Merge pull request 'Add decision tree' (#20) from decision_tree into main

Reviewed-on: s473601/Machine_learning_2023#20
Reviewed-by: Tim Barvenov <timbar@st.amu.edu.pl>

decisionTree/data.csv

@@ -0,0 +1,22 @@
+1-2-3-4-5;1-green 2-yellow 3-orange 4-black 5-while 6-blue;in dB 0-100;0-24;0/1;in cm;in C;0/1
+Size;Color;Sound;Time;Smell;Height;Temperature;ToRemove
+1;2;0;16;1;10;25;1
+2;1;0;12;0;50;24;0
+2;3;30;13;1;38;38;0
+1;4;0;7;1;5;27;1
+1;2;0;16;1;10;25;1
+2;1;0;12;0;50;24;0
+2;3;30;13;1;38;38;0
+1;4;0;7;1;5;27;1
+1;2;0;16;1;10;25;1
+2;1;0;12;0;50;24;0
+2;3;30;13;1;38;38;0
+1;4;0;7;1;5;27;1
+1;2;0;16;1;10;25;1
+2;1;0;12;0;50;24;0
+2;3;30;13;1;38;38;0
+1;4;0;7;1;5;27;1
+1;2;0;16;1;10;25;1
+2;1;0;12;0;50;24;0
+2;3;30;13;1;38;38;0
+1;4;0;7;1;5;27;1

decisionTree/evaluate.py

@@ -0,0 +1,9 @@
+import joblib
+
+def evaluate(data):
+    # Load the model
+    clf = joblib.load('decisionTree/decision_tree_model.pkl')
+
+    # Make a prediction
+    prediction = clf.predict(data)
+    return prediction

decisionTree/prepare.py

@@ -0,0 +1,21 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+import joblib
+
+pima = pd.read_csv("data.csv", header=1, delimiter=';')
+
+feature_cols = ['Size', 'Color', 'Sound', 'Time','Smell', 'Height','Temperature']
+X = pima[feature_cols]
+y = pima.ToRemove
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
+clf = DecisionTreeClassifier()
+clf = clf.fit(X_train,y_train)
+
+joblib.dump(clf, 'decision_tree_model.pkl')
+
+y_pred = clf.predict(X_test)
+
+print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

domain/entities/cat.py

@@ -13,3 +13,5 @@ class Cat(Entity):
         self.busy = False
         self.sleeping = False
         self.direction = 0
+        
+        self.props = [1,2,0,16,1,10,25]

domain/entities/earring.py

@@ -0,0 +1,8 @@
+from domain.entities.entity import Entity
+from domain.world import World
+
+
+class Earring(Entity):
+    def __init__(self, x: int, y: int):
+        super().__init__(x, y, "EARRING")
+        self.props = [2,1,0,12,0,50,24]

domain/entities/garbage.py

@@ -1,11 +1,11 @@
 from domain.entities.entity import Entity
-from domain.world import World
 
 
 class Garbage(Entity):
     def __init__(self, x: int, y: int):
-        super().__init__(x, y, "GARBAGE")
+        super().__init__(x, y, "PEEL")
         self.wet = False
         self.size = 0
+        self.props = [1,2,0,16,1,10,25]
 
         # TODO GARBAGE: add more properties

domain/world.py

@@ -1,3 +1,4 @@
+from decisionTree.evaluate import evaluate
 from domain.entities.entity import Entity
 
 
@@ -15,6 +16,8 @@ class World:
     def add_entity(self, entity: Entity):
         if entity.type == "PEEL":
             self.dust[entity.x][entity.y].append(entity)
+        elif entity.type == "EARRING":
+            self.dust[entity.x][entity.y].append(entity)
         elif entity.type == "VACUUM":
             self.vacuum = entity
         elif entity.type == "DOC_STATION":
@@ -29,7 +32,10 @@ class World:
         return bool(self.obstacles[x][y])
 
     def is_garbage_at(self, x: int, y: int) -> bool:
-        return bool(self.dust[x][y])
+        if len(self.dust[x][y]) == 0:
+            return False
+        tmp = evaluate([self.dust[x][y][0].props])
+        return bool(tmp[0])
 
     def is_docking_station_at(self, x: int, y: int) -> bool:
         return bool(self.doc_station.x == x and self.doc_station.y == y)

main.py

@@ -8,6 +8,8 @@ from domain.commands.vacuum_move_command import VacuumMoveCommand
 from domain.entities.cat import Cat
 from domain.entities.entity import Entity
 from domain.entities.vacuum import Vacuum
+from domain.entities.garbage import Garbage
+from domain.entities.earring import Earring
 from domain.entities.docking_station import Doc_Station
 from domain.world import World
 from view.renderer import Renderer
@@ -135,7 +137,7 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
     for _ in range(10):
         temp_x = randint(0, tiles_x - 1)
         temp_y = randint(0, tiles_y - 1)
-        world.add_entity(Entity(temp_x, temp_y, "PEEL"))
+        world.add_entity(Garbage(temp_x, temp_y))
     world.vacuum = Vacuum(1, 1)
     world.doc_station = Doc_Station(9, 8)
     if config.getboolean("APP", "cat"):
@@ -146,6 +148,7 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
     world.add_entity(Entity(3, 4, "PLANT2"))
     world.add_entity(Entity(8, 8, "PLANT2"))
     world.add_entity(Entity(9, 3, "PLANT3"))
+    world.add_entity(Earring(5, 5))
     return world
 
 

requirements.txt

@@ -1,3 +1,6 @@
 pygame
 configparser
-formaFormatting: Provider - black
+pandas
+scikit-learn
+joblib
+# formaFormatting: Provider - black

view/renderer.py

@@ -94,6 +94,13 @@ class Renderer:
                     self.tile_height + self.tile_height / 4,
                 ),
             ),
+             "EARRING": pygame.transform.scale(
+                pygame.image.load("media/sprites/earrings.webp"),
+                (
+                    self.tile_width + self.tile_width / 4,
+                    self.tile_height + self.tile_height / 4,
+                ),
+            ),
         }
 
         self.cat_direction_sprite = {