503 lines
25 KiB
Python
503 lines
25 KiB
Python
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################################### 报告内容 ##################################
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# 根据真实值分组，去掉最高最小预测值画图逻辑
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# content.append(Graphs.draw_text('图示说明：'))
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# content.append(Graphs.draw_text('1. 将所有模型的预测结果进行分组，大于真实值的为一组，小于真实值的为一组，去掉最高的预测值，去掉最小的预测值'))
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# content.append(Graphs.draw_text('2. 确定通道上界：在大于真实值的分组中，取最大的预测值'))
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# content.append(Graphs.draw_text('3. 确定通道下界：在小于真实值的分组中，取第二小的预测值'))
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# content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据，通道上界取近20个交易日内预测在上界值的模型对应的预测值，通道下界同理；'))
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# content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内，最多接近真实值的模型的预测值对应的预测结果；'))
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# content.append(Graphs.draw_text('6. 预测结果在通道外的，代表最接近真实值的预测结果不在置信波动范围内。'))
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# 波动率画图逻辑
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# content.append(Graphs.draw_text('图示说明：'))
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# content.append(Graphs.draw_text('1. 确定波动率置信区间：统计近60个交易日的真实价格波动率，找出在 10% ，90% 的分位值作为波动率置信区间;'))
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# content.append(Graphs.draw_text('2. 确定通道上界：在所有模型的预测结果中 <= 前一天真实价格 乘以 90%的置信波动分位数'))
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# content.append(Graphs.draw_text('3. 确定通道下界：在所有模型的预测结果中 >= 前一天真实价格 乘以 10%的置信波动分位数'))
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# content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据，通道上界取近20个交易日内预测在上界值的模型对应的预测值，通道下界同理；'))
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# content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内，最多接近真实值的模型的预测值对应的预测结果；'))
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# content.append(Graphs.draw_text('6. 预测结果在通道外的，代表最接近真实值的预测结果不在置信波动范围内。'))
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# # 计算特征相关性
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# data.rename(columns={y: 'y'}, inplace=True)
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# data['ds'] = pd.to_datetime(data['ds'])
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# data.drop(columns=['ds'], inplace=True)
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# # 创建一个空的 DataFrame 来保存相关系数
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# correlation_df = pd.DataFrame(columns=['Feature', 'Correlation'])
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# # 计算各特征与目标列的皮尔逊相关系数，并保存到新的 Data 中
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# for col in data.columns:
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# if col!= 'y':
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# pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1]
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# spearman_correlation, _ = spearmanr(data[col], data['y'])
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# new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)}
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# correlation_df = correlation_df._append(new_row, ignore_index=True)
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# correlation_df.drop('Correlation', axis=1, inplace=True)
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# correlation_df.dropna(inplace=True)
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# correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False)
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# data = correlation_df['Pearson_Correlation'].values.tolist()
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# # 生成 -1 到 1 的 20 个区间
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# bins = np.linspace(-1, 1, 21)
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# # 计算每个区间的统计数（这里是区间内数据的数量）
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# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
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# #设置画布大小
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# plt.figure(figsize=(10, 6))
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# # 绘制直方图
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# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
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# # 添加标题和坐标轴标签
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# plt.title('皮尔逊相关系数分布图')
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# plt.xlabel('区间')
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# plt.ylabel('统计数')
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# plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
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# plt.close()
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# #设置画布大小
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# plt.figure(figsize=(10, 6))
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# data = correlation_df['Spearman_Correlation'].values.tolist()
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# # 计算每个区间的统计数（这里是区间内数据的数量）
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# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
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# # 绘制直方图
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# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
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# # 添加标题和坐标轴标签
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# plt.title('斯皮尔曼相关系数分布图')
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# plt.xlabel('区间')
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# plt.ylabel('统计数')
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# plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
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# plt.close()
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# content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
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# # 皮尔逊正相关 不相关 负相关 的表格
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# content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png')))
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# content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
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# content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
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# content.append(Graphs.draw_text('''
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# 相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
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# content.append(Graphs.draw_text('''当前特征中正相关前十的有：'''))
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# top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list()
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# top10 = ','.join(top10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# feature_df = feature_data_df[['ds','y']+top10_columns]
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# # 遍历X每一列，和yy画散点图 ，
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# for i, col in enumerate(feature_df.columns):
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# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
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# if col not in ['ds', 'y']:
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# fig, ax1 = plt.subplots(figsize=(10, 6))
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# # 在第一个坐标轴上绘制数据
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# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
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# ax1.set_xlabel('日期')
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# ax1.set_ylabel('y', color='b')
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# ax1.tick_params('y', colors='b')
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# # 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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# for j in range(1,len(feature_df),2):
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# value = feature_df['y'].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
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# # 创建第二个坐标轴
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# ax2 = ax1.twinx()
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# # 在第二个坐标轴上绘制数据
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# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
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# ax2.set_ylabel(col, color='r')
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# ax2.tick_params('y', colors='r')
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# # 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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# for j in range(0,len(feature_df),2):
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# value = feature_df[col].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
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# # 添加标题
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# plt.title(col)
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# # 设置横坐标为日期格式并自动调整
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# locator = mdates.AutoDateLocator()
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# formatter = mdates.AutoDateFormatter(locator)
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# ax1.xaxis.set_major_locator(locator)
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# ax1.xaxis.set_major_formatter(formatter)
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# # 文件名特殊字符处理
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# col = col.replace('*', '-')
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# col = col.replace(':', '-')
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# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
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# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
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# plt.close()
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# content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
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# # 皮尔逊正相关 不相关 负相关 的表格
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# content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png')))
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# content.append(Graphs.draw_text('斯皮尔曼相关系数（Spearmans rank correlation coefficient）是一种用于衡量两个变量之间的单调关系（不一定是线性关系）的统计指标。'))
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# content.append(Graphs.draw_text('它的计算基于变量的秩次（即变量值的排序位置）而非变量的原始值。'))
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# content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。'))
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# content.append(Graphs.draw_text('当系数为 1 时，表示两个变量之间存在完全正的单调关系；'))
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# content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：'''))
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# top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list()
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# top10 = ','.join(top10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# feature_df = feature_data_df[['ds','y']+top10_columns]
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# # 遍历X每一列，和yy画散点图 ，
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# for i, col in enumerate(feature_df.columns):
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# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
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# if col not in ['ds', 'y']:
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# fig, ax1 = plt.subplots(figsize=(10, 6))
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# # 在第一个坐标轴上绘制数据
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# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
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# ax1.set_xlabel('日期')
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# ax1.set_ylabel('y', color='b')
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# ax1.tick_params('y', colors='b')
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# # 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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# for j in range(1,len(feature_df),2):
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# value = feature_df['y'].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
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# # 创建第二个坐标轴
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# ax2 = ax1.twinx()
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# # 在第二个坐标轴上绘制数据
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# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
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# ax2.set_ylabel(col, color='r')
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# ax2.tick_params('y', colors='r')
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# # 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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# for j in range(0,len(feature_df),2):
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# value = feature_df[col].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
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# # 添加标题
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# plt.title(col)
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# # 设置横坐标为日期格式并自动调整
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# locator = mdates.AutoDateLocator()
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# formatter = mdates.AutoDateFormatter(locator)
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# ax1.xaxis.set_major_locator(locator)
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# ax1.xaxis.set_major_formatter(formatter)
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# # 文件名特殊字符处理
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# col = col.replace('*', '-')
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# col = col.replace(':', '-')
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# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
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# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
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# plt.close()
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# content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；'))
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# content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：'''))
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# tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list()
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# top10 = ','.join(tail10_columns)
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# content.append(Graphs.draw_text(f'''{top10}'''))
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# # 获取特征的近一周值
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# feature_df = feature_data_df[['ds','y']+tail10_columns]
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# # 遍历X每一列，和yy画散点图 ，
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# for i, col in enumerate(feature_df.columns):
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# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
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# if col not in ['ds', 'y']:
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# fig, ax1 = plt.subplots(figsize=(10, 6))
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# # 在第一个坐标轴上绘制数据
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# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
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# ax1.set_xlabel('日期')
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# ax1.set_ylabel('y', color='b')
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# ax1.tick_params('y', colors='b')
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# # 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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# for j in range(len(feature_df)):
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# if j%2 == 1:
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# value = feature_df['y'].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
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# # 创建第二个坐标轴
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# ax2 = ax1.twinx()
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# # 在第二个坐标轴上绘制数据
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# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
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# ax2.set_ylabel(col, color='r')
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# ax2.tick_params('y', colors='r')
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# # 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
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# for j in range(1,len(feature_df),2):
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# value = feature_df[col].iloc[j]
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# date = feature_df['ds'].iloc[j]
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# offset = 1.001
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# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
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# # 添加标题
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# plt.title(col)
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# # 设置横坐标为日期格式并自动调整
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# locator = mdates.AutoDateLocator()
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# formatter = mdates.AutoDateFormatter(locator)
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# ax1.xaxis.set_major_locator(locator)
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# ax1.xaxis.set_major_formatter(formatter)
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# # 文件名特殊字符处理
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# col = col.replace('*', '-')
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# col = col.replace(':', '-')
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# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
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# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
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# plt.close()
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# content.append(Graphs.draw_text('当系数为 0 时，表示两个变量之间不存在单调关系。'))
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# content.append(Graphs.draw_text('与皮尔逊相关系数相比，斯皮尔曼相关系数对于数据中的异常值不敏感，更适用于处理非线性关系或存在极端值的数据。'))
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# 附1，特征列表
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# content.append(Graphs.draw_little_title('附1、特征列表：'))
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# df_fuyi = pd.read_csv(os.path.join(dataset,'特征频度统计.csv'),encoding='utf-8')
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# for col in df_fuyi.columns:
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# fuyi = df_fuyi[col]
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# fuyi = fuyi.dropna()
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# content.append(Graphs.draw_text(f'{col}：'))
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# for i in range(len(fuyi)):
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# content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
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################################### 预测值与真实值绘图逻辑 ##################################
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# # 根据真实值y确定最大最小值,去掉最高最低的预测值
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# import heapq # 使用堆来找到最大和最小的值
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# def find_min_max_within_quantile(row):
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# true_value = row['y']
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# row.drop(['ds','y'], inplace=True)
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# row = row.astype(float).round(2)
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# max_heap = []
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# min_heap = []
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# for col in row.index:
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# # 对比真实值进行分类
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# if row[col] < true_value:
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# heapq.heappush(min_heap, row[col])
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# elif row[col] > true_value:
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# heapq.heappush(max_heap, -row[col]) # 使用负号来实现最大堆
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# if len(max_heap) == 1:
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# max_y = max_heap[0]
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# elif len(max_heap) == 0:
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# max_y = -min_heap[-1]
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# else:
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# max_y = heapq.nsmallest(2, max_heap)[1]
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# if len(min_heap) < 2 :
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# min_y = -max_heap[-1]
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# else:
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# min_y = heapq.nsmallest(2, min_heap)[-1]
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# # 获取最大和最小的值
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# q10 = min_y
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# q90 = -max_y
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# # 获取最大和最小的模型名称
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# min_model = row[row == q10].idxmin()
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# max_model = row[row == q90].idxmax()
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# # 设置上下界比例
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# rote = 1
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# q10 = q10 * rote
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# q90 = q90 * rote
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# logger.info(min_model,q10,max_model,q90)
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# return pd.Series([q10, q90, min_model, max_model], index=['min_within_quantile', 'max_within_quantile', 'min_model', 'max_model'])
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# # # 遍历行
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# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
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# df_combined = df_combined.round(4)
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# print(df_combined3)
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#使用最佳五个模型进行绘图
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# best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()
|
||
# def find_min_max_within_quantile(row):
|
||
# row = row[best_models]
|
||
# q10 = row.min()
|
||
# q90 = row.max()
|
||
# # 获取 row行最大最小值模型名称
|
||
# min_model = row[row == q10].idxmin()
|
||
# max_model = row[row == q90].idxmin()
|
||
|
||
# # # 判断flot值是否为空值
|
||
# # if pd.isna(q10) or pd.isna(q90):
|
||
# return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
|
||
|
||
# # 遍历行
|
||
# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
|
||
# df_combined = df_combined.round(4)
|
||
# print(df_combined3)
|
||
|
||
# # 通道使用模型评估前80%作为置信度
|
||
# def find_min_max_within_quantile(row):
|
||
# row.drop(['ds','y'], inplace=True)
|
||
# row = row.astype(float).round(2)
|
||
|
||
# row_sorted = row
|
||
# # 计算 10% 和 90% 位置的索引
|
||
# index_10 = 0
|
||
# index_90 = int(len(row_sorted) * 0.8)
|
||
# q10 = row_sorted[index_10]
|
||
# q90 = row_sorted[index_90]
|
||
# # 获取模型名称
|
||
# min_model = row[row == q10].idxmin()
|
||
# max_model = row[row == q90].idxmin()
|
||
|
||
|
||
# # # 判断flot值是否为空值
|
||
# # if pd.isna(q10) or pd.isna(q90):
|
||
# return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
|
||
|
||
# # 重新排列
|
||
# df_combined3 = df_combined3[['ds','y'] + allmodelnames]
|
||
# # 遍历行
|
||
# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
|
||
# df_combined = df_combined.round(4)
|
||
# print(df_combined3)
|
||
|
||
|
||
# # 通道使用预测模型的80%置信度
|
||
# def find_min_max_within_quantile(row):
|
||
# row.drop(['ds','y'], inplace=True)
|
||
# row = row.astype(float).round(2)
|
||
|
||
# row_sorted = row.sort_values(ascending=True).reset_index(drop=True)
|
||
# # 计算 10% 和 90% 位置的索引
|
||
# index_10 = int(len(row_sorted) * 0.1)
|
||
# index_90 = int(len(row_sorted) * 0.9)
|
||
# q10 = row_sorted[index_10]
|
||
# q90 = row_sorted[index_90]
|
||
# # 获取模型名称
|
||
# min_model = row[row == q10].idxmin()
|
||
# max_model = row[row == q90].idxmin()
|
||
|
||
|
||
# # # 判断flot值是否为空值
|
||
# # if pd.isna(q10) or pd.isna(q90):
|
||
# return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
|
||
|
||
# # 遍历行
|
||
# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
|
||
# df_combined = df_combined.round(4)
|
||
# print(df_combined3)
|
||
|
||
|
||
|
||
|
||
# # 计算波动率
|
||
# df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)
|
||
# # 计算近60日的波动率 10% 90%分位数
|
||
# df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1)
|
||
# df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9)
|
||
# df_combined3 = df_combined3.round(4)
|
||
# # 计算分位数对应的价格
|
||
# df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10'])
|
||
# df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90'])
|
||
|
||
# # 遍历行
|
||
# def find_min_max_within_quantile(row):
|
||
# # 获取分位数10%和90%的值
|
||
# q10 = row['quantile_10_price']
|
||
# q90 = row['quantile_90_price']
|
||
|
||
# # 判断flot值是否为空值
|
||
# if pd.isna(q10) or pd.isna(q90):
|
||
# return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
|
||
|
||
# # 初始化最小和最大值为None
|
||
# min_value = None
|
||
# max_value = None
|
||
# min_value_model = ''
|
||
# max_value_model = ''
|
||
|
||
|
||
# # 遍历指定列，找出在分位数范围内的最大最小值
|
||
# for model in modelnames:
|
||
# value = row[model]
|
||
# if value >= q10 and value <= q90:
|
||
# if min_value is None or value < min_value:
|
||
# min_value = value
|
||
# min_value_model = model
|
||
|
||
# if max_value is None or value > max_value:
|
||
# max_value = value
|
||
# max_value_model = model
|
||
|
||
# 返回最大最小值
|
||
# return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model'])
|
||
|
||
# # # 应用函数到每一行
|
||
# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
|
||
|
||
# # 去除有空值的行
|
||
# df_combined3.dropna(inplace=True)
|
||
# # # 保存到数据库
|
||
# df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)
|
||
# df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
|
||
|
||
|
||
# # 去掉方差最大的模型，其余模型预测最大最小值确定通道边界
|
||
|
||
|
||
# # 历史数据+预测数据
|
||
# # 拼接未来时间预测
|
||
# df_predict = loadcsv(os.path.join(dataset,'predict.csv'))
|
||
# df_predict.drop('unique_id',inplace=True,axis=1)
|
||
# df_predict.dropna(axis=1,inplace=True)
|
||
# df_predict2 = df_predict.copy()
|
||
# try:
|
||
# df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')
|
||
# except ValueError :
|
||
# df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
|
||
|
||
# # 取第一行数据存储到数据库中
|
||
# first_row = df_predict.head(1)
|
||
# first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
||
|
||
# # # 将预测结果保存到数据库
|
||
# df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
|
||
# # # 判断 df 的数值列转为float
|
||
# for col in df_combined3.columns:
|
||
# try:
|
||
# if col != 'ds':
|
||
# df_combined3[col] = df_combined3[col].astype(float)
|
||
# df_combined3[col] = df_combined3[col].round(2)
|
||
# except ValueError:
|
||
# pass
|
||
# df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
|
||
# df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
||
# # # 判断表存在
|
||
# if not sqlitedb.check_table_exists('testandpredict_groupby'):
|
||
# df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
|
||
# else:
|
||
# for row in df_combined3.itertuples(index=False):
|
||
# row_dict = row._asdict()
|
||
# check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f"ds = '{row.ds}'")
|
||
# if len(check_query) > 0:
|
||
# set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||
# sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f"ds = '{row.ds}'")
|
||
# continue
|
||
# sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())
|
||
|
||
# ten_models = allmodelnames
|
||
# # 计算每个模型的方差
|
||
# variances = df_combined3[ten_models].var()
|
||
# # 找到方差最大的模型
|
||
# max_variance_model = variances.idxmax()
|
||
# # 打印方差最大的模型
|
||
# print("方差最大的模型是：", max_variance_model)
|
||
# # 去掉方差最大的模型
|
||
# df_combined3 = df_combined3.drop(columns=[max_variance_model])
|
||
# if max_variance_model in allmodelnames:
|
||
# allmodelnames.remove(max_variance_model)
|
||
# df_combined3['min'] = df_combined3[allmodelnames].min(axis=1)
|
||
# df_combined3['max'] = df_combined3[allmodelnames].max(axis=1)
|
||
# print(df_combined3[['min','max']])
|
||
# # 历史价格+预测价格
|
||
# df_combined3 = df_combined3[-50:] # 取50个数据点画图
|
||
# plt.figure(figsize=(20, 10))
|
||
# plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值',marker='o')
|
||
# plt.plot(df_combined3['ds'], df_combined3[most_model], label=most_model_name)
|
||
# plt.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2)
|
||
# plt.grid(True)
|
||
# # # 显示历史值
|
||
# for i, j in zip(df_combined3['ds'][:-5], df_combined3['y'][:-5]):
|
||
# plt.text(i, j, str(j), ha='center', va='bottom')
|
||
# # 当前日期画竖虚线
|
||
# plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')
|
||
# plt.legend()
|
||
# plt.xlabel('日期')
|
||
# plt.ylabel('价格')
|
||
|
||
# plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
|
||
# plt.close() |