separate decisionTree from main()
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xVulpeSx
parent
f7cccd4b4d
commit
353b5f0174
87
main.py
87
main.py
@ -17,6 +17,7 @@ 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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from tree.DecisionTree import DecisionTree
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colors = [
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'blue', 'cyan', 'orange', 'yellow', 'magenta', 'purple', '#103d3e', '#9fc86c',
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@ -60,80 +61,24 @@ def agent_portrayal(agent):
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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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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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with open("data/TEST/generatedData.csv", 'w', newline='') as file:
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writer = csv.writer(file)
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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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writer.writerow(header)
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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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for i in range(200):
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data = test.get_client_params()
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grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
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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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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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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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server.port = 8888
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server.launch()
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86
tree/DecisionTree.py
Normal file
86
tree/DecisionTree.py
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@ -0,0 +1,86 @@
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import csv
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import pandas
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import sklearn
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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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class DecisionTree:
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def __init__(self) -> None:
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super().__init__()
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def generate_data(self, generator: ClientParamsFactory, n:int):
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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(n):
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data = generator.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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def get_normalized_data(self, X):
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label_BP = preprocessing.LabelEncoder()
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label_BP.fit(
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['CompanySize.NO', 'CompanySize.SMALL', 'CompanySize.NORMAL', 'CompanySize.BIG', 'CompanySize.HUGE',
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'CompanySize.GIGANTISHE'])
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X[:, 7] = label_BP.transform(X[:, 7])
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return X
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def print_logs(self, x, y, prediction):
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for i in range(len(prediction)):
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print("{}. {} \n predicted: {}, actual: {}".format(i, x[i, :], prediction[i], y[i]))
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print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y, prediction))
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def get_decision_tree(self) -> DecisionTreeClassifier:
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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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X = self.get_normalized_data(X)
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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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self.print_logs(X_test, y_test, predicted)
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print(sklearn.tree.export_text(clf, feature_names=X_headers))
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return drugTree
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