Design a family of algorithms, encapsulate each one, and make them interchangeable. Strategy lets the algorithm vary independently from the clients that use it.
- Context
- Provides a service by delegating some computation to interchangeable components that implement alternative algorithms.
- In the ecommerce example, the context is an Order, which is configured to apply a promotional discount according to one of several algorithms
- Strategy
- The interface common to the components that implement the different algorithms.
- In our example, this role is played by an abstract class called Promotion
- Concrete Strategy
- One of the concrete subclasses of Strategy.
from abc import ABC, abstractmethod
from collections import namedtuple
from dataclasses import dataclass
Customer = namedtuple('Customer', 'name fidelity')
@dataclass
class LineItem:
product: str
quantity: int
price: float
def total(self):
return self.quantity * self.price
class Order:
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):
@abstractmethod
def discount(self, order):
"""Return discount as a positive dollar amount"""
class FidelityPromo(Promotion):
"""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):
"""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):
"""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# promotions.py
def fidelity_promo(order):
"""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
# main.py
# we can use inspect module to collect all strategy functions
promos = [func for name, func in inspect.getmembers(promotions, inspect.isfunction)]
# and automatically pick the best one
def best_promo(order):
"""Select best discount available"""
return max(promo(order) for promo in promos)Use decorator to improve previous example
promos = []
def promotion(promo_func):
promos.append(promo_func)
return promo
@promotion
def fidelity(order):
"""5% discount for customers with 1000 or more fidelity points"""
return order.total() * .05 if order.customer.fidelity >= 1000 else 0
@promotion
def bulk_item(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
@promotion
def large_order(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
# automatically pick the best one
def best_promo(order):
"""Select best discount available"""
return max(promo(order) for promo in promos)- Each concrete strategy only has a single method and has no state
- A function is more lightweight than an instance of a user-defined class
- A plain function is an inborn flyweight
- flyweight: a shared object that can be used in multiple contexts simultaneously
- Without flyweight, the strategy instances need to be constructed over and over again