feat(save): for save

.gitignore

@@ -0,0 +1 @@
+*

data.py

@@ -1,11 +1,8 @@
-import logging
-import traceback
-
 from  keras.models import Sequential
 from  keras.layers import Dense, Dropout, Embedding
 from  keras.layers import InputLayer
 from  keras.layers import LSTM
-from  keras import backend 
+# from  keras import backend  as K
 
 import pymysql
 import pickle
@@ -13,24 +10,6 @@ import os
 import numpy
 import time, datetime
 
-regions = [
-    "all",
-   "Region_Arab",
-   "Region_China",
-   "Region_English",
-   "Region_Germany",
-   "Region_India",
-   "Region_Indonesia",
-   "Region_Japan",
-   "Region_Philippines",
-   "Region_Portuguese",
-   "Region_Russian",
-   "Region_Spanish",
-   "Region_Thailand",
-   "Region_Turkey",
-   "Region_Vietnam",
-]
-
 def get_collect():
     collect = {} 
     loadfile = "./collect.pickle"
@@ -39,126 +18,77 @@ def get_collect():
         collect = pickle.load(open(loadfile, 'rb'))
     except Exception as e:
         print(e)
-        try:
-        
         # 打开数据库连接
-            db = pymysql.connect(host="sg-board1.livenono.com", port=3306,user="root",passwd="Nono-databoard",db="databoard",charset="utf8")
+        db = pymysql.connect(host="sg-board1.livenono.com", port=3306,user="root",passwd="Nono-databoard",db="databoard",charset="utf8")
-            
+        
-            # 使用 cursor() 方法创建一个游标对象 cursor
+        # 使用 cursor() 方法创建一个游标对象 cursor
-            cursor = db.cursor()
+        cursor = db.cursor()
-            today = time.strftime("%Y-%m-%d", time.localtime())
+        today = time.strftime("%Y-%m-%d", time.localtime())
-            
+         
-            for region in regions:
+        # 使用 execute()  方法执行 SQL 查询 
+        cursor.execute(
+            '''SELECT coin, extra_coins, pay_users, create_at from pay_items_hour pih where region = "all" and platform="all" and create_at <= %s''',
+             (today),
+        )
+        
+        collect_pay = {}
 
-                # 使用 execute()  方法执行 SQL 查询 
+        for row in cursor.fetchall():
-                cursor.execute('''SELECT coin, extra_coins, pay_users, create_at from pay_items_hour pih where region = %s and country = "all" and platform="all" and create_at >= "2021-02-23" and create_at <= %s''',(region , today))
+            # print(row)
-                collect_pay = []
+            coin, extra_coins, pay_users, create_at = row
-                
+            d = str(create_at.date())
-                for row in cursor.fetchall():
+            if d in collect_pay:
-                    # print(row)
+                collect_pay[d].append(row)
-                    coin, extra_coins, pay_users, create_at = row
+            else:
-                    rowlist = [coin, extra_coins, pay_users, create_at.hour, create_at]
+                collect_pay[d] = [ row ] 
-                    # print(dir(create_at), create_at.hour)
+        # print(dir(create_at), create_at.timestamp(), create_at.date())
-                    collect_pay.append(rowlist)
+        print('共查找出', cursor.rowcount, '条数据')
-                    # d = str(create_at.date())
+        deletelist = []
-                    # if d in collect_pay:
+        for k in collect_pay:
-                    #     collect_pay.append(row)
+            if len(collect_pay[k]) != 24:
-                    # else:
+                deletelist.append(k)
-                    #     collect_pay[d] = [ row ] 
-                # print(dir(create_at), create_at.timestamp(), create_at.date())
-                print('共查找出', cursor.rowcount, '条数据')
 
+        for k in deletelist:
+            del collect_pay[k]
 
+        querydate= []
+        for k in collect_pay:
+            querydate.append(k)
+        
+        querydate.sort()
+        cursor.execute(
+            '''SELECT coin, users, create_at from gift_items_hour pih where region = "all" and create_at >= %s and create_at <= %s''',
+            (querydate[0], querydate[-1]),
+            )
 
-                # if cursor.rowcount <= 500: 
+        collect_gift = {}
-                #     collect["pay-" + region] = None
+        for row in cursor.fetchall():
-                #     collect["gift-" + region] = None
+    
-                #     continue
+            coin, users, create_at = row
+            d = str(create_at.date())
+            if d in collect_gift:
+                collect_gift[d].append(row)
+            else:
+                collect_gift[d] = [ row ] 
 
-                # deletelist = []
+        for k in collect_pay:
-                # for k in collect_pay:
+            l = collect_pay[k]
-                #     if len(collect_pay[k]) != 24:
+            l.sort(key=lambda x:x[3])
-                #         deletelist.append(k)
 
-                # for k in deletelist:
+        for k in collect_gift:
-                #     del collect_pay[k]
+            l = collect_gift[k]
+            l.sort(key=lambda x:x[2])
 
-                # querydate= []
+        collect["pay"] = collect_pay
-                # for k in collect_pay:
+        collect["gift"] = collect_gift
-                #     querydate.append(k)
-                
-                
-
-                # querydate.sort()
-                cursor.execute(
-                    '''SELECT coin, users, create_at from gift_items_hour pih where region = %s and country = "all" and create_at >= "2021-02-23" and create_at <= %s''',
-                    (region, today),
-                    )
-
-                collect_gift = []
-                for row in cursor.fetchall():
-            
-                    coin, users, create_at = row
-                    rowlist = [coin, users, create_at.hour, create_at]
-                    collect_gift.append(rowlist)
-
-                    # d = str(create_at.date())
-                    # if d in collect_gift:
-                    #     collect_gift[d].append(row)
-                    # else:
-                    #     collect_gift[d] = [ row ] 
-
-  
-                collect_pay.sort(key=lambda x:x[-1])
-                collect_gift.sort(key=lambda x:x[-1])
-                # for k in collect_gift:
-                #     l = collect_gift[k]
-                #     l.sort(key=lambda x:x[2])
-                yesterday = {}
-                for v in collect_pay:
-                    print(v[-1])
-                    date = (v[-1].date() - datetime.timedelta(days=1)).__str__()
-                    print(date)
-                    if date not in yesterday:
-                        cursor.execute(
-                            '''SELECT coin, extra_coins, pay_users, create_at from pay_items_day pid where region = %s and country = "all" and platform="all" and create_at = %s''',
-                            (region , date),
-                            )
-                        row = cursor.fetchone()
-                        coin, extra_coins, pay_users, create_at = row
-                        yesterday[date] = coin + extra_coins
-                    v.insert(-2, yesterday[date])
-                    
-                yesterday = {}
-                for v in collect_gift:
-                    print(v[-1])
-                    date = (v[-1].date() - datetime.timedelta(days=1)).__str__()
-                    print(date)
-                    if date not in yesterday:
-                        cursor.execute(
-                            '''SELECT coin, users, create_at from gift_items_day where region = %s and country = "all" and create_at = %s''',
-                            (region , date),
-                            )
-                        row = cursor.fetchone()
-                        coin, users, create_at = row
-                        yesterday[date] = coin 
-                    v.insert(-2, yesterday[date])
-
-                collect["pay-" + region] = collect_pay
-                collect["gift-" + region] = collect_gift
-        except Exception as e:
-            # print(e)
-            logging.error(traceback.format_exc())
-            
 
         pickle.dump(collect, open(loadfile, 'wb+'))
-    
     finally:
         return collect
 
  
  
-def load_pay_data(textNum = 80, region = "all"):
+def load_pay_data(textNum = 80):
  
 
     collect = get_collect()
@@ -168,13 +98,11 @@ def load_pay_data(textNum = 80, region = "all"):
     x_train = []
     y_train = []
 
-    rkey = "pay-" + region
+    collect_pay = []
-    collect_pay = collect[rkey]
+    for k in collect["pay"]:
-     
+        collect_pay.append(collect["pay"][k])
-    # for k in collect[rkey]:
-        # collect_pay.append(collect[rkey][k])
     
-    # collect_pay.sort(key=lambda x:x[0][3])
+    collect_pay.sort(key=lambda x:x[0][3])
     lastday_v = collect_pay[0]
     for cur_v in collect_pay[1:]:
     
@@ -182,6 +110,9 @@ def load_pay_data(textNum = 80, region = "all"):
         users = 0
         last_total_coin = 0
 
+        for v2 in lastday_v:
+            last_total_coin += v2[0] + v2[1]
+
         count = 0
         for v1, v2 in zip(cur_v,lastday_v):
             total_coin += v1[0] + v1[1]
@@ -194,7 +125,7 @@ def load_pay_data(textNum = 80, region = "all"):
             # print(compare)
     
             # 时刻. 前一个小时 时刻. 当前支付总币数. 当前支付总币数 昨天币数
-            x_train.append([v1[-2]  ,total_coin / v1[-3] , total_coin])
+            x_train.append([count  ,total_coin / last_total_coin , total_coin])
             count+=1
 
         for i in range(count):
@@ -210,23 +141,21 @@ def load_pay_data(textNum = 80, region = "all"):
     tx_train = x_train[len(x_train) - textNum:]
     ty_train = y_train[len(y_train) - textNum:]
 
-    # x_train = x_train[:len(x_train) - textNum]
+    x_train = x_train[:len(x_train) - textNum]
-    # y_train = y_train[:len(y_train) - textNum]
+    y_train = y_train[:len(y_train) - textNum]
 
     return x_train, y_train, tx_train, ty_train, input_shape
 
-def load_gift_data(textNum = 80, region = "all"):
+def load_gift_data(textNum = 80):
 
     collect = get_collect()
 
     x_train = []
     y_train = []
 
-
-    rkey = "gift-" + region
     collect_gift = []
-    for k in collect[rkey]:
+    for k in collect["gift"]:
-        collect_gift.append(collect[rkey][k])
+        collect_gift.append(collect["gift"][k])
     
     collect_gift.sort(key=lambda x:x[0][2])
     lastday_v = collect_gift[0]
@@ -268,7 +197,7 @@ def load_gift_data(textNum = 80, region = "all"):
     tx_train = x_train[len(x_train) - textNum:]
     ty_train = y_train[len(y_train) - textNum:]
 
-    # x_train = x_train[:len(x_train) - textNum]
+    x_train = x_train[:len(x_train) - textNum]
-    # y_train = y_train[:len(y_train) - textNum]
+    y_train = y_train[:len(y_train) - textNum]
 
     return x_train, y_train, tx_train, ty_train, input_shape

predict.py

@@ -1,37 +0,0 @@
-import numpy
-from keras.models import load_model
-from data import load_pay_data, load_gift_data
-
-import matplotlib.pyplot as plt
-
-
-# x_train, y_train, tx_train, ty_train, _ = load_pay_data(160)
-# model = load_model("./predict_pay")
-
-# p_data = model.predict(tx_train)
-# for i in range(len(p_data)):
-#     comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
-#     print(comp, p_data[i][0], ty_train[i])
-#     if abs(comp) >= 1:
-#         print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])    
-
-
-x_train, y_train, tx_train, ty_train, _ = load_gift_data(160)
-model = load_model("./predict_gift")
-p_data = model.predict(tx_train)
-for i in range(len(p_data)):
-    comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
-    print(comp, p_data[i][0], ty_train[i])
-    if abs(comp) >= 0.1:
-        print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])    
-
-
-plt.plot(ty_train)
-plt.plot(p_data)
-plt.show()
-# data = numpy.reshape([[15, 2359688 / 10000000,  255968 / 1000000, 10 / 10000]],(1, 4, 1))
-# print( model.predict(data))
-
-
-
- 

predict_gift.py

@@ -0,0 +1,25 @@
+import numpy
+from keras.models import load_model
+from data import load_pay_data, load_gift_data
+
+import matplotlib.pyplot as plt 
+
+
+x_train, y_train, tx_train, ty_train, _ = load_gift_data(160)
+model = load_model("./predict_gift")
+p_data = model.predict(tx_train)
+for i in range(len(p_data)):
+    comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
+    print(comp, p_data[i][0], ty_train[i])
+    if abs(comp) >= 0.1:
+        print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])    
+
+
+plt.plot(ty_train)
+plt.plot(p_data)
+plt.show()
+ 
+
+
+
+ 

predict_pay.py

@@ -0,0 +1,20 @@
+import numpy
+from keras.models import load_model
+from data import load_pay_data, load_gift_data
+
+import matplotlib.pyplot as plt
+
+
+x_train, y_train, tx_train, ty_train, _ = load_pay_data(160)
+model = load_model("./predict_pay")
+
+p_data = model.predict(tx_train)
+for i in range(len(p_data)):
+    comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
+    print(comp, p_data[i][0], ty_train[i])
+    if abs(comp) >= 1:
+        print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])  
+
+plt.plot(ty_train)
+plt.plot(p_data)
+plt.show()  

requirements.txt

@@ -2,4 +2,5 @@ tensorflow
 keras
 numpy
 pymysql
-grpc_tools
+grpcio
+grpc_tools 

train_pay.py

@@ -4,7 +4,8 @@ from  keras.models import Sequential
 from  keras.layers import Dense, Dropout, Embedding
 from  keras.layers import InputLayer
 from  keras.layers import LSTM
-from  keras import backend 
+from  keras import backend as K
+from keras.losses import mean_squared_error
 from keras.layers.recurrent import SimpleRNN
 
 import pymysql
@@ -14,11 +15,12 @@ import numpy
 
 from data import load_pay_data
 
+def mean_squared_error(y_true, y_pred):
+    return K.mean(K.square(y_pred - y_true), axis=-1)
 
 if __name__ == "__main__":
 
-    region = "Region_Arab"
+    x_train, y_train, tx_train, ty_train, input_shape = load_pay_data(80)
-    x_train, y_train, tx_train, ty_train, input_shape = load_pay_data(80, region)
 
     
     model = Sequential()
@@ -29,10 +31,10 @@ if __name__ == "__main__":
     # model.add(Dropout(0.1))
     model.add(Dense(1))
     model.summary()
-    model.compile(loss = 'mse', optimizer = 'adam')
+    model.compile(loss = 'msle', optimizer = 'adam')
 
-    model.fit(x_train, y_train, batch_size=96, epochs=1200)
+    model.fit(x_train, y_train, batch_size=96, epochs=600)
-    model.save("./predict_pay_" + region)
+    model.save("./predict_pay")
 
     p_data = model.predict(tx_train)
     for i in range(len(p_data)):