implement decision tree saving with joblib library

.gitignore

@@ -3,4 +3,9 @@ __pycache__/
 
 # ignore pdf files 
 *.pdf
+
+# ignore data file
 data
+
+# ignore .joblib files
+*.joblib

learning.py

@@ -1,11 +1,15 @@
 from sklearn import tree
 import graphviz
+from joblib import dump, load
 
 class Learning():
         
     X = []
     Y = []
     clf = tree.DecisionTreeClassifier()
+    saved_clf = None
+    s = None
+
     def load_data(self):
         file = open('dane.txt', "r")
         data_str = []
@@ -27,11 +31,30 @@ class Learning():
     def learn(self):    
         #clf = tree.DecisionTreeClassifier()
         self.clf = self.clf.fit(self.X, self.Y)
+        dump(self.clf, 'decision_tree.joblib')
+        
+    def load_tree(self):
+        self.saved_clf = load('decision_tree.joblib')
+
     def draw_tree(self):
         dot_data = tree.export_graphviz(self.clf, out_file=None, filled=True, class_names= ['1', '2', '3', '4', '5'], rounded=True, special_characters=True) 
         graph = graphviz.Source(dot_data) 
         graph.render("data")
 
+    def predict_on_saved_tree(self, param_array):
+        self.load_tree()
+        print(self.saved_clf.predict([param_array]))
+        if self.saved_clf.predict([param_array]) == 1:
+            print("oflagowac")
+        if self.saved_clf.predict([param_array]) == 2:
+            print("zdetonowac")
+        if self.saved_clf.predict([param_array]) == 3:
+            print("sprzedac na allegro")
+        if self.saved_clf.predict([param_array]) == 4:
+            print("sprzedac na czarnym rynku")
+        if self.saved_clf.predict([param_array]) == 5:
+            print("obejrzec")
+
     def predict(self, param_array):
         print(self.clf.predict([param_array]))
         if self.clf.predict([param_array]) == 1:
@@ -44,3 +67,5 @@ class Learning():
             print("sprzedac na czarnym rynku")
         if self.clf.predict([param_array]) == 5:
             print("obejrzec")
+        
+    

main.py

@@ -18,6 +18,7 @@ from pizza import *
 from learning import *
 
 
+
 class Game:
     def __init__(self):
         pg.init()
@@ -47,7 +48,7 @@ class Game:
             for col, tile in enumerate(tiles):
                 if tile == '2':
                     Mine(self, col, row)
-                    Mine.set_parameters(Mine,-5,32,6,7,1,0,0,0)
+                    Mine.set_parameters(Mine,18,32,6,7,0,0,0,0)
                 if tile == '3':
                     Bomb(self, col, row)
                 if tile == '4':
@@ -130,7 +131,8 @@ class Game:
                     
                     # Test.run()
                 if event.key == pg.K_F5:
-                    print("lol xD")
+                    self.player.decision_tree_learning()
+                    #print("lol xD")
                     pg.event.clear()
                 if event.key == pg.K_F6:
                     pg.event.clear()

sprites.py

@@ -6,6 +6,7 @@ from settings import *
 from maze import *
 from learning import *
 
+
 class Player(pg.sprite.Sprite):
     def __init__(self, game, x, y, direction = 'Right'):
         self.groups = game.all_sprites
@@ -24,7 +25,7 @@ class Player(pg.sprite.Sprite):
         self.maze = Maze()
         self.moves = ''
         self.my_learning = Learning()
-        self.decision_tree_learning()
+        #self.decision_tree_learning()
 
     def set_direction(self, direction):
         self.direction = direction
@@ -94,6 +95,9 @@ class Player(pg.sprite.Sprite):
         self.check_bomb()
 
     
+
+            
+
     def check_border(self, dx=0, dy=0):
         if (self.x + dx) < 0 or (self.y + dy) < 0 or (self.x + dx) >= MAP_SIZE or (self.y + dy) >= MAP_SIZE :
             return False
@@ -188,14 +192,18 @@ class Player(pg.sprite.Sprite):
         self.my_learning.load_data()
         self.my_learning.learn()
         self.my_learning.draw_tree()
+        print("new decision tree created")
+        print("restart to use saved decision tree")
         #my_learning.predict()
     
+
+
     """ sprawdzenie danych miny """
     def check_bomb(self):
         if self.check_if_on_mine():
             current_mine = self.get_my_mine_object()
             mine_params = current_mine.get_parameters()
-            self.my_learning.predict(mine_params)
+            self.my_learning.predict_on_saved_tree(mine_params)
         return