Validating historical features with Great Expectations

In this tutorial, we will use the public dataset of Chicago taxi trips to present data validation capabilities of Feast.

• The original dataset is stored in BigQuery and consists of raw data for each taxi trip (one row per trip) since 2013.

• We will generate several training datasets (aka historical features in Feast) for different periods and evaluate expectations made on one dataset against another.

Types of features we're ingesting and generating:

• Features that aggregate raw data with daily intervals (eg, trips per day, average fare or speed for a specific day, etc.).

• Features using SQL while pulling data from BigQuery (like total trips time or total miles travelled).

• Features calculated on the fly when requested using Feast's on-demand transformations

Our plan:

1. Prepare environment

2. Pull data from BigQuery (optional)

3. Declare & apply features and feature views in Feast

4. Generate reference dataset

5. Develop & test profiler function

6. Run validation on different dataset using reference dataset & profiler

0. Setup

Install Feast Python SDK and great expectations:

1. Dataset preparation (Optional)

You can skip this step if you don't have GCP account. Please use parquet files that are coming with this tutorial instead

Running some basic aggregations while pulling data from BigQuery. Grouping by taxi_id and day:

2. Declaring features

3. Generating training (reference) dataset

Generating range of timestamps with daily frequency:

Cross merge (aka relation multiplication) produces entity dataframe with each taxi_id repeated for each timestamp:

156984 rows × 2 columns

Retrieving historical features for resulting entity dataframe and persisting output as a saved dataset:

4. Developing dataset profiler

Dataset profiler is a function that accepts dataset and generates set of its characteristics. This charasteristics will be then used to evaluate (validate) next datasets.

Important: datasets are not compared to each other! Feast use a reference dataset and a profiler function to generate a reference profile. This profile will be then used during validation of the tested dataset.

Loading saved dataset first and exploring the data:

156984 rows × 10 columns

Testing our profiler function:

Verify that all expectations that we coded in our profiler are present here. Otherwise (if you can't find some expectations) it means that it failed to pass on the reference dataset (do it silently is default behavior of Great Expectations).

Now we can create validation reference from dataset and profiler function:

and test it against our existing retrieval job

Validation successfully passed as no exception were raised.

5. Validating new historical retrieval

Creating new timestamps for Dec 2020:

35448 rows × 2 columns

Execute retrieval job with validation reference:

Validation failed since several expectations didn't pass:

• Trip count (mean) decreased more than 10% (which is expected when comparing Dec 2020 vs June 2019)

• Average Fare increased - all quantiles are higher than expected

• Earn per hour (mean) increased more than 10% (most probably due to increased fare)