decision_tree/decisionTree.py

@@ -0,0 +1,66 @@
+import joblib
+import matplotlib.pyplot as plt
+import pandas
+from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree
+
+'''
+atrybuty w pliku csv muszą być integerami, wstępnie ustaliłem:
+season = {"wiosna": 1, "lato": 2, "jesien":3, "zima":4}
+enough_space_in_trashmaster = { "no": 1, "yes":2}
+time_since_flush = [1,2,3,4,5,6,7,8,9,10]
+type_of_trash = {"bio":1, "szklo":2, "plastik":3, "papier":4, "mieszane":5}
+access_to_bin = { "no":1, "yes":2}
+distance = [1,2,3,4,5,6,7,8,9,10]
+decision = [1,2,3,4,5]
+'''
+decisions = ["decision"]
+attributes = ["season", "enough_space_in_trashmaster", "time_since_flush", "type_of_trash", "access_to_bin", "distance",
+              "decision"]
+
+
+# return tree made from attributes
+def tree():
+    dataset = pandas.read_csv('./decision_tree/drzewo_decyzyjne.csv')
+
+    x = dataset[attributes]
+    y = dataset[decisions]
+    decision_tree = DecisionTreeClassifier()
+    decision_tree = decision_tree.fit(x, y)
+
+    return decision_tree
+
+
+# return decision made from tree and attributes
+def decision(decision_tree, season, enough_space_in_trashmaster, time_since_flush, type_of_trash, access_to_bin,
+             distance):
+    decision = decision_tree.predict(
+        [[season, enough_space_in_trashmaster, time_since_flush, type_of_trash, access_to_bin, distance]])
+
+    return decision
+
+
+'''
+we shall save output of our decision tree. It is possible for a few ways:
+txt, png or structure
+'''
+
+
+def tree_as_txt(decision_tree):
+    with open('./decision_tree/tree_as_txt.txt', "w") as file:
+        file.write(export_text(decision_tree))
+
+
+def tree_to_png(decision_tree):
+    plt.figure()
+    plot_tree(decision_tree, feature_names=attributes, filled=True)
+    plt.title("Decision tree")
+    plt.show()
+
+
+def tree_to_structure(decision_tree):
+    joblib.dump(decision_tree, './decision_tree/tree_model')
+
+#drzewo = tree()
+#tree_as_txt(drzewo)
+#tree_to_png(drzewo)
+#tree_to_structure(drzewo)

decision_tree/drzewo_decyzyjne.csv

@@ -0,0 +1,101 @@
+season,enough_space_in_trashmaster,time_since_flush,type_of_trash,access_to_bin,distance,decision
+2,2,1,1,2,10,1
+2,2,2,2,2,8,1
+2,2,3,3,2,6,1
+2,2,4,4,2,4,2
+2,2,5,5,2,2,3
+2,2,6,1,2,1,5
+2,2,7,2,2,3,4
+2,2,8,3,2,5,4
+2,2,9,4,2,7,3
+2,2,10,5,2,9,5
+2,2,1,1,2,2,1
+2,2,2,2,2,1,1
+2,2,3,3,2,3,2
+2,2,4,4,2,4,2
+2,2,5,5,2,5,3
+2,2,6,1,2,6,3
+2,2,7,2,2,7,2
+2,2,8,3,2,8,2
+2,2,9,4,2,9,4
+2,2,10,5,2,10,4
+2,2,1,1,2,7,1
+2,2,2,2,2,6,2
+2,2,3,3,2,5,3
+2,2,4,4,1,4,0
+2,2,5,5,1,3,0
+2,2,6,1,1,2,0
+2,2,7,2,1,1,0
+2,2,8,3,1,9,0
+2,2,9,4,1,8,0
+2,2,10,5,1,7,0
+2,2,1,1,1,3,0
+2,2,2,2,1,2,0
+2,2,3,3,1,1,0
+2,2,4,4,1,4,0
+2,1,5,5,1,5,0
+2,1,6,1,1,6,0
+2,1,7,2,1,10,0
+2,1,8,3,1,9,0
+2,1,9,4,1,8,0
+2,1,10,5,1,7,0
+2,1,1,1,1,2,0
+2,1,2,2,1,4,0
+2,1,3,3,1,6,0
+2,1,4,4,1,8,0
+2,1,5,5,2,10,0
+2,1,6,1,2,1,0
+2,1,7,2,2,3,0
+2,1,8,3,2,5,0
+2,1,9,4,2,7,0
+2,1,10,5,2,9,0
+3,2,1,1,2,2,1
+3,2,2,2,2,1,1
+3,2,3,3,2,4,2
+3,2,4,4,2,3,3
+3,2,5,5,2,6,4
+3,2,6,1,2,5,4
+3,2,7,2,2,8,3
+3,2,8,3,2,7,3
+3,2,9,4,2,9,4
+3,2,10,5,2,10,5
+3,2,1,1,2,7,1
+3,2,2,2,2,6,1
+3,2,3,3,2,4,3
+3,2,4,4,2,1,3
+3,2,5,5,2,2,4
+3,2,6,1,2,3,4
+3,2,7,2,2,9,3
+3,2,8,3,2,8,3
+3,2,9,4,2,5,5
+3,2,10,5,2,4,5
+3,2,1,1,2,1,1
+3,2,2,2,1,7,0
+3,2,3,3,1,9,0
+3,2,4,4,1,10,0
+3,2,5,5,1,3,0
+3,2,6,1,1,2,0
+3,2,7,2,1,5,0
+3,2,8,3,1,6,0
+3,2,9,4,1,8,0
+3,1,10,5,1,3,0
+3,1,1,1,1,1,0
+3,1,2,2,1,2,0
+3,1,3,3,1,6,0
+3,1,4,4,1,9,0
+3,1,5,5,1,7,0
+3,1,6,1,1,4,0
+3,1,7,2,1,3,0
+3,1,8,3,1,5,0
+3,1,9,4,1,10,0
+3,1,10,5,1,8,0
+3,1,1,1,2,2,0
+3,1,2,2,2,4,0
+3,1,3,3,2,6,0
+3,1,4,4,2,7,0
+3,1,5,5,2,1,0
+3,1,6,1,2,9,0
+3,1,7,2,2,3,0
+3,1,8,3,2,9,0
+3,1,9,4,2,9,0
+3,1,10,5,2,1,0

decision_tree/tree_as_txt.txt

@@ -0,0 +1,16 @@
+|--- feature_6 <= 0.50
+|   |--- class: 0
+|--- feature_6 >  0.50
+|   |--- feature_6 <= 1.50
+|   |   |--- class: 1
+|   |--- feature_6 >  1.50
+|   |   |--- feature_6 <= 3.50
+|   |   |   |--- feature_6 <= 2.50
+|   |   |   |   |--- class: 2
+|   |   |   |--- feature_6 >  2.50
+|   |   |   |   |--- class: 3
+|   |   |--- feature_6 >  3.50
+|   |   |   |--- feature_6 <= 4.50
+|   |   |   |   |--- class: 4
+|   |   |   |--- feature_6 >  4.50
+|   |   |   |   |--- class: 5

game_objects/player.py

@@ -19,6 +19,7 @@ class Player(pg.sprite.Sprite):
         self.pos = vec(x, y)
         self.rot = 0
         self.__rotation = a_star_utils.Rotation.RIGHT
+        self.mass = 0
 
     def rotation(self) -> a_star_utils.Rotation:
         return self.__rotation

main.py

@@ -10,9 +10,18 @@ from map import map
 from map import map_utils
 from path_search_algorthms import bfs
 from path_search_algorthms import a_star, a_star_utils
-
+from decision_tree import decisionTree
 
 from game_objects import aiPlayer
+
+
+def printTree():
+    tree = decisionTree.tree()
+    decisionTree.tree_as_txt(tree)
+    decisionTree.tree_to_png(tree)
+    decisionTree.tree_to_structure(tree)
+
+
 class Game():
 
     def __init__(self):
@@ -125,7 +134,9 @@ class Game():
                 pos = pg.mouse.get_pos()
                 offset_x, offset_y = self.camera.offset()
                 clicked_coords = [math.floor(pos[0] / TILESIZE) - offset_x, math.floor(pos[1] / TILESIZE) - offset_y]
-                actions = a_star.search_path(math.floor(self.player.pos[0] / TILESIZE), math.floor(self.player.pos[1] / TILESIZE), self.player.rotation(), clicked_coords[0], clicked_coords[1], self.mapArray)
+                actions = a_star.search_path(math.floor(self.player.pos[0] / TILESIZE),
+                                             math.floor(self.player.pos[1] / TILESIZE), self.player.rotation(),
+                                             clicked_coords[0], clicked_coords[1], self.mapArray)
                 print(actions)
                 if (actions != None):
                     t = aiPlayer.aiPlayer(self.player, game=self)
@@ -137,11 +148,13 @@ class Game():
     def show_go_screen(self):
         pass
 
+
 # create the game object
 
 if __name__ == "__main__":
     g = Game()
     g.show_start_screen()
+    printTree()
 
     g.run()
     g.show_go_screen()

requirements.txt

@@ -15,3 +15,7 @@ toml==0.10.2
 tomli==2.0.1
 typing_extensions==4.1.1
 wrapt==1.14.0
+joblib~=1.1.0
+matplotlib~=3.5.1
+pandas~=1.1.1
+scikit-learn~=1.0.2