Refactor file creation and feature names

dtree_learner.py

@@ -6,31 +6,32 @@ import csv
 import grammar_generation_utils
 import json
 
+
 class DTreeLearner(LearnerBase):
     def __init__(self, state_dim: int, perc_dim: int,
                  timeout: int = 10000) -> None:
         super().__init__()
-        
+
         self._state_dim: int = state_dim
         self._perc_dim: int = perc_dim
 
         self._s2f_func_list: List = []
 
-        #check tempLocation dir exists, if not create it.
-        self.dir_name = "tempLocation"
-        check_temp_dir: bool = os.path.isdir(self.dir_name)
-        if not check_temp_dir:
+        # check directory name exists, if not create it.
+        self.dir_name = "out"
+        if not os.path.isdir(self.dir_name):
             os.makedirs(self.dir_name)
 
-        # create empty files
-        self.data_file = self.dir_name+"/pre.data"
-        self.names_file = self.dir_name+"/pre.names"
-        open(self.data_file,'w').close() 
-        open(self.names_file,'w').close()
+        path_prefix = self.dir_name+"/pre"
+        self.data_file = path_prefix + ".data"
+        self.names_file = path_prefix + ".names"
+        self.tree_out = path_prefix + ".json"
 
-        self.exec = 'c50exact/c5.0dbg  -I 1 -m 1 -f tempLocation/pre'
+        # create empty files or truncate existing contents in files
+        open(self.data_file, 'w').close()
+        open(self.names_file, 'w').close()
 
-        self.tree_out = "tempLocation/pre.json"
+        self.exec = f'c50exact/c5.0dbg  -I 1 -m 1 -f {path_prefix}'
 
     @property
     def state_dim(self) -> int:
@@ -41,16 +42,13 @@ class DTreeLearner(LearnerBase):
         return self._perc_dim
 
     def set_grammar(self, grammar) -> None:
-        count = 1
-        numerical_vars = []
-        for a_mat, b_vec in grammar:
+        numerical_vars: List[str] = []
+        for count, (a_mat, b_vec) in enumerate(grammar):
             self._s2f_func_list.append(
                 construct_sample_to_feature_func(a_mat, b_vec))
+            numerical_vars.extend(f"fvar{i}_A{count}B{count}"
+                                  for i in range(self.perc_dim))
 
-            numerical_vars.append("dbar"+f'A{count}B{count}')
-            numerical_vars.append("psibar"+f'A{count}B{count}')
-            count += 1
-        
         grammar_generation_utils.generateInputLanguageFile(self.names_file, numerical_vars, [])
 
     def add_implication_examples(self, *args) -> None:
@@ -65,7 +63,7 @@ class DTreeLearner(LearnerBase):
             feature_vec_list.append(feature_vec)
 
         print("Positive feature vectors (dbar, psibar):", feature_vec_list)
-        self.write_to_file(self.data_file,feature_vec_list, "true")
+        self.write_to_file(self.data_file, feature_vec_list, "true")
 
     def add_negative_examples(self, *args) -> None:
         feature_vec_list = []
@@ -78,14 +76,13 @@ class DTreeLearner(LearnerBase):
         print("Negative feature vectors (dbar, psibar):", feature_vec_list)
         self.write_to_file(self.data_file, feature_vec_list, "false")
 
-    def write_to_file(self, file:str, feature_vec_list, label:str ):
+    def write_to_file(self, file: str, feature_vec_list, label: str):
         with open(file, 'a') as d_file:
             data_out = csv.writer(d_file)
             for f in feature_vec_list:
                 print(f)
                 data_out.writerow(f+[label])
 
-
     def learn(self) -> Tuple:
         res = os.popen(self.exec).read()
         print(res)