140 lines
4.8 KiB
Python
140 lines
4.8 KiB
Python
import csv
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import random
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import pandas
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import sklearn.tree
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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 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 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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'#b4c2ed', '#31767d', '#31a5fa', '#ba96e0', '#fef3e4', '#6237ac', '#f9cacd', '#1e8123'
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]
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def agent_portrayal(agent):
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if isinstance(agent, ForkliftAgent):
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shape = ""
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if agent.current_rotation == Direction.top:
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shape = "img/image_top.png"
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elif agent.current_rotation == Direction.right:
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shape = "img/image_right.png"
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elif agent.current_rotation == Direction.down:
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shape = "img/image_down.png"
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elif agent.current_rotation == Direction.left:
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shape = "img/image_left.png"
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portrayal = {"Shape": shape, "scale": 1.0, "Layer": 0}
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if isinstance(agent, PatchAgent):
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color = colors[0]
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if agent.patch_type == PatchType.wall:
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portrayal = {"Shape": "img/brick.webp", "scale": 1.0, "Layer": 0}
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elif agent.patch_type == PatchType.dropOff:
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portrayal = {"Shape": "img/truck.png", "scale": 1.0, "Layer": 0}
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elif agent.patch_type == PatchType.pickUp:
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portrayal = {"Shape": "img/okB00mer.png", "scale": 1.0, "Layer": 0}
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elif agent.patch_type == PatchType.diffTerrain:
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portrayal = {"Shape": "img/puddle.png", "scale": 1.0, "Layer": 0}
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else:
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color = colors[random.randrange(13) + 3]
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portrayal = {"Shape": "rect",
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"Filled": "true",
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"Layer": 0,
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"Color": color,
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"w": 1,
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"h": 1}
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return portrayal
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if __name__ == '__main__':
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test = ClientParamsFactory()
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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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with open("data/TEST/generatedData.csv", 'w', newline='') as file:
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writer = csv.writer(file)
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writer.writerow(header)
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for i in range(200):
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data = test.get_client_params()
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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_headers = ['DELAY','PAYED','NET-WORTH','INFLUENCE','SKARBOWKA','MEMBER','HAT','SIZE']
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X = data_input[X_headers].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=0.2, train_size=0.8)
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drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
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clf = drugTree.fit(X_train, y_train)
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predicted = drugTree.predict(X_test)
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y_test = y_test.to_list()
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for i in range(len(predicted)):
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print("{}. {} \n predicted: {}, actual: {}".format(i, X_test[i,:], predicted[i], y_test[i]))
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print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y_test, predicted))
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print(sklearn.tree.export_text(clf, feature_names=X_headers))
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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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