From 662fbd7e5554f426cc34e42402e48fc5ab407621 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 11:43:02 +0800 Subject: [PATCH] chore. --- examples/mi/machine_learning.py | 44 ++++++++++++++++----------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/examples/mi/machine_learning.py b/examples/mi/machine_learning.py index a76561a37..689335db3 100644 --- a/examples/mi/machine_learning.py +++ b/examples/mi/machine_learning.py @@ -2,35 +2,35 @@ import fire from metagpt.roles.mi.interpreter import Interpreter - DATA_DIR = "examples/mi/data" -requirements = { - "wine": "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.", +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." - # sales_forecast data from https://www.kaggle.com/datasets/aslanahmedov/walmart-sales-forecast/data - "sales_forecast": f""" - # Goal - Use time series regression machine learning to make predictions for Dept sales of the stores as accurate as possible. +# sales_forecast data from https://www.kaggle.com/datasets/aslanahmedov/walmart-sales-forecast/data, +# new_train, new_test from train.csv. +SALES_FORECAST_REQ = f""" +# Goal +Use time series regression machine learning to make predictions for Dept sales of the stores as accurate as possible. - # Datasets Available - - train_data: {DATA_DIR}/WalmartSalesForecast/new_train.csv - - test_data: {DATA_DIR}/WalmartSalesForecast/new_test.csv - - additional data: {DATA_DIR}/WalmartSalesForecast/features.csv; To merge on train, test data. - - stores data: {DATA_DIR}/WalmartSalesForecast/stores.csv; To merge on train, test data. +# Datasets Available +- train_data: {DATA_DIR}/WalmartSalesForecast/new_train.csv +- test_data: {DATA_DIR}/WalmartSalesForecast/new_test.csv +- additional data: {DATA_DIR}/WalmartSalesForecast/features.csv; To merge on train, test data. +- stores data: {DATA_DIR}/WalmartSalesForecast/stores.csv; To merge on train, test data. - # Metric - The metric of the competition is weighted mean absolute error (WMAE) for test data. +# 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. - - Perform data analysis by plotting sales trends, holiday effects, distribution of sales across stores/departments using box/violin on the train data. - - Make sure the DataFrame.dtypes must be int, float or bool, and drop date column. - - Plot scatter plots of groud truth and predictions on test data. - """ -} +# Notice +- *print* key variables to get more information for next task step. +- Perform data analysis by plotting sales trends, holiday effects, distribution of sales across stores/departments using box/violin on the train data. +- Make sure the DataFrame.dtypes must be int, float or bool, and drop date column. +- Plot scatter plots of groud truth and predictions on test data. +""" + +requirements = {"wine": WINE_REQ, "sales_forecast": SALES_FORECAST_REQ} -async def main(auto_run: bool = True, use_case: str = 'wine'): +async def main(auto_run: bool = True, use_case: str = "wine"): mi = Interpreter(auto_run=auto_run) await mi.run(requirements[use_case])