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methods.py
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methods.py
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from pulp import LpVariable, LpProblem, LpMaximize, LpMinimize, lpSum, value, LpStatus
import pandas as pd
import numpy as np
from helpers import Connect
class Winning(Connect):
def __init__(self, tansyo, umatan_, umatan__ = None):
super().__init__(tansyo, umatan_, umatan__)
self.df = self.connect()
self.odds = self.df['オッズ']
self.l = len(self.df)
# pulpで使う時の変数を設定する
self.V = [LpVariable('v'+n, lowBound = 0) for n in self.df.index]
def model_0(self):
# ベット枚数を最小にする
model = LpProblem(name = 'horse', sense = LpMinimize)
model += lpSum(v for v in self.V)
for l in range(self.l):
# どの馬が1着になっても、総ベット枚数＊１００円を超えるようにする
model += self.odds[l] * self.V[l] - lpSum(v for v in self.V) >= 0
# ベット枚数は最小でも１枚はかける
model += self.V[l] >= 1
return model
def model_1(self, betsum):
# 総払い戻し金が最大になるようにする
model = LpProblem(name = 'horse', sense = LpMaximize)
odds_V = np.sum(self.odds * self.V)
sum_V = lpSum(v for v in self.V)
model += odds_V - self.l * sum_V
# 総ベット枚数が最初に引数で渡したベット枚数を超えないようにする
model += sum_V <= betsum
for l in range(self.l):
# どの馬が1着になっても、総ベット枚数＊１００円を超えるようにする
model += self.odds[l] * self.V[l] - lpSum(v for v in self.V) >= 0
# ベット枚数は最小でも１枚はかける
model += self.V[l] >= 1
return model
# 結果を表示
def result(self, model):
res = model.solve()
bet = np.array([v.value() for v in self.V])
# ベット数は小数点になるので切り上げる
bet = np.ceil(bet)
result_betsum = np.sum(bet)
if_win = self.odds * bet * 100
diff = (self.odds * bet - result_betsum) * 100
self.df['ベット(枚)'] = bet
self.df['払い戻し金(円)'] = if_win
self.df['差額(円)'] = diff
status = 'Solved' if LpStatus[res] == 'Optimal' else 'Not solved'
return self.df, status
# 期待値を出す
class Expect(Connect):
def __init__(self, tansyo, umatan_ = None, umatan__ = None, propability = None):
super().__init__(tansyo, umatan_ = None, umatan__ = None)
self.df = self.connect()
self.l = len(self.df)
self.odds = self.df['オッズ']
self.pro = propability
# 確率を引数に持たせない場合オッズから計算したものを使う
if self.pro == None:
self.pro = self.df['確率']
self.V = [LpVariable('v'+n, lowBound = 0) for n in self.df.index]
def model_0(self, betsum, k):
# 期待値を最大にする
model = LpProblem(name = 'horse', sense = LpMaximize)
model += lpSum(o * v * p for o, p, v in zip(self.odds, self.pro, self.V))
# 総ベット枚数が最初に引数で渡したベット枚数を超えないようにする
model += lpSum(v for v in self.V) <= betsum
for i in range(self.l):
# ベット枚数は最小でも１枚はかける
model += self.V[i] >= k
return model
def model_1(self, betsum):
# 期待値を最大にする
model = LpProblem(name = 'horse', sense = LpMaximize)
model += lpSum(o * v * p for o, p, v in zip(self.odds, self.pro, self.V))
# 総ベット枚数が最初に引数で渡したベット枚数を超えないようにする
model += lpSum(v for v in self.V) <= betsum
pre_df = self.df.copy()
pre_df['V'] = self.V
pre_df = pre_df.sort_values('オッズ')
V = pre_df['V']
# オッズが高いものからベット枚数を大きくしていく オッズが一番高いものでも最小でも１枚はかける
for i in range(self.l):
if i < self.l - 1:
model += V[i] - V[i+1] >= 1
elif i == self.l:
model += V[i] >= 1
return model
# 結果を出力
def result(self, model):
result = model.solve()
bet = np.array([value(v) for v in self.V])
bet = np.ceil(bet)
result_betsum = np.sum(bet)
if_win = self.odds * bet * 100
diff = self.odds * bet * 100 - result_betsum * 100
expected_value = np.sum(self.odds * self.pro * bet * 100)
self.df['ベット(枚)'] = bet
self.df['払い戻し金(円)'] = if_win
self.df['差額(円)'] = diff
print('Solved' if LpStatus[result] == 'Optimal' else 'Not solved')
print('ベット総額(枚) : {}'.format(int(result_betsum)))
print('期待値(円) : {}'.format(int(expected_value)))
return self.df