fin implemented DecisionTree and GeneticOrder
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16
GameModel.py
16
GameModel.py
@ -183,15 +183,23 @@ class GameModel(Model):
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if self.phase == Phase.CLIENT_SORTING:
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orders: [Order] = self.orderList
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tree: DecisionTree = DecisionTree()
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readyTree = tree.get_decision_tree()
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# CLIENT RECOGNITION
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orders_with_prio = tree.get_data_good(orders)
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# TODO CLIENT RECOGNITION
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# print("before:" )
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# for i in range(len(orders_with_prio)):
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# print("ORDER {}, PRIO: {}".format(orders_with_prio[i].id, orders_with_prio[i].priority))
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# GENERICS SORTING
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genericOrder: GeneticOrder = GeneticOrder(orders)
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self.orderList = genericOrder.get_orders_sorted(orders)
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genericOrder: GeneticOrder = GeneticOrder(orders_with_prio)
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new_orders = genericOrder.get_orders_sorted(orders)
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# print("after:" )
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# for i in range(len(new_orders)):
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# print("ORDER {}, PRIO: {}".format(new_orders[i].id, new_orders[i].priority))
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self.orderList = new_orders
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print("FINISHED CLIENT ORDER SORTING")
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self.phase = Phase.EXECUTION
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@ -15,7 +15,7 @@ class GeneticOrder:
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best_fit_super_special = 20
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population_size = 200
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number_of_populations = 10000
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number_of_populations = 1000
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punish_low = 500
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punish_med = 300
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130
main.py
130
main.py
@ -100,71 +100,69 @@ if __name__ == '__main__':
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[grid, readyText, provided_itesm, recognised_items, ordersText,
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fulfilled_orders],
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"Automatyczny Wózek Widłowy",
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dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=model))
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dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=20, classificator=model))
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def order_rule(order: Order):
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return order.id
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# def order_rule(order: Order):
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# return order.id
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#
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#
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# punish_low = 500
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# punish_med = 300
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# def sum_wrong(member: [Order]) -> int:
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# last_high = 0
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# last_med = 0
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#
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# sum_high = 0
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# sum_med = 0
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# sum_low = 0
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#
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# counter = 0
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#
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# for i in range(len(member)):
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# o: Order = member[i]
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# if o.priority == Priority.HIGH :
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# sum_high += 1
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# last_high = i
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# elif o.priority == Priority.MEDIUM:
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# sum_med += 1
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# last_med = i
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# else:
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# sum_low += 1
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#
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# for i in range(last_high):
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# o: Order = member[i]
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# if o.priority == Priority.MEDIUM:
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# counter += punish_med
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# elif o.priority == Priority.LOW:
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# counter += punish_low
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#
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# for i in range(last_med):
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# o: Order = member[i]
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# if o.priority == Priority.LOW:
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# counter += punish_low
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#
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# print("sum: high:", sum_high, "med:", sum_med, "low:", sum_low)
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# print("last_high:", last_high, "last_med:", last_med, "sum:", counter)
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# return counter
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#
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# orders = InitialStateFactory.generate_order_list_XD(20)
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# test: GeneticOrder = GeneticOrder(orders)
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# punish_low = test.punish_low
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# punish_med = test.punish_med
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#
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# print("SIEMA before: ")
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# sum_wrong(orders)
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# for i in orders:
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# print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
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# # print("id:", i.id, "priority:", i.priority)
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#
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# newOrders = test.get_orders_sorted(orders)
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#
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# print("NAURA after:")
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# sum_wrong(newOrders)
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# for i in newOrders:
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# print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
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# # print("id:", i.id, "priority:", i.priority)
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punish_low = 500
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punish_med = 300
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def sum_wrong(member: [Order]) -> int:
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last_high = 0
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last_med = 0
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sum_high = 0
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sum_med = 0
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sum_low = 0
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counter = 0
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for i in range(len(member)):
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o: Order = member[i]
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if o.priority == Priority.HIGH :
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sum_high += 1
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last_high = i
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elif o.priority == Priority.MEDIUM:
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sum_med += 1
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last_med = i
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else:
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sum_low += 1
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for i in range(last_high):
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o: Order = member[i]
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if o.priority == Priority.MEDIUM:
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counter += punish_med
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elif o.priority == Priority.LOW:
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counter += punish_low
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for i in range(last_med):
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o: Order = member[i]
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if o.priority == Priority.LOW:
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counter += punish_low
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print("sum: high:", sum_high, "med:", sum_med, "low:", sum_low)
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print("last_high:", last_high, "last_med:", last_med, "sum:", counter)
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return counter
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orders = InitialStateFactory.generate_order_list_XD(20)
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test: GeneticOrder = GeneticOrder(orders)
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punish_low = test.punish_low
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punish_med = test.punish_med
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print("SIEMA before: ")
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sum_wrong(orders)
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for i in orders:
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print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
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# print("id:", i.id, "priority:", i.priority)
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newOrders = test.get_orders_sorted(orders)
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print("NAURA after:")
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sum_wrong(newOrders)
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for i in newOrders:
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print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
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# print("id:", i.id, "priority:", i.priority)
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# server.port = 8888
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# server.launch()
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server.port = 8888
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server.launch()
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@ -1,11 +1,17 @@
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import csv
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import numpy as np
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import pandas
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import sklearn
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from sklearn import metrics, preprocessing
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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from InitialStateFactory import InitialStateFactory
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from data.ClientParams import ClientParams
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from data.Order import Order
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from data.enum.CompanySize import CompanySize
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from data.enum.Priority import Priority
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from util.ClientParamsFactory import ClientParamsFactory
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@ -58,6 +64,63 @@ class DecisionTree:
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print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y, prediction))
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def get_data_good(self, orders: [Order]) -> [Order]:
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n_array_input = []
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for i in range(len(orders)):
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o:Order = orders[i]
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cp: ClientParams = o.client_params
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pom = []
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pom.append(cp.payment_delay)
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pom.append(cp.payed)
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pom.append(cp.net_worth)
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pom.append(cp.infuence_rate)
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pom.append(cp.is_skarbowka)
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pom.append(cp.membership)
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pom.append(cp.is_hat)
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size: CompanySize = cp.company_size
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if(size == CompanySize.NO):
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pom.append(0)
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if (size == CompanySize.SMALL):
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pom.append(1)
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if (size == CompanySize.NORMAL):
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pom.append(2)
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if (size == CompanySize.BIG):
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pom.append(3)
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if (size == CompanySize.HUGE):
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pom.append(4)
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if (size == CompanySize.GIGANTISHE):
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pom.append(5)
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n_array_input.append(pom)
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n_array = np.array(n_array_input)
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# print(n_array)
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# print(n_array[0])
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tree = self.get_decision_tree()
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priority = tree.predict(n_array)
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for i in range(len(orders)):
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print(orders[i].priority)
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orders[i].priority = priority[i]
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if priority[i] == "LOW":
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orders[i].priority = Priority.LOW
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if priority[i] == "MEDIUM":
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orders[i].priority = Priority.MEDIUM
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if priority[i] == "HIGH":
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orders[i].priority = Priority.HIGH
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print(orders[i].priority)
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return orders
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def get_decision_tree(self) -> DecisionTreeClassifier:
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@ -72,15 +135,25 @@ class DecisionTree:
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X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, train_size=0.8)
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# print(len(X_train[0]))
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# print(X_train[0])
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drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
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clf = drugTree.fit(X_train, y_train)
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predicted = drugTree.predict(X_test)
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# print(type(X_test))
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y_test = y_test.to_list()
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self.print_logs(X_test, y_test, predicted)
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# self.print_logs(X_test, y_test, predicted)
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print(sklearn.tree.export_text(clf, feature_names=X_headers))
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# print(sklearn.tree.export_text(clf, feature_names=X_headers))
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return drugTree
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return drugTree
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# kurwa = DecisionTree()
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# kurwa.get_data_good(InitialStateFactory.generate_order_list(50))
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