Chapter 3. Defining machine learning features

Procedure

1. From the OpenShift AI dashboard, click Projects.
2. Click the name of the project in which your cluster administrator has set up the Feature Store instance.
3. In the project in which the cluster administrator set up Feature Store, create a workbench, as described in Creating a workbench.
4. To open the IDE (for example, JuypterLab), in a new window, click the open icon ( ) next to the workbench.
5. Add a feature_store.yaml file to your notebook environment. For example, upload a local file or clone a Git repo that contains the file, as described in Uploading an existing notebook file to JupyterLab from a Git repository by using the CLI.
6. Open a new Python notebook.

7. In a cell, run the following command to install the feast CLI:

   ! pip install feast

Verification

1. Run the following command to list the available features:

   ! feast features list

   The output should show a list of features, Feature View and data type similar to the following:

   Feature	Feature         View          Data Type
   credit_card_due	        credit_history			Int64
   mortgage_due	        credit_history			Int64
   student_loan_due	    credit_history			Int64
   vehicle_loan_due	    credit_history			Int64
   city			        zipcode_features		String
   state			        zipcode_features		String
   location_type		    zipcode_features		String

2. Optionally, run the following commands to list the registered feast projects, feature views, and entities.

   ! feast projects list
   
   ! feast feature-views list
   
   ! feast entities list

Procedure

1. In the editor of your choice, create a new Python file.

2. At the beginning of the file, specify the data source for the features that you want to define within the file.

   For example, use the following code to specify the data source as a Parquet-formatted file:

   from feast import FileSource
   from feast.data_format import ParquetFormat
   
   parquet_file_source = FileSource(
       file_format=ParquetFormat(),
       path="file:///feast/customer.parquet",
   )

3. Save the file.

Procedure

1. In your IDE, such as JupyterLab, open the feature_repo/example_features.py file that contains example feature definitions or create a new Python (.py) file in the feature_repo directory.

2. Create a feature view that is relevant to your use case based on the structure shown in the following example:

   credit_history_source = FileSource(   

   1

   
   	name="Credit history",
   	path="data/credit_history.parquet",
   	file_format=ParquetFormat(),
   	timestamp_field="event_timestamp",
   	created_timestamp_column="created_timestamp",
   )
   credit_history = FeatureView(       

   2

   
   	name="credit_history",
   	entities=[dob_ssn],             

   3

   
   	ttl=timedelta(days=90),         

   4

   
   	schema=[                        

   5

   
       	Field(name="credit_card_due", dtype=Int64),
       	Field(name="mortgage_due", dtype=Int64),
       	Field(name="student_loan_due", dtype=Int64),
       	Field(name="vehicle_loan_due", dtype=Int64),
       	Field(name="hard_pulls", dtype=Int64),
       	Field(name="missed_payments_2y", dtype=Int64),
       	Field(name="missed_payments_1y", dtype=Int64),
       	Field(name="missed_payments_6m", dtype=Int64),
       	Field(name="bankruptcies", dtype=Int64),
   	],
   	source=credit_history_source,  

   6

   
       tags={"origin": "internet"},   

   7

   
   )

   1

   A data source that provides time-stamped tabular data. A feature view must always have a data source for the generation of training datasets and when materializing feature values into the online store. Possible data sources are batch data sources from data warehouses (BigQuery, Snowflake, Redshift), data lakes (S3, GCS), or stream sources. Users can push features from data sources into Feature Store, and make the features available for training or batch scoring ("offline"), for realtime feature serving ("online"), or both.

   2

   A name that identifies the feature view in the project. Within a feature view, feature names must be unique.

   3

   Zero or more entities. Feature views generally contain features that are properties of a specific object, in which case that object is defined as an entity and included in the feature view. If the features are not related to a specific object, the feature view might not have entities.

   4

   (Optional) Time-to-live (TTL) to limit how far back to look when Feature Store generates historical datasets.

   5

   One or more feature definitions.

   6

   A reference to the data source.

   7

   (Optional) You can add metadata, such as tags that enable filtering of features when viewing them in the UI, listing them by using a CLI command, or by querying the registry directly.

3. Save the file.