added proper implementation for learn in decisiontree

src/classes/decision_tree.py

@@ -17,40 +17,49 @@ class DecisionTree:
 	# TODO
 	# IMPLEMENTATION IS NOT WORKING
 
-	def classify(self, feature_vector: FeatureVector) -> tuple[Concept, int]:
+	def classify(self, feature_vector: FeatureVector) -> Concept:
 		if len(self.children) == 0:
-			return self.concept, self.depth
+			return self.concept
 		
 		feature_value = feature_vector.features_list[self.depth] # get feature value of vector based on current depth
 		
 		try:
 			new_decision_tree: Self = self.children[feature_value] # get sub decition tree for feature
 		except KeyError:
-			return Concept.UNKNOWN, self.depth
+			return Concept.UNKNOWN
 		
 		return new_decision_tree.classify(feature_vector) # classify new desicion tree
 
 	
 	def learn(self, training_set: list[FeatureVector]) -> None:
 		i = 0
-		while not self._finished(i, len(training_set)*2):
+		while not self._finished(i, len(training_set)):
 			vector: FeatureVector = training_set[i % len(training_set)]
-			concept, depth = self.classify(vector)
+			concept = self.classify(vector)
-			print(f"concept is {vector.concept}, was classified as {concept} in depth: {depth}")
+			#print(f"{i}\t: actual: {vector.concept} \t classified as: {concept}")
-			if concept == Concept.UNKNOWN:
+			if concept == vector.concept:
-				self.concept = vector.concept
+				i += 1
-				i+=1
-
-			elif concept == vector.concept:
-				i+=1
-
 			else:
-				self.concept = Concept.UNKNOWN
+				self._insert(vector)
-				self.children.update({vector.features_list[depth]: DecisionTree(concept=vector.concept, parent=self, depth=depth+1, children={})})
+				#i += 1
+
+
+	def _insert(self, vector: FeatureVector) -> None:
+		if self.concept == Concept.UNKNOWN:
+			self.concept = vector.concept
+
+		else:
+			feature_value = vector.features_list[self.depth]
+
+			try:
+				sub_decision_tree: Self = self.children[feature_value] # get sub decition tree for feature
+				sub_decision_tree._insert(vector)
+			except KeyError:
+				self.children.update({vector.features_list[self.depth]: DecisionTree(concept=vector.concept, parent=self, depth=self.depth+1, children={})})
 		
 		
 	def _finished(self, i: int, num_training_data: int):
 		# TODO implement proper finished condition
-		if i < num_training_data:
+		if i < num_training_data*3:
 			return False
 		return True

src/main.py

@@ -31,20 +31,37 @@ def knn():
     print(f"Correctness: {(cnt / len(testing_data)) * 100}%")
 
 
-if __name__ == "__main__":
+def decision_tree():
-
     from classes.decision_tree import DecisionTree
 
     training = os.path.abspath(os.path.join(__file__, "..", "training.csv"))
     testing = os.path.abspath(os.path.join(__file__, "..", "testing.csv"))
 
-    testing_data = []
+    training_data = []
-    with open(testing, 'r') as csv_file:
+    with open(training, 'r') as csv_file:
         reader = csv.reader(csv_file, delimiter=";")
         next(reader)
         for row in reader:
             fv = FeatureVector(concept = Concept.identify_by_str(row[1].split(".")[1]), features_list = ast.literal_eval(row[2]), loaded = True)
-            testing_data.append(fv)
+            training_data.append(fv)
 
     tree = DecisionTree()
-    tree.learn(testing_data)
+    tree.learn(training_data)
+
+    cnt_data = 0
+    cnt_correct = 0
+    with open(testing, 'r') as csv_file:
+        reader = csv.reader(csv_file, delimiter=";")
+        next(reader)
+        for row in reader:
+            cnt_data += 1
+            fv = FeatureVector(concept = Concept.identify_by_str(row[1].split(".")[1]), features_list = ast.literal_eval(row[2]), loaded = True)
+            classified_concept = tree.classify(fv)
+            cnt_correct += 1 if fv.concept == classified_concept else 0
+            print(f"{fv.concept} was classified as {classified_concept}")
+
+    print(f"classified {cnt_correct}/{cnt_data} correctly  ({round(cnt_correct/cnt_data*100, 3)}%)")
+
+
+if __name__ == "__main__":
+    decision_tree()