fin implemented DecisionTree and GeneticOrder

GameModel.py

@@ -183,15 +183,23 @@ class GameModel(Model):
         if self.phase == Phase.CLIENT_SORTING:
             orders: [Order] = self.orderList
             tree: DecisionTree = DecisionTree()
-            readyTree = tree.get_decision_tree()
 
+            # CLIENT RECOGNITION
+            orders_with_prio = tree.get_data_good(orders)
 
-            # TODO CLIENT RECOGNITION
+            # print("before:" )
+            # for i in range(len(orders_with_prio)):
+            #     print("ORDER {}, PRIO: {}".format(orders_with_prio[i].id, orders_with_prio[i].priority))
 
             # GENERICS SORTING
-            genericOrder: GeneticOrder = GeneticOrder(orders)
+            genericOrder: GeneticOrder = GeneticOrder(orders_with_prio)
-            self.orderList = genericOrder.get_orders_sorted(orders)
+            new_orders = genericOrder.get_orders_sorted(orders)
 
+            # print("after:" )
+            # for i in range(len(new_orders)):
+            #     print("ORDER {}, PRIO: {}".format(new_orders[i].id, new_orders[i].priority))
+
+            self.orderList = new_orders
 
             print("FINISHED CLIENT ORDER SORTING")
             self.phase = Phase.EXECUTION

genetic_order/GeneticOrder.py

@@ -15,7 +15,7 @@ class GeneticOrder:
     best_fit_super_special = 20
 
     population_size = 200
-    number_of_populations = 10000
+    number_of_populations = 1000
 
     punish_low = 500
     punish_med = 300

main.py

@@ -100,71 +100,69 @@ if __name__ == '__main__':
                            [grid, readyText, provided_itesm, recognised_items, ordersText,
                             fulfilled_orders],
                            "Automatyczny Wózek Widłowy",
-                           dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=model))
+                           dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=20, classificator=model))
 
-    def order_rule(order: Order):
+    # def order_rule(order: Order):
-        return order.id
+    #     return order.id
+    #
+    #
+    # punish_low = 500
+    # punish_med = 300
+    # def sum_wrong(member: [Order]) -> int:
+    #     last_high = 0
+    #     last_med = 0
+    #
+    #     sum_high = 0
+    #     sum_med = 0
+    #     sum_low = 0
+    #
+    #     counter = 0
+    #
+    #     for i in range(len(member)):
+    #         o: Order = member[i]
+    #         if o.priority == Priority.HIGH :
+    #             sum_high += 1
+    #             last_high = i
+    #         elif o.priority == Priority.MEDIUM:
+    #             sum_med += 1
+    #             last_med = i
+    #         else:
+    #             sum_low += 1
+    #
+    #     for i in range(last_high):
+    #         o: Order = member[i]
+    #         if o.priority == Priority.MEDIUM:
+    #             counter += punish_med
+    #         elif o.priority == Priority.LOW:
+    #             counter += punish_low
+    #
+    #     for i in range(last_med):
+    #         o: Order = member[i]
+    #         if o.priority == Priority.LOW:
+    #             counter += punish_low
+    #
+    #     print("sum: high:", sum_high, "med:", sum_med, "low:", sum_low)
+    #     print("last_high:", last_high, "last_med:", last_med, "sum:", counter)
+    #     return counter
+    #
+    # orders = InitialStateFactory.generate_order_list_XD(20)
+    # test: GeneticOrder = GeneticOrder(orders)
+    # punish_low = test.punish_low
+    # punish_med = test.punish_med
+    #
+    # print("SIEMA before: ")
+    # sum_wrong(orders)
+    # for i in orders:
+    #     print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
+    #     # print("id:", i.id, "priority:", i.priority)
+    #
+    # newOrders = test.get_orders_sorted(orders)
+    #
+    # print("NAURA after:")
+    # sum_wrong(newOrders)
+    # for i in newOrders:
+    #     print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
+    #     # print("id:", i.id, "priority:", i.priority)
 
-
+    server.port = 8888
-    punish_low = 500
+    server.launch()
-    punish_med = 300
-    def sum_wrong(member: [Order]) -> int:
-        last_high = 0
-        last_med = 0
-
-        sum_high = 0
-        sum_med = 0
-        sum_low = 0
-
-        counter = 0
-
-        for i in range(len(member)):
-            o: Order = member[i]
-            if o.priority == Priority.HIGH :
-                sum_high += 1
-                last_high = i
-            elif o.priority == Priority.MEDIUM:
-                sum_med += 1
-                last_med = i
-            else:
-                sum_low += 1
-
-        for i in range(last_high):
-            o: Order = member[i]
-            if o.priority == Priority.MEDIUM:
-                counter += punish_med
-            elif o.priority == Priority.LOW:
-                counter += punish_low
-
-        for i in range(last_med):
-            o: Order = member[i]
-            if o.priority == Priority.LOW:
-                counter += punish_low
-
-        print("sum: high:", sum_high, "med:", sum_med, "low:", sum_low)
-        print("last_high:", last_high, "last_med:", last_med, "sum:", counter)
-        return counter
-
-    orders = InitialStateFactory.generate_order_list_XD(20)
-    test: GeneticOrder = GeneticOrder(orders)
-    punish_low = test.punish_low
-    punish_med = test.punish_med
-
-    print("SIEMA before: ")
-    sum_wrong(orders)
-    for i in orders:
-        print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
-        # print("id:", i.id, "priority:", i.priority)
-
-    newOrders = test.get_orders_sorted(orders)
-
-    print("NAURA after:")
-    sum_wrong(newOrders)
-    for i in newOrders:
-        print("id:", i.id, "priority:", i.priority, "sum/time:", i.sum/i.time)
-        # print("id:", i.id, "priority:", i.priority)
-
-
-
-    # server.port = 8888
-    # server.launch()

tree/DecisionTree.py

@@ -1,11 +1,17 @@
 import csv
 
+import numpy as np
 import pandas
 import sklearn
 from sklearn import metrics, preprocessing
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 
+from InitialStateFactory import InitialStateFactory
+from data.ClientParams import ClientParams
+from data.Order import Order
+from data.enum.CompanySize import CompanySize
+from data.enum.Priority import Priority
 from util.ClientParamsFactory import ClientParamsFactory
 
 
@@ -58,6 +64,63 @@ class DecisionTree:
         print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y, prediction))
 
 
+    def get_data_good(self, orders: [Order]) -> [Order]:
+
+        n_array_input = []
+        for i in range(len(orders)):
+            o:Order = orders[i]
+            cp: ClientParams = o.client_params
+            pom = []
+
+            pom.append(cp.payment_delay)
+            pom.append(cp.payed)
+            pom.append(cp.net_worth)
+            pom.append(cp.infuence_rate)
+            pom.append(cp.is_skarbowka)
+            pom.append(cp.membership)
+            pom.append(cp.is_hat)
+
+            size: CompanySize = cp.company_size
+            if(size == CompanySize.NO):
+                pom.append(0)
+            if (size == CompanySize.SMALL):
+                pom.append(1)
+            if (size == CompanySize.NORMAL):
+                pom.append(2)
+            if (size == CompanySize.BIG):
+                pom.append(3)
+            if (size == CompanySize.HUGE):
+                pom.append(4)
+            if (size == CompanySize.GIGANTISHE):
+                pom.append(5)
+
+
+
+            n_array_input.append(pom)
+
+        n_array = np.array(n_array_input)
+        # print(n_array)
+
+        # print(n_array[0])
+        tree = self.get_decision_tree()
+        priority = tree.predict(n_array)
+
+
+        for i in range(len(orders)):
+            print(orders[i].priority)
+            orders[i].priority = priority[i]
+
+            if priority[i] == "LOW":
+                orders[i].priority = Priority.LOW
+            if priority[i] == "MEDIUM":
+                orders[i].priority = Priority.MEDIUM
+            if priority[i] == "HIGH":
+                orders[i].priority = Priority.HIGH
+
+            print(orders[i].priority)
+
+
+        return orders
 
 
     def get_decision_tree(self) -> DecisionTreeClassifier:
@@ -72,15 +135,25 @@ class DecisionTree:
 
         X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, train_size=0.8)
 
+        # print(len(X_train[0]))
+        # print(X_train[0])
+
         drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
 
         clf = drugTree.fit(X_train, y_train)
         predicted = drugTree.predict(X_test)
 
+        # print(type(X_test))
+
         y_test = y_test.to_list()
 
-        self.print_logs(X_test, y_test, predicted)
+        # self.print_logs(X_test, y_test, predicted)
 
-        print(sklearn.tree.export_text(clf, feature_names=X_headers))
+        # print(sklearn.tree.export_text(clf, feature_names=X_headers))
 
         return drugTree
+
+
+
+# kurwa = DecisionTree()
+# kurwa.get_data_good(InitialStateFactory.generate_order_list(50))