bd35ff10b4c52b7b6d5a1a089f23bd89eea5346e,examples/pipelines/multitable/multitable.py,,,#,96

Before Change

                              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"])

After Change

    order_products = pd.read_csv("data/Retail/order_products.csv")
    label_times = pd.read_csv("data/Retail/label_times.csv")

    X = label_times.sample(frac=0.8)
    X_test = label_times.drop(X.index)
    y = X["label"]
    y_test = X_test["label"]

    es = make_entity_set(orders, order_products)

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

Instances