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刘棒棒 2024-02-21 11:43:02 +08:00
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examples/mi/machine_learning.py
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@ -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])