SI_InteligentnyWozekWidlowy/main.py

import csv
import random

import pandas
import sklearn.tree

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 PatchAgent import PatchAgent
from PatchType import PatchType
from data.enum.CompanySize import CompanySize
from data.enum.Direction import Direction
from data.enum.Priority import Priority

colors = [
    'blue', 'cyan', 'orange', 'yellow', 'magenta', 'purple', '#103d3e', '#9fc86c',
    '#b4c2ed', '#31767d', '#31a5fa', '#ba96e0', '#fef3e4', '#6237ac', '#f9cacd', '#1e8123'
]


def agent_portrayal(agent):
    if isinstance(agent, ForkliftAgent):
        shape = ""
        if agent.current_rotation == Direction.top:
            shape = "img/image_top.png"
        elif agent.current_rotation == Direction.right:
            shape = "img/image_right.png"
        elif agent.current_rotation == Direction.down:
            shape = "img/image_down.png"
        elif agent.current_rotation == Direction.left:
            shape = "img/image_left.png"

        portrayal = {"Shape": shape, "scale": 1.0, "Layer": 0}

    if isinstance(agent, PatchAgent):
        color = colors[0]
        if agent.patch_type == PatchType.wall:
            portrayal = {"Shape": "img/brick.webp", "scale": 1.0, "Layer": 0}
        elif agent.patch_type == PatchType.dropOff:
            portrayal = {"Shape": "img/truck.png", "scale": 1.0, "Layer": 0}
        elif agent.patch_type == PatchType.pickUp:
            portrayal = {"Shape": "img/okB00mer.png", "scale": 1.0, "Layer": 0}
        elif agent.patch_type == PatchType.diffTerrain:
            portrayal = {"Shape": "img/puddle.png", "scale": 1.0, "Layer": 0}
        else:
            color = colors[random.randrange(13) + 3]
            portrayal = {"Shape": "rect",
                         "Filled": "true",
                         "Layer": 0,
                         "Color": color,
                         "w": 1,
                         "h": 1}
    return portrayal


if __name__ == '__main__':
    test = ClientParamsFactory()

    header = ['DELAY',
              'PAYED',
              'NET-WORTH',
              'INFLUENCE',
              'SKARBOWKA',
              'MEMBER',
              'HAT',
              'SIZE']

    with open("data/TEST/generatedData.csv", 'w', newline='') as file:
        writer = csv.writer(file)

        writer.writerow(header)

        for i in range(200):
            data = test.get_client_params()

            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_headers = ['DELAY','PAYED','NET-WORTH','INFLUENCE','SKARBOWKA','MEMBER','HAT','SIZE']

    X = data_input[X_headers].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=0.2, train_size=0.8)

    drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)

    clf = drugTree.fit(X_train, y_train)
    predicted = drugTree.predict(X_test)

    y_test = y_test.to_list()

    for i in range(len(predicted)):
        print("{}. {} \n predicted: {}, actual: {}".format(i, X_test[i,:], predicted[i], y_test[i]))

    print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y_test, predicted))

    print(sklearn.tree.export_text(clf, feature_names=X_headers))

    # 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()