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debugdemo.ipynb
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debugdemo.ipynb
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@ -289,7 +289,7 @@ def model_losss(sqlitedb):
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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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# 去除有空值的行
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# df_combined3.dropna(inplace=True)
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df_combined3.dropna(inplace=True)
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# 保存到数据库
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df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)
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df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
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@ -503,7 +503,7 @@ def model_losss_juxiting(sqlitedb):
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# 删除模型生成的cutoff列
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df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)
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# 获取模型名称
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modelnames = df_combined.columns.to_list()[1:]
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modelnames = df_combined.columns.to_list()[2:]
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if 'y' in modelnames:
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modelnames.remove('y')
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df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要
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@ -534,101 +534,26 @@ def model_losss_juxiting(sqlitedb):
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with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
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f.write(','.join(modelnames) + '\n')
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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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# 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()
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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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row = row[best_models]
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q10 = row.min()
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q90 = row.max()
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# 获取 row行最大最小值模型名称
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min_model = row[row == q10].idxmin()
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max_model = row[row == q90].idxmax()
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max_model = row[row == q90].idxmin()
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# # 判断flot值是否为空值
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# if pd.isna(q10) or pd.isna(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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# 设置上下界比例
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q10 = q10 * 0.99
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q90 = q90 * 0.99
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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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# 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()
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# def find_min_max_within_quantile(row):
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# row = row[best_models]
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# q10 = row.min()
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# q90 = row.max()
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# # 获取 row行最大最小值模型名称
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# min_model = row[row == q10].idxmin()
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# max_model = row[row == q90].idxmin()
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# # # 判断flot值是否为空值
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# # if pd.isna(q10) or pd.isna(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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# # 通道使用模型评估前80%作为置信度
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# def find_min_max_within_quantile(row):
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# row.drop(['ds','y'], inplace=True)
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# row = row.astype(float).round(2)
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# row_sorted = row
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# # 计算 10% 和 90% 位置的索引
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# index_10 = 0
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# index_90 = int(len(row_sorted) * 0.8)
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# q10 = row_sorted[index_10]
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# q90 = row_sorted[index_90]
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# # 获取模型名称
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# min_model = row[row == q10].idxmin()
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# max_model = row[row == q90].idxmin()
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# # # 判断flot值是否为空值
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# # if pd.isna(q10) or pd.isna(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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# # 重新排列
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# df_combined3 = df_combined3[['ds','y'] + allmodelnames]
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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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# # 通道使用预测模型的80%置信度
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@ -716,7 +641,7 @@ def model_losss_juxiting(sqlitedb):
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.figure(figsize=(15, 10))
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# 设置有5个子图的画布
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for n,model in enumerate(modelnames[:5]):
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for n,model in enumerate(modelnames):
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plt.subplot(3, 2, n+1)
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plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
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plt.plot(df_combined3['ds'], df_combined3[model], label=model)
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@ -746,11 +671,11 @@ def model_losss_juxiting(sqlitedb):
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if not sqlitedb.check_table_exists('trueandpredict'):
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first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
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else:
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for col in first_row.columns:
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sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
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for row in first_row.itertuples(index=False):
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row_dict = row._asdict()
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columns=row_dict.keys()
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for col in columns:
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sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
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check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
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if len(check_query) > 0:
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set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
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@ -763,10 +688,6 @@ def model_losss_juxiting(sqlitedb):
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# 最多频率的模型名称
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min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()
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max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax()
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if min_model_max_frequency_model == max_model_max_frequency_model:
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# 取20天第二多的模型
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max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1]
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df_predict['min_model'] = min_model_max_frequency_model
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df_predict['max_model'] = max_model_max_frequency_model
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df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
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