liurui/PriceForecast
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计算准确率逻辑修改

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liurui 2024-12-17 09:31:36 +08:00
parent 1ad1553e01
commit 34c4a9e205
1 changed files with 15 additions and 6 deletions
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21
models/nerulforcastmodels.py
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@ -400,7 +400,7 @@ def model_losss(sqlitedb,end_time):
# 保存到数据库 # 保存到数据库
if not sqlitedb.check_table_exists('accuracy'): if not sqlitedb.check_table_exists('accuracy'):
columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE']) columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE','min_price','max_price'])
sqlitedb.create_table('accuracy',columns=columns) sqlitedb.create_table('accuracy',columns=columns)
existing_data = sqlitedb.select_data(table_name = "accuracy") existing_data = sqlitedb.select_data(table_name = "accuracy")
@ -429,9 +429,11 @@ def model_losss(sqlitedb,end_time):
# 模型评估前五最大最小 # 模型评估前五最大最小
# predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']] # predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']]
# 模型评估前五均值 df_combined3['mean'] = df_combined3[modelnames].mean(axis=1) # 模型评估前五均值 df_combined3['mean'] = df_combined3[modelnames].mean(axis=1)
# predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1
predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1 # predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1 # 模型评估前十均值
predict_y['min_price'] = predict_y[allmodelnames[0:5]].min(axis=1)
predict_y['max_price'] = predict_y[allmodelnames[0:5]].max(axis=1)
for id in ids: for id in ids:
row = predict_y[predict_y['id'] == id] row = predict_y[predict_y['id'] == id]
try: try:
@ -447,12 +449,19 @@ def model_losss(sqlitedb,end_time):
df['ds'] = pd.to_datetime(df['ds']) df['ds'] = pd.to_datetime(df['ds'])
df = df.reindex() df = df.reindex()
def calculate_accuracy(row): def calculate_accuracy(row):
if row['HIGH_PRICE'] > row['min_price'] or row['max_price'] > row['LOW_PRICE']: # 子集情况：
if (row['HIGH_PRICE'] >= row['max_price'] and row['min_price'] >= row['LOW_PRICE']) or \
(row['LOW_PRICE'] >= row['min_price'] and row['max_price'] >= row['HIGH_PRICE']):
return 1
# 有交集情况：
if row['HIGH_PRICE'] > row['min_price'] or \
row['max_price'] > row['LOW_PRICE']:
sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']]) sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']])
middle_diff = sorted_prices[2] - sorted_prices[1] middle_diff = sorted_prices[2] - sorted_prices[1]
price_range = row['HIGH_PRICE'] - row['LOW_PRICE'] price_range = row['HIGH_PRICE'] - row['LOW_PRICE']
accuracy = middle_diff / price_range accuracy = middle_diff / price_range
return accuracy return accuracy
# 无交集情况：
else: else:
return 0 return 0
columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price'] columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price']
@ -586,7 +595,7 @@ def model_losss(sqlitedb,end_time):
_plt_model_results3() _plt_model_results3()
return model_results3 return model_results3
# 聚烯烃计算预测评估指数 # 聚烯烃计算预测评估指数
def model_losss_juxiting(sqlitedb): def model_losss_juxiting(sqlitedb):