Parameters are a place for you to store variables for your pipeline that can be accessed by any node that needs it, and can be easily changed by changing your environment. Parameters are stored in the repository in yaml files.
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You can have multiple parameters files and choose which ones to load by setting
your environment. By default kedro will give you a
The base environment should contain all of the default values you want to run.
# /conf/base/parameters.yml test_size: 0.2 random_state: 3 features: - engines - passenger_capacity - crew - d_check_complete - moon_clearance_complete - iata_approved - company_rating - review_scores_rating
NOTE base will always be loaded first.
Parameters can be accessed through context or through the catalog. Generally when you are working with nodes it will be loaded through the catalog.
Loding with the context.
context = session.load_context() context.params
Loading with the catalog.
Loading a specific key with the catalog.
Here is an example from the complete spaceflights demo. The entire parameters
dict is passed in, then the
key is accessed.
def split_data(data: pd.DataFrame, parameters: Dict) -> Tuple: """Splits data into features and targets training and test sets. Args: data: Data containing features and target. parameters: Parameters defined in parameters.yml. Returns: Split data. """ X = data[parameters["features"]] y = data["price"] X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=parameters["test_size"], random_state=parameters["random_state"] ) return X_train, X_test, y_train, y_test
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The local parameters by default are in
. They will
override the base parameters in a shallow fashion. If a top level key exists
in local, it will override that entire key in your parameters.
# /conf/local/parameters.yml env: local features: - company_rating - review_scores_rating
NOTE If you have not explicitly set your environment, local will be the default environment selected to override base.
You can also have other environments that override the base environment.
# /conf/new/parameters.yml env: new
NOTE if you use an env local will not be applied
Following the configuration docs we can activate the environment by setting an environment variable in our shell or passing in --env to our kedro cli command.
Setting an environment variable.
Passing in the env to a kedro cli command.
kedro run --env=test
Setting the Environment Variable in python.
import os os.environ['KEDRO_ENV'] = 'new'