6a8929b5ccce8004f4babc3102390b3461911304,examples/pipelines/multitable/multitable.py,,,#,96

Before Change

    )

    es = load_entityset("partitioned_data/part_1/")
    label_times = make_labels(es=es,
                              product_name="Banana",
                              cutoff_time=pd.Timestamp("March 15, 2015"),
                              prediction_window=ft.Timedelta("4 weeks"),
                              training_window=ft.Timedelta("60 days"))
    y = label_times["label"]

    multitable = MLPipeline.from_ml_json(["dfs", "random_forest_classifier"])
    multitable.update_fixed_hyperparams({

After Change

    assert expected_steps == steps

    // Check that we can score properly.
    produce_params = {
        ("dfs", "entityset"): es,
        ("dfs", "cutoff_time_in_index"): True
    }
    print("\nFitting pipeline...")
    fit_params = {
        ("dfs", "entityset"): es,
        ("dfs", "target_entity"): "users",

Instances