add sales_forecast in machine_learning.

examples/mi/machine_learning.py

@@ -3,10 +3,36 @@ import fire
 from metagpt.roles.mi.interpreter import Interpreter
 
 
-async def main(auto_run: bool = True):
-    requirement = "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"
+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.",
+
+    # 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.
+
+    # 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.
+
+    # 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.
+    """
+}
+
+
+async def main(auto_run: bool = True, use_case: str = 'wine'):
     mi = Interpreter(auto_run=auto_run)
-    await mi.run(requirement)
+    await mi.run(requirements[use_case])
 
 
 if __name__ == "__main__":