Decision Tree

bin/Classess/DecisionTree.py

@@ -0,0 +1,45 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import classification_report, confusion_matrix
+from joblib import dump, load
+
+
+class DecisionTree:
+    def __init__(self):
+        self.classifier = load('../../files/decision tree/classifier.joblib')
+
+
+def Learning():
+    # Uploading data from file
+    dataset = pd.read_csv("../../files/decision tree/database.csv")
+
+    print(f'Shape: {dataset.shape}')
+    print(f'Head:\n{dataset.head()}')
+
+    X = dataset.drop('decision', axis=1)
+    y = dataset['decision']
+
+    # Split data to training and test sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10)
+
+    # Training
+    classifier = DecisionTreeClassifier()
+    classifier.fit(X_train, y_train)
+
+    # Predictions test
+    y_pred = classifier.predict(X_test)
+    # print(y_pred)
+
+    # my_dict = {'known': [1], 'power': [1], 'new': [1], 'location': [0], 'stable': [1], 'chain_reaction': [1]}
+    # s = pd.DataFrame.from_dict(my_dict)
+    # predict = self.classifier.predict(s)
+    #
+    # print(predict)
+
+    print()
+    print(confusion_matrix(y_test, y_pred))
+    print(classification_report(y_test, y_pred))
+
+    dump(classifier, '../../files/decision tree/classifier.joblib')
+

bin/Classess/Field.py

@@ -24,12 +24,16 @@ class Field(object):
         self.columns = 10
         self.x_start = 5
         self.y_start = 5
-        self.state_of_cell_array = [[0 for i in range(3)] for j in range(200)]
+        # For red and green rectangles
+        # self.state_of_cell_array = [[0 for i in range(3)] for j in range(200)]
+        # Is on this position mine (True, False)
         self.field_state_array = [[False for i in range(self.rows)] for j in range(self.columns)]
         self.small_image_array = [[0 for i in range(self.rows)] for j in range(self.columns)]
         self.large_image_array = [[0 for i in range(self.rows)] for j in range(self.columns)]
         self.cell_expense = [0 for i in range(self.rows * self.columns)]
-        self.visited_mines = []
+        # Array rows * columns, if on [x][y] mine, object mine will be in the array in this position
+        self.state_of_cell_array = [["None" for _ in range(self.rows)] for __ in range(self.columns)]
+        # self.visited_mines = []
 
         # Modified by Artem to search in the status area
         self.canvas_small_images = []
@@ -47,7 +51,11 @@ class Field(object):
                                          bg='gray')
         self.large_image_canvas.place(x=FRAME_WIDTH + 5, y=3)
 
-        self.flag_img = PhotoImage(master=self.small_field_canvas, file="../../files/flag/Flaga.png")
+        self.flag_img = PhotoImage(master=self.small_field_canvas, file="../../files/flag/FlagRed.png")
+        self.flag_green_img = PhotoImage(master=self.small_field_canvas, file="../../files/flag/FlagGreen.png")
+        self.flag_yellow_img = PhotoImage(master=self.small_field_canvas, file="../../files/flag/FlagYellow.png")
+        self.flag_red_img = PhotoImage(master=self.small_field_canvas, file="../../files/flag/FlagRed.png")
+        self.flag_bleu_img = PhotoImage(master=self.small_field_canvas, file="../../files/flag/FlagBlue.png")
 
     # Clear Canvases
     def Moving(self):

bin/Classess/Mine.py

@@ -1,7 +1,15 @@
 class Mine:
-    def __init__(self, x, y):
+    def __init__(self, x, y, known, power, new, location, stable):
         self.array_x = x
         self.array_y = y
         self.status = True
         self.small_img = None
         self.large_img = None
+
+        # Attributes for decision tree
+        self.known = known     # 0 - 'no', 1 - 'yes'
+        self.power = power     # 0 - 'no', 1 - 'yes'
+        self.new = new       # 0 - 'no', 1 - 'yes'
+        self.location = location  # 0 - 'standard', 1 - 'sand', 2 - 'water', 3 - 'swamp'
+        self.stable = stable    # 0 - 'no', 1 - 'yes'
+        self.chain_reaction = 0    # 0 - 'no', 1 - 'yes'

bin/Main/LearnDecisionTree.py

@@ -0,0 +1,9 @@
+from bin.Classess.DecisionTree import Learning
+
+
+def LearnDecisionTree():
+    Learning()
+
+
+if __name__ == '__main__':
+    LearnDecisionTree()

bin/Main/test.py

@@ -0,0 +1,15 @@
+import pandas as pd
+
+
+def main():
+    my_dict = {'known': [1], 'power': [1], 'new': [1], 'location': 0, 'stable': [1], 'chain_reaction': [1]}
+
+    s = pd.DataFrame.from_dict(my_dict)
+
+    print(s)
+
+    # data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
+    # print(pd.DataFrame.from_dict(data))
+
+
+main()

bin/main/main.py

@@ -2,6 +2,7 @@ import os
 import random
 import time
 from tkinter import *
+import pandas as pd
 
 from bin.Classess.Field import Field
 from bin.Classess.Mine import Mine
@@ -10,6 +11,7 @@ from bin.Classess.Player import Player
 import bin.Classess.Node as nd
 import bin.Classess.Travel as tr
 from resources.Globals import *
+from bin.Classess.DecisionTree import DecisionTree
 
 # WINDOW_X = 533 + 1200
 # WINDOW_Y = 950
@@ -27,12 +29,16 @@ from resources.Globals import *
 player = Player()
 field = Field()
 travel = Travel()
+decision_tree = DecisionTree()
 
+# Globals
 fringe = []
 explored = []
 action_list = []
 images_coord = []
 
+label = None
+
 
 def Arrow(direction):
     image = ""
@@ -62,18 +68,17 @@ def Fill(bool):
 
         print(travel.points_map)
 
-
         for i in range(0, len(field.canvas_small_images)):
             images_coord.append(field.small_field_canvas.coords(field.canvas_small_images[i]))
         # print("Coords List: ", images_coord)
 
         nd.init_data(images_coord, field.cell_expense)
 
-    # Drawing red/green rectangles
+    # # Drawing red/green rectangles
-    for el in field.state_of_cell_array:
+    # for el in field.state_of_cell_array:
-        if el[0] != 0:
+    #     if el[0] != 0:
-            field.small_field_canvas.create_rectangle(el[0], el[1], el[0] + player.step - 2,
+    #         field.small_field_canvas.create_rectangle(el[0], el[1], el[0] + player.step - 2,
-                                                      el[1] + player.step - 2, width=3, outline=el[2])
+    #                                                   el[1] + player.step - 2, width=3, outline=el[2])
 
     DrawingLargeImage()
 
@@ -179,11 +184,11 @@ def CellDesignation(array, color):
 def Action(action):
     if action in ["Right", "Left", "Up", "space"]:
         Moving(action)
-    elif action in ["1", "2"]:
+    # elif action in ["1", "2"]:
-        if action == "1":
+    #     if action == "1":
-            CellDesignation(field.state_of_cell_array, "red")
+    #         CellDesignation(field.state_of_cell_array, "red")
-        else:
+    #     else:
-            CellDesignation(field.state_of_cell_array, "green")
+    #         CellDesignation(field.state_of_cell_array, "green")
 
 
 # Modified by Artem to search in the status area
@@ -199,6 +204,42 @@ def create_action_list(states, index):
     create_action_list(states, states.index(state_parent))
 
 
+def MakeDecision():
+    if player.current_array_x != 0 or player.current_array_y != 0:
+        mine = field.state_of_cell_array[player.current_array_y][player.current_array_x]
+        # print(field.state_of_cell_array)
+        # print(mine)
+        attributes_dict = {'known': [mine.known], 'power': [mine.power], 'new': [mine.new], 'location': [mine.location],
+                           'stable': [mine.stable], 'chain_reaction': [mine.chain_reaction]}
+
+        attributes = f'{mine.known}; {mine.power}; {mine.new}; {mine.location}; {mine.stable}; {mine.chain_reaction}'
+
+        global label_text
+        label_text += attributes + '\n'
+
+        global label
+        label.config(text=label_text)
+        field.win.update()
+
+        data_frame = pd.DataFrame.from_dict(attributes_dict)
+        predict = decision_tree.classifier.predict(data_frame)
+
+        return predict
+
+
+def MarkMine(prediction):
+    if prediction == 0:
+        DrawFlag(field.flag_green_img)
+    if prediction == 1:
+        DrawFlag(field.flag_yellow_img)
+    if prediction == 2:
+        DrawFlag(field.flag_red_img)
+    if prediction == 3:
+        DrawFlag(field.flag_bleu_img)
+
+    field.win.update()
+
+
 def MouseClickEvent(track):
     global fringe
     global explored
@@ -230,7 +271,6 @@ def MouseClickEvent(track):
         explored.clear()
         action_list.clear()
         fringe = nd.graph_search_A(fringe, explored, node.state, end_position)
-        # fringe = nd.graph_search(fringe, explored, node.state, end_position)
 
         states = []
         goal_all = []
@@ -249,80 +289,39 @@ def MouseClickEvent(track):
 
         create_action_list(states, -1)
 
-        # for i in range(0, len(fringe)):
-        #     print('Node{} = State: {} {}, Parent: {} {} {}, Action: {}'.format(i + 1, fringe[i].state.coord, fringe[i].state.direction, fringe[i].parent[0], fringe[i].parent[1], fringe[i].parent[2], fringe[i].action))
-
-        # print(action_list)
-
         # Start moving
         AutoMove()
-        DrawFlag()
+
+        # Decision by tree
+        prediction = MakeDecision()
+        # Draw the right flag
+        MarkMine(prediction)
 
         time.sleep(SLEEP_AFTER_CHECK_MINE)
 
 
-    # start_position = field.small_field_canvas.coords(player.image_canvas_id)
+# Check in which locations is mine
-    # end_position = []
+def CheckLocation(x, y):
-    #
+    # Add x + y like strings that create xy number
-    # # print("Pierwsza pozycja: {} {}".format(start_position[0], start_position[1]))
+    temp_x = str(x)
-    #
+    temp_y = str(y)
-    # for i in range(0, len(field.canvas_small_images)):
+    position_str = temp_x + temp_y
-    #     img_coords = field.small_field_canvas.coords(field.canvas_small_images[i])
+    position = int(position_str)
-    #     if (img_coords[0] <= event.x and event.x <= img_coords[0] + IMAGE_SIZE) and (img_coords[1] <= event.y and event.y <= img_coords[1] + IMAGE_SIZE):
+
-    #         end_position = img_coords
+    color_number = -1
-    #         print("Color cost: ", field.cell_expense[i])
+    if field.cell_expense[position] == standard_cell_cost:
-    #
+        color_number = 0
-    # # if len(end_position) == 2:
+    elif field.cell_expense[position] == sand_cell_cost:
-    # #     print("Koncowa pozycja: {} {}".format(end_position[0], end_position[1]))
+        color_number = 1
-    #
+    elif field.cell_expense[position] == water_cell_cost:
-    # node = nd.Node()
+        color_number = 2
-    # if len(fringe) == 0:
+    elif field.cell_expense[position] == swamp_cell_cost:
-    #     node.state.coord = start_position
+        color_number = 3
-    #     node.state.direction = "east"
+
-    # else:
+    return color_number
-    #     states = []
-    #     for k in range(0, len(fringe)):
-    #         new_state = fringe[k].state.coord
-    #         states.append(new_state)
-    #     start_node = fringe[-1]
-    #
-    #     node.state.coord = start_node.state.coord
-    #     node.state.direction = start_node.state.direction
-    #
-    # fringe.clear()
-    # explored.clear()
-    # action_list.clear()
-    # fringe = nd.graph_search_A(fringe, explored, node.state, end_position)
-    # # fringe = nd.graph_search(fringe, explored, node.state, end_position)
-    #
-    # states = []
-    # goal_all = []
-    # for i in range(0, len(fringe)):
-    #     new_state = [fringe[i].state.coord, fringe[i].state.direction]
-    #     states.append(new_state)
-    #     if end_position[0] == fringe[i].state.coord[0] and end_position[1] == fringe[i].state.coord[1]:
-    #         goal_all.append(fringe[i])
-    #
-    # elem_min = goal_all[0]
-    # for i in range(1, len(goal_all)):
-    #     if elem_min.priority > goal_all[i].priority:
-    #         elem_min = goal_all[i]
-    # index = fringe.index(elem_min)
-    # fringe = fringe[:index + 1]
-    #
-    # create_action_list(states, -1)
-    #
-    # # for i in range(0, len(fringe)):
-    # #     print('Node{} = State: {} {}, Parent: {} {} {}, Action: {}'.format(i + 1, fringe[i].state.coord, fringe[i].state.direction, fringe[i].parent[0], fringe[i].parent[1], fringe[i].parent[2], fringe[i].action))
-    #
-    # print(action_list)
-    #
-    #
-    #
-    # # Start moving
-    # AutoMove()
 
 
+# Add mines on the field and to arrays
 def PutMines(mines_array):
     counter = 0
 
@@ -339,9 +338,18 @@ def PutMines(mines_array):
             if x == 0 and y == 0:
                 continue
             else:
-                mine = Mine(x, y)
+                known = random.randint(0, 1)
+                power = random.randint(1, 10)
+                new = random.randint(0, 1)
+                location = CheckLocation(x, y)
+                stable = random.randint(0, 1)
+
+                mine = Mine(x, y, known, power, new, location, stable)
                 mines_array.append(mine)
 
+                # Add mine to array at the right position
+                field.state_of_cell_array[x][y] = mine
+
                 field.field_state_array[x][y] = True
 
                 counter += 1
@@ -391,8 +399,8 @@ def MinesInArrays(mines_array, directory, imgs_array, bool_mines_coord):
             field.mines_coord.append([mines_array[i].array_x, mines_array[i].array_y])
 
 
-def DrawFlag():
+def DrawFlag(image):
-    field.small_field_canvas.create_image(player.current_x, player.current_y, anchor=NW, image=field.flag_img)
+    field.small_field_canvas.create_image(player.current_x, player.current_y, anchor=NW, image=image)
 
 
 # def IsItMine():
@@ -439,15 +447,14 @@ def DrawRectangle():
         elif field.cell_expense[i] == swamp_cell_cost:
             color = "green4"
         if color != "None":
-            field.small_field_canvas.create_rectangle(x, y, x + IMAGE_SIZE + 2, y + IMAGE_SIZE + 2, width=2, outline=color)
+            field.small_field_canvas.create_rectangle(x, y, x + IMAGE_SIZE + 2, y + IMAGE_SIZE + 2, width=2,
+                                                      outline=color)
         x += player.step
         if x + IMAGE_SIZE + 2 > field.width:
             x = 4
             y += player.step
 
 
-
-
 def AddCostCellsToArray(amount, cost):
     counter = 0
     while counter < amount:
@@ -461,7 +468,9 @@ def CostingOfCells():
     AddCostCellsToArray(amount_of_sand_cells, sand_cell_cost)
     AddCostCellsToArray(amount_of_water_cells, water_cell_cost)
     AddCostCellsToArray(amount_of_swamp_cells, swamp_cell_cost)
-    AddCostCellsToArray(field.rows * field.columns - (amount_of_sand_cells + amount_of_water_cells + amount_of_swamp_cells), standard_cell_cost)
+    AddCostCellsToArray(
+        field.rows * field.columns - (amount_of_sand_cells + amount_of_water_cells + amount_of_swamp_cells),
+        standard_cell_cost)
 
     # Draw rectangles
     DrawRectangle()
@@ -469,8 +478,9 @@ def CostingOfCells():
 
 def click_button():
     btn.destroy()
-    label = Label(field.win, text="Wait...\nAI conquers the world...", fg='black', font="20")
+    global label
-    label.place(x=50, y=570)
+    label = Label(field.win, text="Wait...\nAI conquers the world...", fg='black', font="20", bg='gray')
+    label.place(x=10, y=560)
     field.win.update()
     track = tr.genetic_algorithm(travel.points_map)
 
@@ -480,6 +490,41 @@ def click_button():
     MouseClickEvent(track)
 
 
+# Check if there mines near and if, mark it
+def CheckForMinesNear(x, y):
+    if x > 0:
+        if field.state_of_cell_array[x - 1][y] != "None":
+            # Mark by chain reaction current mine
+            field.state_of_cell_array[x][y].chain_reaction = 1
+            # Mark by chain reaction mine that near
+            field.state_of_cell_array[x - 1][y].chain_reaction = 1
+    if x < 9:
+        if field.state_of_cell_array[x + 1][y] != "None":
+            # Mark by chain reaction current mine
+            field.state_of_cell_array[x][y].chain_reaction = 1
+            # Mark by chain reaction mine that near
+            field.state_of_cell_array[x + 1][y].chain_reaction = 1
+    if y > 0:
+        if field.state_of_cell_array[x][y - 1] != "None":
+            # Mark by chain reaction current mine
+            field.state_of_cell_array[x][y].chain_reaction = 1
+            # Mark by chain reaction mine that near
+            field.state_of_cell_array[x][y - 1].chain_reaction = 1
+    if y < 9:
+        if field.state_of_cell_array[x][y + 1] != "None":
+            # Mark by chain reaction current mine
+            field.state_of_cell_array[x][y].chain_reaction = 1
+            # Mark by chain reaction mine that near
+            field.state_of_cell_array[x][y + 1].chain_reaction = 1
+
+
+def CheckForChainReaction():
+    for x in range(field.columns):
+        for y in range(field.rows):
+            if field.state_of_cell_array[x][y] != "None":
+                CheckForMinesNear(x, y)
+
+
 def main():
     # Creating the main window of an application
     win_size = f'{WINDOW_X}x{WINDOW_Y}'
@@ -487,6 +532,7 @@ def main():
     field.win.configure(bg='gray')
     field.win.geometry(win_size)
     print(f'Amount of mines: {AMOUNT_OF_MINES}')
+
     global btn
     btn = Button(field.win,
                  text="Search for mines",  # текст кнопки
@@ -498,13 +544,19 @@ def main():
                  command=click_button
                  )
 
-    btn.place(x=50, y=570)
+    btn.place(x=10, y=560)
 
     # Create array with mines objects
     mines_array = []
+
+    CostingOfCells()
+
     # Put mines on coordinates
     PutMines(mines_array)
 
+    CheckForChainReaction()
+
+    # Add images of mines in arrays
     MinesInArrays(mines_array, "../../files/small_mines_images", field.small_image_array, True)
     MinesInArrays(mines_array, "../../files/large_mines_images", field.large_image_array, False)
 
@@ -514,8 +566,6 @@ def main():
     large_directory = "../../files/large_images"
     ImagesInArray(large_directory, field.large_image_array)
 
-    CostingOfCells()
-
     # Add arrow image to Player class
     images = []
     for file in os.listdir("../../files/arrow"):
@@ -540,6 +590,7 @@ def main():
     # Binding keyboard press to function
     # field.win.bind("<Key>", Action)
     # field.small_field_canvas.bind("<Button-1>", MouseClickEvent)
+
     # Starting mainloop for window
     field.win.mainloop()
 

files/decision tree/database.csv

@@ -0,0 +1,641 @@
+known,power,new,location,stable,chain_reaction,decision
+1,1,1,0,1,1,1
+1,1,1,0,1,0,2
+1,1,1,0,0,1,1
+1,1,1,0,0,0,2
+1,1,0,0,1,1,1
+1,1,0,0,1,0,2
+1,1,0,0,0,1,1
+1,1,0,0,0,0,2
+1,1,1,1,1,1,1
+1,1,1,1,1,0,2
+1,1,1,1,0,1,1
+1,1,1,1,0,0,2
+1,1,0,1,1,1,1
+1,1,0,1,1,0,2
+1,1,0,1,0,1,1
+1,1,0,1,0,0,2
+1,1,1,2,1,1,1
+1,1,1,2,1,0,2
+1,1,1,2,0,1,1
+1,1,1,2,0,0,2
+1,1,0,2,1,1,1
+1,1,0,2,1,0,2
+1,1,0,2,0,1,1
+1,1,0,2,0,0,2
+1,1,1,3,1,1,1
+1,1,1,3,1,0,2
+1,1,1,3,0,1,1
+1,1,1,3,0,0,2
+1,1,0,3,1,1,1
+1,1,0,3,1,0,2
+1,1,0,3,0,1,1
+1,1,0,3,0,0,2
+1,2,1,0,1,1,1
+1,2,1,0,1,0,2
+1,2,1,0,0,1,1
+1,2,1,0,0,0,2
+1,2,0,0,1,1,1
+1,2,0,0,1,0,2
+1,2,0,0,0,1,1
+1,2,0,0,0,0,2
+1,2,1,1,1,1,1
+1,2,1,1,1,0,2
+1,2,1,1,0,1,1
+1,2,1,1,0,0,2
+1,2,0,1,1,1,1
+1,2,0,1,1,0,2
+1,2,0,1,0,1,1
+1,2,0,1,0,0,2
+1,2,1,2,1,1,1
+1,2,1,2,1,0,2
+1,2,1,2,0,1,1
+1,2,1,2,0,0,2
+1,2,0,2,1,1,1
+1,2,0,2,1,0,2
+1,2,0,2,0,1,1
+1,2,0,2,0,0,2
+1,2,1,3,1,1,1
+1,2,1,3,1,0,2
+1,2,1,3,0,1,1
+1,2,1,3,0,0,2
+1,2,0,3,1,1,1
+1,2,0,3,1,0,2
+1,2,0,3,0,1,1
+1,2,0,3,0,0,2
+1,3,1,0,1,1,1
+1,3,1,0,1,0,2
+1,3,1,0,0,1,1
+1,3,1,0,0,0,2
+1,3,0,0,1,1,1
+1,3,0,0,1,0,2
+1,3,0,0,0,1,1
+1,3,0,0,0,0,2
+1,3,1,1,1,1,1
+1,3,1,1,1,0,2
+1,3,1,1,0,1,1
+1,3,1,1,0,0,2
+1,3,0,1,1,1,1
+1,3,0,1,1,0,2
+1,3,0,1,0,1,1
+1,3,0,1,0,0,2
+1,3,1,2,1,1,1
+1,3,1,2,1,0,2
+1,3,1,2,0,1,1
+1,3,1,2,0,0,2
+1,3,0,2,1,1,1
+1,3,0,2,1,0,2
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resources/Globals.py

@@ -14,7 +14,7 @@ MAX_AMOUNT_OF_MINES = 11
 AMOUNT_OF_MINES = random.randint(MIN_AMOUNT_OF_MINES, MAX_AMOUNT_OF_MINES)
 
 DELAY_TIME = 0.2
-SLEEP_AFTER_CHECK_MINE = 1
+SLEEP_AFTER_CHECK_MINE = 2
 
 STEP = IMAGE_SIZE + 5
 
@@ -36,3 +36,6 @@ NUMBER_OF_INDIVIDUALS_FOR_DUEL = 4
 NUMBER_OF_POINTS_PERMUTATION = 10
 PERCENT_OF_MUTATION = 0.01
 PERCENT_OF_OUTGOING_INDIVIDUALS = 0.03
+
+
+label_text = ""