From 6652aa09ce8e2e19ba4a8ffd89013fae2fccb23f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=88=98=E6=A3=92=E6=A3=92?= Date: Wed, 21 Feb 2024 22:52:18 +0800 Subject: [PATCH] delete feature.csv, store.csv, test.csv. --- examples/mi/machine_learning.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/examples/mi/machine_learning.py b/examples/mi/machine_learning.py index 5f9d5b0cd..652e7c908 100644 --- a/examples/mi/machine_learning.py +++ b/examples/mi/machine_learning.py @@ -10,22 +10,17 @@ DATA_DIR = "examples/mi/data/WalmartSalesForecast2" SALES_FORECAST_REQ = f""" # Goal Train a model to predict sales for each department in every store (split the last 40 weeks records as validation dataset, -the others is train dataset), include plot sales trends, holiday effects, distribution of sales across stores/departments, -using box on the train dataset, print metric and plot scatter plots of groud truth and predictions on validation data. -save predictions on test data. +the others is train dataset), include plot sales trends, print metric and plot scatter plots of +groud truth and predictions on validation data. # Datasets Available - train_data: {DATA_DIR}/train.csv -- test_data: {DATA_DIR}/test.csv, no label data. -- additional data: {DATA_DIR}/features.csv -- stores data: {DATA_DIR}/stores.csv # Metric The metric of the competition is weighted mean absolute error (WMAE) for test data. # Notice - *print* key variables to get more information for next task step. -- Only When you fit the model, make the DataFrame.dtypes to be int, float or bool, and drop date column. """ requirements = {"wine": WINE_REQ, "sales_forecast": SALES_FORECAST_REQ} @@ -36,6 +31,7 @@ async def main(auto_run: bool = True, use_case: str = "wine"): if use_case == "wine": requirement = requirements[use_case] else: + mi.use_tools = True assert DATA_DIR != "your/path/to/data", f"Please set DATA_DIR for the use_case: {use_case}!" requirement = requirements[use_case] await mi.run(requirement)