From 041a2d61097bad45b5b7e28a880b37268a285c5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=88=98=E6=A3=92=E6=A3=92?= Date: Thu, 22 Feb 2024 21:56:02 +0800 Subject: [PATCH] chore. --- examples/mi/machine_learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/mi/machine_learning.py b/examples/mi/machine_learning.py index 53168e0e9..56c68f69e 100644 --- a/examples/mi/machine_learning.py +++ b/examples/mi/machine_learning.py @@ -4,7 +4,7 @@ from metagpt.roles.mi.interpreter import Interpreter WINE_REQ = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy." -DATA_DIR = "examples/mi/data/WalmartSalesForecast2" +DATA_DIR = "path/to/your/data" # sales_forecast data from https://www.kaggle.com/datasets/aslanahmedov/walmart-sales-forecast/data SALES_FORECAST_REQ = f"""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 total sales trends, print metric and plot scatter plots of groud truth and predictions on validation data. Dataset is {DATA_DIR}/train.csv, the metric is weighted mean absolute error (WMAE) for test data. Notice: *print* key variables to get more information for next task step.