Quickstart (Local)
A quick start guide for Featureform localmode. This is not meant for production, but rather for local experimentation or to test our the Featureform API.
You can follow the instructions below to install Featureform locally and try out the dashboard.
- Python 3.7+
Install the Featureform SDK via Pip.
pip install featureform
For this quickstart, we'll use a fraudulent transaction dataset that can be found here: https://featureform-demo-files.s3.amazonaws.com/transactions.csv
The data contains 9 columns, almost all of would require some feature engineering before using in a typical model.
TransactionID,CustomerID,CustomerDOB,CustLocation,CustAccountBalance,TransactionAmount (INR),Timestamp,IsFraud
T1,C5841053,10/1/94,JAMSHEDPUR,17819.05,25,2022-04-09 11:33:09,False
T2,C2142763,4/4/57,JHAJJAR,2270.69,27999,2022-03-27 01:04:21,False
T3,C4417068,26/11/96,MUMBAI,17874.44,459,2022-04-07 00:48:14,False
T4,C5342380,14/9/73,MUMBAI,866503.21,2060,2022-04-14 07:56:59,True
T5,C9031234,24/3/88,NAVI MUMBAI,6714.43,1762.5,2022-04-13 07:39:19,False
T6,C1536588,8/10/72,ITANAGAR,53609.2,676,2022-03-26 17:02:51,True
T7,C7126560,26/1/92,MUMBAI,973.46,566,2022-03-29 08:00:09,True
T8,C1220223,27/1/82,MUMBAI,95075.54,148,2022-04-12 07:01:02,True
T9,C8536061,19/4/88,GURGAON,14906.96,833,2022-04-10 20:43:10,True
We can write a config file in Python that registers our test data file.
definitions.py
import featureform as ff
ff.register_user("featureformer").make_default_owner()
local = ff.register_local()
transactions = local.register_file(
name="transactions",
variant="quickstart",
description="A dataset of fraudulent transactions",
path="transactions.csv"
)
Next, we'll define a Dataframe transformation on our dataset.
definitions.py
@local.df_transformation(variant="quickstart",
inputs=[("transactions", "quickstart")])
def average_user_transaction(transactions):
"""the average transaction amount for a user """
return transactions.groupby("CustomerID")["TransactionAmount"].mean()
Next, we'll register a user entity to associate with a feature and label.
definitions.py
@ff.entity
class User:
avg_transactions = ff.Feature(
average_user_transaction[["CustomerID", "TransactionAmount"]], # We can optional include the `timestamp_column` "Timestamp" here
variant="quickstart",
type=ff.Float32,
inference_store=local,
)
fraudulent = ff.Label(
transactions[["CustomerID", "IsFraud"]], variant="quickstart", type=ff.Bool
)
The
ff.entity decorator will use the lowercased class name as the entity name. The class attributes avg_transactions and fraudulent will be registered as a feature and label, respectively, associated with the user entity. Indexing into the sources (e.g. average_user_transaction) with a [["<ENTITY COLUMN>", "<FEATURE/LABEL COLUMN>"]], returns the required parameters to the Feature and Label registration classes.When registering more than one variant, we can use the
Variants registration class:definitions.py
@ff.entity
class User:
avg_transactions = ff.Variants(
{
"quickstart": ff.Feature(
average_user_transaction[["CustomerID", "TransactionAmount"]],
type=ff.Float32,
inference_store=local,
),
"quickstart_v2": ff.Feature(
average_user_transaction[["CustomerID", "TransactionAmount"]],
type=ff.Float32,
inference_store=local,
),
}
)
fraudulent = ff.Label(
transactions[["CustomerID", "IsFraud"]], variant="quickstart", type=ff.Bool
)
Finally, we'll join together the feature and label into a training set.
definitions.py
ff.register_training_set(
"fraud_training", "quickstart",
label=("fraudulent", "quickstart"),
features=[("avg_transactions", "quickstart")],
)
Now that our definitions are complete, we can apply it to our Featureform instance.
featureform apply definitions.py --local
Once we have our training set and features registered, we can train our model.
import featureform as ff
client = ff.Client(local=True)
dataset = client.training_set("fraud_training", "quickstart")
training_dataset = dataset.repeat(10).shuffle(1000).batch(8)
for row in training_dataset:
print(row.features(), row.label())
We can serve features in production once we deploy our trained model as well.
import featureform as ff
client = ff.Client(local=True)
fpf = client.features([("avg_transactions", "quickstart")], {"user": "C1010012"})
# Run features through model
Last modified 24d ago