trained decision tree
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Mateusz
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/__pycache__
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digraph Tree {
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node [shape=box, fontname="helvetica"] ;
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edge [fontname="helvetica"] ;
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0 [label="distance <= 14.5\ngini = 0.484\nsamples = 200\nvalue = [118, 82]\nclass = collect"] ;
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1 [label="paid_on_time <= 0.5\ngini = 0.437\nsamples = 93\nvalue = [30, 63]\nclass = no-collect"] ;
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0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
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2 [label="gini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = collect"] ;
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1 -> 2 ;
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3 [label="fuel <= 1508.0\ngini = 0.375\nsamples = 84\nvalue = [21, 63]\nclass = no-collect"] ;
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1 -> 3 ;
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4 [label="gini = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = collect"] ;
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3 -> 4 ;
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5 [label="space_occupied <= 0.856\ngini = 0.298\nsamples = 77\nvalue = [14, 63]\nclass = no-collect"] ;
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3 -> 5 ;
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6 [label="days_since_last_collection <= 4.5\ngini = 0.187\nsamples = 67\nvalue = [7, 60]\nclass = no-collect"] ;
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5 -> 6 ;
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7 [label="fuel <= 11519.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2]\nclass = collect"] ;
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6 -> 7 ;
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8 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = collect"] ;
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7 -> 8 ;
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9 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = no-collect"] ;
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7 -> 9 ;
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10 [label="fuel <= 16955.0\ngini = 0.121\nsamples = 62\nvalue = [4, 58]\nclass = no-collect"] ;
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6 -> 10 ;
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11 [label="garbage_weight <= 0.612\ngini = 0.094\nsamples = 61\nvalue = [3, 58]\nclass = no-collect"] ;
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10 -> 11 ;
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12 [label="odour_intensity <= 5.682\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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11 -> 12 ;
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13 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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12 -> 13 ;
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14 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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12 -> 14 ;
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15 [label="garbage_type <= 2.5\ngini = 0.065\nsamples = 59\nvalue = [2, 57]\nclass = no-collect"] ;
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11 -> 15 ;
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16 [label="gini = 0.0\nsamples = 46\nvalue = [0, 46]\nclass = no-collect"] ;
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15 -> 16 ;
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17 [label="garbage_weight <= 15.925\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = no-collect"] ;
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15 -> 17 ;
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18 [label="fuel <= 13561.0\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
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17 -> 18 ;
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19 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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18 -> 19 ;
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20 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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18 -> 20 ;
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21 [label="gini = 0.0\nsamples = 10\nvalue = [0, 10]\nclass = no-collect"] ;
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17 -> 21 ;
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22 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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10 -> 22 ;
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23 [label="garbage_type <= 1.0\ngini = 0.42\nsamples = 10\nvalue = [7, 3]\nclass = collect"] ;
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5 -> 23 ;
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24 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = no-collect"] ;
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23 -> 24 ;
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25 [label="odour_intensity <= 8.841\ngini = 0.219\nsamples = 8\nvalue = [7, 1]\nclass = collect"] ;
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23 -> 25 ;
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26 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = collect"] ;
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25 -> 26 ;
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27 [label="distance <= 7.0\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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25 -> 27 ;
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28 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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27 -> 28 ;
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29 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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27 -> 29 ;
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30 [label="odour_intensity <= 7.156\ngini = 0.292\nsamples = 107\nvalue = [88, 19]\nclass = collect"] ;
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0 -> 30 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
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31 [label="garbage_weight <= 49.659\ngini = 0.116\nsamples = 81\nvalue = [76, 5]\nclass = collect"] ;
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30 -> 31 ;
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32 [label="days_since_last_collection <= 24.5\ngini = 0.095\nsamples = 80\nvalue = [76, 4]\nclass = collect"] ;
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31 -> 32 ;
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33 [label="gini = 0.0\nsamples = 65\nvalue = [65, 0]\nclass = collect"] ;
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32 -> 33 ;
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34 [label="distance <= 19.0\ngini = 0.391\nsamples = 15\nvalue = [11, 4]\nclass = collect"] ;
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32 -> 34 ;
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35 [label="fuel <= 6122.0\ngini = 0.444\nsamples = 6\nvalue = [2, 4]\nclass = no-collect"] ;
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34 -> 35 ;
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36 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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35 -> 36 ;
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37 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = no-collect"] ;
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35 -> 37 ;
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38 [label="gini = 0.0\nsamples = 9\nvalue = [9, 0]\nclass = collect"] ;
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34 -> 38 ;
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39 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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31 -> 39 ;
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40 [label="days_since_last_collection <= 13.5\ngini = 0.497\nsamples = 26\nvalue = [12, 14]\nclass = no-collect"] ;
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30 -> 40 ;
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41 [label="gini = 0.0\nsamples = 8\nvalue = [8, 0]\nclass = collect"] ;
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40 -> 41 ;
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42 [label="distance <= 20.0\ngini = 0.346\nsamples = 18\nvalue = [4, 14]\nclass = no-collect"] ;
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40 -> 42 ;
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43 [label="gini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = no-collect"] ;
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42 -> 43 ;
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44 [label="distance <= 24.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
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42 -> 44 ;
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45 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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44 -> 45 ;
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46 [label="space_occupied <= 0.243\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
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44 -> 46 ;
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47 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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46 -> 47 ;
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48 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
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46 -> 48 ;
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}
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22
main.py
22
main.py
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@ -1,5 +1,11 @@
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import pygame
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import random
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import pandas as pd
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from sklearn import tree
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from sklearn.preprocessing import LabelEncoder
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import graphviz
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from astar import astar
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from state import State
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import time
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@ -71,12 +77,26 @@ def draw_window(agent, fields, flip):
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def main():
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train_data = pd.read_csv('./data_set.csv')
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attributes = train_data.drop('collect', axis='columns')
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e_type = LabelEncoder()
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attributes['type_num'] = e_type.fit_transform(attributes['garbage_type'])
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attr_encoded = attributes.drop(['garbage_type'], axis='columns')
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attr_names = ['fuel','distance','space_occupied','days_since_last_collection','paid_on_time','odour_intensity','garbage_weight', 'garbage_type']
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label_names = ['collect', 'no-collect']
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label = train_data['collect']
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print(attr_encoded)
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print(label)
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classifier = tree.DecisionTreeClassifier()
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classifier.fit(attr_encoded, label)
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dot_data = tree.export_graphviz(classifier, out_file=None, feature_names=attr_names, class_names=label_names)
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graph = graphviz.Source(dot_data)
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graph.render('collect')
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clock = pygame.time.Clock()
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run = True
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x, y = [0, 0]
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agent = GarbageTruck(0, 0, pygame.Rect(x, y, 50, 50), 0) # tworzenie pola dla agenta
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fields, priority_array = randomize_map()
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print(priority_array)
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final_x, final_y = [100, 300]
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while run:
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clock.tick(FPS)
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