From e8ecec120a5496a3851e91cc9c694e1473027cb2 Mon Sep 17 00:00:00 2001 From: liurui Date: Thu, 21 Nov 2024 13:27:54 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B3=A8=E9=87=8A=E4=BB=A3=E7=A0=81=E5=A4=87?= =?UTF-8?q?=E4=BB=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- codeback.py | 494 +++++++++++++++++++++++++++++++++ models/nerulforcastmodels.py | 515 +---------------------------------- 2 files changed, 497 insertions(+), 512 deletions(-) create mode 100644 codeback.py diff --git a/codeback.py b/codeback.py new file mode 100644 index 0000000..2b88642 --- /dev/null +++ b/codeback.py @@ -0,0 +1,494 @@ + +################################### 报告内容 ################################## + + # 根据真实值分组，去掉最高最小预测值画图逻辑 + # content.append(Graphs.draw_text('图示说明：')) + # 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')) + # 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')) + # 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')) + # 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；')) + # 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；')) + # 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。')) + # 波动率画图逻辑 + # content.append(Graphs.draw_text('图示说明：')) + # 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;')) + # 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')) + # 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')) + # 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；')) + # 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；')) + # 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。')) + + # # 计算特征相关性 + # data.rename(columns={y: 'y'}, inplace=True) + # data['ds'] = pd.to_datetime(data['ds']) + # data.drop(columns=['ds'], inplace=True) + # # 创建一个空的 DataFrame 来保存相关系数 + # correlation_df = pd.DataFrame(columns=['Feature', 'Correlation']) + # # 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 + # for col in data.columns: + # if col!= 'y': + # pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1] + # spearman_correlation, _ = spearmanr(data[col], data['y']) + # new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)} + # correlation_df = correlation_df._append(new_row, ignore_index=True) + + # correlation_df.drop('Correlation', axis=1, inplace=True) + # correlation_df.dropna(inplace=True) + # correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False) + + # data = correlation_df['Pearson_Correlation'].values.tolist() + # # 生成 -1 到 1 的 20 个区间 + # bins = np.linspace(-1, 1, 21) + # # 计算每个区间的统计数（这里是区间内数据的数量） + # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] + + # #设置画布大小 + # plt.figure(figsize=(10, 6)) + # # 绘制直方图 + # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) + + # # 添加标题和坐标轴标签 + # plt.title('皮尔逊相关系数分布图') + # plt.xlabel('区间') + # plt.ylabel('统计数') + # plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png')) + # plt.close() + + + # #设置画布大小 + # plt.figure(figsize=(10, 6)) + # data = correlation_df['Spearman_Correlation'].values.tolist() + # # 计算每个区间的统计数（这里是区间内数据的数量） + # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] + + # # 绘制直方图 + # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) + + # # 添加标题和坐标轴标签 + # plt.title('斯皮尔曼相关系数分布图') + # plt.xlabel('区间') + # plt.ylabel('统计数') + # plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png')) + # plt.close() + # content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：')) + # # 皮尔逊正相关 不相关 负相关 的表格 + # content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png'))) + # content.append(Graphs.draw_text('''皮尔逊相关系数说明：''')) + # content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。''')) + # content.append(Graphs.draw_text(''' + # 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。''')) + # content.append(Graphs.draw_text('''当前特征中正相关前十的有：''')) + # top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list() + # top10 = ','.join(top10_columns) + # content.append(Graphs.draw_text(f'''{top10}''')) + + # feature_df = feature_data_df[['ds','y']+top10_columns] + # # 遍历X每一列，和yy画散点图 ， + # for i, col in enumerate(feature_df.columns): + # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') + # if col not in ['ds', 'y']: + # fig, ax1 = plt.subplots(figsize=(10, 6)) + # # 在第一个坐标轴上绘制数据 + # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') + # ax1.set_xlabel('日期') + # ax1.set_ylabel('y', color='b') + # ax1.tick_params('y', colors='b') + # # 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 + # for j in range(1,len(feature_df),2): + # value = feature_df['y'].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) + # # 创建第二个坐标轴 + # ax2 = ax1.twinx() + # # 在第二个坐标轴上绘制数据 + # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') + # ax2.set_ylabel(col, color='r') + # ax2.tick_params('y', colors='r') + # # 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 + # for j in range(0,len(feature_df),2): + # value = feature_df[col].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) + # # 添加标题 + # plt.title(col) + # # 设置横坐标为日期格式并自动调整 + # locator = mdates.AutoDateLocator() + # formatter = mdates.AutoDateFormatter(locator) + # ax1.xaxis.set_major_locator(locator) + # ax1.xaxis.set_major_formatter(formatter) + # # 文件名特殊字符处理 + # col = col.replace('*', '-') + # col = col.replace(':', '-') + # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) + # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) + # plt.close() + + + # content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：')) + # # 皮尔逊正相关 不相关 负相关 的表格 + # content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png'))) + # 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。')) + # 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。')) + # content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。')) + # 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；')) + # content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：''')) + # top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list() + # top10 = ','.join(top10_columns) + # content.append(Graphs.draw_text(f'''{top10}''')) + + # feature_df = feature_data_df[['ds','y']+top10_columns] + # # 遍历X每一列，和yy画散点图 ， + # for i, col in enumerate(feature_df.columns): + # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') + # if col not in ['ds', 'y']: + # fig, ax1 = plt.subplots(figsize=(10, 6)) + # # 在第一个坐标轴上绘制数据 + # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') + # ax1.set_xlabel('日期') + # ax1.set_ylabel('y', color='b') + # ax1.tick_params('y', colors='b') + # # 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 + # for j in range(1,len(feature_df),2): + # value = feature_df['y'].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) + # # 创建第二个坐标轴 + # ax2 = ax1.twinx() + # # 在第二个坐标轴上绘制数据 + # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') + # ax2.set_ylabel(col, color='r') + # ax2.tick_params('y', colors='r') + # # 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 + # for j in range(0,len(feature_df),2): + # value = feature_df[col].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) + # # 添加标题 + # plt.title(col) + # # 设置横坐标为日期格式并自动调整 + # locator = mdates.AutoDateLocator() + # formatter = mdates.AutoDateFormatter(locator) + # ax1.xaxis.set_major_locator(locator) + # ax1.xaxis.set_major_formatter(formatter) + # # 文件名特殊字符处理 + # col = col.replace('*', '-') + # col = col.replace(':', '-') + # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) + # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) + # plt.close() + + # content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；')) + # content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：''')) + # tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list() + # top10 = ','.join(tail10_columns) + # content.append(Graphs.draw_text(f'''{top10}''')) + # # 获取特征的近一周值 + # feature_df = feature_data_df[['ds','y']+tail10_columns] + # # 遍历X每一列，和yy画散点图 ， + # for i, col in enumerate(feature_df.columns): + # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') + # if col not in ['ds', 'y']: + # fig, ax1 = plt.subplots(figsize=(10, 6)) + # # 在第一个坐标轴上绘制数据 + # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') + # ax1.set_xlabel('日期') + # ax1.set_ylabel('y', color='b') + # ax1.tick_params('y', colors='b') + # # 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 + # for j in range(len(feature_df)): + # if j%2 == 1: + # value = feature_df['y'].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) + # # 创建第二个坐标轴 + # ax2 = ax1.twinx() + # # 在第二个坐标轴上绘制数据 + # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') + # ax2.set_ylabel(col, color='r') + # ax2.tick_params('y', colors='r') + # # 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 + # for j in range(1,len(feature_df),2): + # value = feature_df[col].iloc[j] + # date = feature_df['ds'].iloc[j] + # offset = 1.001 + # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) + # # 添加标题 + # plt.title(col) + # # 设置横坐标为日期格式并自动调整 + # locator = mdates.AutoDateLocator() + # formatter = mdates.AutoDateFormatter(locator) + # ax1.xaxis.set_major_locator(locator) + # ax1.xaxis.set_major_formatter(formatter) + # # 文件名特殊字符处理 + # col = col.replace('*', '-') + # col = col.replace(':', '-') + # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) + # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) + # plt.close() + # 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。')) + # 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。')) + + + + + + +################################### 预测值与真实值绘图逻辑 ################################## + # # 根据真实值y确定最大最小值,去掉最高最低的预测值 + # import heapq # 使用堆来找到最大和最小的值 + # def find_min_max_within_quantile(row): + # true_value = row['y'] + # row.drop(['ds','y'], inplace=True) + # row = row.astype(float).round(2) + + # max_heap = [] + # min_heap = [] + # for col in row.index: + # # 对比真实值进行分类 + # if row[col] < true_value: + # heapq.heappush(min_heap, row[col]) + # elif row[col] > true_value: + # heapq.heappush(max_heap, -row[col]) # 使用负号来实现最大堆 + + # if len(max_heap) == 1: + # max_y = max_heap[0] + # elif len(max_heap) == 0: + # max_y = -min_heap[-1] + # else: + # max_y = heapq.nsmallest(2, max_heap)[1] + + # if len(min_heap) < 2 : + # min_y = -max_heap[-1] + # else: + # min_y = heapq.nsmallest(2, min_heap)[-1] + + + # # 获取最大和最小的值 + # q10 = min_y + # q90 = -max_y + + # # 获取最大和最小的模型名称 + # min_model = row[row == q10].idxmin() + # max_model = row[row == q90].idxmax() + + # # 设置上下界比例 + # rote = 1 + + # q10 = q10 * rote + # q90 = q90 * rote + + # logger.info(min_model,q10,max_model,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) + + #使用最佳五个模型进行绘图 + # 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() \ No newline at end of file diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 4ef8e96..d2beaa6 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -218,7 +218,7 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien return nf_test_preds -# 计算预测评估指数 +# 原油计算预测评估指数 def model_losss(sqlitedb): global dataset # 预测数据处理 predict @@ -483,10 +483,7 @@ def model_losss(sqlitedb): plt.close() return model_results3 - - - -# 计算预测评估指数 +# 聚烯烃计算预测评估指数 def model_losss_juxiting(sqlitedb): global dataset global rote @@ -619,264 +616,6 @@ def model_losss_juxiting(sqlitedb): df_combined3[['upper_bound','lower_bound']] = names_df.apply(add_upper_lower_bound, axis=1) - - - # # 根据真实值y确定最大最小值,去掉最高最低的预测值 - # import heapq # 使用堆来找到最大和最小的值 - # def find_min_max_within_quantile(row): - # true_value = row['y'] - # row.drop(['ds','y'], inplace=True) - # row = row.astype(float).round(2) - - # max_heap = [] - # min_heap = [] - # for col in row.index: - # # 对比真实值进行分类 - # if row[col] < true_value: - # heapq.heappush(min_heap, row[col]) - # elif row[col] > true_value: - # heapq.heappush(max_heap, -row[col]) # 使用负号来实现最大堆 - - # if len(max_heap) == 1: - # max_y = max_heap[0] - # elif len(max_heap) == 0: - # max_y = -min_heap[-1] - # else: - # max_y = heapq.nsmallest(2, max_heap)[1] - - # if len(min_heap) < 2 : - # min_y = -max_heap[-1] - # else: - # min_y = heapq.nsmallest(2, min_heap)[-1] - - - # # 获取最大和最小的值 - # q10 = min_y - # q90 = -max_y - - # # 获取最大和最小的模型名称 - # min_model = row[row == q10].idxmin() - # max_model = row[row == q90].idxmax() - - # # 设置上下界比例 - # rote = 1 - - # q10 = q10 * rote - # q90 = q90 * rote - - # logger.info(min_model,q10,max_model,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) - - #使用最佳五个模型进行绘图 - # 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() - ''' - - def find_most_common_model(): # 最多频率的模型名称 min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax() @@ -1520,7 +1259,6 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu except TimeoutError as e: print(f"请求超时: {e}") - def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf',sqlitedb='jbsh_yuanyou.db'): global y # 创建内容对应的空列表 @@ -1578,32 +1316,13 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi # except Exception as e: # print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}') - - + ### 添加标题 content.append(Graphs.draw_title(f'{y}{time}预测报告')) - ### 预测结果 content.append(Graphs.draw_little_title('一、预测结果：')) # 添加历史走势及预测价格的走势图片 content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值.png'))) - # 根据真实值分组，去掉最高最小预测值画图逻辑 - # content.append(Graphs.draw_text('图示说明：')) - # 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')) - # 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')) - # 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')) - # 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；')) - # 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；')) - # 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。')) - # 波动率画图逻辑 - # content.append(Graphs.draw_text('图示说明：')) - # 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;')) - # 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')) - # 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')) - # 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；')) - # 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；')) - # 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。')) - # 取df中y列为空的行 import pandas as pd @@ -1755,222 +1474,6 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi content.append(Graphs.draw_text('气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。')) logger.info(f'绘制相关性总和的气泡图结束') - - - # # 计算特征相关性 - # data.rename(columns={y: 'y'}, inplace=True) - # data['ds'] = pd.to_datetime(data['ds']) - # data.drop(columns=['ds'], inplace=True) - # # 创建一个空的 DataFrame 来保存相关系数 - # correlation_df = pd.DataFrame(columns=['Feature', 'Correlation']) - # # 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 - # for col in data.columns: - # if col!= 'y': - # pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1] - # spearman_correlation, _ = spearmanr(data[col], data['y']) - # new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)} - # correlation_df = correlation_df._append(new_row, ignore_index=True) - - # correlation_df.drop('Correlation', axis=1, inplace=True) - # correlation_df.dropna(inplace=True) - # correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False) - - # data = correlation_df['Pearson_Correlation'].values.tolist() - # # 生成 -1 到 1 的 20 个区间 - # bins = np.linspace(-1, 1, 21) - # # 计算每个区间的统计数（这里是区间内数据的数量） - # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] - - # #设置画布大小 - # plt.figure(figsize=(10, 6)) - # # 绘制直方图 - # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) - - # # 添加标题和坐标轴标签 - # plt.title('皮尔逊相关系数分布图') - # plt.xlabel('区间') - # plt.ylabel('统计数') - # plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png')) - # plt.close() - - - # #设置画布大小 - # plt.figure(figsize=(10, 6)) - # data = correlation_df['Spearman_Correlation'].values.tolist() - # # 计算每个区间的统计数（这里是区间内数据的数量） - # hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)] - - # # 绘制直方图 - # plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0])) - - # # 添加标题和坐标轴标签 - # plt.title('斯皮尔曼相关系数分布图') - # plt.xlabel('区间') - # plt.ylabel('统计数') - # plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png')) - # plt.close() - # content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：')) - # # 皮尔逊正相关 不相关 负相关 的表格 - # content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png'))) - # content.append(Graphs.draw_text('''皮尔逊相关系数说明：''')) - # content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。''')) - # content.append(Graphs.draw_text(''' - # 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。''')) - # content.append(Graphs.draw_text('''当前特征中正相关前十的有：''')) - # top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list() - # top10 = ','.join(top10_columns) - # content.append(Graphs.draw_text(f'''{top10}''')) - - # feature_df = feature_data_df[['ds','y']+top10_columns] - # # 遍历X每一列，和yy画散点图 ， - # for i, col in enumerate(feature_df.columns): - # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') - # if col not in ['ds', 'y']: - # fig, ax1 = plt.subplots(figsize=(10, 6)) - # # 在第一个坐标轴上绘制数据 - # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') - # ax1.set_xlabel('日期') - # ax1.set_ylabel('y', color='b') - # ax1.tick_params('y', colors='b') - # # 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 - # for j in range(1,len(feature_df),2): - # value = feature_df['y'].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) - # # 创建第二个坐标轴 - # ax2 = ax1.twinx() - # # 在第二个坐标轴上绘制数据 - # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') - # ax2.set_ylabel(col, color='r') - # ax2.tick_params('y', colors='r') - # # 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 - # for j in range(0,len(feature_df),2): - # value = feature_df[col].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) - # # 添加标题 - # plt.title(col) - # # 设置横坐标为日期格式并自动调整 - # locator = mdates.AutoDateLocator() - # formatter = mdates.AutoDateFormatter(locator) - # ax1.xaxis.set_major_locator(locator) - # ax1.xaxis.set_major_formatter(formatter) - # # 文件名特殊字符处理 - # col = col.replace('*', '-') - # col = col.replace(':', '-') - # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) - # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) - # plt.close() - - - # content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：')) - # # 皮尔逊正相关 不相关 负相关 的表格 - # content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png'))) - # 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。')) - # 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。')) - # content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。')) - # 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；')) - # content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：''')) - # top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list() - # top10 = ','.join(top10_columns) - # content.append(Graphs.draw_text(f'''{top10}''')) - - # feature_df = feature_data_df[['ds','y']+top10_columns] - # # 遍历X每一列，和yy画散点图 ， - # for i, col in enumerate(feature_df.columns): - # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') - # if col not in ['ds', 'y']: - # fig, ax1 = plt.subplots(figsize=(10, 6)) - # # 在第一个坐标轴上绘制数据 - # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') - # ax1.set_xlabel('日期') - # ax1.set_ylabel('y', color='b') - # ax1.tick_params('y', colors='b') - # # 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 - # for j in range(1,len(feature_df),2): - # value = feature_df['y'].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) - # # 创建第二个坐标轴 - # ax2 = ax1.twinx() - # # 在第二个坐标轴上绘制数据 - # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') - # ax2.set_ylabel(col, color='r') - # ax2.tick_params('y', colors='r') - # # 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 - # for j in range(0,len(feature_df),2): - # value = feature_df[col].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) - # # 添加标题 - # plt.title(col) - # # 设置横坐标为日期格式并自动调整 - # locator = mdates.AutoDateLocator() - # formatter = mdates.AutoDateFormatter(locator) - # ax1.xaxis.set_major_locator(locator) - # ax1.xaxis.set_major_formatter(formatter) - # # 文件名特殊字符处理 - # col = col.replace('*', '-') - # col = col.replace(':', '-') - # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) - # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) - # plt.close() - - # content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；')) - # content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：''')) - # tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list() - # top10 = ','.join(tail10_columns) - # content.append(Graphs.draw_text(f'''{top10}''')) - # # 获取特征的近一周值 - # feature_df = feature_data_df[['ds','y']+tail10_columns] - # # 遍历X每一列，和yy画散点图 ， - # for i, col in enumerate(feature_df.columns): - # print(f'正在绘制第{i+1}个特征{col}与价格散点图...') - # if col not in ['ds', 'y']: - # fig, ax1 = plt.subplots(figsize=(10, 6)) - # # 在第一个坐标轴上绘制数据 - # ax1.plot(feature_df['ds'], feature_df['y'], 'b-') - # ax1.set_xlabel('日期') - # ax1.set_ylabel('y', color='b') - # ax1.tick_params('y', colors='b') - # # 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 - # for j in range(len(feature_df)): - # if j%2 == 1: - # value = feature_df['y'].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10) - # # 创建第二个坐标轴 - # ax2 = ax1.twinx() - # # 在第二个坐标轴上绘制数据 - # line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-') - # ax2.set_ylabel(col, color='r') - # ax2.tick_params('y', colors='r') - # # 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 - # for j in range(1,len(feature_df),2): - # value = feature_df[col].iloc[j] - # date = feature_df['ds'].iloc[j] - # offset = 1.001 - # ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10) - # # 添加标题 - # plt.title(col) - # # 设置横坐标为日期格式并自动调整 - # locator = mdates.AutoDateLocator() - # formatter = mdates.AutoDateFormatter(locator) - # ax1.xaxis.set_major_locator(locator) - # ax1.xaxis.set_major_formatter(formatter) - # # 文件名特殊字符处理 - # col = col.replace('*', '-') - # col = col.replace(':', '-') - # plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png')) - # content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png'))) - # plt.close() - # 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。')) - # 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。')) content.append(Graphs.draw_little_title('模型选择：')) content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：')) @@ -1992,12 +1495,9 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi eval_df[col] = eval_df[col].round(3) # 筛选 fivemodels_list.tolist() 的行 eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)] - # df转置 eval_df = eval_df.T - # df重置索引 eval_df = eval_df.reset_index() eval_df = eval_df.T - # # 添加表格 data = eval_df.values.tolist() col_width = 500/len(eval_df.columns) content.append(Graphs.draw_table(col_width,*data)) @@ -2006,7 +1506,6 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi content.append(Graphs.draw_text('2. 平均绝对误差(MAE)：平均绝对误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。')) content.append(Graphs.draw_text('3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。')) content.append(Graphs.draw_text('模型拟合：')) - # 添加图片 content.append(Graphs.draw_img(os.path.join(dataset,'预测值与真实值对比图.png'))) # 附1，特征列表 @@ -2019,14 +1518,11 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi for i in range(len(fuyi)): content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}')) - - ### 生成pdf文件 doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter) # doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter) doc.build(content) # pdf 上传到数字化信息平台 - # 读取pdf并转为base64 try: if is_update_report: with open(os.path.join(dataset,reportname), 'rb') as f: @@ -2038,8 +1534,6 @@ def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsi except TimeoutError as e: print(f"请求超时: {e}") - - def pp_export_pdf_v1(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf'): global y # 创建内容对应的空列表 @@ -2376,9 +1870,6 @@ def pp_export_pdf_v1(num_indicators=475,num_models=21, num_dayindicator=202,inpu except TimeoutError as e: print(f"请求超时: {e}") - - - def tansuanli_export_pdf(num_indicators=475,num_models=22, num_dayindicator=202,inputsize=5,dataset='dataset',y='电碳价格',end_time='2024-07-30',reportname='tansuanli.pdf'): # 创建内容对应的空列表 content = list()