输出预测结果

浙江行业电量/prophet_行业电量.py

@@ -2,8 +2,50 @@ from prophet import Prophet
 import pandas as pd
 import os
 import datetime
+import numpy as np
+
+
+def normal(data):
+    high = data.describe()['75%'] + 1.5 * (data.describe()['75%'] - data.describe()['25%'])
+    low = data.describe()['25%'] - 1.5 * (data.describe()['75%'] - data.describe()['25%'])
+    return data[(data<=high)&(data>=low)]
+
 
 file_dir = './浙江各地市行业电量数据'
-city = os.listdir(file_dir)[0]
+for city in os.listdir(file_dir):
-df_city = pd.read_excel(os.path.join(file_dir,city))
+    df_city = pd.read_excel(os.path.join(file_dir, city))
-print(df_city.columns)
+    df_city['stat_date'] = df_city['stat_date'].map(lambda x: str(x).strip()[:10])
+    df_city['stat_date'] = pd.to_datetime(df_city['stat_date'])
+    list_goal = []
+    list_industry = []
+    for industry in df_city.columns[2:]:
+        s1 = df_city[['stat_date', industry]]
+        s1 = s1[(s1['stat_date'] >= '2022-09-30') & (s1['stat_date'] <= '2023-10-31')]
+        s1 = s1.loc[normal(s1[industry]).index]
+        s1.rename(columns={'stat_date': 'ds', industry: 'y'}, inplace=True)
+
+        df_train = s1[(s1['ds'] >= '2022-08-31') & (s1['ds'] <= '2023-10-31')].sort_values(by='ds')
+        df_test = s1[(s1['ds'] >= '2022-08-31') & (s1['ds'] <= '2023-10-31')].sort_values(by='ds')
+
+        model = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=True)
+        model.add_country_holidays(country_name="CN")
+        model.fit(df_train)
+        future = model.make_future_dataframe(periods=3, freq='D')
+
+        predict = model.predict(future)
+        predict = predict[['ds', 'yhat']].set_index('ds')
+        print(city,industry)
+        print(predict.loc['2023-10'])
+
+        # df = predict.join(s1.set_index('ds')).loc['2023-8']
+        # df['偏差率'] = (df['y'] - df['yhat']) / df['y']
+        # df['goal'] = (df['y'] - df['yhat'])[-3:].sum() / df['y'].sum()
+        # list_goal.append((df['y'] - df['yhat'])[-3:].sum() / df['y'].sum())
+        # list_industry.append(industry)
+
+    # df = pd.DataFrame({'industry': list_industry, 'goal': list_goal})
+    # df.to_csv(fr'C:\Users\鸽子\Desktop\行业8月偏差\{city[:2]}_goal.csv', index=False, encoding='gbk')
+    #
+    # with open(r'C:\Users\鸽子\Desktop\goal_8.txt','a') as f:
+    #     f.write(f'{city[:2]}\n')
+    #     df['goal'].value_counts(bins=[-np.inf,-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.02, 0.05,np.inf], sort=False).to_csv(f,header=False,sep='\t')

浙江行业电量/test1.py

@@ -53,30 +53,62 @@ def normal(df):
             pass
     return df
 
-file_dir = './浙江各地市行业电量数据'
+# file_dir = './浙江各地市行业电量数据'
-city1 = os.listdir(file_dir)[0]
+# city1 = os.listdir(file_dir)[0]
-df_city = pd.read_excel(os.path.join(file_dir, city1))
+# df_city = pd.read_excel(os.path.join(file_dir, city1))
-df_city = normal(df_city)
+# df_city = normal(df_city)
-df_city = df_city.drop(columns='地市')
+# df_city = df_city.drop(columns='地市')
-df_city[df_city.columns[1:]] /= 10000
+# df_city[df_city.columns[1:]] /= 10000
-df_city['stat_date'] = df_city['stat_date'].map(lambda x: str(x).strip()[:10])
+# df_city['stat_date'] = df_city['stat_date'].map(lambda x: str(x).strip()[:10])
-df_city.stat_date = pd.to_datetime(df_city.stat_date)
+# df_city.stat_date = pd.to_datetime(df_city.stat_date)
-print(df_city.describe())
+# print(df_city.describe())
+#
+# list_1000 = []
+# list_100 = []
+# list_10 = []
+# list_1 = []
+# for i in df_city.columns[1:]:
+#     if df_city[i].describe()['mean']>=1000:
+#         list_1000.append(i)
+#     if df_city[i].describe()['mean'] < 1000 and df_city[i].describe()['mean']  >= 100:
+#         list_100.append(i)
+#     if df_city[i].describe()['mean'] < 100 and df_city[i].describe()['mean']  >= 10:
+#         list_10.append(i)
+#     else:
+#         list_1.append(i)
+# print('list_1:',list_1)
+# print('list_10:',list_10)
+# print('list_100:',list_100)
+# print('list_1000:',list_1000)
+import pandas as pd
+
+# 创建一个简单的DataFrame
+data = pd.DataFrame({'A': [1, 2, 3000, 4, 500],
+        'B': [10, 20, 30, 40, 50]})
+
+
+Q1 = data['A'].quantile(0.25)
+Q3 = data['A'].quantile(0.75)
+IQR = Q3 - Q1
+
+lower_threshold = Q1 - 1.5 * IQR
+upper_threshold = Q3 + 1.5 * IQR
+# 向下移动一行
+outliers = (data['A'] < lower_threshold) | (data['A'] > upper_threshold)
+print(outliers)
+print( data['A'].shift(1))
+# 替换异常值为临近一个值
+
+data = {'A': [1, 2, 3, 4, 5],
+        'B': [10, 20, 30, 40, 50]}
+df = pd.DataFrame(data)
+
+# 将满足条件的元素替换为新值
+condition = df['A'] > 3
+df_new = df.where(condition, other=-1)
+
+print("原始数据:")
+print(df)
 
-list_1000 = []
+print("\n根据条件替换后的数据:")
-list_100 = []
+print(df_new)
-list_10 = []
-list_1 = []
-for i in df_city.columns[1:]:
-    if df_city[i].describe()['mean']>=1000:
-        list_1000.append(i)
-    if df_city[i].describe()['mean'] < 1000 and df_city[i].describe()['mean']  >= 100:
-        list_100.append(i)
-    if df_city[i].describe()['mean'] < 100 and df_city[i].describe()['mean']  >= 10:
-        list_10.append(i)
-    else:
-        list_1.append(i)
-print('list_1:',list_1)
-print('list_10:',list_10)
-print('list_100:',list_100)
-print('list_1000:',list_1000)

浙江行业电量/分类归一化.py

@@ -0,0 +1,76 @@
+import os
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+
+
+def normal(data):
+    high = data.describe()['75%'] + 1.5 * (data.describe()['75%'] - data.describe()['25%'])
+    low = data.describe()['25%'] - 1.5 * (data.describe()['75%'] - data.describe()['25%'])
+    return (data >= low) & (data <= high)
+
+
+# file_dir = './浙江各地市行业电量数据'
+#
+# # 合并11个市
+# df = pd.DataFrame({})
+# for city in os.listdir(file_dir):
+#
+#     df_city = pd.read_excel(os.path.join(file_dir, city))
+#
+#     # 对每个市的每一个行业异常值 向后填充
+#     for industry in df_city.columns[2:]:
+#         outliers_index = normal(df_city[industry]).index
+#         df_city[industry] = df_city[industry].where(normal(df_city[industry]), other=np.nan).bfill()
+#         df_city[industry].fillna(method='ffill',inplace=True)
+#     df = pd.concat([df,df_city])
+#     print(df.shape)
+#
+# df.to_csv('11市行业数据(已处理异常).csv',index=False,encoding='GBK')
+df = pd.read_csv('11市行业数据(已处理异常).csv', encoding='gbk')
+print(sum(df.isnull().sum()))
+print(df.describe())
+# 对df每一行业进行归一化
+column_params = {}
+for column in df.columns[2:]:
+    scaler = MinMaxScaler()
+
+    df[column] = scaler.fit_transform(df[[column]])
+
+    column_params[column] = {'min': scaler.data_min_[0], 'max': scaler.data_max_[0]}
+
+print(column_params)
+print(df.head())
+
+
+def create_dataset(data, days_for_train=10) -> (np.array, np.array):
+    dataset_x, dataset_y = [], []
+    for i in range(len(data) - days_for_train - 3):
+        dataset_x.append(data[i:(i + days_for_train)])
+        dataset_y.append(data[i + days_for_train:i + days_for_train + 3])
+
+    return (np.array(dataset_x), np.array(dataset_y))
+
+
+# 切分x,y数据集，步长为10.最小单位为单个城市的单个行业。
+# 先从第一个行业切分，合并所有城市。
+
+industry = df.columns[2:][0]
+city = df['地市'].drop_duplicates()[0]
+df_city_industry = df[df['地市'] == city][industry]
+dataset_x, dataset_y = create_dataset(df_city_industry)
+
+for city in df['地市'].drop_duplicates()[1:]:
+    df_city_industry = df[df['地市'] == city][industry]
+    x, y = create_dataset(df_city_industry)
+    dataset_x,dataset_y = np.concatenate([dataset_x,x]),np.concatenate([dataset_y,y])
+
+for industry in df.columns[2:][1:]:
+    for city in df['地市'].drop_duplicates():
+        df_city_industry = df[df['地市'] == city][industry]
+        x, y = create_dataset(df_city_industry)
+        dataset_x, dataset_y = np.concatenate([dataset_x, x]), np.concatenate([dataset_y, y])
+
+print(dataset_x.shape, dataset_y.shape)
+
+

浙江行业电量/行业电量_输出为3_步长为10.py

@@ -28,9 +28,9 @@ class LSTM_Regression(nn.Module):
 
 def create_dataset(data, days_for_train=5) -> (np.array, np.array):
     dataset_x, dataset_y = [], []
-    for i in range(len(data) - days_for_train-5):
+    for i in range(len(data) - days_for_train-3):
         dataset_x.append(data[i:(i + days_for_train)])
-        dataset_y.append(data[i + days_for_train:i + days_for_train+5])
+        dataset_y.append(data[i + days_for_train:i + days_for_train+3])
         # print(dataset_x,dataset_y)
     return (np.array(dataset_x), np.array(dataset_y))
 
@@ -103,13 +103,13 @@ train_y = dataset_y[:train_size]
 
 # # 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
 train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
-train_y = train_y.reshape(-1, 1, 5)
+train_y = train_y.reshape(-1, 1, 3)
 
 # # 转为pytorch的tensor对象
 train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
 train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
 print('=====================================',train_x.shape)
-model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, num_layers=2).to(device)  # 导入模型并设置模型的参数输入输出层、隐藏层等
+model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=3, num_layers=2).to(device)  # 导入模型并设置模型的参数输入输出层、隐藏层等
 #
 train_loss = []
 loss_function = nn.MSELoss()
@@ -146,7 +146,7 @@ for i in range(1500):
 # plt.legend(loc='best')
 # plt.show()
 
-model.load_state_dict(torch.load('hy5.pth',map_location=torch.device('cpu')))
+model.load_state_dict(torch.load('hy3.pth',map_location=torch.device('cpu')))
 max_value = 354024930.8
 min_value = 0.0