Dodanie drzewa decyzjnego v1

This commit is contained in:
trzmielewskiR 2022-05-09 19:18:24 +02:00
parent 0f090f9a85
commit 8e331a8803
7 changed files with 213 additions and 12 deletions

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@ -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)

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@ -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
1 season enough_space_in_trashmaster time_since_flush type_of_trash access_to_bin distance decision
2 2 2 1 1 2 10 1
3 2 2 2 2 2 8 1
4 2 2 3 3 2 6 1
5 2 2 4 4 2 4 2
6 2 2 5 5 2 2 3
7 2 2 6 1 2 1 5
8 2 2 7 2 2 3 4
9 2 2 8 3 2 5 4
10 2 2 9 4 2 7 3
11 2 2 10 5 2 9 5
12 2 2 1 1 2 2 1
13 2 2 2 2 2 1 1
14 2 2 3 3 2 3 2
15 2 2 4 4 2 4 2
16 2 2 5 5 2 5 3
17 2 2 6 1 2 6 3
18 2 2 7 2 2 7 2
19 2 2 8 3 2 8 2
20 2 2 9 4 2 9 4
21 2 2 10 5 2 10 4
22 2 2 1 1 2 7 1
23 2 2 2 2 2 6 2
24 2 2 3 3 2 5 3
25 2 2 4 4 1 4 0
26 2 2 5 5 1 3 0
27 2 2 6 1 1 2 0
28 2 2 7 2 1 1 0
29 2 2 8 3 1 9 0
30 2 2 9 4 1 8 0
31 2 2 10 5 1 7 0
32 2 2 1 1 1 3 0
33 2 2 2 2 1 2 0
34 2 2 3 3 1 1 0
35 2 2 4 4 1 4 0
36 2 1 5 5 1 5 0
37 2 1 6 1 1 6 0
38 2 1 7 2 1 10 0
39 2 1 8 3 1 9 0
40 2 1 9 4 1 8 0
41 2 1 10 5 1 7 0
42 2 1 1 1 1 2 0
43 2 1 2 2 1 4 0
44 2 1 3 3 1 6 0
45 2 1 4 4 1 8 0
46 2 1 5 5 2 10 0
47 2 1 6 1 2 1 0
48 2 1 7 2 2 3 0
49 2 1 8 3 2 5 0
50 2 1 9 4 2 7 0
51 2 1 10 5 2 9 0
52 3 2 1 1 2 2 1
53 3 2 2 2 2 1 1
54 3 2 3 3 2 4 2
55 3 2 4 4 2 3 3
56 3 2 5 5 2 6 4
57 3 2 6 1 2 5 4
58 3 2 7 2 2 8 3
59 3 2 8 3 2 7 3
60 3 2 9 4 2 9 4
61 3 2 10 5 2 10 5
62 3 2 1 1 2 7 1
63 3 2 2 2 2 6 1
64 3 2 3 3 2 4 3
65 3 2 4 4 2 1 3
66 3 2 5 5 2 2 4
67 3 2 6 1 2 3 4
68 3 2 7 2 2 9 3
69 3 2 8 3 2 8 3
70 3 2 9 4 2 5 5
71 3 2 10 5 2 4 5
72 3 2 1 1 2 1 1
73 3 2 2 2 1 7 0
74 3 2 3 3 1 9 0
75 3 2 4 4 1 10 0
76 3 2 5 5 1 3 0
77 3 2 6 1 1 2 0
78 3 2 7 2 1 5 0
79 3 2 8 3 1 6 0
80 3 2 9 4 1 8 0
81 3 1 10 5 1 3 0
82 3 1 1 1 1 1 0
83 3 1 2 2 1 2 0
84 3 1 3 3 1 6 0
85 3 1 4 4 1 9 0
86 3 1 5 5 1 7 0
87 3 1 6 1 1 4 0
88 3 1 7 2 1 3 0
89 3 1 8 3 1 5 0
90 3 1 9 4 1 10 0
91 3 1 10 5 1 8 0
92 3 1 1 1 2 2 0
93 3 1 2 2 2 4 0
94 3 1 3 3 2 6 0
95 3 1 4 4 2 7 0
96 3 1 5 5 2 1 0
97 3 1 6 1 2 9 0
98 3 1 7 2 2 3 0
99 3 1 8 3 2 9 0
100 3 1 9 4 2 9 0
101 3 1 10 5 2 1 0

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|--- 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

BIN
decision_tree/tree_model Normal file

Binary file not shown.

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

17
main.py
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@ -10,9 +10,18 @@ from map import map
from map import map_utils from map import map_utils
from path_search_algorthms import bfs from path_search_algorthms import bfs
from path_search_algorthms import a_star, a_star_utils from path_search_algorthms import a_star, a_star_utils
from decision_tree import decisionTree
from game_objects import aiPlayer 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(): class Game():
def __init__(self): def __init__(self):
@ -125,7 +134,9 @@ class Game():
pos = pg.mouse.get_pos() pos = pg.mouse.get_pos()
offset_x, offset_y = self.camera.offset() offset_x, offset_y = self.camera.offset()
clicked_coords = [math.floor(pos[0] / TILESIZE) - offset_x, math.floor(pos[1] / TILESIZE) - offset_y] 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) print(actions)
if (actions != None): if (actions != None):
t = aiPlayer.aiPlayer(self.player, game=self) t = aiPlayer.aiPlayer(self.player, game=self)
@ -137,11 +148,13 @@ class Game():
def show_go_screen(self): def show_go_screen(self):
pass pass
# create the game object # create the game object
if __name__ == "__main__": if __name__ == "__main__":
g = Game() g = Game()
g.show_start_screen() g.show_start_screen()
printTree()
g.run() g.run()
g.show_go_screen() g.show_go_screen()

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