upgrade decision tree model
This commit is contained in:
Vadzim Valchkovich
parent
dd25c7a145
commit
e9b3a685e6
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.gitignore
vendored
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@ -57,8 +57,6 @@ docs/_build/
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Pipfile
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Pipfile.lock
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decision_tree
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decision_tree.pdf
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Source.gv.pdf
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Source.gv
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decision_tree.txt
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# output
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/out
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14
README.MD
14
README.MD
@ -41,8 +41,14 @@
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---
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- [ ] **Drzewa decyzyjne: wymagania dot. trzeciego przyrostu**
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- [ ] Należy wykorzystać algorytm ID3 (tj. schemat indukcyjnego uczenia drzewa decyzyjnego oraz procedurę wyboru atrybutu o największym przyroście informacji) lub któreś z jego uogólnień.
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- [ ] Należy przygotować zbiór uczący złożony z co najmniej 200 przykładów.
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- [x] **Drzewa decyzyjne: wymagania dot. trzeciego przyrostu**
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- [x] Należy wykorzystać algorytm ID3 (tj. schemat indukcyjnego uczenia drzewa decyzyjnego oraz procedurę wyboru atrybutu o największym przyroście informacji) lub któreś z jego uogólnień.
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- [x] Należy przygotować zbiór uczący złożony z co najmniej 200 przykładów.
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- [x] Decyzja stanowiąca cel uczenia powinna zostać opisana przynajmniej ośmioma atrybutami.
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- [ ] Powinna pojawić się opcja podglądu wyuczonego drzewa (np. w logach lub w pliku z graficzną reprezentacją drzewa).
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- [x] Powinna pojawić się opcja podglądu wyuczonego drzewa (np. w logach lub w pliku z graficzną reprezentacją drzewa).
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---
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- [ ] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
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- [ ] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
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- [ ] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.
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@ -1,38 +0,0 @@
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from sklearn import tree
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import pandas as pd #for manipulating the csv data
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import numpy as np
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import graphviz
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import os
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os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
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#importing the dataset from the disk
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train_data_m=np.genfromtxt("dataset/converted_dataset.csv", delimiter=",",skip_header=1);
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# Separate the attributes and labels
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X_train = [data[:-1] for data in train_data_m]
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y_train = [data[-1] for data in train_data_m]
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# Create the decision tree classifier using the ID3 algorithm
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clf = tree.DecisionTreeClassifier(criterion='entropy')
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#clf = tree.DecisionTreeClassifier(criterion='gini')
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# Train the decision tree on the training data
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clf.fit(X_train, y_train)
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# Visualize the trained decision tree
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tree_text = tree.export_text(clf,feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'])
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with open('decision_tree.txt', 'w') as f:
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f.write(tree_text) # Save the visualization as a text file
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dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True,rounded=True)
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graph = graphviz.Source(dot_data)
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graph.render("decision_tree") # Save the visualization as a PDF file
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# Test the decision tree with a new example
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#Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
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new_example = [2, 0, 1, 1, 1 ,2, 0]
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predicted_label = clf.predict([new_example])
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if predicted_label[0]>0:
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result="YES"
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else:
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result="NO"
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print("Predicted Label:", result)
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@ -1,201 +0,0 @@
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Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error,To go
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||||
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|
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
@ -1,200 +0,0 @@
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3,1,0,0,1,0,1,0
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||||
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|
||||
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|
||||
3,1,0,0,1,1,0,0
|
||||
1,1,0,1,0,1,1,0
|
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|
||||
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|
||||
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|
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|
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|
||||
2,1,0,1,1,0,0,0
|
||||
2,1,0,1,1,1,0,1
|
||||
0,1,0,0,0,2,1,0
|
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1,1,0,0,0,1,0,0
|
||||
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|
||||
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|
||||
1,0,1,1,0,1,0,0
|
||||
2,0,1,1,0,1,1,0
|
||||
1,0,0,0,0,2,0,0
|
||||
2,1,1,1,0,1,1,0
|
||||
3,0,1,0,1,1,0,1
|
||||
0,0,1,1,0,0,1,0
|
||||
3,0,0,0,0,2,1,0
|
||||
2,0,1,1,1,1,0,1
|
||||
2,1,1,1,1,0,0,0
|
||||
0,1,0,1,1,2,0,0
|
||||
0,0,0,0,0,0,0,0
|
||||
2,1,0,0,0,1,0,0
|
||||
2,0,1,1,0,2,1,0
|
||||
3,1,1,0,1,1,1,0
|
||||
2,0,0,1,0,0,1,0
|
||||
1,0,0,0,1,0,0,0
|
||||
3,1,1,1,1,0,1,0
|
||||
3,1,1,1,0,2,1,0
|
||||
1,1,1,1,0,2,1,0
|
||||
1,1,0,0,0,0,1,0
|
||||
2,1,0,0,0,1,1,0
|
||||
3,1,0,1,1,1,1,0
|
||||
1,0,0,0,0,0,0,0
|
||||
3,0,1,0,0,0,1,0
|
||||
2,1,0,1,1,2,0,1
|
||||
1,0,1,1,1,2,1,0
|
||||
1,1,1,0,0,2,1,0
|
||||
2,1,1,0,0,0,0,0
|
||||
2,1,1,1,0,2,0,0
|
||||
2,1,0,1,0,0,1,0
|
||||
2,0,1,0,0,0,1,0
|
||||
2,0,0,0,1,2,0,0
|
||||
0,1,0,0,1,0,0,0
|
||||
1,1,1,1,1,1,1,0
|
||||
1,1,1,0,0,2,0,0
|
||||
3,1,1,0,1,1,0,0
|
||||
1,1,0,1,1,1,0,0
|
||||
0,0,0,1,1,2,0,0
|
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1,1,0,1,0,0,0,0
|
||||
3,1,0,0,0,2,0,0
|
||||
2,1,0,0,1,2,1,0
|
||||
1,0,0,1,1,1,0,0
|
||||
0,0,0,1,0,0,0,0
|
||||
0,1,1,0,1,0,1,0
|
||||
0,0,1,1,1,1,0,0
|
||||
0,0,1,0,0,1,0,0
|
||||
0,0,0,0,0,2,0,0
|
||||
2,0,1,0,0,2,0,0
|
||||
0,0,1,0,1,1,1,0
|
||||
0,0,0,0,0,2,1,0
|
||||
3,1,1,1,0,0,0,0
|
||||
3,0,1,1,1,2,1,0
|
||||
3,0,1,0,1,0,0,0
|
||||
1,0,0,1,0,1,0,0
|
||||
2,1,0,1,0,0,0,0
|
||||
2,0,0,1,1,2,1,0
|
||||
2,0,1,1,1,2,0,1
|
||||
3,0,0,1,0,0,1,0
|
||||
1,1,1,1,1,2,0,0
|
||||
2,0,0,1,1,2,0,0
|
||||
2,0,1,0,1,2,0,1
|
||||
0,1,1,1,0,2,0,0
|
||||
1,0,0,1,1,0,0,0
|
||||
3,1,0,1,1,0,1,0
|
||||
3,0,0,0,1,1,1,0
|
||||
0,0,1,0,0,0,0,0
|
||||
3,1,0,1,0,0,1,0
|
||||
0,1,0,1,0,2,0,0
|
||||
0,1,1,0,1,0,0,0
|
||||
2,1,0,0,1,2,0,0
|
||||
2,1,1,1,0,0,1,0
|
||||
0,1,1,1,0,0,1,0
|
||||
1,0,0,0,0,1,0,0
|
||||
1,1,0,0,0,0,0,0
|
||||
1,1,1,1,0,0,1,0
|
||||
1,0,0,1,1,0,1,0
|
||||
0,1,1,0,0,2,0,0
|
||||
1,1,1,0,1,2,0,0
|
||||
3,1,1,1,1,1,1,0
|
||||
0,1,0,1,1,1,1,0
|
||||
1,1,0,0,1,1,0,0
|
||||
0,1,1,1,1,0,0,0
|
||||
2,1,1,0,0,1,1,0
|
||||
0,0,0,0,1,1,0,0
|
||||
3,1,0,1,0,0,0,0
|
||||
1,0,1,1,1,2,0,0
|
||||
1,0,0,1,1,2,1,0
|
||||
3,1,0,1,1,1,0,1
|
||||
0,1,1,0,1,1,1,0
|
||||
3,1,0,0,1,0,0,0
|
||||
1,0,1,1,0,2,0,0
|
||||
1,1,1,0,1,1,1,0
|
||||
3,0,0,0,0,0,0,0
|
||||
1,1,0,0,0,2,1,0
|
||||
3,0,0,1,1,0,1,0
|
||||
0,1,1,1,0,0,0,0
|
||||
1,0,1,1,0,0,0,0
|
||||
1,1,1,0,0,0,1,0
|
||||
0,0,1,1,0,2,0,0
|
||||
2,1,1,0,1,1,1,0
|
||||
3,1,1,0,1,2,0,0
|
||||
1,1,0,1,1,1,1,0
|
||||
1,0,0,1,0,1,1,0
|
||||
3,1,1,1,0,1,1,0
|
||||
3,1,0,0,0,1,1,0
|
||||
1,1,1,1,1,0,1,0
|
||||
1,1,1,1,0,2,0,0
|
||||
0,1,0,0,1,1,0,0
|
||||
0,0,1,0,1,0,1,0
|
||||
3,1,1,0,0,2,0,0
|
||||
3,1,0,1,0,2,0,0
|
||||
1,0,1,1,0,1,1,0
|
||||
3,0,1,1,1,0,1,0
|
||||
0,1,0,0,0,0,0,0
|
||||
3,1,0,1,0,1,1,0
|
||||
3,0,1,0,0,1,0,0
|
||||
0,0,1,0,1,2,1,0
|
||||
3,0,1,0,0,1,1,0
|
||||
0,0,0,0,0,1,0,0
|
||||
3,0,0,1,1,2,0,0
|
||||
2,0,0,0,0,0,1,0
|
||||
2,1,1,0,0,2,0,0
|
||||
3,1,0,0,0,0,0,0
|
||||
3,0,0,0,0,1,0,0
|
||||
2,1,1,0,1,0,0,0
|
||||
3,0,0,0,1,2,1,0
|
||||
0,1,1,1,1,2,0,0
|
||||
2,1,1,1,0,0,0,0
|
||||
0,1,1,0,1,1,0,0
|
||||
0,1,1,1,0,1,1,0
|
||||
0,0,1,0,1,2,0,0
|
||||
1,0,0,0,1,2,1,0
|
||||
0,1,0,1,0,1,1,0
|
||||
2,1,0,1,0,1,1,0
|
||||
2,1,1,0,1,0,1,0
|
||||
2,1,1,0,0,2,1,0
|
||||
2,1,1,1,1,2,0,1
|
||||
2,1,0,0,1,0,0,0
|
||||
3,1,0,1,1,2,0,1
|
||||
1,0,1,1,0,0,1,0
|
||||
1,1,0,1,0,2,1,0
|
||||
2,1,1,1,1,2,1,0
|
||||
3,1,1,1,1,0,0,0
|
||||
2,1,0,0,0,2,1,0
|
||||
2,1,0,1,0,2,0,0
|
||||
3,0,0,1,0,2,1,0
|
||||
0,0,0,1,1,1,0,0
|
||||
0,0,1,0,0,2,0,0
|
||||
3,0,1,1,0,2,1,0
|
||||
2,0,1,1,0,0,1,0
|
||||
2,1,0,0,0,0,0,0
|
||||
3,0,0,0,1,0,0,0
|
||||
3,1,1,0,0,1,0,0
|
||||
3,1,1,0,0,0,1,0
|
||||
0,0,1,1,1,0,1,0
|
||||
3,0,0,0,0,0,1,0
|
||||
2,0,0,0,1,0,0,0
|
||||
1,1,0,1,0,1,0,0
|
||||
1,1,0,1,1,2,1,0
|
||||
3,1,0,1,0,2,1,0
|
||||
2,1,0,1,1,1,1,0
|
||||
0,0,0,1,0,2,1,0
|
||||
2,0,1,0,0,1,1,0
|
||||
0,0,0,1,0,0,1,0
|
||||
3,0,0,1,0,1,0,0
|
||||
2,0,1,0,1,0,1,0
|
||||
2,1,1,1,1,1,0,1
|
||||
3,1,1,0,0,1,1,0
|
||||
3,1,1,1,0,1,0,0
|
||||
1,1,1,0,1,1,0,0
|
||||
2,0,1,0,0,2,1,0
|
||||
2,0,1,0,1,1,1,0
|
||||
3,0,0,0,1,2,0,0
|
||||
0,1,1,0,0,2,1,0
|
||||
1,0,1,0,0,0,1,0
|
||||
0,1,1,1,1,0,1,0
|
||||
2,0,1,1,0,1,0,0
|
||||
2,0,0,0,0,2,0,0
|
||||
2,1,1,1,0,1,0,0
|
||||
0,1,1,1,1,1,0,0
|
||||
1,0,1,0,1,0,0,0
|
||||
|
@ -1,57 +0,0 @@
|
||||
import random
|
||||
import csv
|
||||
|
||||
attributes = {
|
||||
1: ["empty", "low", "medium", "high"],
|
||||
2: ["empty", "full"],
|
||||
3: ["none", "available"],
|
||||
4: ["none", "available"],
|
||||
5: ["unavailable", "available"],
|
||||
6: ["low", "medium", "high"],
|
||||
7: ["no", "yes"],
|
||||
8: ["no", "yes"]
|
||||
}
|
||||
|
||||
dataset = []
|
||||
while len(dataset) < 200:
|
||||
data = {}
|
||||
|
||||
# Generate random values for attributes 1-7
|
||||
for attr in range(1, 8):
|
||||
data[attr] = random.choice(attributes[attr])
|
||||
|
||||
# Apply the rules to determine the value of attribute 8
|
||||
if data[1] in ["empty", "low"]:
|
||||
data[8] = "no"
|
||||
elif data[2] == "full" and data[4] == "none":
|
||||
data[8] = "no"
|
||||
elif data[2] == "empty" and data[3] == "none":
|
||||
data[8] = "no"
|
||||
elif data[3] == "none" and data[4] == "none":
|
||||
data[8] = "no"
|
||||
elif data[5] == "unavailable":
|
||||
data[8] = "no"
|
||||
elif data[6] == "low":
|
||||
data[8] = "no"
|
||||
elif data[7] == "yes":
|
||||
data[8] = "no"
|
||||
else:
|
||||
data[8] = "yes"
|
||||
|
||||
# Check if the generated data already exists in the dataset
|
||||
if data not in dataset:
|
||||
dataset.append(data)
|
||||
|
||||
# Print the generated dataset
|
||||
for data in dataset:
|
||||
print(data)
|
||||
with open("dataset/Dataset.csv", "w", newline="") as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
|
||||
# Write the header row
|
||||
writer.writerow(["Battery Charge", "Fullness", "Ready orders", "Waiting tables","Availability", "Cleanliness", "Error", "To go"])
|
||||
|
||||
# Write the data rows
|
||||
for data in dataset:
|
||||
row = [data[attr] for attr in range(1, 9)]
|
||||
writer.writerow(row)
|
||||
@ -1,2 +1,5 @@
|
||||
-i https://pypi.org/simple
|
||||
pygame==2.3.0
|
||||
pandas
|
||||
scikit-learn
|
||||
graphviz
|
||||
@ -4,6 +4,8 @@ from .obj.Object import Object
|
||||
from .obj.Waiter import Waiter
|
||||
from .UserController import UserController
|
||||
from .StateController import StateController
|
||||
from .decisionTree.TreeConcept import TreeEngine
|
||||
from queue import PriorityQueue
|
||||
|
||||
|
||||
class Engine:
|
||||
@ -12,6 +14,7 @@ class Engine:
|
||||
pygame.display.set_caption('Waiter Agent')
|
||||
|
||||
self.action_clock = 0
|
||||
self.tree = TreeEngine()
|
||||
|
||||
self.user: Waiter = user
|
||||
self.state: StateController = state
|
||||
@ -59,6 +62,7 @@ class Engine:
|
||||
if self.goals:
|
||||
self.state.graphsearch(self)
|
||||
self.user.handler(self)
|
||||
self.predict()
|
||||
else:
|
||||
# went path
|
||||
|
||||
@ -105,3 +109,49 @@ class Engine:
|
||||
|
||||
def appendGoalPosition(self, position):
|
||||
self.goals.append(position)
|
||||
|
||||
def predict(self):
|
||||
|
||||
goal_queue = PriorityQueue()
|
||||
|
||||
for o in self.objects:
|
||||
|
||||
condition = o.agent_role in [
|
||||
"order",
|
||||
"wait",
|
||||
"done"
|
||||
]
|
||||
|
||||
if not condition or o.compare_pos(self.user.obj.position):
|
||||
continue
|
||||
|
||||
battery = self.user.obj.battary_status()[0]
|
||||
distance = o.distance_to(
|
||||
self.user.obj.position) // (self.num_squares // 2)
|
||||
mood = o.get_mood(self.action_clock)[0] if condition else 0
|
||||
memory = self.user.obj.memory_size
|
||||
dishes_held = self.user.obj.basket_size
|
||||
customers = o.customers if condition else 0
|
||||
waiting_for_order = o.is_order() if condition else 0
|
||||
waiting_for_dish = o.is_done() if condition else 0
|
||||
|
||||
p = self.tree.make_predict(
|
||||
battery,
|
||||
distance,
|
||||
mood,
|
||||
memory,
|
||||
dishes_held,
|
||||
customers,
|
||||
waiting_for_order,
|
||||
waiting_for_dish
|
||||
)
|
||||
|
||||
goal_queue.put((p, o.position))
|
||||
|
||||
if len(goal_queue.queue):
|
||||
priority, goal = goal_queue.queue.pop()
|
||||
if priority:
|
||||
print(goal, priority, end='\r')
|
||||
self.appendGoalPosition(goal)
|
||||
else:
|
||||
print(goal, priority, end='\r')
|
||||
|
||||
@ -13,7 +13,6 @@ class UserController:
|
||||
pos = pygame.mouse.get_pos()
|
||||
pos = [pos[0] // engine.square_size,
|
||||
pos[1] // engine.square_size]
|
||||
# for o in engine.objects:
|
||||
# if o.compare_pos(pos):
|
||||
# o.set_order(engine.action_clock)
|
||||
engine.appendGoalPosition(pos)
|
||||
for o in engine.objects:
|
||||
if o.compare_pos(pos):
|
||||
o.set_order(engine.action_clock)
|
||||
|
||||
70
src/decisionTree/TreeConcept.py
Normal file
70
src/decisionTree/TreeConcept.py
Normal file
@ -0,0 +1,70 @@
|
||||
import time
|
||||
from sklearn import tree
|
||||
import numpy as np
|
||||
import graphviz
|
||||
from src.decisionTree.datasetGenerator import generateRawDataset
|
||||
from src.decisionTree.datasetConverter import convertDataset
|
||||
|
||||
|
||||
class TreeEngine():
|
||||
def __init__(self):
|
||||
generateRawDataset()
|
||||
convertDataset()
|
||||
|
||||
# importing the dataset from the disk
|
||||
train_data_m = np.genfromtxt(
|
||||
"out/dataset.csv", delimiter=",", skip_header=1)
|
||||
|
||||
# Separate the attributes and labels
|
||||
self.X_train = [data[:-1] for data in train_data_m]
|
||||
self.y_train = [data[-1] for data in train_data_m]
|
||||
|
||||
# Create the decision tree classifier using the ID3 algorithm
|
||||
self.clf = tree.DecisionTreeClassifier(
|
||||
criterion='entropy', splitter="best")
|
||||
# clf = tree.DecisionTreeClassifier(criterion='gini')
|
||||
|
||||
# Train the decision tree on the training data
|
||||
self.clf.fit(self.X_train, self.y_train)
|
||||
|
||||
self.exportText()
|
||||
self.exportPdf()
|
||||
|
||||
def exportText(self):
|
||||
# Visualize the trained decision tree
|
||||
tree_text = tree.export_text(self.clf, feature_names=[
|
||||
"Battery level",
|
||||
"Distance between kitchen and table",
|
||||
"Customers mood",
|
||||
"Basket is empty",
|
||||
"Dish is ready",
|
||||
"Dish in basket",
|
||||
"Table status",
|
||||
"Is actual",
|
||||
])
|
||||
|
||||
with open('out/decision_tree.txt', 'w') as f:
|
||||
f.write(tree_text) # Save the visualization as a text file
|
||||
|
||||
def exportPdf(self):
|
||||
dot_data = tree.export_graphviz(self.clf, out_file=None, feature_names=[
|
||||
"Battery level",
|
||||
"Distance between kitchen and table",
|
||||
"Customers mood",
|
||||
"Basket is empty",
|
||||
"Dish is ready",
|
||||
"Dish in basket",
|
||||
"Table status",
|
||||
"Is actual",
|
||||
], class_names=[
|
||||
'High priority',
|
||||
'Low priority',
|
||||
'Return to kitchen',
|
||||
], filled=True, rounded=True)
|
||||
|
||||
graph = graphviz.Source(dot_data)
|
||||
# Save the visualization as a PDF file
|
||||
graph.render("out/decision_tree")
|
||||
|
||||
def make_predict(self, dataset):
|
||||
return self.clf.predict([dataset])
|
||||
@ -1,30 +1,33 @@
|
||||
import csv
|
||||
|
||||
def convert_dataset(input_file, output_file):
|
||||
|
||||
def convertDataset():
|
||||
attributes = {
|
||||
1: ["empty", "low", "medium", "high"],
|
||||
2: ["empty", "full"],
|
||||
3: ["none", "available"],
|
||||
4: ["none", "available"],
|
||||
5: ["unavailable", "available"],
|
||||
6: ["low", "medium", "high"],
|
||||
7: ["no", "yes"],
|
||||
8: ["no", "yes"]
|
||||
1: ["low", "high"],
|
||||
2: ["far", "close"],
|
||||
3: ['bad', 'good', 'undefined'],
|
||||
4: ["no", "yes"],
|
||||
5: ["no", "yes"],
|
||||
6: ["no", "yes"],
|
||||
7: ["empty", "new order", "waiting for dish", "have a dish"],
|
||||
8: ["no", "yes"],
|
||||
9: ["high priority", "low priority", "return to kitchen"]
|
||||
}
|
||||
|
||||
|
||||
# Create a mapping dictionary for attribute values
|
||||
mapping = {}
|
||||
for attr, values in attributes.items():
|
||||
mapping[attr] = {value: index for index, value in enumerate(values)}
|
||||
|
||||
|
||||
converted_dataset = []
|
||||
|
||||
|
||||
# Read the input CSV file
|
||||
with open(input_file, "r") as csvfile:
|
||||
with open("out/rawDataset.csv", "r") as csvfile:
|
||||
reader = csv.reader(csvfile)
|
||||
|
||||
|
||||
header = next(reader) # Skip the header row
|
||||
|
||||
converted_dataset.append(header)
|
||||
|
||||
# Convert the data rows
|
||||
for row in reader:
|
||||
converted_row = []
|
||||
@ -34,21 +37,15 @@ def convert_dataset(input_file, output_file):
|
||||
converted_value = mapping[i + 1][value]
|
||||
else:
|
||||
converted_value = value
|
||||
|
||||
|
||||
converted_row.append(converted_value)
|
||||
|
||||
|
||||
converted_dataset.append(converted_row)
|
||||
|
||||
|
||||
# Write the converted dataset to a new CSV file
|
||||
with open(output_file, "w", newline="") as csvfile:
|
||||
with open("out/dataset.csv", "w", newline="") as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
|
||||
# Write the header row
|
||||
#writer.writerow(header)
|
||||
|
||||
|
||||
# Write the converted data rows
|
||||
for row in converted_dataset:
|
||||
writer.writerow(row)
|
||||
|
||||
# Example usage:
|
||||
convert_dataset("dataset/Dataset.csv", "dataset/converted_dataset.csv")
|
||||
111
src/decisionTree/datasetGenerator.py
Normal file
111
src/decisionTree/datasetGenerator.py
Normal file
@ -0,0 +1,111 @@
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def generateRawDataset():
|
||||
battery_values = ['high', 'low']
|
||||
distance_values = ['far', 'close']
|
||||
mood_values = ['undefined', 'good', 'bad']
|
||||
status_values = ['empty', 'new order', 'waiting for dish', 'have a dish']
|
||||
other = ['no', 'yes']
|
||||
|
||||
training_data = []
|
||||
training_data.append([
|
||||
"Battery level",
|
||||
"Distance between kitchen and table",
|
||||
"Customers mood",
|
||||
"Basket is empty",
|
||||
"Dish is ready",
|
||||
"Dish in basket",
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||||
"Table status",
|
||||
"Is actual",
|
||||
"Priority"
|
||||
])
|
||||
|
||||
for battery in battery_values:
|
||||
for distance in distance_values:
|
||||
for mood in mood_values:
|
||||
for basket in other:
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||||
for ready in other:
|
||||
for dish in other:
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for status in status_values:
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for actual in other:
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||||
|
||||
dataset = buildDataset(
|
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battery,
|
||||
distance,
|
||||
mood,
|
||||
basket,
|
||||
ready,
|
||||
dish,
|
||||
status,
|
||||
actual
|
||||
)
|
||||
|
||||
training_data.append(dataset)
|
||||
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||||
Path("out/").mkdir(parents=True, exist_ok=True)
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with open('out/rawDataset.csv', 'w', newline='') as file:
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writer = csv.writer(file)
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writer.writerows(training_data)
|
||||
|
||||
|
||||
def buildDataset(b, d, m, e, r, i, s, a) -> list:
|
||||
dataset = [
|
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b, # battery
|
||||
d, # distance
|
||||
m, # mood
|
||||
e, # basket is empty
|
||||
r, # ready
|
||||
i, # dish in basket
|
||||
s, # status
|
||||
a, # actual
|
||||
]
|
||||
|
||||
dataset.append(getPriority(dataset))
|
||||
|
||||
return dataset
|
||||
|
||||
|
||||
def getPriority(dataset) -> str:
|
||||
PRIORITY = {
|
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'high': 'high priority',
|
||||
'low': 'low priority',
|
||||
'kitchen': 'return to kitchen'
|
||||
}
|
||||
|
||||
BATTERY_LEVEL = dataset[0]
|
||||
DISTANCE_TO_OBJECT = dataset[1]
|
||||
MOOD = dataset[2]
|
||||
BASKET_IS_EMPTY = dataset[3]
|
||||
DISH_IS_READY = dataset[4]
|
||||
DISH_IN_BASKET = dataset[5]
|
||||
STATUS_OF_TABLE = dataset[6]
|
||||
IS_ACTUAL = dataset[7]
|
||||
|
||||
def mood_and_distance() -> str:
|
||||
if MOOD == "undefined":
|
||||
return PRIORITY['kitchen']
|
||||
elif MOOD == "good":
|
||||
return PRIORITY['low']
|
||||
elif DISTANCE_TO_OBJECT == "far":
|
||||
return PRIORITY['high']
|
||||
else:
|
||||
return PRIORITY['low']
|
||||
|
||||
if BATTERY_LEVEL == "low":
|
||||
return PRIORITY['kitchen']
|
||||
elif IS_ACTUAL == "no":
|
||||
return PRIORITY['high']
|
||||
elif STATUS_OF_TABLE == "empty" or STATUS_OF_TABLE == "have a dish":
|
||||
return PRIORITY['kitchen']
|
||||
elif STATUS_OF_TABLE == "new order":
|
||||
return mood_and_distance()
|
||||
elif BASKET_IS_EMPTY == "yes":
|
||||
return PRIORITY['kitchen']
|
||||
elif DISH_IS_READY == "no":
|
||||
return PRIORITY['kitchen']
|
||||
elif DISH_IN_BASKET == "no":
|
||||
return PRIORITY['kitchen']
|
||||
else:
|
||||
return mood_and_distance()
|
||||
@ -1,33 +0,0 @@
|
||||
import csv
|
||||
import random
|
||||
|
||||
battery_values = ['high', 'medium', 'low']
|
||||
distance_values = ['far', 'medium', 'close']
|
||||
mood_values = ['good', 'medium', 'bad']
|
||||
other = ['yes', 'no']
|
||||
|
||||
|
||||
training_data = []
|
||||
|
||||
for _ in range(200):
|
||||
battery = random.choice(battery_values)
|
||||
distance = random.choice(distance_values)
|
||||
mood = random.choice(mood_values)
|
||||
memory = random.randint(0,4)
|
||||
dishes_held = random.randint(0,4)
|
||||
"""empty_basket = random.choice(other)
|
||||
if empty_basket == 'yes':
|
||||
dish_in_basket = 'no'
|
||||
else:
|
||||
dish_in_basket = random.choice(other)
|
||||
dish_in_basket = random.choice(other)"""
|
||||
waiting_for_order = random.choice(other)
|
||||
waiting_for_dish = random.choice(other)
|
||||
|
||||
example = [battery, distance, mood, memory, dishes_held, waiting_for_order, waiting_for_dish]
|
||||
training_data.append(example)
|
||||
|
||||
with open('data.csv', 'w', newline='') as file:
|
||||
writer = csv.writer(file)
|
||||
writer.writerows(training_data)
|
||||
|
||||
@ -37,9 +37,6 @@ class Table(Object):
|
||||
def is_done(self):
|
||||
return self.agent_role == "done"
|
||||
|
||||
def waiting_for_dish(self):
|
||||
return self.agent_role in ["wait", "done"]
|
||||
|
||||
def get_customers_count(self) -> int:
|
||||
return self.customers
|
||||
|
||||
@ -48,12 +45,12 @@ class Table(Object):
|
||||
return None
|
||||
|
||||
diff = current_time - self.order_time
|
||||
if diff < 200:
|
||||
return "good"
|
||||
elif diff < 400:
|
||||
return "medium"
|
||||
if diff < 100:
|
||||
return (0, "good")
|
||||
elif diff < 200:
|
||||
return (1, "medium")
|
||||
else:
|
||||
return "bad"
|
||||
return (2, "bad")
|
||||
|
||||
def action(self, waiter):
|
||||
if self.is_order():
|
||||
|
||||
@ -18,92 +18,11 @@ class Waiter(Object):
|
||||
self.orientation = copy.copy(state.orientation)
|
||||
self.basket = copy.copy(state.basket)
|
||||
self.battery -= state.cost
|
||||
# self.calcTree()
|
||||
return state
|
||||
|
||||
def dish_in_basket(self, table) -> bool:
|
||||
return table in self.basket
|
||||
|
||||
'''
|
||||
def calcTree(self):
|
||||
from sklearn import tree
|
||||
import pandas as pd # for manipulating the csv data
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
# importing the dataset from the disk
|
||||
train_data_m = np.genfromtxt(
|
||||
"dataset/converted_dataset.csv", delimiter=",", skip_header=1)
|
||||
|
||||
X_train = [data[:-1] for data in train_data_m]
|
||||
y_train = [data[-1] for data in train_data_m]
|
||||
# Create the decision tree classifier using the ID3 algorithm
|
||||
clf = tree.DecisionTreeClassifier(criterion='entropy')
|
||||
|
||||
# Train the decision tree on the training data
|
||||
clf.fit(X_train, y_train)
|
||||
|
||||
# Visualize the trained decision tree
|
||||
tree_text = tree.export_text(clf, feature_names=[
|
||||
'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
|
||||
with open('decision_tree.txt', 'w') as f:
|
||||
f.write(tree_text) # Save the visualization as a text file
|
||||
|
||||
# Test the decision tree with a new example
|
||||
# Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
|
||||
new_example = [self.battery, 0, self.orderReadiness,
|
||||
self.waitingTables, self.availability, self.cleanliness, self.error]
|
||||
predicted_label = clf.predict([new_example])
|
||||
if predicted_label[0] > 0:
|
||||
result = "YES"
|
||||
else:
|
||||
result = "NO"
|
||||
print("Predicted Label:", result)
|
||||
|
||||
def calcTreePDF(self):
|
||||
from sklearn import tree
|
||||
import pandas as pd # for manipulating the csv data
|
||||
import numpy as np
|
||||
import graphviz
|
||||
import os
|
||||
os.environ["PATH"] += os.pathsep + \
|
||||
'C:/Program Files (x86)/Graphviz/bin/'
|
||||
|
||||
# importing the dataset from the disk
|
||||
train_data_m = np.genfromtxt(
|
||||
"dataset/converted_dataset.csv", delimiter=",", skip_header=1)
|
||||
|
||||
X_train = [data[:-1] for data in train_data_m]
|
||||
y_train = [data[-1] for data in train_data_m]
|
||||
# Create the decision tree classifier using the ID3 algorithm
|
||||
clf = tree.DecisionTreeClassifier(criterion='entropy')
|
||||
|
||||
# Train the decision tree on the training data
|
||||
clf.fit(X_train, y_train)
|
||||
|
||||
# Visualize the trained decision tree
|
||||
tree_text = tree.export_text(clf, feature_names=[
|
||||
'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
|
||||
with open('decision_tree.txt', 'w') as f:
|
||||
f.write(tree_text) # Save the visualization as a text file
|
||||
dot_data = tree.export_graphviz(clf, out_file=None, feature_names=[
|
||||
'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True, rounded=True)
|
||||
graph = graphviz.Source(dot_data)
|
||||
graph.render("decision_tree") # Save the visualization as a PDF file
|
||||
|
||||
# Test the decision tree with a new example
|
||||
# Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
|
||||
new_example = [self.battery, 0, self.orderReadiness,
|
||||
self.waitingTables, self.availability, self.cleanliness, self.error]
|
||||
predicted_label = clf.predict([new_example])
|
||||
if predicted_label[0] > 0:
|
||||
result = "YES"
|
||||
else:
|
||||
result = "NO"
|
||||
print("Predicted Label:", result)
|
||||
|
||||
'''
|
||||
|
||||
def basket_is_full(self) -> bool:
|
||||
return self.basket_size == 0
|
||||
|
||||
@ -140,11 +59,11 @@ class Waiter(Object):
|
||||
|
||||
def battary_status(self) -> str:
|
||||
if self.battery >= 200:
|
||||
return "hight"
|
||||
return (2, "hight")
|
||||
elif self.battery >= 100:
|
||||
return "medium"
|
||||
return (1, "medium")
|
||||
else:
|
||||
return "low"
|
||||
return (0, "low")
|
||||
|
||||
def recharge(self):
|
||||
self.battery = 300
|
||||
|
||||
Loading…
Reference in New Issue
Block a user