Python Resolvers

If you want to skip ahead, you can find the full source code for this tutorial on GitHub.

So far, we’ve mapped SQL tables into feature classes. But there’s a lot more we can do with Chalk. In this step, we’ll add features to our Account and User feature classes that are gathered from API calls and computed downstream of other features.

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Derived Features

We’ve noticed that some fraudsters try to link stolen accounts to our platform and attempt to transfer money through our system. To detect this behavior, we want to compute a similarity score between the user’s name and the account’s title.

We’ll start by adding this new feature, account_name_match, to our User feature class.

@features
class User:
  id: int
  name: str
  email: str
  account: "Account"

  # The similarity between the user's name and the account's title.
  account_name_match: float

Next, we’ll define a resolver that computes this feature. We’ll use Jaccard similarity to compute the similarity score.

from src.models import User
from chalk import online

@online
def account_name_match(
    title: User.account.title,
    name: User.name,
) -> User.account_name_match:
    """Docstrings show up in the Chalk dashboard"""
    intersection = set(title) & set(name)
    union = set(title) | set(name)
    return len(intersection) / len(union)

The @online decorator tells Chalk that this resolver should be called in realtime when the User.account_name_match feature is requested. Our feature dependencies are declared in the function signature as User.account.title and User.name. Chalk will automatically retrieve User.account_id and User.name from our user.chalk.sql resolver. Then, using this account id, Chalk will retrieve Account.title from the online store, where it has been cached from our cron run of the balance.chalk.sql resolver.

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Testing

Resolvers are callable functions, so we can test them like any other Python function. Let’s test our new resolver by writing a unit test:

from src.resolvers import account_name_match

def test_names_match():
    """Resolvers can be unit tested exactly as you would expect.

    Here, the `account_name_match` resolver should return 1.0
    because the `title` and `name` are identical.
    """
    assert 1 == account_name_match(
        title="John Coltrane",
        name="John Coltrane",
    )

def test_names_completely_different():
    """The `account_name_match` resolver should return 0
    because the `title` and `name` don't share any characters.
    """
    assert 0 == account_name_match(
        title="John Coltrane",
        name="Zyx",
    )

You can read more about testing resolvers in the API docs.

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API Calls

Any Python function can be used as a resolver. This means that we can call APIs to compute features. Let’s add a feature that computes the user’s FICO score from our credit scoring vendor, Experian.

As before, we’ll first add the features that we want to compute:

from chalk.features import feature

@features
class User:
  id: int
  name: str
  email: str
  account_name_match: float

  # The fraud score, as provided by a third-party vendor.
  fico_score: int = feature(min=300, max=850, strict=True)

  # Tags from our credit scoring vendor.
  credit_score_tags: list[str]

We are adding strict validation to our fico_score feature to ensure that we only store and utilize valid FICO scores.

Now, we can write a resolver to fetch the user’s FICO score from Experian.

from src.models import User
from src.mocks import experian
from chalk.features import online, Features

@online
def get_fraud_score(
    name: User.name,
    email: User.email,
) -> Features[User.fico_score, User.credit_score_tags]:
    response = experian.get_credit_score(name, email)

    # We don't need to provide all the features for
    # the `User` class, only the ones that we want to update.
    return User(
        fico_score=response['fico_score'],
        credit_score_tags=response['tags'],
    )

Here, we are returning two features of the user, User.fico_score and User.credit_score_tags. We use the Features type to indicate which feature we expect to return. Also note that we are initializing the User class with only the features that we want to update. This partial initialization is the primary difference between Python’s @dataclass and Chalk’s @features.

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Deploying

Finally, we’ll want to deploy our new resolvers. As before, we can check our work by using a branch deployment:

$ chalk apply --branch tutorial
✓ Found resolvers
✓ Deployed branch

We can then query our new features:

$ chalk query --branch tutorial  \
              --in     user.id=1 \
              --out    user.name_match_score