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You can follow the instructions below to install Featureform locally and try out the dashboard. You can also try local mode in this example 📔 Google Colab notebook 📔 here.

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Step 1: Install Featureform

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Requirements

  • Python 3.9+
Install the Featureform SDK via Pip. 
pip install featureform

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Step 2: Download test data

For this quickstart, we’ll use a fraudulent transaction dataset that can be found here: https://featureform-demo-files.s3.amazonaws.com/transactions.csvThe data contains 9 columns, almost all of which would require some feature engineering before being used 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

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Step 3: Register files

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 them to our Featureform instance. 
featureform apply definitions.py --local

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Step 4: Serve features for training and inference

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