Testing a Feature Aggregation

Developing features for Triage experiments can be a daunting task. There are a lot of things to configure, a small amount of configuration can result in a ton of SQL, and it can take a long time to validate your feature configuration in the context of an Experiment being run on real data.

To speed up the process of iterating on features, you can run a list of feature aggregations, without imputation, on just one as-of-date. This functionality can be accessed through the triage command line tool or called directly from code (say, in a Jupyter notebook) using the FeatureGenerator component.

Using Triage CLI

The command-line interface for testing features takes in two arguments: - An experiment config file. Refer to the example_experiment_config.yaml's feature_aggregations section. It consists of a YAML list, with one or more feature_aggregation rows present. - An as-of-date. This should be in the format 2016-01-01.

Example: triage experiment featuretest example/config/experiment.yaml 2016-01-01

All given feature aggregations will be processed for the given date. You will see a bunch of queries pass by in your terminal, populating tables in the features_test schema which you can inspect afterwards.

Using Python Code

If you'd like to call this from a notebook or from any other Python code, the arguments look similar but are a bit different. You have to supply your own sqlalchemy database engine to create a 'FeatureGenerator' object, and then call the create_features_before_imputation method with your feature config as a list of dictionaries, along with an as-of-date as a string. Make sure your logging level is set to INFO if you want to see all of the queries.

from triage.component.architect.feature_generators import FeatureGenerator
from triage.util.db import create_engine
import logging
import yaml

logging.basicConfig(level=logging.INFO)

# create a db_engine 
db_url = 'your db url here'
db_engine = create_engine(db_url)

feature_config = [{
    'prefix': 'aprefix',
    'aggregates': [
        {
        'quantity': 'quantity_one',
        'metrics': ['sum', 'count'],
    ],
    'categoricals': [
        {
            'column': 'cat_one',
            'choices': ['good', 'bad'],
            'metrics': ['sum']
        },
    ],
    'intervals': ['all'],
    'knowledge_date_column': 'knowledge_date',
    'from_obj': 'data'
}]

FeatureGenerator(db_engine, 'features_test').create_features_before_imputation(
    feature_aggregation_config=feature_config,
    feature_dates=['2016-01-01']
)