upgrade decision tree model

.gitignore

@@ -57,8 +57,6 @@ docs/_build/
 
 Pipfile
 Pipfile.lock
-decision_tree
+
-decision_tree.pdf
+# output
-Source.gv.pdf
+/out
-Source.gv
-decision_tree.txt

README.MD

@@ -41,8 +41,14 @@
 
 ---
 
-- [ ] **Drzewa decyzyjne: wymagania dot. trzeciego przyrostu**
+- [x] **Drzewa decyzyjne: wymagania dot. trzeciego przyrostu**
-  - [ ] Należy wykorzystać algorytm ID3 (tj. schemat indukcyjnego uczenia drzewa decyzyjnego oraz procedurę wyboru atrybutu o największym przyroście informacji) lub któreś z jego uogólnień.
+  - [x] Należy wykorzystać algorytm ID3 (tj. schemat indukcyjnego uczenia drzewa decyzyjnego oraz procedurę wyboru atrybutu o największym przyroście informacji) lub któreś z jego uogólnień.
-  - [ ] Należy przygotować zbiór uczący złożony z co najmniej 200 przykładów.
+  - [x] Należy przygotować zbiór uczący złożony z co najmniej 200 przykładów.
   - [x] Decyzja stanowiąca cel uczenia powinna zostać opisana przynajmniej ośmioma atrybutami.
-  - [ ] Powinna pojawić się opcja podglądu wyuczonego drzewa (np. w logach lub w pliku z graficzną reprezentacją drzewa).
+  - [x] Powinna pojawić się opcja podglądu wyuczonego drzewa (np. w logach lub w pliku z graficzną reprezentacją drzewa).
+
+---
+
+- [ ] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
+  - [ ] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
+  - [ ] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.

TreeConcept.py

@@ -1,38 +0,0 @@
-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);
-
-# Separate the attributes and labels
-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')
-#clf = tree.DecisionTreeClassifier(criterion='gini')
-
-# 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 = [2, 0, 1, 1, 1 ,2, 0]
-predicted_label = clf.predict([new_example])
-if predicted_label[0]>0:
-    result="YES"
-else:
-    result="NO"
-print("Predicted Label:", result)

dataset/Dataset.csv

@@ -1,201 +0,0 @@
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dataset/converted_dataset.csv

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-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

dataset/datasetGenerator.py

@@ -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)

requirements.txt

@@ -1,2 +1,5 @@
 -i https://pypi.org/simple
 pygame==2.3.0
+pandas
+scikit-learn
+graphviz

src/Engine.py

@@ -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')

src/UserController.py

@@ -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:
+                for o in engine.objects:
-                #    if o.compare_pos(pos):
+                    if o.compare_pos(pos):
-                #        o.set_order(engine.action_clock)
+                        o.set_order(engine.action_clock)
-                engine.appendGoalPosition(pos)

src/decisionTree/TreeConcept.py

@@ -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])

src/decisionTree/datasetConverter.py

@@ -1,15 +1,17 @@
 import csv
 
-def convert_dataset(input_file, output_file):
+
+def convertDataset():
     attributes = {
-        1: ["empty", "low", "medium", "high"],
+        1: ["low", "high"],
-        2: ["empty", "full"],
+        2: ["far", "close"],
-        3: ["none", "available"],
+        3: ['bad', 'good', 'undefined'],
-        4: ["none", "available"],
+        4: ["no", "yes"],
-        5: ["unavailable", "available"],
+        5: ["no", "yes"],
-        6: ["low", "medium", "high"],
+        6: ["no", "yes"],
-        7: ["no", "yes"],
+        7: ["empty", "new order", "waiting for dish", "have a dish"],
-        8: ["no", "yes"]
+        8: ["no", "yes"],
+        9: ["high priority", "low priority", "return to kitchen"]
     }
 
     # Create a mapping dictionary for attribute values
@@ -20,10 +22,11 @@ def convert_dataset(input_file, output_file):
     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:
@@ -40,15 +43,9 @@ def convert_dataset(input_file, output_file):
             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")

src/decisionTree/datasetGenerator.py

@@ -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",
+        "Table status",
+        "Is actual",
+        "Priority"
+    ])
+
+    for battery in battery_values:
+        for distance in distance_values:
+            for mood in mood_values:
+                for basket in other:
+                    for ready in other:
+                        for dish in other:
+                            for status in status_values:
+                                for actual in other:
+
+                                    dataset = buildDataset(
+                                        battery,
+                                        distance,
+                                        mood,
+                                        basket,
+                                        ready,
+                                        dish,
+                                        status,
+                                        actual
+                                    )
+
+                                    training_data.append(dataset)
+
+    Path("out/").mkdir(parents=True, exist_ok=True)
+    with open('out/rawDataset.csv', 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerows(training_data)
+
+
+def buildDataset(b, d, m, e, r, i, s, a) -> list:
+    dataset = [
+        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 = {
+        '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()

src/decisionTree/dataset_generator.py

@@ -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)
-

src/obj/Table.py

@@ -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:
+        if diff < 100:
-            return "good"
+            return (0, "good")
-        elif diff < 400:
+        elif diff < 200:
-            return "medium"
+            return (1, "medium")
         else:
-            return "bad"
+            return (2, "bad")
 
     def action(self, waiter):
         if self.is_order():

src/obj/Waiter.py

@@ -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