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decisionTree update

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This commit is contained in:
xVulpeSx 2022-05-11 19:05:44 +02:00
parent f7b6da279d
commit 77625d79e4
6 changed files with 131 additions and 21 deletions
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1
ClientParamsFactory.py
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@ -3,7 +3,6 @@ import random
from data.ClientParams import ClientParams
from data.enum.CompanySize import CompanySize
class ClientParamsFactory:
def __init__(self) -> None:

2
ForkliftAgent.py
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@ -1,7 +1,7 @@
from typing import Tuple, List
from AgentBase import AgentBase
from data.Direction import Direction
from data.enum.Direction import Direction
from decision.ActionType import ActionType

51
data/TEST/importedData.csv Normal file
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@ -0,0 +1,51 @@
DELAY,PAYED,NET-WORTH,INFLUENCE,SKARBOWKA,MEMBER,HAT,SIZE,PRIORITY
11,FALSE,41,97,TRUE,FALSE,TRUE,CompanySize.HUGE,LOW
7,FALSE,22,80,TRUE,FALSE,FALSE,CompanySize.NORMAL,LOW
3,FALSE,58,0,TRUE,TRUE,TRUE,CompanySize.BIG,LOW
11,FALSE,3,15,FALSE,TRUE,FALSE,CompanySize.NO,LOW
5,FALSE,42,18,TRUE,FALSE,FALSE,CompanySize.SMALL,LOW
4,TRUE,51,54,TRUE,FALSE,TRUE,CompanySize.NORMAL,HIGH
7,TRUE,18,47,FALSE,TRUE,TRUE,CompanySize.SMALL,LOW
3,TRUE,96,61,FALSE,TRUE,TRUE,CompanySize.SMALL,MEDIUM
13,FALSE,42,44,TRUE,TRUE,TRUE,CompanySize.NORMAL,LOW
4,TRUE,6,3,FALSE,FALSE,FALSE,CompanySize.NORMAL,LOW
10,TRUE,91,36,FALSE,FALSE,TRUE,CompanySize.NO,MEDIUM
1,TRUE,80,11,TRUE,FALSE,TRUE,CompanySize.GIGANTISHE,HIGH
6,FALSE,91,82,FALSE,TRUE,TRUE,CompanySize.NO,LOW
4,FALSE,1,93,TRUE,TRUE,FALSE,CompanySize.BIG,LOW
14,FALSE,67,13,TRUE,TRUE,TRUE,CompanySize.SMALL,LOW
0,FALSE,7,58,FALSE,FALSE,FALSE,CompanySize.NORMAL,LOW
8,TRUE,74,67,TRUE,TRUE,TRUE,CompanySize.NORMAL,HIGH
4,TRUE,33,43,FALSE,TRUE,FALSE,CompanySize.BIG,MEDIUM
8,TRUE,74,44,TRUE,FALSE,TRUE,CompanySize.HUGE,HIGH
14,FALSE,59,33,TRUE,FALSE,FALSE,CompanySize.NORMAL,LOW
6,FALSE,87,80,TRUE,TRUE,FALSE,CompanySize.GIGANTISHE,LOW
10,FALSE,2,45,FALSE,FALSE,FALSE,CompanySize.BIG,LOW
7,FALSE,74,17,TRUE,FALSE,FALSE,CompanySize.SMALL,LOW
14,FALSE,14,80,FALSE,TRUE,FALSE,CompanySize.NO,LOW
1,FALSE,74,82,TRUE,TRUE,FALSE,CompanySize.NO,LOW
13,FALSE,66,50,FALSE,TRUE,TRUE,CompanySize.HUGE,LOW
12,TRUE,55,82,TRUE,TRUE,TRUE,CompanySize.NO,HIGH
0,TRUE,63,1,TRUE,TRUE,TRUE,CompanySize.NO,HIGH
0,FALSE,39,70,FALSE,FALSE,TRUE,CompanySize.NORMAL,LOW
1,FALSE,14,66,FALSE,FALSE,FALSE,CompanySize.BIG,LOW
7,FALSE,48,86,TRUE,TRUE,TRUE,CompanySize.BIG,LOW
7,FALSE,39,41,FALSE,TRUE,FALSE,CompanySize.HUGE,LOW
6,TRUE,29,90,FALSE,FALSE,FALSE,CompanySize.GIGANTISHE,HIGH
8,TRUE,79,49,FALSE,TRUE,FALSE,CompanySize.BIG,HIGH
14,TRUE,51,51,FALSE,TRUE,FALSE,CompanySize.NO,LOW
1,FALSE,92,97,FALSE,TRUE,TRUE,CompanySize.HUGE,LOW
6,FALSE,92,90,TRUE,FALSE,FALSE,CompanySize.GIGANTISHE,LOW
9,FALSE,89,34,FALSE,TRUE,TRUE,CompanySize.NO,LOW
14,FALSE,85,8,FALSE,FALSE,TRUE,CompanySize.HUGE,LOW
14,TRUE,86,30,FALSE,TRUE,FALSE,CompanySize.GIGANTISHE,MEDIUM
3,TRUE,82,57,FALSE,TRUE,FALSE,CompanySize.BIG,MEDIUM
8,TRUE,18,44,FALSE,TRUE,FALSE,CompanySize.HUGE,LOW
0,FALSE,87,32,FALSE,FALSE,FALSE,CompanySize.NO,LOW
10,TRUE,97,26,FALSE,TRUE,TRUE,CompanySize.HUGE,HIGH
0,FALSE,88,98,FALSE,TRUE,FALSE,CompanySize.NO,LOW
10,TRUE,27,82,FALSE,FALSE,FALSE,CompanySize.HUGE,MEDIUM
8,TRUE,28,36,TRUE,FALSE,FALSE,CompanySize.HUGE,HIGH
14,FALSE,48,94,TRUE,FALSE,TRUE,CompanySize.HUGE,LOW
7,FALSE,40,63,TRUE,TRUE,TRUE,CompanySize.BIG,LOW
8,FALSE,90,20,TRUE,TRUE,FALSE,CompanySize.NO,LOW
1 DELAY PAYED NET-WORTH INFLUENCE SKARBOWKA MEMBER HAT SIZE PRIORITY
2 11 FALSE 41 97 TRUE FALSE TRUE CompanySize.HUGE LOW
3 7 FALSE 22 80 TRUE FALSE FALSE CompanySize.NORMAL LOW
4 3 FALSE 58 0 TRUE TRUE TRUE CompanySize.BIG LOW
5 11 FALSE 3 15 FALSE TRUE FALSE CompanySize.NO LOW
6 5 FALSE 42 18 TRUE FALSE FALSE CompanySize.SMALL LOW
7 4 TRUE 51 54 TRUE FALSE TRUE CompanySize.NORMAL HIGH
8 7 TRUE 18 47 FALSE TRUE TRUE CompanySize.SMALL LOW
9 3 TRUE 96 61 FALSE TRUE TRUE CompanySize.SMALL MEDIUM
10 13 FALSE 42 44 TRUE TRUE TRUE CompanySize.NORMAL LOW
11 4 TRUE 6 3 FALSE FALSE FALSE CompanySize.NORMAL LOW
12 10 TRUE 91 36 FALSE FALSE TRUE CompanySize.NO MEDIUM
13 1 TRUE 80 11 TRUE FALSE TRUE CompanySize.GIGANTISHE HIGH
14 6 FALSE 91 82 FALSE TRUE TRUE CompanySize.NO LOW
15 4 FALSE 1 93 TRUE TRUE FALSE CompanySize.BIG LOW
16 14 FALSE 67 13 TRUE TRUE TRUE CompanySize.SMALL LOW
17 0 FALSE 7 58 FALSE FALSE FALSE CompanySize.NORMAL LOW
18 8 TRUE 74 67 TRUE TRUE TRUE CompanySize.NORMAL HIGH
19 4 TRUE 33 43 FALSE TRUE FALSE CompanySize.BIG MEDIUM
20 8 TRUE 74 44 TRUE FALSE TRUE CompanySize.HUGE HIGH
21 14 FALSE 59 33 TRUE FALSE FALSE CompanySize.NORMAL LOW
22 6 FALSE 87 80 TRUE TRUE FALSE CompanySize.GIGANTISHE LOW
23 10 FALSE 2 45 FALSE FALSE FALSE CompanySize.BIG LOW
24 7 FALSE 74 17 TRUE FALSE FALSE CompanySize.SMALL LOW
25 14 FALSE 14 80 FALSE TRUE FALSE CompanySize.NO LOW
26 1 FALSE 74 82 TRUE TRUE FALSE CompanySize.NO LOW
27 13 FALSE 66 50 FALSE TRUE TRUE CompanySize.HUGE LOW
28 12 TRUE 55 82 TRUE TRUE TRUE CompanySize.NO HIGH
29 0 TRUE 63 1 TRUE TRUE TRUE CompanySize.NO HIGH
30 0 FALSE 39 70 FALSE FALSE TRUE CompanySize.NORMAL LOW
31 1 FALSE 14 66 FALSE FALSE FALSE CompanySize.BIG LOW
32 7 FALSE 48 86 TRUE TRUE TRUE CompanySize.BIG LOW
33 7 FALSE 39 41 FALSE TRUE FALSE CompanySize.HUGE LOW
34 6 TRUE 29 90 FALSE FALSE FALSE CompanySize.GIGANTISHE HIGH
35 8 TRUE 79 49 FALSE TRUE FALSE CompanySize.BIG HIGH
36 14 TRUE 51 51 FALSE TRUE FALSE CompanySize.NO LOW
37 1 FALSE 92 97 FALSE TRUE TRUE CompanySize.HUGE LOW
38 6 FALSE 92 90 TRUE FALSE FALSE CompanySize.GIGANTISHE LOW
39 9 FALSE 89 34 FALSE TRUE TRUE CompanySize.NO LOW
40 14 FALSE 85 8 FALSE FALSE TRUE CompanySize.HUGE LOW
41 14 TRUE 86 30 FALSE TRUE FALSE CompanySize.GIGANTISHE MEDIUM
42 3 TRUE 82 57 FALSE TRUE FALSE CompanySize.BIG MEDIUM
43 8 TRUE 18 44 FALSE TRUE FALSE CompanySize.HUGE LOW
44 0 FALSE 87 32 FALSE FALSE FALSE CompanySize.NO LOW
45 10 TRUE 97 26 FALSE TRUE TRUE CompanySize.HUGE HIGH
46 0 FALSE 88 98 FALSE TRUE FALSE CompanySize.NO LOW
47 10 TRUE 27 82 FALSE FALSE FALSE CompanySize.HUGE MEDIUM
48 8 TRUE 28 36 TRUE FALSE FALSE CompanySize.HUGE HIGH
49 14 FALSE 48 94 TRUE FALSE TRUE CompanySize.HUGE LOW
50 7 FALSE 40 63 TRUE TRUE TRUE CompanySize.BIG LOW
51 8 FALSE 90 20 TRUE TRUE FALSE CompanySize.NO LOW

92
main.py
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@ -1,14 +1,21 @@
import csv
import random
import pandas
from mesa.visualization.ModularVisualization import ModularServer
from mesa.visualization.modules import CanvasGrid
from sklearn import metrics, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from ClientParamsFactory import ClientParamsFactory
from ForkliftAgent import ForkliftAgent
from GameModel import GameModel
from PatchAgent import PatchAgent
from PatchType import PatchType
from data.enum.CompanySize import CompanySize
from data.enum.Direction import Direction
from util.PathDefinitions import GridWithWeights
from data.enum.Priority import Priority
colors = [
'blue', 'cyan', 'orange', 'yellow', 'magenta', 'purple', '#103d3e', '#9fc86c',
@ -52,23 +59,74 @@ def agent_portrayal(agent):
if __name__ == '__main__':
base = 512
gridWidth = 10
gridHeight = 10
scale = base / gridWidth
test = ClientParamsFactory()
diagram4 = GridWithWeights(gridWidth, gridHeight)
diagram4.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (3, 4), (4, 4), (6, 4)]
header = ['DELAY',
'PAYED',
'NET-WORTH',
'INFLUENCE',
'SKARBOWKA',
'MEMBER',
'HAT',
'SIZE']
diagram5 = GridWithWeights(gridWidth, gridHeight)
diagram5.puddles = [(2, 2), (2, 5), (2, 6), (5, 4)]
with open("data/TEST/generatedData.csv", 'w', newline='') as file:
writer = csv.writer(file)
grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
writer.writerow(header)
server = ModularServer(GameModel,
[grid],
"Automatyczny Wózek Widłowy",
{"width": gridHeight, "height": gridWidth, "graph": diagram4, "graph2": diagram5},)
for i in range(50):
data = test.get_client_params()
server.port = 8888
server.launch()
writer.writerow([data.payment_delay,
data.payed,
data.net_worth,
data.infuence_rate,
data.is_skarbowka,
data.membership,
data.is_hat,
data.company_size])
file.close()
data_input = pandas.read_csv('data/TEST/importedData.csv', delimiter=",")
X = data_input[['DELAY','PAYED','NET-WORTH','INFLUENCE','SKARBOWKA','MEMBER','HAT','SIZE']].values
Y = data_input["PRIORITY"]
label_BP = preprocessing.LabelEncoder()
label_BP.fit(['CompanySize.NO', 'CompanySize.SMALL', 'CompanySize.NORMAL', 'CompanySize.BIG', 'CompanySize.HUGE', 'CompanySize.GIGANTISHE'])
X[:, 7] = label_BP.transform(X[:, 7])
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, train_size=0.9)
drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
drugTree.fit(X_train, y_train)
predicted = drugTree.predict(X_test)
print(X_test)
print(predicted)
print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y_test, predicted))
# base = 512
# gridWidth = 10
# gridHeight = 10
# scale = base / gridWidth
#
# diagram4 = GridWithWeights(gridWidth, gridHeight)
# diagram4.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (3, 4), (4, 4), (6, 4)]
#
# diagram5 = GridWithWeights(gridWidth, gridHeight)
# diagram5.puddles = [(2, 2), (2, 5), (2, 6), (5, 4)]
#
# grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
#
# server = ModularServer(GameModel,
# [grid],
# "Automatyczny Wózek Widłowy",
# {"width": gridHeight, "height": gridWidth, "graph": diagram4, "graph2": diagram5},)
#
# server.port = 8888
# server.launch()

3
pathfinding/PathfinderOnStates.py
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@ -1,8 +1,9 @@
from typing import List
from typing import Tuple
from data.Direction import Direction
from data.GameConstants import GameConstants
from data.enum.Direction import Direction
from decision.ActionType import ActionType
from pathfinding.PathFinderState import PathFinderState
from pathfinding.PrioritizedItem import PrioritizedItem

1
requirements.txt
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@ -2,3 +2,4 @@ jupyter
matplotlib
mesa
numpy
sklearn