重構電商例子（策略模式）

案例分析:重构“策略”模式

《设计模式:可复用面向对象软件的基础》一书是这样概述“策略”模式的:

定义一系列算法,把它们一一封装起来,并且使它们可以相互替换。本模式使得算法可以独立于使用它的客户而变化。

假如一个网店制定了下述折扣规则：

• 有 1000 或以上积分的顾客,每个订单享 5% 折扣。
• 同一订单中,单个商品的数量达到 20 个或以上,享 10% 折扣。
• 订单中的不同商品达到 10 个或以上,享 7% 折扣。
• 简单起见,我们假定一个订单一次只能享用一个折扣

“策略”模式的 UML 类图见图 6-1,其中涉及下列内容。

实现 Order 类,支持插入式折扣策略

# classic_strategy.py
# Strategy pattern -- classic implementation

"""
# BEGIN CLASSIC_STRATEGY_TESTS

    >>> joe = Customer(‘John Doe‘, 0)  # <1>
    >>> ann = Customer(‘Ann Smith‘, 1100)
    >>> cart = [LineItem(‘banana‘, 4, .5),  # <2>
    ...         LineItem(‘apple‘, 10, 1.5),
    ...         LineItem(‘watermellon‘, 5, 5.0)]
    >>> Order(joe, cart, FidelityPromo())  # <3>
    <Order total: 42.00 due: 42.00>
    >>> Order(ann, cart, FidelityPromo())  # <4>
    <Order total: 42.00 due: 39.90>
    >>> banana_cart = [LineItem(‘banana‘, 30, .5),  # <5>
    ...                LineItem(‘apple‘, 10, 1.5)]
    >>> Order(joe, banana_cart, BulkItemPromo())  # <6>
    <Order total: 30.00 due: 28.50>
    >>> long_order = [LineItem(str(item_code), 1, 1.0) # <7>
    ...               for item_code in range(10)]
    >>> Order(joe, long_order, LargeOrderPromo())  # <8>
    <Order total: 10.00 due: 9.30>
    >>> Order(joe, cart, LargeOrderPromo())
    <Order total: 42.00 due: 42.00>

# END CLASSIC_STRATEGY_TESTS
"""
# BEGIN CLASSIC_STRATEGY

from abc import ABC, abstractmethod
from collections import namedtuple

Customer = namedtuple(‘Customer‘, ‘name fidelity‘)


class LineItem:

    def __init__(self, product, quantity, price):
        self.product = product
        self.quantity = quantity
        self.price = price

    def total(self):
        return self.price * self.quantity


class Order:  # the Context

    def __init__(self, customer, cart, promotion=None):
        self.customer = customer
        self.cart = list(cart)
        self.promotion = promotion

    def total(self):
        if not hasattr(self, ‘__total‘):
            self.__total = sum(item.total() for item in self.cart)
        return self.__total

    def due(self):
        if self.promotion is None:
            discount = 0
        else:
            discount = self.promotion.discount(self)
        return self.total() - discount

    def __repr__(self):
        fmt = ‘<Order total: {:.2f} due: {:.2f}>‘
        return fmt.format(self.total(), self.due())


class Promotion(ABC):  # the Strategy: an Abstract Base Class

    @abstractmethod
    def discount(self, order):
        """Return discount as a positive dollar amount"""


class FidelityPromo(Promotion):  # first Concrete Strategy
    """5% discount for customers with 1000 or more fidelity points"""

    def discount(self, order):
        return order.total() * .05 if order.customer.fidelity >= 1000 else 0


class BulkItemPromo(Promotion):  # second Concrete Strategy
    """10% discount for each LineItem with 20 or more units"""

    def discount(self, order):
        discount = 0
        for item in order.cart:
            if item.quantity >= 20:
                discount += item.total() * .1
        return discount


class LargeOrderPromo(Promotion):  # third Concrete Strategy
    """7% discount for orders with 10 or more distinct items"""

    def discount(self, order):
        distinct_items = {item.product for item in order.cart}
        if len(distinct_items) >= 10:
            return order.total() * .07
        return 0

# END CLASSIC_STRATEGY

使用函数实现“策略”模式

每个具体策略都是一个类,而且都只定义了一个方法,即discount。此外,策略实例没有状态(没有实例属性)。

提示 當一個類只有一個函數時，應該將其重構成一個函數。因爲函數的開銷比類小很多。

# strategy.py
# Strategy pattern -- function-based implementation

"""
# BEGIN STRATEGY_TESTS

    >>> joe = Customer(‘John Doe‘, 0)  # <1>
    >>> ann = Customer(‘Ann Smith‘, 1100)
    >>> cart = [LineItem(‘banana‘, 4, .5),
    ...         LineItem(‘apple‘, 10, 1.5),
    ...         LineItem(‘watermellon‘, 5, 5.0)]
    >>> Order(joe, cart, fidelity_promo)  # <2>
    <Order total: 42.00 due: 42.00>
    >>> Order(ann, cart, fidelity_promo)
    <Order total: 42.00 due: 39.90>
    >>> banana_cart = [LineItem(‘banana‘, 30, .5),
    ...                LineItem(‘apple‘, 10, 1.5)]
    >>> Order(joe, banana_cart, bulk_item_promo)  # <3>
    <Order total: 30.00 due: 28.50>
    >>> long_order = [LineItem(str(item_code), 1, 1.0)
    ...               for item_code in range(10)]
    >>> Order(joe, long_order, large_order_promo)
    <Order total: 10.00 due: 9.30>
    >>> Order(joe, cart, large_order_promo)
    <Order total: 42.00 due: 42.00>

# END STRATEGY_TESTS
"""
# BEGIN STRATEGY

from collections import namedtuple

Customer = namedtuple(‘Customer‘, ‘name fidelity‘)


class LineItem:

    def __init__(self, product, quantity, price):
        self.product = product
        self.quantity = quantity
        self.price = price

    def total(self):
        return self.price * self.quantity


class Order:  # the Context

    def __init__(self, customer, cart, promotion=None):
        self.customer = customer
        self.cart = list(cart)
        self.promotion = promotion

    def total(self):
        if not hasattr(self, ‘__total‘):
            self.__total = sum(item.total() for item in self.cart)
        return self.__total

    def due(self):
        if self.promotion is None:
            discount = 0
        else:
            discount = self.promotion(self)  # <1>
        return self.total() - discount

    def __repr__(self):
        fmt = ‘<Order total: {:.2f} due: {:.2f}>‘
        return fmt.format(self.total(), self.due())

# <2>

def fidelity_promo(order):  # <3>
    """5% discount for customers with 1000 or more fidelity points"""
    return order.total() * .05 if order.customer.fidelity >= 1000 else 0


def bulk_item_promo(order):
    """10% discount for each LineItem with 20 or more units"""
    discount = 0
    for item in order.cart:
        if item.quantity >= 20:
            discount += item.total() * .1
    return discount


def large_order_promo(order):
    """7% discount for orders with 10 or more distinct items"""
    distinct_items = {item.product for item in order.cart}
    if len(distinct_items) >= 10:
        return order.total() * .07
    return 0

# END STRATEGY

选择最佳策略:简单的方式

promos = [fidelity_promo, bulk_item_promo, large_order_promo]  # <1>promos 列出以函数实现的各个策略。

def best_promo(order):  
    """Select best discount available
    """
    return max(promo(order) for promo in promos)

但是有些重复可能会导致不易察觉的缺陷:若想添加新的促销策略,要定义相应的函数,还要记得把它添加到promos列表中;否则,当新促销函数显式地作为参数传给 Order 时,它是可用的,但是 best_promo 不会考虑它。

找出模块中的全部策略

promos = [globals()[name] for name in globals()  # <1>迭代 globals() 返回字典中的各个 name。
          if name.endswith(‘_promo‘)  # <2>只选择以 _promo 结尾的名称。
          and name != ‘best_promo‘]  # <3>过滤掉 best_promo 自身,防止无限递归。


def best_promo(order):
    """Select best discount available
    """
    return max(promo(order) for promo in promos)  # <4>best_promo 内部的代码没有变化。

這裏需要注意的地方有：

1. 策略模式所定義的函數結尾均是_promo
2. 不要忘記類globals()

globals()

from pprint import pprint
def fun():
    pass


dd = dict()
ll = list()

pprint(globals())

"""output
{‘__annotations__‘: {},
 ‘__builtins__‘: <module ‘builtins‘ (built-in)>,
 ‘__cached__‘: None,
 ‘__doc__‘: None,
 ‘__file__‘: ‘/home/yuanoung/Projects/fluent-python/other/test.py‘,
 ‘__loader__‘: <_frozen_importlib_external.SourceFileLoader object at 0x7f35590ca080>,
 ‘__name__‘: ‘__main__‘,
 ‘__package__‘: None,
 ‘__spec__‘: None,
 ‘dd‘: {},
 ‘fun‘: <function fun at 0x7f35590f0ea0>,
 ‘ll‘: [],
 ‘pprint‘: <function pprint at 0x7f355735b840>}
"""