test/predict-coin
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feat(save): for save

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eson 2021-04-02 17:22:46 +08:00
parent a7ce020942
commit 5d3130169e
7 changed files with 128 additions and 187 deletions
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1
.gitignore vendored Normal file
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@ -0,0 +1 @@
*

179
data.py
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@ -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"
@ -39,8 +18,6 @@ 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")
@ -48,117 +25,70 @@ def get_collect():
cursor = db.cursor()
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 = %s and country = "all" and platform="all" and create_at >= "2021-02-23" and create_at <= %s''',(region , today))
collect_pay = []
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 = {}
for row in cursor.fetchall():
# print(row)
coin, extra_coins, pay_users, create_at = row
rowlist = [coin, extra_coins, pay_users, create_at.hour, create_at]
# print(dir(create_at), create_at.hour)
collect_pay.append(rowlist)
# d = str(create_at.date())
# if d in collect_pay:
# collect_pay.append(row)
# else:
# collect_pay[d] = [ row ]
d = str(create_at.date())
if d in collect_pay:
collect_pay[d].append(row)
else:
collect_pay[d] = [ row ]
# print(dir(create_at), create_at.timestamp(), create_at.date())
print('共查找出', cursor.rowcount, '条数据')
deletelist = []
for k in collect_pay:
if len(collect_pay[k]) != 24:
deletelist.append(k)
for k in deletelist:
del collect_pay[k]
querydate= []
for k in collect_pay:
querydate.append(k)
# if cursor.rowcount <= 500:
# collect["pay-" + region] = None
# collect["gift-" + region] = None
# continue
# deletelist = []
# for k in collect_pay:
# if len(collect_pay[k]) != 24:
# deletelist.append(k)
# for k in deletelist:
# del collect_pay[k]
# querydate= []
# for k in collect_pay:
# querydate.append(k)
# querydate.sort()
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),
'''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]),
)
collect_gift = []
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 ]
# d = str(create_at.date())
# if d in collect_gift:
# collect_gift[d].append(row)
# else:
# collect_gift[d] = [ row ]
for k in collect_pay:
l = collect_pay[k]
l.sort(key=lambda x:x[3])
for k in collect_gift:
l = collect_gift[k]
l.sort(key=lambda x:x[2])
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())
collect["pay"] = collect_pay
collect["gift"] = collect_gift
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[rkey]
collect_pay = []
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]
# y_train = y_train[:len(y_train) - textNum]
x_train = x_train[:len(x_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]:
collect_gift.append(collect[rkey][k])
for k in collect["gift"]:
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]
# y_train = y_train[:len(y_train) - textNum]
x_train = x_train[:len(x_train) - textNum]
y_train = y_train[:len(y_train) - textNum]
return x_train, y_train, tx_train, ty_train, input_shape

37
predict.py
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@ -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))

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predict_gift.py Normal file
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@ -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()

20
predict_pay.py Normal file
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@ -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()

1
requirements.txt
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@ -2,4 +2,5 @@ tensorflow
keras
numpy
pymysql
grpcio
grpc_tools

14
train_pay.py
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@ -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, region)
x_train, y_train, tx_train, ty_train, input_shape = load_pay_data(80)
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.save("./predict_pay_" + region)
model.fit(x_train, y_train, batch_size=96, epochs=600)
model.save("./predict_pay")
p_data = model.predict(tx_train)
for i in range(len(p_data)):