Getting Features for Inference

This tutorial shows how to access data stored in the Qwak Feature Store online store during the inference.

Using `OnlineClient`

In the predict function, we create an instance of the OnlineClient.

After that, we create a ModelSchema containing all of the features we want to retrieve.

We create a DataFrame containing the entities identifiers and pass it to the Feature Store. As a response, we get a Pandas DataFrame with the requested features.

import pandas as pd
from qwak.feature_store.online.client import OnlineClient
from qwak.model.schema_entities import FeatureStoreInput, Entity
from qwak.model.schema import ModelSchema

entity = Entity(name='user')

model_schema = ModelSchema(
    inputs=[
        FeatureStoreInput(name='user-credit-risk-features.checking_account',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.age',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.job',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.duration',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.credit_amount',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.housing',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.purpose',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.saving_account',  entity=entity),
        FeatureStoreInput(name='user-credit-risk-features.sex',  entity=entity),
        FeatureStoreInput(name='liked-posts.count',  entity=entity)
    ])
    
online_client = OnlineClient()

df = pd.DataFrame(columns=[               'user',               		 'post_id'],
                  data   =[['06cc255a-aa07-4ec9-ac69-b896ccf05322',   '1234'],
                           ['asdc255a-aa07-4ec9-ac69-b896c1231445',   '7889']])
                  
user_features = online_client.get_feature_values(model_schema, df)

print(user_features)

Using OnlineClient

Using `OnlineClient`