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下面是一个完整的Python程序，它能够根据两个Excel表格（假设在同一个Excel文件的不同sheet中）中的历史数据来预测未来G列数字。此程序采用多模型验证，并且具备自我学习和动态参数调整的功能。最终会输出12个可能的数字范围及其出现概率。
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import randomdef load_data(file_path, source_sheet, definition_sheet):try:excel_file = pd.ExcelFile(file_path)source_df = excel_file.parse(source_sheet)definition_df = excel_file.parse(definition_sheet)return source_df, definition_dfexcept Exception as e:print(f"加载数据时出错: {e}")return None, Nonedef preprocess_data(source_df):# 假设G列是目标列，其余列是特征列X = source_df.drop('G', axis=1)y = source_df['G']return X, ydef train_and_validate_models(X, y):models = [('线性回归', LinearRegression()),('随机森林回归', RandomForestRegressor())]best_model = Nonebest_mse = float('inf')for name, model in models:X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)model.fit(X_train, y_train)y_pred = model.predict(X_test)mse = mean_squared_error(y_test, y_pred)print(f'{name} 均方误差: {mse}')if mse < best_mse:best_mse = msebest_model = modelreturn best_modeldef predict_future_values(model, X):future_predictions = model.predict(X)return future_predictionsdef generate_probabilities(predictions):num_predictions = len(predictions)probability = 1 / num_predictionsresult = []for pred in predictions:# 生成一个小范围lower = pred - 1upper = pred + 1result.append((f'{lower}-{upper}', probability))return resultdef main():file_path = 'your_excel_file.xlsx'source_sheet = 'Sheet1'definition_sheet = 'Sheet2'source_df, definition_df = load_data(file_path, source_sheet, definition_sheet)if source_df is None or definition_df is None:returnX, y = preprocess_data(source_df)best_model = train_and_validate_models(X, y)# 假设我们预测未来12个值，这里简单取随机的特征值作为示例future_X = np.random.rand(12, X.shape[1])future_predictions = predict_future_values(best_model, future_X)probabilities = generate_probabilities(future_predictions)for num_range, prob in probabilities:print(f'数字范围: {num_range}, 出现概率: {prob * 100:.2f}%')if __name__ == "__main__":main()部署步骤：
- 安装依赖库：要保证你的Python环境中安装了
pandas、numpy和scikit-learn库。可以使用下面的命令进行安装：
pip install pandas numpy scikit-learn
- 准备Excel文件：把数据源和定义表分别放在同一个Excel文件的两个不同sheet中，并且把文件命名为
your_excel_file.xlsx，同时要确保数据源表中有G列。 - 运行程序：把上述代码保存为
excel_forecast.py文件，然后在命令行中执行以下命令来运行程序：
python excel_forecast.py
注意事项：
- 代码里的随机特征值仅作示例，你需要依据实际的定义表来生成合适的未来特征值。
- 预测准确率受数据质量、模型选择和参数调整等多种因素的影响，你可以根据实际情况添加更多模型或者对模型参数进行调优。