| import importlib |
| |
| import ray |
| |
| from hamilton import base, driver, log_setup |
| from hamilton.experimental import h_ray |
| |
| if __name__ == "__main__": |
| log_setup.setup_logging() |
| ray.init() |
| module_names = [ |
| "data_loaders", # functions to help load data |
| "business_logic", # where our important logic lives |
| ] |
| modules = [importlib.import_module(m) for m in module_names] |
| initial_columns = { # could load data here via some other means, or delegate to a module as we have done. |
| # 'signups': pd.Series([1, 10, 50, 100, 200, 400]), |
| "signups_location": "some_path", |
| # 'spend': pd.Series([10, 10, 20, 40, 40, 50]), |
| "spend_location": "some_other_path", |
| } |
| rga = h_ray.RayGraphAdapter(result_builder=base.PandasDataFrameResult()) |
| dr = driver.Driver(initial_columns, *modules, adapter=rga) # can pass in multiple modules |
| # we need to specify what we want in the final dataframe. |
| output_columns = [ |
| "spend", |
| "signups", |
| "avg_3wk_spend", |
| "spend_per_signup", |
| "spend_zero_mean_unit_variance", |
| ] |
| # let's create the dataframe! |
| df = dr.execute(output_columns) |
| # To visualize do `pip install "sf-hamilton[visualization]"` if you want these to work |
| # dr.visualize_execution(output_columns, './my_dag.dot', {}) |
| # dr.display_all_functions('./my_full_dag.dot') |
| print(df.to_string()) |
| ray.shutdown() |
