from zenml.integrations.constants import FACETS, LIGHTGBM, XGBOOST
from zenml.pipelines import pipeline


@pipeline(
    enable_cache=False,
    required_integrations=[FACETS, LIGHTGBM, XGBOOST],
    requirements="requirements.txt"
)
def data_analysis_pipeline(ingest_data, data_splitter):
    """Pipeline for analyzing data.

    Args:
        ingest_data  : Ingest data from the data source.
        data_splitter: Splits the data into train and test data.
    """
    customer_churn_df = ingest_data()
    train, test = data_splitter(customer_churn_df)

from zenml.integrations.constants import FACETS, LIGHTGBM, XGBOOST
from zenml.pipelines import pipeline


@pipeline(
    enable_cache=False,
    required_integrations=[FACETS, LIGHTGBM, XGBOOST],
    requirements="requirements.txt"
)
def data_analysis_pipeline(ingest_data, data_splitter):
    """Pipeline for analyzing data.

    Args:
        ingest_data  : Ingest data from the data source.
        data_splitter: Splits the data into train and test data.
    """
    customer_churn_df = ingest_data()
    train, test = data_splitter(customer_churn_df)

Pipeline for analyzing data.      Args:         ingest_data  : Ingest data from the data source.         data_splitter: Splits the data into train and test data.