it to be healthy before proceeding fb.playground()

02:07:16 | INFO     | Using configuration file at: /home/chester/.featurebyte/config.yaml
02:07:16 | INFO     | Active profile: local (http://127.0.0.1:8088)
02:07:16 | INFO     | SDK version: 0.2.2
02:07:16 | INFO     | Active catalog: default
02:07:16 | INFO     | 0 feature list, 0 feature deployed
02:07:16 | INFO     | (1/4) Starting featurebyte services
 Container mongo-rs  Running
 Container spark-thrift  Running
 Container redis  Running
 Container featurebyte-server  Running
 Container featurebyte-worker  Running
 Container redis  Waiting
 Container mongo-rs  Waiting
 Container mongo-rs  Waiting
 Container redis  Healthy
 Container mongo-rs  Healthy
 Container mongo-rs  Healthy
02:07:17 | INFO     | (2/4) Creating local spark feature store
02:07:17 | INFO     | (3/4) Import datasets
02:07:18 | INFO     | Dataset grocery already exists, skipping import
02:07:18 | INFO     | Dataset healthcare already exists, skipping import
02:07:18 | INFO     | Dataset creditcard already exists, skipping import
02:07:18 | INFO     | (4/4) Playground environment started successfully. Ready to go! 🚀

Create a pre-built catalog for this tutorial, with the data, metadata, and features already set up¶

Note that creating a pre-built catalog is not a step you will do in real-life. This is a function specific to this quick-start tutorial to quickly skip over many of the preparatory steps and get you to a point where you can materialize features.

In a real-life project you would do data modeling, declaring the tables, entities, and the associated metadata. This would not be a frequent task, but forms the basis for best-practice feature engineering.

Load the featurebyte library and connect to the local instance of featurebyte¶

In [2]:

# get the functions to create a pre-built catalog
from prebuilt_catalogs import *

# create a new catalog for this tutorial
catalog = create_tutorial_catalog(PrebuiltCatalog.Playground_CreditCard)

# get the functions to create a pre-built catalog from prebuilt_catalogs import * # create a new catalog for this tutorial catalog = create_tutorial_catalog(PrebuiltCatalog.Playground_CreditCard)

Cleaning up existing tutorial catalogs

02:07:19 | INFO     | Catalog activated: deep dive materializing features 20230511:0202

Cleaning catalog: deep dive materializing features 20230511:0202
  1 batch feature tables
  1 batch request tables
  1 historical feature tables
  2 observation tables
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.0s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.0s (0.17%/s)         

02:07:50 | INFO     | Catalog activated: default
02:07:50 | INFO     | Catalog activated: credit card playground 20230511:0207

Building a playground catalog for credit cards named [credit card playground 20230511:0207]
Creating new catalog
Catalog created
Registering the source tables
Registering the entities
Tagging the entities to columns in the data tables

##################################################################
# suggested script to load the tables and views into your notebook

# get the table objects
cardtransactiongroups_table = catalog.get_table("CARDTRANSACTIONGROUPS")
cardfraudstatus_table = catalog.get_table("CARDFRAUDSTATUS")
cardtransactions_table = catalog.get_table("CARDTRANSACTIONS")
creditcard_table = catalog.get_table("CREDITCARD")
statedetails_table = catalog.get_table("STATEDETAILS")
bankcustomer_table = catalog.get_table("BANKCUSTOMER")

# get the view objects
cardtransactiongroups_view = cardtransactiongroups_table.get_view()
cardfraudstatus_view = cardfraudstatus_table.get_view()
cardtransactions_view = cardtransactions_table.get_view()
creditcard_view = creditcard_table.get_view()
statedetails_view = statedetails_table.get_view()
bankcustomer_view = bankcustomer_table.get_view()

##################################################################