created first Decision Tree; minor changes to project structure

algorithms/learn/decision_tree.py

@@ -0,0 +1,80 @@
+import os
+import json
+from joblib import dump, load
+from sklearn import tree
+from sklearn.feature_extraction import DictVectorizer
+
+from mines.disarming.mine_parameters import MineParameters
+
+
+class DecisionTree:
+    def __init__(self, clf_source: str = None, vec_source: str = None):
+        if clf_source is not None and vec_source is not None:
+            self.load(clf_source, vec_source)
+        else:
+            self.clf = None
+            self.vec = None
+
+    def build(self, training_file: str, depth: int):
+        path = os.path.join("..", "..", "resources", "data", training_file)
+
+        samples = list()
+        results = list()
+
+        with open(path, "r") as training_file:
+            for sample in training_file:
+                s, r = self._process_input_line(sample)
+                samples.append(s)
+                results.append(r)
+
+        # vec transforms X (a list of dictionaries of string-string pairs) to binary arrays for tree to work on
+        self.vec = DictVectorizer()
+
+        # create and run Tree Clasifier upon provided data
+        self.clf = tree.DecisionTreeClassifier(max_depth=depth)
+        self.clf = self.clf.fit(self.vec.fit_transform(samples).toarray(), results)
+
+        # print a tree (not necessary)
+        print(tree.export_text(self.clf, feature_names=self.vec.get_feature_names()))
+
+    def save(self):
+        dump(self.clf, 'decision_tree.joblib')
+        dump(self.vec, 'dict_vectorizer.joblib')
+
+    def load(self, clf_file, vec_file):
+        self.clf = load(clf_file)
+        self.vec = load(vec_file)
+
+    def get_answer(self, mine_params):
+        return self.clf.predict(self.vec.transform(mine_params).toarray())
+
+    def test(self):
+        mistakes = 0
+        for _ in range(1000):
+            mine_params = MineParameters().jsonifyable_dict()
+            correct = mine_params['wire']
+            del mine_params['wire']
+
+            answer = self.get_answer(mine_params)
+
+            if correct != answer:
+                print(f"Answer: {answer}\nCorrect: {correct}")
+                mistakes += 1
+
+        print(f"Accuracy: {100 - (mistakes / 1000)}")
+
+    @staticmethod
+    def _process_input_line(line):
+        data = json.loads(line.strip())
+        result = data['wire']
+        del data['wire']
+        sample = data
+
+        return sample, result
+
+
+if __name__ == "__main__":
+    decision_tree = DecisionTree()
+    decision_tree.build("params3.txt", 15)
+    decision_tree.test()
+    decision_tree.save()

algorithms/learning_algorithms/decision_tree.py

@@ -1,22 +0,0 @@
-from joblib import dump, load
-from sklearn import tree
-from sklearn.feature_extraction import DictVectorizer
-
-# X is a list of dictionaries with samples, Y is a list of samples' results
-X = list()
-Y = list()
-
-# TODO: load training data
-
-# vec transforms X (a list of dictionaries of string-string pairs) to binary arrays for tree to work on
-vec = DictVectorizer()
-
-# create and run Tree Clasifier upon provided data
-clf = tree.DecisionTreeClassifier(max_depth=3)
-clf = clf.fit(vec.fit_transform(X).toarray(), Y)
-
-# save decision tree to file
-dump(clf, 'decision_tree.joblib')
-
-# print a tree (not necessary)
-print(tree.export_text(clf, feature_names=vec.get_feature_names()))

game.py

@@ -3,7 +3,7 @@ from random import randint
 import project_constants as const
 
 from display_assets import blit_graphics
-from algorithms.searching_algorithms import a_star
+from algorithms.search import a_star
 
 from minefield import Minefield
 

mines/disarming/mine_parameters.py

@@ -1,5 +1,5 @@
 import random
-import hash_function as hf
+from mines.disarming import hash_function as hf
 
 
 class MineParameters:

mines/disarming/parameter_json.py

@@ -4,7 +4,7 @@ import os
 
 
 # this module is self contained, used to generate a json file
-DIR_DATA = os.path.join("../resources", "data")
+DIR_DATA = os.path.join("../../resources", "data")
 
 
 # just to show, how mine parameters works