add exception handling when importing decision tree or neural network

src/const.py

@@ -2,8 +2,10 @@ import os
 import pickle
 
 import pygame as pg
+import torch
 
 from image import Image
+from machine_learning.neural_network.net import Net
 
 
 main_path = os.path.dirname(os.getcwd())
@@ -91,5 +93,33 @@ for name in [
 
 ROCK_INDEXES = (5, 6, 23, 24)
 
-with open(f'{main_path}/src/machine_learning/decision_tree.pkl', 'rb') as file:
-    TREE_ROOT = pickle.load(file)
+try:
+    with open(f'{main_path}/src/machine_learning/decision_tree/decision_tree.pkl', 'rb') as file:
+        TREE_ROOT = pickle.load(file)
+
+except FileNotFoundError:
+    print('Decision tree not detected\nInitializing...')
+
+    from machine_learning.decision_tree.decision_tree import main
+    main()
+
+    with open(f'{main_path}/src/machine_learning/decision_tree/decision_tree.pkl', 'rb') as file:
+        TREE_ROOT = pickle.load(file)
+
+try:
+    checkpoint = torch.load(f'{main_path}/src/machine_learning/neural_network/mine_recognizer.model')
+    model = Net(num_classes=2)
+    model.load_state_dict(checkpoint)
+    model.eval()
+
+except FileNotFoundError:
+    print('Neural network not detected\nInitializing...')
+
+    from machine_learning.neural_network.learning import main
+    main()
+
+    checkpoint = torch.load(f'{main_path}/src/machine_learning/neural_network/mine_recognizer.model')
+
+    model = Net(num_classes=2)
+    model.load_state_dict(checkpoint)
+    model.eval()

src/machine_learning/decision_tree/data_set.py

@@ -1,10 +1,9 @@
-import os
 import json
 from typing import List
 from itertools import product
 
+from machine_learning.decision_tree.helpers import path
 
-data_path = os.path.dirname(os.path.abspath(__file__))
 
 visibility = ('bad', 'medium', 'good')
 stability = ('unstable', 'stable')
@@ -67,7 +66,7 @@ def main():
         json.dump(data_set, outfile)
 
 
-with open(f'{data_path}/data.txt', 'r') as f:
+with open(f'{path}/data.txt', 'r') as f:
     file_data = f.read()
     json_data = json.loads(file_data)
 

src/machine_learning/decision_tree/decision_tree.py

@@ -5,6 +5,7 @@ from collections import Counter
 
 from anytree import Node, RenderTree
 
+from machine_learning.decision_tree.helpers import path
 from machine_learning.decision_tree.data_set import training_set, test_set, attributes as attribs
 
 
@@ -84,7 +85,7 @@ def main():
             score += 1
 
     print(f'Accuracy: {score/len(test_set)}')
-    with open('decision_tree.pkl', 'wb') as file:
+    with open(f'{path}/decision_tree.pkl', 'wb') as file:
         pickle.dump(tree_root, file, pickle.HIGHEST_PROTOCOL)
 
 

src/machine_learning/decision_tree/helpers.py

@@ -1,3 +1,4 @@
+import os
 from tile import Tile
 
 
@@ -10,3 +11,6 @@ def get_dataset_from_tile(tile: Tile):
         'mine_type': tile.mine.mine_type,
         'pressure_gt_two': True if tile.mine.pressure > 2 else False
     }
+
+
+path = os.path.dirname(os.path.abspath(__file__))