- 用户从桶中选择 1 到 {N} 个产品
- 对于每个产品，供应商提供 {N} 个报价
- 我想给用户机会告诉他
我想要的输出:
- 如果您从供应商 (A) 处购买 5 件产品的总成本为 87 美元
- 如果您从供应商 (A) 和 (B) 那里购买 5 件产品的总成本为 80 美元 + (x2 运费)
- 如果您从供应商 (A)(B) 和 (C) 那里购买 5 件产品的总成本为 72 美元 + (x3 运费)
python Pandas
x = pd.DataFrame.from_dict(OFFERS)
print(x)
offer_price product_id ventor
0 5.0 1 A
1 6.0 1 B
2 7.0 1 C
3 8.0 1 D
4 9.0 1 E
5 5.1 2 A
6 6.1 2 B
7 7.1 2 C
8 8.1 2 D
9 9.1 2 E
10 5.2 3 A
11 6.2 3 B
12 7.2 3 C
13 8.2 3 D
14 9.2 3 E
15 77.2 3 F
16 66.2 3 G
I get a list with JSON format from server
OFFERS = [
{'offer_id': 100, 'product_id': 1, 'vendor':'A', 'offer_price':5.00},
{'offer_id': 101, 'product_id': 1, 'vendor':'B', 'offer_price':6.00},
{'offer_id': 102, 'product_id': 1, 'vendor':'C', 'offer_price':7.00},
{'offer_id': 103, 'product_id': 1, 'vendor':'D', 'offer_price':8.00},
{'offer_id': 104, 'product_id': 1, 'vendor':'E', 'offer_price':9.00},
{'offer_id': 105, 'product_id': 2, 'vendor':'A', 'offer_price':5.10},
{'offer_id': 106, 'product_id': 2, 'vendor':'B', 'offer_price':6.10},
{'offer_id': 107, 'product_id': 2, 'vendor':'C', 'offer_price':7.10},
{'offer_id': 108, 'product_id': 2, 'vendor':'D', 'offer_price':8.10},
{'offer_id': 109, 'product_id': 2, 'vendor':'E', 'offer_price':9.10},
{'offer_id': 110, 'product_id': 3, 'vendor':'A', 'offer_price':5.20},
{'offer_id': 111, 'product_id': 3, 'vendor':'B', 'offer_price':6.20},
{'offer_id': 112, 'product_id': 3, 'vendor':'C', 'offer_price':7.20},
{'offer_id': 113, 'product_id': 3, 'vendor':'D', 'offer_price':8.20},
{'offer_id': 114, 'product_id': 3, 'vendor':'E', 'offer_price':9.20}
]
问题 1
How I get the best combination of offers that gonna limit the vendors(shipping) and I get best possible price
我想要的输出:
- 如果您从供应商 (A) 处购买 5 件产品的总成本为 87 美元
- 如果您从供应商 (A) 和 (B) 那里购买 5 件产品的总成本为 80 美元 + (x2 运费)
- 如果您从供应商 (A)(B) 和 (C) 处购买 5 件产品的总费用为 72 美元 +(x3 运费)
我的问题是:
- 我想我想解决的问题的名称是什么？
- 我是否需要使用机器学习来解决该问题？
My code so far is with python 3.6
=========================== TRY No1 ============================
After 3 hours of digging I come with this but I think this algorithm is very slow
My data have this format:
OFFERS = [
{'offer_id':'oid_1','product_id': 'pid_1', 'vendor':'B', 'offer_price':5.00},
{'offer_id':'oid_1','product_id': 'pid_1', 'vendor':'B', 'offer_price':6.00},
{'offer_id':'oid_2','product_id': 'pid_1', 'vendor':'C', 'offer_price':7.00},
{'offer_id':'oid_3','product_id': 'pid_1', 'vendor':'D', 'offer_price':2.00},
{'offer_id':'oid_4','product_id': 'pid_1', 'vendor':'E', 'offer_price':9.00},
{'offer_id':'oid_5','product_id': 'pid_2', 'vendor':'A', 'offer_price':5.10},
{'offer_id':'oid_6','product_id': 'pid_2', 'vendor':'B', 'offer_price':6.10},
{'offer_id':'oid_7','product_id': 'pid_2', 'vendor':'C', 'offer_price':7.10},
{'offer_id':'oid_8','product_id': 'pid_2', 'vendor':'D', 'offer_price':18.10},
{'offer_id':'oid_9','product_id': 'pid_2', 'vendor':'E', 'offer_price':9.10},
{'offer_id':'oid_10','product_id': 'pid_3', 'vendor':'A', 'offer_price':5.20},
{'offer_id':'oid_11','product_id': 'pid_3', 'vendor':'B', 'offer_price':6.20},
{'offer_id':'oid_12','product_id': 'pid_3', 'vendor':'C', 'offer_price':37.20},
{'offer_id':'oid_13','product_id': 'pid_3', 'vendor':'D', 'offer_price':8.20},
{'offer_id':'oid_14','product_id': 'pid_3', 'vendor':'E', 'offer_price':9.20},
{'offer_id':'oid_15','product_id': 'pid_3', 'vendor':'F', 'offer_price':77.20},
{'offer_id':'oid_16','product_id': 'pid_3', 'vendor':'G', 'offer_price':66.20},
]
应用.py
import pandas as pd
import json
from collections import defaultdict, Counter
import itertools
import random
from timeit import default_timer as timer
# START MY TIMER TO ESTIMATE HOW LONG TAKE TO CALCULATE
start = timer()
print('Timer Start')
def generate_random_offers():
''' Generate random offers with this format:
{'offer_id':'oid_1','product_id': 'pid_1', 'ventor':'B', 'offer_price':5.00}
'''
_offers = []
_vendors = ['A','B','C','D','E']
_pids_1 = ['pid_1']
_pids_2 = ['pid_1','pid_2']
_pids_3 = ['pid_1','pid_2','pid_3']
_pids_4 = ['pid_1','pid_2','pid_3','pid_4']
_pids_5 = ['pid_1','pid_2','pid_3','pid_4','pid_5']
_pids_6 = ['pid_1','pid_2','pid_3','pid_4','pid_5','pid_6']
_pids_7 = ['pid_1','pid_2','pid_3','pid_4','pid_5','pid_6','pid_7']
_pids_5 = ['pid_1','pid_2','pid_3','pid_4','pid_5']
for i in range(1, 100):
random_price = round(random.uniform(1, 80), 2)
random_vendor = random.choice(_vendors)
random_pid = random.choice(_pids_4)
print(i)
schema = {}
schema['offer_id'] = f'oid_{i}'
schema['product_id'] = random_pid
schema['ventor'] = random_vendor
schema['offer_price'] = random_price
_offers.append(schema)
# print(_offers)
# write_json_file(_offers)
return _offers
#end
# initiate the variable that gona hold all the offers
OFFERS = []
OFFERS = generate_random_offers()
def get_the_vendors():
''' Return array of all individuals vendors in offers array '''
_vendors = []
for offer in OFFERS:
if offer['ventor'] not in _vendors:
_vendors.append(offer['ventor'])
# print(vendors)
return _vendors
#end
def get_the_products():
''' Get the products that is inside the array '''
_products = []
for offer in OFFERS:
if offer['product_id'] not in _products:
_products.append(offer['product_id'])
# print('products => ', _products)
return _products
#end
def get_offers_base_on_product():
''' Get the offers base on products '''
_offers_by_product = []
PRODUCTS = get_the_products()
for product in PRODUCTS:
_prod = {}
p = []
for offer in OFFERS:
if offer['product_id'] == product:
p.append(offer['offer_id'])
# _prod[offer['product_id']] = p
_prod = p
_offers_by_product.append(_prod)
# print('_offers_by_product', _offers_by_product)
return _offers_by_product
#end
def get_the_vendors_total_product_price():
''' Return the sum of the vendors offers '''
_v = []
VENDORS = get_the_vendors()
for vendor in VENDORS:
v = []
_sum = 0
for offer in OFFERS:
x = {}
if offer['ventor'] == vendor:
_sum += offer['offer_price']
print('sum of ' + vendor + ' => ', _sum)
x['vendor'] = vendor
x['sum'] = _sum
_v.append(x)
print(_v)
return _v
#end
def compinations():
list_of_offers_by_product = get_offers_base_on_product()
a = []
for _list in list_of_offers_by_product:
a.append(_list)
super_compinations = list(itertools.product(*a))
# print('ALL POSSIBLE COMBINATIONS', super_compinations)
print(super_compinations[0])
print(super_compinations[1])
print(super_compinations[2])
return super_compinations
#end
def get_sums():
super_compinations = compinations()
_sums = []
best_price = {}
min_price = 1000
min_set = ''
# for i in range(30):
for i in range(len(super_compinations)):
price = 0
for ii in range(len(super_compinations[i])):
offer_id = super_compinations[i][ii]
for _offer in OFFERS:
try:
if _offer['offer_id'] == offer_id:
price += _offer['offer_price']
# print(price)
except KeyboardInterrupt:
print('Interrupted')
_sums.append(price)
if price < min_price:
min_price = price
min_set = super_compinations[i]
print('========')
print('OFFERS SUMS => ', _sums)
print('========')
print('Min Price: ', min_price)
print('Min Set: ', min_set)
# STOP MY TIMER
elapsed_time = timer() - start # in seconds
print('TOOK: ', elapsed_time)
#end
# Heare a start the program to calculate all the combinations and
after I get all the combinations I try to get the sum of every combination one by one
get_sums()
_offers_by_product [['oid_1', 'oid_1', 'oid_2', 'oid_3', 'oid_4'], ['oid_5', 'oid_6', 'oid_7', 'oid_8', 'oid_9'], ['oid_10', 'oid_11', 'oid_12', 'oid_1
3', 'oid_14', 'oid_15', 'oid_16']]
ALL POSSIBLE COMBINATIONS [
('oid_1', 'oid_5', 'oid_10'),
('oid_1', 'oid_5', 'oid_11'),
('oid_1', 'oid_5', 'oid_12'),
('oid_1', 'oid_5', 'oid_13'),
('oid_1', 'oid_5', 'oid_14'),
('oid_1', 'oid_5', 'oid_15'),
('oid_1', 'oid_5', 'oid_16'),
('oid_1', 'oid_6', 'oid_10'),
('oid_1', 'oid_6', 'oid_11'),
### N..... Possible combinations mabe 1.000.000 milion
]
这是输出 1Trillion combinations take 20sec
[4, 177.64, 206.63, 227.38, 152.29, 202.47, 211.85, 195.35, 171.37, 191.94,
187.51999999999998, 122.53999999999999, 139.34, 166.43, 135.62, 167.49, 182.12, 169.79
, 193.42000000000002, 147.42, 176.41, 197.16, 122.07, 172.25, 181.63, 165.13, 141.15, 161.72, 157.3, 150.73999999999998, 167.54, 194.63, 163.82, 195.69, 210.32,
193.54000000000002, 225.41000000000003, 240.04000000000002, 227.71, 251.34000000000003, 205.34,
234.33, 255.08, 179.99, 230.17000000000002, 239.55, 223.05, 192.67000000000002, 213.24, 208.82]
========
Min Price: 14.08
Min Set: ('oid_22', 'oid_16', 'oid_9', 'oid_71')
TOOK: 19.05843851931529
PS C:\Users\George35mk\Desktop\MACHINE LERNING EXAMPLES\Hello world>
专家能告诉我我的方法是否正确吗
最佳答案
不要使用机器学习，使用现成的求解器 Mixed-integer programming (这是基本的 discrete-optimization problem )或设计您自己的近似算法。这个问题可能是 NP-hard 问题，许多流行的 NP-hard 问题都有一些共同的特征，可以从中学习!
这里有一些演示，应该解释为此使用混合整数编程的基本思想!不过有一些注意事项:
- 此代码尚未准备好用于生产(演示!)
- bigM 需要调整；特别是对于默认求解器(不好；继续阅读!)
- 这段代码使用了我最喜欢的建模工具 cvxpy (尽管为其他用例构建)
- 缺点:默认的 MIP 求解器非常糟糕 -> 只是玩具问题!
- 可能的补救措施:
- cvxpy 支持使用一些商业求解器(Gurobi、CPLEX、Mosek)(如果可用)
- cvxpy 还支持优秀的开源求解器(CBC、GLPK)，但可能需要更复杂的设置(推荐使用 Linux；阅读 cvxpy 的文档)!
- 可能的补救措施:
- 缺点:默认的 MIP 求解器非常糟糕 -> 只是玩具问题!
MIP 求解器对于此类问题应该非常强大。即使在 NP-hard 情况和困难情况下，也应该能够在给定一些时间限制(以及一些已证明的界限!)的情况下获得良好的近似值!
或者你可以试试 pulp ，其中:
- 易于安装(甚至在 Windows 上)
- 带来了一个很好的默认 MIP 求解器 (CBC)
- 不太好(如果有人喜欢类似代数的建模!但有些人喜欢 pulp 的风格，它的安装/打包是一流的!)
好的 MIP 求解器将很难被击败，即使在以最佳或好的近似为目标时使用这种简单的数学公式也是如此!
代码:
import numpy as np
import scipy as sp
import cvxpy as cvx
np.random.seed(1)
""" Synthetic problem """
N = 3 # Products to oder
M = 5 # Vendors
# Shipping costs
v_ship_c = np.random.choice([1, 3, 5], size=M) # vendor shipping costs if vendor used
# indepenent on number of products
# Product prices
product_prices_mean = np.random.random(size=N) * 50
p_price_v = np.repeat(product_prices_mean, M).reshape(N,M) + np.random.normal(size=(N, M)) * 2
p_price_v = np.clip(p_price_v, 1, np.inf) # min cost of 1
# Product availability
p_v_avail = np.random.choice([0,1], size=(N, M), p=[0.2, 0.8])
assert np.all(np.count_nonzero(p_v_avail, axis=1) > 0) # feasible solution
# Print
print('# products ordered: ', N)
print('# vendors: ', M)
print('Vendor shipping costs')
print(v_ship_c)
print('Mean product prices')
print(product_prices_mean)
print('Vendor-specific product prices')
print(p_price_v)
print('Vendor-product availability')
print(p_v_avail)
""" Optimization problem """
bigM = 1e4 # big-M constant / CRITICAL!
# "http://scip.zib.de/workshop/scip_lodi.pdf"
X = cvx.Bool(N, M) # [p,v] == 1 iff p ordered from v
Y = cvx.Bool(M) # [v] == 1 iff vendor v used -> shipping
objective_product_costs = cvx.sum_entries(cvx.mul_elemwise(p_price_v, X))
objective_shipping_costs = sum(v_ship_c * Y)
objective = cvx.Minimize(objective_product_costs + objective_shipping_costs)
constraints = [cvx.sum_entries(X, axis=1) >= 1] # at least one of each product ordered
# >= more relaxed than == and equal solution
# given costs are positive!
# will never order 2 as more exp than 1!
not_available = np.where(p_v_avail == 0)
constraints.append(X[not_available] == 0) # can't order from v if v not having p
constraints.append(cvx.sum_entries(X, axis=0).T <= cvx.mul_elemwise(bigM, Y)) # indicator if vendor used
problem = cvx.Problem(objective, constraints)
problem.solve()
""" Output solution """
print(problem.status)
print('Total costs: ', problem.value)
print('Product costs: ', round(objective_product_costs.value, 2))
print('Shipping costs: ', round(objective_shipping_costs.value, 2))
print('Order matrix')
print(np.round(X.value))
print('Shipping matrix')
print(np.round(Y.value.T))
输出:
# products ordered: 3
# vendors: 5
Vendor shipping costs
[3 1 1 3 3]
Mean product prices
[ 7.33779454 4.61692974 9.31301057]
Vendor-specific product prices
[[ 5.12592439 4.02876364 2.61085733 9.60848524 5.30376627]
[ 5.89165337 2.89711652 8.162145 2.39620363 4.97935827]
[ 10.4417003 8.17999011 10.77296176 10.05899815 10.38063239]]
Vendor-product availability
[[1 1 1 1 1]
[0 1 1 1 1]
[1 0 1 1 1]]
optimal
Total costs: 18.280935453799668
Product costs: 16.28
Shipping costs: 2.0
Order matrix
[[ 0. 0. 1. 0. 0.]
[ 0. 1. 0. 0. 0.]
[ 0. -0. 1. 0. 0.]]
Shipping matrix
[[ 0. 1. 1. 0. 0.]]
这个小例子在 0.01 秒内被这个玩具求解器解决了。更大的实例当然会表现不同!
关于python - 我如何获得限制供应商(运输)的最佳优惠组合，以及我如何使用 python 或机器学习获得最佳价格，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46888182/