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Version: 0.5

On-Demand Feature View Examples

Using Request Data

from tecton import RequestSource, on_demand_feature_view
from tecton.types import Float64, Field, Bool

request_schema = [Field("amt", Float64)]
transaction_request = RequestSource(schema=request_schema)
output_schema = [Field("transaction_amount_is_high", Bool)]

# An example of an on-demand feature view that depends only on a request source.
@on_demand_feature_view(
sources=[transaction_request],
mode="python",
schema=output_schema,
description="The transaction amount is higher than $100.",
)
def transaction_amount_is_high(transaction_request):
return {"transaction_amount_is_high": transaction_request["amt"] > 100}

Using Feature View Dependencies

from tecton import RequestSource, on_demand_feature_view
from tecton.types import String, Timestamp, Float64, Field, Bool
from fraud.features.stream_features.user_transaction_amount_metrics import (
user_transaction_amount_metrics,
)

request_schema = [Field("amt", Float64)]
transaction_request = RequestSource(schema=request_schema)
output_schema = [Field("transaction_amount_is_higher_than_average", Bool)]


@on_demand_feature_view(
sources=[transaction_request, user_transaction_amount_metrics],
mode="python",
schema=output_schema,
description="The transaction amount is higher than the 1 day average.",
)
def transaction_amount_is_higher_than_average(transaction_request, user_transaction_amount_metrics):
amount_mean = (
0
if user_transaction_amount_metrics["amt_mean_1d_10m"] is None
else user_transaction_amount_metrics["amt_mean_1d_10m"]
)
return {"transaction_amount_is_higher_than_average": transaction_request["amt"] > amount_mean}

Embedding Cosine Similarity

Cosine similarity between embeddings is a common and powerful cross feature that can be implemented with On-Demand Feature Views. In this example, the feature view is computing the similarity between a pre-computed user embedding and a query embedding passed in the request to the Tecton Feature Service.

from tecton import on_demand_feature_view
from tecton.types import Field, Float64
from ads.features.feature_tables.user_embeddings import user_embeddings
from ads.features.feature_tables.ad_embeddings import ad_embeddings
import pandas

output_schema = [Field("cosine_similarity", Float64)]


@on_demand_feature_view(
sources=[ad_embeddings, user_embeddings],
mode="python",
schema=output_schema,
owner="jake@tecton.ai",
tags={"release": "production"},
description="Computes the cosine similarity between a precomputed ad embedding and a precomputed user embedding.",
)
def user_ad_embedding_similarity(ad_embedding, user_embedding):
import numpy as np
from numpy.linalg import norm

def cosine_similarity(a: np.ndarray, b: np.ndarray):
# Handle the case where one or both entities do not have a precomputed embedding.
if a is None or b is None:
return -1.0

return np.dot(a, b) / (norm(a) * norm(b))

result = {}
result["cosine_similarity"] = cosine_similarity(
user_embedding["user_embedding"], ad_embedding["ad_embedding"]
).astype("float64")
return result

Returning Struct Features

from tecton import on_demand_feature_view, RequestSource
from tecton.types import Array, Field, Float64, String, Struct

request_source = RequestSource([Field("input_float", Float64)])

output_schema = [
Field(
"output_struct",
Struct([Field("string_field", String), Field("float64_field", Float64)]),
)
]


@on_demand_feature_view(
mode="python",
sources=[request_source],
schema=output_schema,
description="Output a struct with two fields.",
)
def simple_struct_example_odfv(request):
input_float = request["input_float"]
return {
"output_struct": {
"string_field": str(input_float * 2),
"float64_field": input_float * 2,
}
}

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