decision tree

Astar.py

@@ -83,7 +83,6 @@ class Pathfinding:
             if(neighbour_x >= 0 and neighbour_x < G_var().DIMENSION_X and neighbour_y >= 0 and neighbour_y < G_var().DIMENSION_Y):
                 neighbours.append(self.grid[neighbour_x][neighbour_y])
         return neighbours
-                
 
     def find_path(self, starting_state, target_state):   # algorytm wyszukiwania trasy
         start_node = self.grid[starting_state.x][starting_state.y]

CompanyFactory.py

@@ -0,0 +1,9 @@
+import random
+
+
+class CompanyFactory:
+    def __init__(self):
+        self.popularity = random.randint(0,5)
+        self.payment_delay = random.randint(0,5)
+        self.shipping_type = random.randint(0,1)
+

Environment.py

@@ -2,7 +2,6 @@ import time
 from Empty import Empty
 from Finding_fields import Finding_fields
 from Moving_truck import Moving_truck
-from Package import Package
 from Package_types import Package_types
 from Packages_spawner import Packages_spawner
 from Shelf import Shelf
@@ -11,7 +10,8 @@ import random
 from Grid import Grid
 from Truck import Truck
 from Global_variables import Global_variables as G_var
-from pygame.constants import *
+from Sectors_types import Sectors_types
+from decision_tree.Decision_tree import DecisionTree
 
 from Astar import Pathfinding, State
 
@@ -23,8 +23,8 @@ class Environment:
         self.initialize_eviroment_2d()
         self.add_shelfs_to_enviroment_2d()
         # TEST CREATE PACKAGE
-        self.package_spawner = Packages_spawner(window,self.enviroment_2d)
-        self.package_spawner.spawn_package()
+        self.package_spawner = Packages_spawner(window, self.enviroment_2d)
+        self.sector_decision = self.package_spawner.spawn_package()
         new_truck = Truck(window, 14, 7)
         self.enviroment_2d[14][7] = new_truck
         self.truck = new_truck
@@ -32,6 +32,7 @@ class Environment:
             self.window, self.enviroment_2d, self.truck, self.package_spawner)
         self.astar = Pathfinding(self.enviroment_2d)
         self.finding_fields = Finding_fields(self.enviroment_2d)
+        self.weekend = random.randint(0, 1)
 
     def draw_all_elements(self):
         for row in self.enviroment_2d:
@@ -45,12 +46,24 @@ class Environment:
         self.use_astar()    # wywyoływanie za każdym razem astar jest bardzo zasobożerne. Lepiej raz na przejście
         self.update_truck()
         time.sleep(0.5)
-        
-    
+
+    # def use_decision_tree(self):
+    #     marking = self.package.type
+    #     if marking == Package_types.fragile:
+    #         marking = 0
+    #     elif marking == Package_types.priority:
+    #         marking = 1
+    #     tree = DecisionTree(marking, self.weekend, self.package.company.popularity,
+    #                         self.package.company.payment_delay, self.package.payed_upfront,
+    #                         self.package.company.shipping_type)
+    #     decision = tree.decision
+    #     return decision
+
     def use_astar(self):
         start_state = State(1,self.truck.x,self.truck.y) # sprawić aby paczka i shelf były wyszukiwane raz
         if self.truck.has_package:
-            end_position = self.finding_fields.find_closest_shelf(self.truck,self.truck.package_type)
+            end_position = self.finding_fields.find_closest_shelf(self.truck, self.truck.package_type,
+                                                                  self.sector_decision)
         else:
             end_position = self.finding_fields.find_package()
         end_state = State(1,end_position.x, end_position.y)
@@ -62,10 +75,14 @@ class Environment:
         next_field_y = next_field_to_move.y - self.truck.y
         self.moving_truck.move(next_field_x,next_field_y)
 
-    def gen_shelf_type(self):
-        shelve_types = list(Package_types)
+    # def gen_shelf_type(self):
+    #     shelve_types = list(Package_types)
+    #     while True:
+    #         yield random.choice(shelve_types)
+    def gen__sectors(self):
+        sectors = [Sectors_types.normal, Sectors_types.shipping_tomorrow, Sectors_types.shipping_today]
         while True:
-            yield random.choice(shelve_types)
+            yield random.choice(sectors)
 
     def initialize_eviroment_2d(self):
         self.enviroment_2d = [[
@@ -76,14 +93,28 @@ class Environment:
 
     def add_shelfs_to_enviroment_2d(self):
         shelf_2_offset = 9
-        avaiable_types = self.gen_shelf_type()
-        for x in range(2, 22, 3):
-            type_of_new_shelf_1 = next(avaiable_types)
-            type_of_new_shelf_2 = next(avaiable_types)
+        avaiable_sectors = self.gen__sectors()
+        for x in range(8, 22, 3):
+            type_of_new_shelf = Package_types.priority
+            sector_of_new_shelf1 = next(avaiable_sectors)
+            # print(sector_of_new_shelf1)
+            sector_of_new_shelf2 = next(avaiable_sectors)
+            # print(sector_of_new_shelf2)
             for y in range(0, 6):
                 self.enviroment_2d[x][y] = Shelf(
-                    self.window, x, y, type_of_new_shelf_1
+                    self.window, x, y, type_of_new_shelf, sector_of_new_shelf1
                 )
                 self.enviroment_2d[x][y + shelf_2_offset] = Shelf(
-                    self.window, x, (y + shelf_2_offset), type_of_new_shelf_2
+                    self.window, x, (y + shelf_2_offset), type_of_new_shelf, sector_of_new_shelf2
+                )
+
+        for x in range(2, 7, 3):
+            type_of_new_shelf = Package_types.fragile
+            sector_of_new_shelf = Sectors_types.fragile
+            for y in range(0, 6):
+                self.enviroment_2d[x][y] = Shelf(
+                    self.window, x, y, type_of_new_shelf, sector_of_new_shelf
+                )
+                self.enviroment_2d[x][y + shelf_2_offset] = Shelf(
+                    self.window, x, (y + shelf_2_offset), type_of_new_shelf, sector_of_new_shelf
                 )

Finding_fields.py

@@ -8,7 +8,7 @@ class Finding_fields:
     def __init__(self, enviroment_2d):
         self.enviroment_2d = enviroment_2d
 
-    def find_closest_shelf(self, start_field, type):
+    def find_closest_shelf(self, start_field, type, sector):
         shelves = []
         for row in self.enviroment_2d:
             for field in row:
@@ -17,7 +17,7 @@ class Finding_fields:
         min_distance = math.inf
         closest_shelf = None
         for shelf in shelves:
-            if shelf.type == type:
+            if shelf.type == type and shelf.sector == sector:
                 distance = abs(start_field.x - shelf.x) + abs(start_field.y - shelf.y)
                 if distance < min_distance:
                     min_distance = distance

Package.py

@@ -6,6 +6,7 @@ from Global_variables import Global_variables as G_var
 from Types_colors import Types_colors
 from Package_types import Package_types
 import math
+from CompanyFactory import CompanyFactory
 
 
 class Package(Field):
@@ -13,13 +14,14 @@ class Package(Field):
         Field.__init__(self, window, x, y)
         self.mark_image = self.get_marking_photo()
         self.type = type
+        self.company = CompanyFactory()
+        self.payed_upfront = random.randint(0,1)
         self.is_placed = False
 
     def get_marking_photo(self):
         file_path_type = ["resources/package_markings/*.jpg"]
         images = glob2.glob(random.choice(file_path_type))
         random_image = random.choice(images)
-        print(random_image)
         return random_image
 
     def draw(self):

Packages_spawner.py

@@ -2,6 +2,7 @@ import random
 from Global_variables import Global_variables as G_var
 from Package import Package
 from Package_types import Package_types
+from decision_tree.Decision_tree import DecisionTree
 
 
 class Packages_spawner:
@@ -10,8 +11,23 @@ class Packages_spawner:
         self.enviroment_2d = enviroment_2d
         
     def spawn_package(self):
-        package_x = random.randrange(22,26)
-        package_y = random.randrange(1,13)
+        package_x = random.randrange(22, 26)
+        package_y = random.randrange(1, 13)
+        weekend = random.randint(0,1)
         package_type = random.choice(list(Package_types))
-        new_package = Package(self.window,package_x,package_y,package_type)
-        self.enviroment_2d[package_x][package_y] = new_package
+        new_package = Package(self.window, package_x, package_y, package_type)
+        self.enviroment_2d[package_x][package_y] = new_package
+        sector_type = self.use_decision_tree(new_package, weekend)
+        return sector_type
+
+    def use_decision_tree(self, package, weekend):
+        marking = package.type
+        if marking == Package_types.fragile:
+            marking = 0
+        elif marking == Package_types.priority:
+            marking = 1
+        tree = DecisionTree(marking, weekend, package.company.popularity,
+                            package.company.payment_delay, package.payed_upfront,
+                            package.company.shipping_type)
+        decision = tree.decision
+        return decision

Sectors_types.py

@@ -0,0 +1,8 @@
+import enum
+
+
+class Sectors_types(enum.Enum):
+    normal = 1
+    shipping_tomorrow = 2
+    shipping_today = 3
+    fragile = 4

Shelf.py

@@ -6,10 +6,11 @@ from Types_colors import Types_colors
 
 
 class Shelf(Field):
-    def __init__(self, window, x, y, type):
+    def __init__(self, window, x, y, type, sector):
         Field.__init__(self, window, x, y)
         self.type = type
         self.color = Types_colors.get_shelf_color(type)
+        self.sector = sector
         self.rect = pygame.Rect(self.x * G_var().RECT_SIZE, self.y *
                                  G_var().RECT_SIZE, G_var().RECT_SIZE, G_var().RECT_SIZE)
 

decision_tree/Decision_tree.py

@@ -0,0 +1,65 @@
+import joblib
+import matplotlib.pyplot as plt
+import pandas
+from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree
+from Sectors_types import Sectors_types
+
+
+decisions = ["decision"]
+attributes = ["marking", "weekend", "c_popularity", "payment_delay", "payed", "shipping_method"]
+
+
+class DecisionTree:
+
+    def __init__(self, marking, weekend, c_popularity, payment_delay, payed, shipping_method):
+        self.decision = self.decision(marking, weekend, c_popularity, payment_delay, payed, shipping_method)
+
+    def tree(self):
+        dataset = pandas.read_csv('./decision_tree/csv_file.csv')
+        x = dataset[attributes]
+        y = dataset[decisions]
+        decision_tree = DecisionTreeClassifier()
+        decision_tree = decision_tree.fit(x.values, y)
+
+        return decision_tree
+
+    # return decision made from tree and attributes
+    def decision(self, marking, weekend, c_popularity, payment_delay, payed, shipping_method):
+        decision_tree = self.tree()
+        decision = decision_tree.predict(
+            [[marking, weekend, c_popularity, payment_delay, payed, shipping_method]])
+        if decision == 1:
+            decision = Sectors_types.fragile
+        elif decision == 2:
+            decision = Sectors_types.normal
+        elif decision == 3:
+            decision = Sectors_types.shipping_tomorrow
+        elif decision == 4:
+            decision = Sectors_types.shipping_today
+        # print(decision)
+        return decision
+
+    def tree_as_txt(self, decision_tree):
+        with open('tree.txt', "w") as file:
+            file.write(export_text(decision_tree))
+
+    def tree_to_png(self, decision_tree):
+        fig = plt.figure()
+        plot_tree(decision_tree, feature_names=attributes, filled=True)
+        plt.title("Decision tree")
+        # plt.show()
+        fig.savefig('tree.png')
+
+    # def tree_to_structure(self,decision_tree):
+    #     joblib.dump(decision_tree, 'tree_model')
+    #
+    # def tree_from_structure(self, file):
+    #     return joblib.load(file)
+    #
+    #
+    #
+    # drzewo = tree()
+    # # tree_as_txt(drzewo)
+    # tree_to_png(drzewo)
+    # # tree_to_structure(drzewo)
+    #

decision_tree/csv_file.csv

@@ -0,0 +1,159 @@
+marking,weekend,c_popularity,payment_delay,payed,shipping_method,decision
+0,1,1,1,1,1,1
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decision_tree/tree.txt

@@ -0,0 +1,22 @@
+|--- feature_0 <= 0.50
+|   |--- class: 1
+|--- feature_0 >  0.50
+|   |--- feature_2 <= 2.50
+|   |   |--- class: 2
+|   |--- feature_2 >  2.50
+|   |   |--- feature_3 <= 4.50
+|   |   |   |--- feature_5 <= 0.50
+|   |   |   |   |--- feature_4 <= 0.50
+|   |   |   |   |   |--- class: 3
+|   |   |   |   |--- feature_4 >  0.50
+|   |   |   |   |   |--- feature_1 <= 0.50
+|   |   |   |   |   |   |--- class: 4
+|   |   |   |   |   |--- feature_1 >  0.50
+|   |   |   |   |   |   |--- class: 2
+|   |   |   |--- feature_5 >  0.50
+|   |   |   |   |--- feature_1 <= 0.50
+|   |   |   |   |   |--- class: 3
+|   |   |   |   |--- feature_1 >  0.50
+|   |   |   |   |   |--- class: 2
+|   |   |--- feature_3 >  4.50
+|   |   |   |--- class: 2