liurui/PriceForecast
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Merge branch 'main' of https://gitea.jayhgq.cn/liurui/PriceForecast

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jingboyitiji 2024-11-05 15:10:49 +08:00
commit bac2919c25
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models/nerulforcastmodels.py
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@ -631,7 +631,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
df_zhibiaoshuju = data.copy() # 气泡图用 df_zhibiaoshuju = data.copy() # 气泡图用
# 绘制特征相关气泡图 # 绘制特征相关气泡图
content.append(Graphs.draw_text('气泡图中，横轴为指标名称，纵轴为指标名称，面积越大表示相关性越大，面积越小表示相关性越小。'))
grouped = df_zhibiaofenlei.groupby('指标分类') grouped = df_zhibiaofenlei.groupby('指标分类')
grouped_corr = pd.DataFrame(columns=['指标分类', '指标数量', '相关性总和']) grouped_corr = pd.DataFrame(columns=['指标分类', '指标数量', '相关性总和'])
@ -705,6 +705,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
plt.savefig(os.path.join(dataset, '指标分类相关性总和的气泡图.png'), bbox_inches='tight') plt.savefig(os.path.join(dataset, '指标分类相关性总和的气泡图.png'), bbox_inches='tight')
plt.close() plt.close()
content.append(Graphs.draw_img(os.path.join(dataset,'指标分类相关性总和的气泡图.png'))) content.append(Graphs.draw_img(os.path.join(dataset,'指标分类相关性总和的气泡图.png')))
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') logger.info(f'绘制相关性总和的气泡图结束')