DecisionTree update

.idea/.name

@@ -1 +1 @@
-main.py
+astar.py

bfs.py

@@ -47,7 +47,7 @@ def bfs(pos, direction, end_pos, houses):
         if not is_house(curr_node.pos, houses) and curr_node.pos == end_pos:
             while curr_node.parent:
 
-                #print(curr_node.pos, end_pos)
+               # print(curr_node.pos, end_pos)
                 actions.append(curr_node.action)
                 curr_node = curr_node.parent
             return actions
@@ -64,3 +64,4 @@ def distance(pos, endpos):
     houses = create_houses(40)
     actions = bfs(pos, 0, endpos, houses)
     return len(actions)
+

csv_gen.py

@@ -0,0 +1,51 @@
+import csv
+
+decision = [0, 1]  # 0 - go to bin, 1 - pick up
+levels = [1, 2, 3, 4, 5]
+# 1 - 0;20  2 - 20;40  3 - 40;60  4 - 60;80  5 - 80;100
+# 1 - 0;40  2 - 40;80  3 - 80;120  4 - 120;160  5 - 160+
+
+
+def enough_free_space(available_space, trash_size, available_mass, mass_trash):
+    if available_space + trash_size <= 5 and available_mass + mass_trash <= 5:
+        return True
+    return False
+
+
+def where_is_closer(bin_distance, trash_distance):
+    if bin_distance <= trash_distance:
+        return 0
+    return 1
+
+
+with open('tree_dataset.csv', 'w', newline='') as csv_file:
+    file_writer = csv.writer(csv_file)
+    file_writer.writerow(["dis_dump", "dis_trash", "mass", "space", "trash_mass", "trash_space", "decision"])
+
+    counter = 0
+
+    for dis_dump in levels:
+        for dis_trash in levels:
+            for mass in levels:
+                for space in levels:
+                    for trash_mass in levels:
+                        for trash_space in levels:
+                            if counter % 10 == 0:
+                                if dis_dump == 1 and space >= 1 and mass >= 1:
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, 0])
+                                elif dis_trash == 1 and enough_free_space(space, trash_space, mass, trash_mass):
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, 1])
+                                elif mass == 4 or space == 4 and not enough_free_space(space, trash_space, mass, trash_mass):
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, 0])
+                                elif mass == 5 or space == 5:
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, 0])
+                                elif mass <= 3 and space <= 3 and enough_free_space(space, trash_space, mass, trash_mass):
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, 1])
+                                elif mass == 4 or space == 4 and enough_free_space(space, trash_space, mass, trash_mass):
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, where_is_closer(dis_dump, dis_trash)])
+                                elif not enough_free_space(space, trash_space, mass, trash_mass):
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, 0])
+                                else:
+                                    file_writer.writerow([dis_dump, dis_trash, mass, space, trash_mass, trash_space, None])
+
+                            counter += 1

main.py

@@ -8,16 +8,16 @@ from random import shuffle, choice
 import numpy as np
 import os
 
-
+import tree
 import pygame
 from time import sleep
-
+from os import path
 from colors import gray
 from house import create_houses
 from truck import Truck
 from trash import Trash
 from TSP import tsp, tspmove
-from bfs import bfs
+from bfs import bfs, distance
 
 
 model = load_model("model.h5")
@@ -38,6 +38,9 @@ def game_keys(truck, multi_trash, houses, auto=False):
                 print('↑')
                 for tindex, trash in enumerate(multi_trash):
                     if truck.pos == trash.pos:
+                        truck.mass += trash.mass
+                        truck.space += trash.space
+                        print(truck.mass, truck.space)
                         prediction = model.predict(multi_trash[tindex].content)
                         for i in range (3):
                             if multi_trash[tindex].names[i][:3] == 'cat':
@@ -106,6 +109,46 @@ def game_loop():
                 if event.key == pygame.K_ESCAPE:
                     pygame.quit()
                     quit()
+                if (event.key == pygame.K_l):
+                    if path.isfile('./tree_model') and not os.stat(
+                            './tree_model').st_size == 0:
+                        decision_tree = tree.load_tree_from_structure('./tree_model')
+                        print("Tree model loaded!")
+
+                if (event.key == pygame.K_k):
+                    print(":>")
+                    trash = multi_trash[0]
+                    dis_dump = distance(truck.pos,[80,80])
+                    dis_trash = distance(truck.pos, trash.pos)
+                    print(dis_dump, dis_trash, truck.mass, truck.space, trash.mass, trash.space)
+                    decision =  tree.making_decision(decision_tree,
+                                                      dis_dump // 12 + 1,
+                                                      dis_trash // 12 + 1,
+                                                      truck.mass // 20 + 1, truck.space // 20 + 1,
+                                                      trash.mass // 20 + 1,
+                                                      trash.space // 20 + 1)
+
+                    print(decision)
+                    if(decision[0]==0):
+                        actions = bfs(truck.pos, truck.dir_control,
+                                      trash.pos, houses)
+                        print(actions)
+                        if not actions:
+                            print('Path couldn\'t be found')
+                            break
+
+                        print('##################################################')
+                        while actions:
+                            action = actions.pop()
+                            pygame.event.post(pygame.event.Event(
+                                pygame.KEYDOWN, {'key': action}))
+
+                            game_keys(truck, multi_trash, houses, True)
+                            update_images(gameDisplay, truck, multi_trash, houses)
+                    else:
+                        truck.space=0
+                        truck.mass=0
+
                 if (event.key == pygame.K_b):
                     trash = multi_trash[0]
                     actions = bfs(truck.pos, truck.dir_control,

trash.py

@@ -37,6 +37,8 @@ class Trash:
         self.size = grid_size
         self.content = draw_trash(filenames)[0]
         self.names = draw_trash(filenames)[1]
+        self.mass = random.randint(0, 25)
+        self.space = random.randint(0, 25)
 
     def new_pos(self, truck_pos, houses, multi):
         self.trash_content, self.trash_names = draw_trash(filenames)

tree.py

@@ -0,0 +1,57 @@
+import joblib
+import matplotlib.pyplot as plt
+import pandas
+from sklearn import tree
+from sklearn.tree import DecisionTreeClassifier
+
+attributes = ["dis_dump", "dis_trash", "mass", "space", "trash_mass", "trash_space"]
+decisions = ["decision"]
+
+
+def learning_tree():
+    dataset = pandas.read_csv('./tree_dataset.csv')
+
+    x = dataset[attributes]
+    y = dataset[decisions]
+
+    decision_tree = DecisionTreeClassifier()
+    decision_tree = decision_tree.fit(x, y)
+
+    return decision_tree
+
+
+def making_decision(decision_tree, distance_to_bin, distance_to_trash, filling_mass, filling_space, trash_mass,
+                    trash_space):
+    decision = decision_tree.predict(
+        [[distance_to_bin, distance_to_trash, filling_mass, filling_space, trash_mass, trash_space]])
+
+    return decision
+
+
+def save_all(decision_tree):
+    save_tree_to_png(decision_tree)
+    save_tree_to_txt(decision_tree)
+    save_tree_to_structure(decision_tree)
+
+
+def save_tree_to_txt(decision_tree):
+    with open('./tree_in_txt.txt', "w") as file:
+        file.write(tree.export_text(decision_tree))
+
+
+def save_tree_to_png(decision_tree):
+    fig = plt.figure(figsize=(25, 20))
+    _ = tree.plot_tree(decision_tree, feature_names=attributes, filled=True)
+    fig.savefig('./decision_tree.png')
+
+
+def save_tree_to_structure(decision_tree):
+    joblib.dump(decision_tree, './tree_model')
+
+
+def load_tree_from_structure(file):
+    return joblib.load(file)
+
+if __name__ == '__main__':
+    tre = learning_tree()
+    save_all(tre)

tree_in_txt.txt

@@ -0,0 +1,112 @@
+|--- feature_2 <= 3.50
+|   |--- feature_4 <= 3.50
+|   |   |--- feature_3 <= 3.50
+|   |   |   |--- feature_0 <= 1.50
+|   |   |   |   |--- class: 0
+|   |   |   |--- feature_0 >  1.50
+|   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |--- class: 1
+|   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |--- feature_2 <= 2.50
+|   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |--- feature_2 >  2.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |--- feature_3 >  3.50
+|   |   |   |--- feature_3 <= 4.50
+|   |   |   |   |--- feature_0 <= 2.50
+|   |   |   |   |   |--- feature_1 <= 1.50
+|   |   |   |   |   |   |--- feature_0 <= 1.50
+|   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |--- feature_0 >  1.50
+|   |   |   |   |   |   |   |--- feature_2 <= 2.50
+|   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |--- feature_2 >  2.50
+|   |   |   |   |   |   |   |   |--- feature_4 <= 2.00
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_4 >  2.00
+|   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_1 >  1.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |   |--- feature_0 >  2.50
+|   |   |   |   |   |--- feature_1 <= 3.50
+|   |   |   |   |   |   |--- feature_1 <= 2.50
+|   |   |   |   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |   |   |   |--- feature_2 <= 2.50
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_2 >  2.50
+|   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |--- feature_1 >  2.50
+|   |   |   |   |   |   |   |--- feature_0 <= 3.50
+|   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |   |--- feature_0 >  3.50
+|   |   |   |   |   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |   |   |   |   |--- feature_2 <= 2.50
+|   |   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |   |--- feature_2 >  2.50
+|   |   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_1 >  3.50
+|   |   |   |   |   |   |--- feature_0 <= 4.50
+|   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |--- feature_0 >  4.50
+|   |   |   |   |   |   |   |--- feature_1 <= 4.50
+|   |   |   |   |   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |   |   |   |   |--- feature_2 <= 2.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |   |--- feature_2 >  2.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |   |--- feature_1 >  4.50
+|   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |--- feature_3 >  4.50
+|   |   |   |   |--- class: 0
+|   |--- feature_4 >  3.50
+|   |   |--- feature_2 <= 1.50
+|   |   |   |--- feature_4 <= 4.50
+|   |   |   |   |--- feature_3 <= 3.50
+|   |   |   |   |   |--- feature_0 <= 1.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_0 >  1.50
+|   |   |   |   |   |   |--- class: 1
+|   |   |   |   |--- feature_3 >  3.50
+|   |   |   |   |   |--- feature_3 <= 4.50
+|   |   |   |   |   |   |--- feature_1 <= 3.50
+|   |   |   |   |   |   |   |--- feature_0 <= 2.50
+|   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |   |--- feature_0 >  2.50
+|   |   |   |   |   |   |   |   |--- feature_1 <= 2.50
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_1 >  2.50
+|   |   |   |   |   |   |   |   |   |--- feature_0 <= 4.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |   |   |   |--- feature_0 >  4.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |--- feature_1 >  3.50
+|   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_3 >  4.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |--- feature_4 >  4.50
+|   |   |   |   |--- class: 0
+|   |   |--- feature_2 >  1.50
+|   |   |   |--- class: 0
+|--- feature_2 >  3.50
+|   |--- feature_1 <= 1.50
+|   |   |--- feature_4 <= 1.50
+|   |   |   |--- feature_2 <= 4.50
+|   |   |   |   |--- feature_3 <= 4.50
+|   |   |   |   |   |--- feature_0 <= 1.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_0 >  1.50
+|   |   |   |   |   |   |--- class: 1
+|   |   |   |   |--- feature_3 >  4.50
+|   |   |   |   |   |--- class: 0
+|   |   |   |--- feature_2 >  4.50
+|   |   |   |   |--- class: 0
+|   |   |--- feature_4 >  1.50
+|   |   |   |--- class: 0
+|   |--- feature_1 >  1.50
+|   |   |--- class: 0

truck.py

@@ -16,6 +16,8 @@ class Truck:
         self.allCats = 0
         self.allTrash = 0
         self.trash = 0
+        self.mass=0
+        self.space=0
 
     def move(self):
         self.pos[0] += self.direction[0] * self.size