decisionTree update
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f7b6da279d
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@ -3,7 +3,6 @@ import random
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from data.ClientParams import ClientParams
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from data.enum.CompanySize import CompanySize
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class ClientParamsFactory:
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def __init__(self) -> None:
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@ -1,7 +1,7 @@
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from typing import Tuple, List
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from AgentBase import AgentBase
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from data.Direction import Direction
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from data.enum.Direction import Direction
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from decision.ActionType import ActionType
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51
data/TEST/importedData.csv
Normal file
51
data/TEST/importedData.csv
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@ -0,0 +1,51 @@
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DELAY,PAYED,NET-WORTH,INFLUENCE,SKARBOWKA,MEMBER,HAT,SIZE,PRIORITY
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11,FALSE,41,97,TRUE,FALSE,TRUE,CompanySize.HUGE,LOW
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7,FALSE,22,80,TRUE,FALSE,FALSE,CompanySize.NORMAL,LOW
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3,FALSE,58,0,TRUE,TRUE,TRUE,CompanySize.BIG,LOW
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11,FALSE,3,15,FALSE,TRUE,FALSE,CompanySize.NO,LOW
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5,FALSE,42,18,TRUE,FALSE,FALSE,CompanySize.SMALL,LOW
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4,TRUE,51,54,TRUE,FALSE,TRUE,CompanySize.NORMAL,HIGH
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7,TRUE,18,47,FALSE,TRUE,TRUE,CompanySize.SMALL,LOW
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3,TRUE,96,61,FALSE,TRUE,TRUE,CompanySize.SMALL,MEDIUM
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13,FALSE,42,44,TRUE,TRUE,TRUE,CompanySize.NORMAL,LOW
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4,TRUE,6,3,FALSE,FALSE,FALSE,CompanySize.NORMAL,LOW
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10,TRUE,91,36,FALSE,FALSE,TRUE,CompanySize.NO,MEDIUM
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1,TRUE,80,11,TRUE,FALSE,TRUE,CompanySize.GIGANTISHE,HIGH
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6,FALSE,91,82,FALSE,TRUE,TRUE,CompanySize.NO,LOW
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4,FALSE,1,93,TRUE,TRUE,FALSE,CompanySize.BIG,LOW
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14,FALSE,67,13,TRUE,TRUE,TRUE,CompanySize.SMALL,LOW
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0,FALSE,7,58,FALSE,FALSE,FALSE,CompanySize.NORMAL,LOW
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8,TRUE,74,67,TRUE,TRUE,TRUE,CompanySize.NORMAL,HIGH
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4,TRUE,33,43,FALSE,TRUE,FALSE,CompanySize.BIG,MEDIUM
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8,TRUE,74,44,TRUE,FALSE,TRUE,CompanySize.HUGE,HIGH
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14,FALSE,59,33,TRUE,FALSE,FALSE,CompanySize.NORMAL,LOW
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6,FALSE,87,80,TRUE,TRUE,FALSE,CompanySize.GIGANTISHE,LOW
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10,FALSE,2,45,FALSE,FALSE,FALSE,CompanySize.BIG,LOW
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7,FALSE,74,17,TRUE,FALSE,FALSE,CompanySize.SMALL,LOW
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14,FALSE,14,80,FALSE,TRUE,FALSE,CompanySize.NO,LOW
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1,FALSE,74,82,TRUE,TRUE,FALSE,CompanySize.NO,LOW
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13,FALSE,66,50,FALSE,TRUE,TRUE,CompanySize.HUGE,LOW
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12,TRUE,55,82,TRUE,TRUE,TRUE,CompanySize.NO,HIGH
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0,TRUE,63,1,TRUE,TRUE,TRUE,CompanySize.NO,HIGH
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0,FALSE,39,70,FALSE,FALSE,TRUE,CompanySize.NORMAL,LOW
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1,FALSE,14,66,FALSE,FALSE,FALSE,CompanySize.BIG,LOW
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7,FALSE,48,86,TRUE,TRUE,TRUE,CompanySize.BIG,LOW
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7,FALSE,39,41,FALSE,TRUE,FALSE,CompanySize.HUGE,LOW
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6,TRUE,29,90,FALSE,FALSE,FALSE,CompanySize.GIGANTISHE,HIGH
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8,TRUE,79,49,FALSE,TRUE,FALSE,CompanySize.BIG,HIGH
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14,TRUE,51,51,FALSE,TRUE,FALSE,CompanySize.NO,LOW
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1,FALSE,92,97,FALSE,TRUE,TRUE,CompanySize.HUGE,LOW
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6,FALSE,92,90,TRUE,FALSE,FALSE,CompanySize.GIGANTISHE,LOW
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9,FALSE,89,34,FALSE,TRUE,TRUE,CompanySize.NO,LOW
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14,FALSE,85,8,FALSE,FALSE,TRUE,CompanySize.HUGE,LOW
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14,TRUE,86,30,FALSE,TRUE,FALSE,CompanySize.GIGANTISHE,MEDIUM
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3,TRUE,82,57,FALSE,TRUE,FALSE,CompanySize.BIG,MEDIUM
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8,TRUE,18,44,FALSE,TRUE,FALSE,CompanySize.HUGE,LOW
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0,FALSE,87,32,FALSE,FALSE,FALSE,CompanySize.NO,LOW
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10,TRUE,97,26,FALSE,TRUE,TRUE,CompanySize.HUGE,HIGH
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0,FALSE,88,98,FALSE,TRUE,FALSE,CompanySize.NO,LOW
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10,TRUE,27,82,FALSE,FALSE,FALSE,CompanySize.HUGE,MEDIUM
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8,TRUE,28,36,TRUE,FALSE,FALSE,CompanySize.HUGE,HIGH
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14,FALSE,48,94,TRUE,FALSE,TRUE,CompanySize.HUGE,LOW
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7,FALSE,40,63,TRUE,TRUE,TRUE,CompanySize.BIG,LOW
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8,FALSE,90,20,TRUE,TRUE,FALSE,CompanySize.NO,LOW
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92
main.py
92
main.py
@ -1,14 +1,21 @@
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import csv
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import random
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import pandas
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from mesa.visualization.ModularVisualization import ModularServer
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from mesa.visualization.modules import CanvasGrid
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from sklearn import metrics, preprocessing
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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from ClientParamsFactory import ClientParamsFactory
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from ForkliftAgent import ForkliftAgent
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from GameModel import GameModel
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from PatchAgent import PatchAgent
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from PatchType import PatchType
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from data.enum.CompanySize import CompanySize
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from data.enum.Direction import Direction
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from util.PathDefinitions import GridWithWeights
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from data.enum.Priority import Priority
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colors = [
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'blue', 'cyan', 'orange', 'yellow', 'magenta', 'purple', '#103d3e', '#9fc86c',
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@ -52,23 +59,74 @@ def agent_portrayal(agent):
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if __name__ == '__main__':
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base = 512
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gridWidth = 10
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gridHeight = 10
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scale = base / gridWidth
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test = ClientParamsFactory()
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diagram4 = GridWithWeights(gridWidth, gridHeight)
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diagram4.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (3, 4), (4, 4), (6, 4)]
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header = ['DELAY',
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'PAYED',
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'NET-WORTH',
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'INFLUENCE',
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'SKARBOWKA',
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'MEMBER',
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'HAT',
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'SIZE']
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diagram5 = GridWithWeights(gridWidth, gridHeight)
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diagram5.puddles = [(2, 2), (2, 5), (2, 6), (5, 4)]
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with open("data/TEST/generatedData.csv", 'w', newline='') as file:
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writer = csv.writer(file)
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grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
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writer.writerow(header)
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server = ModularServer(GameModel,
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[grid],
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"Automatyczny Wózek Widłowy",
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{"width": gridHeight, "height": gridWidth, "graph": diagram4, "graph2": diagram5},)
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for i in range(50):
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data = test.get_client_params()
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server.port = 8888
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server.launch()
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writer.writerow([data.payment_delay,
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data.payed,
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data.net_worth,
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data.infuence_rate,
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data.is_skarbowka,
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data.membership,
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data.is_hat,
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data.company_size])
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file.close()
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data_input = pandas.read_csv('data/TEST/importedData.csv', delimiter=",")
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X = data_input[['DELAY','PAYED','NET-WORTH','INFLUENCE','SKARBOWKA','MEMBER','HAT','SIZE']].values
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Y = data_input["PRIORITY"]
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label_BP = preprocessing.LabelEncoder()
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label_BP.fit(['CompanySize.NO', 'CompanySize.SMALL', 'CompanySize.NORMAL', 'CompanySize.BIG', 'CompanySize.HUGE', 'CompanySize.GIGANTISHE'])
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X[:, 7] = label_BP.transform(X[:, 7])
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X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, train_size=0.9)
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drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
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drugTree.fit(X_train, y_train)
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predicted = drugTree.predict(X_test)
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print(X_test)
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print(predicted)
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print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y_test, predicted))
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# base = 512
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# gridWidth = 10
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# gridHeight = 10
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# scale = base / gridWidth
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#
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# diagram4 = GridWithWeights(gridWidth, gridHeight)
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# diagram4.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (3, 4), (4, 4), (6, 4)]
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#
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# diagram5 = GridWithWeights(gridWidth, gridHeight)
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# diagram5.puddles = [(2, 2), (2, 5), (2, 6), (5, 4)]
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#
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# grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
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#
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# server = ModularServer(GameModel,
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# [grid],
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# "Automatyczny Wózek Widłowy",
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# {"width": gridHeight, "height": gridWidth, "graph": diagram4, "graph2": diagram5},)
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#
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# server.port = 8888
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# server.launch()
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@ -1,8 +1,9 @@
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from typing import List
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from typing import Tuple
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from data.Direction import Direction
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from data.GameConstants import GameConstants
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from data.enum.Direction import Direction
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from decision.ActionType import ActionType
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from pathfinding.PathFinderState import PathFinderState
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from pathfinding.PrioritizedItem import PrioritizedItem
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@ -2,3 +2,4 @@ jupyter
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matplotlib
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mesa
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numpy
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sklearn
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