198 lines
5.7 KiB
Python
198 lines
5.7 KiB
Python
from keras.models import Sequential
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from keras.layers import Dense, Dropout, Embedding
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from keras.layers import InputLayer
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from keras.layers import LSTM
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from keras import backend
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import pymysql
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import pickle
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import os
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import numpy
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import time, datetime
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def get_collect():
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collect = {}
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loadfile = "./collect.pickle"
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try:
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collect = pickle.load(open(loadfile, 'rb'))
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except Exception as e:
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print(e)
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# 打开数据库连接
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db = pymysql.connect(host="sg-board1.livenono.com", port=3306,user="root",passwd="Nono-databoard",db="databoard",charset="utf8")
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = db.cursor()
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today = time.strftime("%Y-%m-%d", time.localtime())
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# 使用 execute() 方法执行 SQL 查询
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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)
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collect_pay = {}
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for row in cursor.fetchall():
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# print(row)
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coin, extra_coins, pay_users, create_at = row
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d = str(create_at.date())
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if d in collect_pay:
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collect_pay[d].append(row)
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else:
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collect_pay[d] = [ row ]
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# print(dir(create_at), create_at.timestamp(), create_at.date())
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print('共查找出', cursor.rowcount, '条数据')
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deletelist = []
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for k in collect_pay:
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if len(collect_pay[k]) != 24:
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deletelist.append(k)
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for k in deletelist:
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del collect_pay[k]
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querydate= []
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for k in collect_pay:
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querydate.append(k)
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querydate.sort()
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cursor.execute(
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'''SELECT coin, users, create_at from gift_items_hour pih where region = "all" and create_at >= %s and create_at <= %s''',
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(querydate[0], querydate[-1]),
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)
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collect_gift = {}
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for row in cursor.fetchall():
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coin, users, create_at = row
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d = str(create_at.date())
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if d in collect_gift:
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collect_gift[d].append(row)
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else:
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collect_gift[d] = [ row ]
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for k in collect_pay:
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l = collect_pay[k]
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l.sort(key=lambda x:x[3])
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for k in collect_gift:
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l = collect_gift[k]
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l.sort(key=lambda x:x[2])
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collect["pay"] = collect_pay
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collect["gift"] = collect_gift
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pickle.dump(collect, open(loadfile, 'wb+'))
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finally:
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return collect
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def load_pay_data(textNum = 80):
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collect = get_collect()
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# TODO: 处理gift pay的波动关系
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x_train = []
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y_train = []
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collect_pay = []
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for k in collect["pay"]:
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collect_pay.append(collect["pay"][k])
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collect_pay.sort(key=lambda x:x[0][3])
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lastday_v = collect_pay[0]
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for cur_v in collect_pay[1:]:
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total_coin = 0
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users = 0
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last_total_coin = 0
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for v2 in lastday_v:
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last_total_coin += v2[0] + v2[1]
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count = 0
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for v1, v2 in zip(cur_v,lastday_v):
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total_coin += v1[0] + v1[1]
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users += v1[2]
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# print(v1[3])
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# last_total_coin += v2[0] + v2[1]
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# print(v2[3])
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# compare = float(total_coin - last_total_coin) / float(last_total_coin)
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# print(compare)
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# 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
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x_train.append([count ,total_coin / last_total_coin , total_coin])
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count+=1
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for i in range(count):
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y_train.append(total_coin)
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lastday_v = cur_v
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input_shape = (len(x_train[0]), 1)
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x_train = numpy.reshape(x_train, (len(x_train) , input_shape[0], input_shape[1]))
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y_train = numpy.reshape(y_train, (len(y_train)))
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# max_features = 1024
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tx_train = x_train[len(x_train) - textNum:]
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ty_train = y_train[len(y_train) - textNum:]
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# x_train = x_train[:len(x_train) - textNum]
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# y_train = y_train[:len(y_train) - textNum]
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return x_train, y_train, tx_train, ty_train, input_shape
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def load_gift_data(textNum = 80):
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collect = get_collect()
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x_train = []
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y_train = []
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collect_gift = []
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for k in collect["gift"]:
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collect_gift.append(collect["gift"][k])
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collect_gift.sort(key=lambda x:x[0][2])
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lastday_v = collect_gift[0]
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for cur_v in collect_gift[1:]:
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total_coin = 0
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last_total_coin = 0
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users = 0
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for v2 in lastday_v:
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last_total_coin += v2[0]
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f = 20000000.0
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count = 0
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for v1, v2 in zip(cur_v,lastday_v):
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total_coin += v1[0]
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# print(v1[3])
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# last_total_coin += v2[0]
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users += v1[1]
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# print(v2[3])
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# compare = float(total_coin - last_total_coin) / float(last_total_coin)
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# print(compare)
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# 参数 前一小个小时. 时刻. 当前金钱. 送礼人数
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x_train.append([count, total_coin / last_total_coin, total_coin ])
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count+=1
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for i in range(count):
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y_train.append(total_coin)
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lastday_v = cur_v
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input_shape = (len(x_train[0]), 1)
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x_train = numpy.reshape(x_train, (len(x_train) , input_shape[0], input_shape[1]))
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y_train = numpy.reshape(y_train, (len(y_train)))
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# max_features = 1024
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tx_train = x_train[len(x_train) - textNum:]
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ty_train = y_train[len(y_train) - textNum:]
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# x_train = x_train[:len(x_train) - textNum]
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# y_train = y_train[:len(y_train) - textNum]
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return x_train, y_train, tx_train, ty_train, input_shape |