Openfs provides a simple api to boost the quality of your training data while keeping your data pipelines clean and manageable.
pip install openfs
import openfs as fs
from openfs.stores import FeatureStore
from openfs.boosters import Booster
import os
# <- import files to upload here
# Connect to store bucket
fs.client.connect(
region_name=os.environ['FSTORE_REGION'],
endpoint_url=os.environ['FSTORE_ENDPOINT_URL'],
access_key_id=os.environ['FSTORE_ACCESS_KEY'],
secret_access_key=os.environ['FSTORE_SECRET_KEY']
)
# Create store
store = FeatureStore("store_name", "description of store", "some_primary_key")
# Upload store
response = store.upload(files, filenames)
# Create booster
booster = Booster(store_id=response['store_id'])
# Add features
booster.add_single("feature_1", alias="alias_for_feature")
booster.add_group(["feature_2", "feature_3"], alias="grouped_feature", how='sum')
# pull features from store (for testing)
df = booster.create_df()
# upload booster
booster.upload(name="booster_name", description="booster description")
fb.client.list_stores()
We'd love to welcome contributors to openfs
to help make training data
richer and more open for everyone. We're working on our contributor docs at the
moment, but if you're interested in contributing, please send us a message at
contact@xplainable.io.
Thanks for trying openfs!
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