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Version: v3.0.0

Maintain a feature set

Refreshing feature set​

To refresh the feature set to contain the latest information, call:

fs.refresh()

Marking feature as target variable​

When feature sets are used to train ML models, it can be beneficial to know which feature was used as model's target variable. In order to communicate this knowledge between different feature set users, there is a possibility to mark/discard a feature as a target variable and list those marked features.

feature_state = fs.features["state"]
feature_state.mark_as_target_variable()

fs.list_features_used_as_target_variable()

feature_state.discard_as_target_variable()

Open feature set in Web UI​

This method opens the given feature set in Feature Store Web UI.

fs.open_website()

Optimizing feature set storage (Delta lake backend only)​

In special cases, there can be a performance benefit when a feature set's data gets optimized. In order to manually enforce a storage optimization use following call. By default, feature set storage gets optimized by Z-order optimization for primary key(s). In case an optimization for different feature's list is needed, you can specify the optimization explicitly when making the call.

The optimization call returns optimization metrics provided by storage. Furthermore, a new minor feature set version gets created. The updated feature set version contains optimization input as one of its attributes.

from h2o_featurestore import ZOrderByOptimization

# z-order optimization for primary key(s) by default
metrics = fs.optimize_storage()

# optimize_storage returns the optimization metrics string directly
print(metrics)

# z-order optimization for specific columns
fs.optimize_storage(ZOrderByOptimization(["name", "age"]))

# refresh version and show optimization input
fs.refresh()
fs.storage_optimization
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