using decisiontree to predict

app.py

@@ -11,11 +11,16 @@ import threading
 import time
 import random
 from classes.data.klient import Klient
+from classes.data.klient import Klient
+import xml.etree.ElementTree as ET
+from decisiontree import predict_client
+
 
 pygame.init()
 window = pygame.display.set_mode((prefs.WIDTH, prefs.HEIGHT))
 pygame.display.set_caption("Game Window")
 table_coords = [(4, 4), (4, prefs.GRID_SIZE-5), (prefs.GRID_SIZE-5, 4), (prefs.GRID_SIZE-5, prefs.GRID_SIZE-5)]
+from classes.data.data_initializer import clients
 
 chosen_coords = random.choice(table_coords)
 chosen_index = random.randint(0, len(table_coords)-1)
@@ -117,6 +122,7 @@ def watekDlaSciezkiAgenta():
         time.sleep(1)
 
 def watekDlaSciezkiKlienta():
+    assigned = False
     time.sleep(3)
     while True:
         if len(path2) > 0:
@@ -132,10 +138,18 @@ def watekDlaSciezkiKlienta():
                 x, y = element2
                 klient.moveto(x, y)
 
-        if klient.current_cell == cells[klientx_target][klienty_target]:
+        if not assigned and klient.current_cell == cells[klientx_target][klienty_target]:
             klient.przyStoliku = True
             klient.stolik = klient.current_cell
+            random_client_data = random.choice(clients) 
+            prediction = predict_client(random_client_data)
+            print("\nClient data:")
+            print(random_client_data)
+            print("Prediction (Adult):", prediction)
+            assigned = True
         
+        if assigned:
+            break
 
         time.sleep(1)
 

classes/agent.py

@@ -290,3 +290,5 @@ class Agent:
 
    
 
+
+

classes/data/data_initializer.py

@@ -1,9 +1,11 @@
 import xml.etree.ElementTree as ET
-from posilek import Posilek
-from klient import Klient
-from kelner import Kelner
-from stolik import Stolik
-from zamowenie import Zamowienie
+from classes.data.posilek import Posilek
+from classes.data.klient import KlientCechy
+from classes.data.klient import Klient
+from classes.data.kelner import Kelner
+from classes.data.stolik import Stolik
+from classes.data.zamowenie import Zamowienie
+
 with open('database\\meals.xml', 'r') as file:
     xml_data = file.read()
 tree = ET.ElementTree(ET.fromstring(xml_data))
@@ -71,15 +73,47 @@ for person in root.findall('person'):
     if favorite_meal in [meal.nazwa for meal in meals]:
         favorite_meal = next((meal for meal in meals if meal.nazwa == favorite_meal), None)
     
+    wrinkles_element = person.find('Wrinkles')
+    wrinkles = wrinkles_element.text if wrinkles_element is not None else ''
+        
+    balding_element = person.find('Balding')
+    balding = balding_element.text if balding_element is not None else ''
+    
+    beard_element = person.find('Beard')
+    beard = beard_element.text if beard_element is not None else ''
+        
+    outfit_element = person.find('Outfit')
+    outfit = outfit_element.text if outfit_element is not None else ''
+        
+    glasses_element = person.find('Glasses')
+    glasses = glasses_element.text if glasses_element is not None else ''
+        
+    tattoo_element = person.find('Tattoo')
+    tattoo = tattoo_element.text if tattoo_element is not None else ''
+        
+    hair_element = person.find('Hair')
+    hair = hair_element.text if hair_element is not None else ''
+        
+    behaviour_element = person.find('Behaviour')
+    behaviour = behaviour_element.text if behaviour_element is not None else ''
+
     person_data = {
         'imie': name,
         'nazwisko': surname,
         'wiek': age,
         'ulubiony_posilek': favorite_meal,
-        'restrykcje_dietowe': restrictions
+        'restrykcje_dietowe': restrictions,
+        'zmarszczki': wrinkles,
+        'lysienie': balding,
+        'broda': beard,
+        'ubior': outfit,
+        'okulary': glasses,
+        'tatuaz': tattoo,
+        'wlosy': hair,
+        'zachowanie': behaviour
     }
 
-    clients.append(Klient(**person_data))
+    clients.append(KlientCechy(**person_data))
     # Use the person_instance as needed
 
 
@@ -109,9 +143,6 @@ for server in root.findall('server'):
     servers.append(Kelner(**server_data))
 
 
-
-
-
 # Prezentacja uzycia przykładowego
 import random
 stoliki = []
@@ -143,7 +174,7 @@ for klient in [k for k in clients if k.stolik is not None]:
 
 print("----------------\n\n")
 
-import logic_test as logic
+import classes.data.logic_test as logic
 
 for klient in [k for k in clients if k.stolik is not None]:
     for zamownienie in klient.rachunek.zamowienia:

classes/data/klient.py

@@ -4,6 +4,8 @@ import prefs
 import random
 import heapq
 from collections import deque
+from classes.data.rachunek import Rachunek
+
 
 class Klient:
     def __init__(self,x,y,cells):
@@ -12,14 +14,7 @@ class Klient:
         self.current_cell = cells[x][y]
         self.current_x = x
         self.current_y = y
-        # self.imie = imie
-        # self.nazwisko = nazwisko
-        # self.wiek = wiek
         przyStoliku = False
-        self.stolik = None
-        # self.rachunek = Rachunek(random.randint(1,1000))
-        # self.ulubiony_posilek = ulubiony_posilek
-        # self.restrykcje_dietowe = restrykcje_dietowe
         self.cells = cells
         self.X = x
         self.Y = y
@@ -97,19 +92,6 @@ class Klient:
             print(self.direction)
         
     
-
-    def zloz_zamowienie(self,zamowienie,stolik):
-        if self.stolik is None:
-            self.stolik = stolik
-            stolik.przypisz_kelner(stolik.kelner)
-            self.rachunek.dodaj_zamowienie(zamowienie) 
-            print(f"Klinet {self.imie} {self.nazwisko} zlozyl zamowienie przy stoliku {stolik.numer_stolika} i przyjal je kelner {stolik.kelner.numer_pracowniczy}.")
-        else:
-            print("Klient ma juz przypisany stolik.")
-
-    def __str__(self):
-        return f"Klient: {self.imie} {self.nazwisko} {self.wiek}, ulubione Danie: {self.ulubiony_posilek}, restrykcje diet: {self.restrykcje_dietowe}"
-    
     def get_possible_moves(self):
         possible_moves = []
 
@@ -199,3 +181,33 @@ class Klient:
         dy = abs(current[1] - target[1])
         return dx + dy
     
+
+class KlientCechy:
+    def __init__(self,imie,nazwisko,wiek,ulubiony_posilek,restrykcje_dietowe,zmarszczki, lysienie, broda, ubior, okulary, tatuaz, wlosy, zachowanie):
+        self.imie = imie
+        self.nazwisko = nazwisko
+        self.wiek = wiek
+        self.ulubiony_posilek = ulubiony_posilek
+        self.restrykcje_dietowe = restrykcje_dietowe
+        self.zmarszczki = zmarszczki
+        self.lysienie = lysienie
+        self.broda = broda
+        self.ubior = ubior
+        self.okulary = okulary
+        self.tatuaz = tatuaz
+        self.wlosy = wlosy
+        self.zachowanie = zachowanie
+        self.stolik = None
+        self.rachunek = Rachunek(random.randint(1,1000))
+        
+    def zloz_zamowienie(self,zamowienie,stolik):
+        if self.stolik is None:
+            self.stolik = stolik
+            stolik.przypisz_kelner(stolik.kelner)
+            self.rachunek.dodaj_zamowienie(zamowienie) 
+            print(f"Klinet {self.imie} {self.nazwisko} zlozyl zamowienie przy stoliku {stolik.numer_stolika} i przyjal je kelner {stolik.kelner.numer_pracowniczy}.")
+        else:
+            print("Klient ma juz przypisany stolik.")
+
+    def __str__(self):
+        return f"Klient: {self.imie} {self.nazwisko} {self.wiek}, ulubione Danie: {self.ulubiony_posilek}, restrykcje diet: {self.restrykcje_dietowe}. Jego cechy to: zmarszczki: {self.zmarszczki}, lysienie: {self.lysienie}, broda: {self.broda}, ubior: {self.ubior}, okulary: {self.okulary}, tatuaz: {self.tatuaz}, wlosy: {self.wlosy}, zachowanie: {self.zachowanie}"

classes/data/rachunek.py

@@ -1,6 +1,6 @@
 import pygame
 
-from zamowenie import Zamowienie
+from classes.data.zamowenie import Zamowienie
 class Rachunek:
     def __init__(self,numer_zamowienia):
         self.numer_zamowienia = numer_zamowienia

classes/data/zamowenie.py

@@ -1,7 +1,7 @@
 import pygame
-from stolik import Stolik
-from kelner import Kelner
-from posilek import Posilek
+from classes.data.stolik import Stolik
+from classes.data.kelner import Kelner
+from classes.data.posilek import Posilek
 class Zamowienie:
     def __init__(self,numer_zamowienia,posilek):
         self.numer_zamowienia = numer_zamowienia

database/clients.xml

@@ -5,6 +5,14 @@
     <age>21</age>
     <favoriteMeal>Tatar</favoriteMeal>
     <restrictions>Meat</restrictions>
+    <Wrinkles>No</Wrinkles>
+    <Balding>Yes</Balding>
+    <Beard>Yes</Beard>
+    <Outfit>Messy</Outfit>
+    <Glasses>No</Glasses>
+    <Tattoo>No</Tattoo>
+    <Hair>Color</Hair>
+    <Behaviour>Energetic</Behaviour>
   </person>
   <person>
     <name>Kamil</name>
@@ -12,6 +20,14 @@
     <age>17</age>
     <favoriteMeal>Pomidorowa z Makaronem</favoriteMeal>
     <restrictions>Vegetarian</restrictions>
+    <Wrinkles>No</Wrinkles>
+    <Balding>No</Balding>
+    <Beard>No</Beard>
+    <Outfit>Messy</Outfit>
+    <Glasses>No</Glasses>
+    <Tattoo>No</Tattoo>
+    <Hair>Color</Hair>
+    <Behaviour>Energetic</Behaviour>
   </person>
   <person>
     <name>Jon</name>
@@ -19,7 +35,14 @@
     <age>23</age>
     <favoriteMeal>Grochówka</favoriteMeal>
     <restrictions>Vegan</restrictions>
-
+    <Wrinkles>No</Wrinkles>
+    <Balding>No</Balding>
+    <Beard>No</Beard>
+    <Outfit>Messy</Outfit>
+    <Glasses>No</Glasses>
+    <Tattoo>No</Tattoo>
+    <Hair>Color</Hair>
+    <Behaviour>Energetic</Behaviour>
   </person>
   <person>
     <name>Andrzej</name>
@@ -27,5 +50,13 @@
     <age>44</age>
     <favoriteMeal>Spaghetti Bolognese</favoriteMeal>
     <restrictions>Meat</restrictions>
+    <Wrinkles>No</Wrinkles>
+    <Balding>No</Balding>
+    <Beard>No</Beard>
+    <Outfit>Messy</Outfit>
+    <Glasses>No</Glasses>
+    <Tattoo>No</Tattoo>
+    <Hair>Color</Hair>
+    <Behaviour>Energetic</Behaviour>
   </person>
 </people>

decisiontree.py

@@ -38,13 +38,13 @@ clf_en = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0
 clf_en.fit(X_train, y_train)
 
 y_pred_en = clf_en.predict(X_test)
-print('Model accuracy score with criterion entropy: {0:0.4f}'.format(accuracy_score(y_test, y_pred_en)))
+#print('Model accuracy score with criterion entropy: {0:0.4f}'.format(accuracy_score(y_test, y_pred_en)))
 
 y_pred_train_en = clf_en.predict(X_train)
-print('Training-set accuracy score: {0:0.4f}'.format(accuracy_score(y_train, y_pred_train_en)))
+#print('Training-set accuracy score: {0:0.4f}'.format(accuracy_score(y_train, y_pred_train_en)))
 
-print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train)))
-print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test)))
+#print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train)))
+#print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test)))
 
 dot_data = tree.export_graphviz(clf_en, out_file=None, 
                               feature_names=X_train.columns,  
@@ -53,7 +53,7 @@ dot_data = tree.export_graphviz(clf_en, out_file=None,
                               special_characters=True)
 
 #nowy klient testowo
-new_client = {
+"""new_client = {
     "Wrinkles": random.choice(['Yes', 'No']),
     "Balding": random.choice(['Yes', 'No']),
     "Beard": random.choice(['Yes', 'No']),
@@ -62,15 +62,33 @@ new_client = {
     "Tattoo": random.choice(['Yes', 'No']),
     "Hair": random.choice(['Color', 'Grey', 'Natural']),
     "Behaviour": random.choice(['Energetic', 'Stressed', 'Calm'])
-}
+} """
 
+def predict_client(client_data):
+    new_client = {
+    "Wrinkles": client_data.zmarszczki,
+    "Balding": client_data.lysienie,
+    "Beard": client_data.broda,
+    "Outfit": client_data.ubior,
+    "Glasses": client_data.okulary,
+    "Tattoo": client_data.tatuaz,
+    "Hair": client_data.wlosy,
+    "Behaviour": client_data.zachowanie
+    }
     new_client_df = pd.DataFrame(new_client, index=[0])
     new_client_df_encoded = encoder.transform(new_client_df)
     prediction = clf_en.predict(new_client_df_encoded)
+    print('Model accuracy score with criterion entropy: {0:0.4f}'.format(accuracy_score(y_test, y_pred_en)))
+    print('Training-set accuracy score: {0:0.4f}'.format(accuracy_score(y_train, y_pred_train_en)))
 
-print("\nNew client:")
-print(new_client_df)
-print("Prediction:", prediction[0])
+    print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train)))
+    print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test)))
+
+    return prediction[0]
+
+#print("\nNew client:")
+#print(new_client_df)
+#print("Prediction:", prediction[0])
 
 #graph = graphviz.Source(dot_data) 
 #graph.render("decision_tree", format='png')