Fix type info warnings

dtree_learner.py

@@ -4,7 +4,7 @@ import itertools
 import json
 import logging
 import os
-from typing import Any, Callable, Dict, List, Literal, MutableSet, Tuple
+from typing import Any, Callable, Dict, List, Literal, Set, Tuple
 
 import numpy as np
 import z3
@@ -16,13 +16,13 @@ class DTreeLearner(LearnerBase):
     def __init__(self, state_dim: int, perc_dim: int,
                  timeout: int = 10000) -> None:
         super().__init__()
-        self.debug_neg_conc = set()  # type: MutableSet[Tuple[float,...]]
-        self.debug_neg_perc = set()  # type: MutableSet[Tuple[float,...]]
+        self.debug_neg_conc = set()  # type: Set[Tuple[float,...]]
+        self.debug_neg_perc = set()  # type: Set[Tuple[float,...]]
         self._state_dim: int = state_dim
         self._perc_dim: int = perc_dim
         self.count_neg_dup = 0
         self._s2f_func = lambda x: x
-        self._cons_s2f_method = None
+        self._cons_s2f_method = None  # type: Literal["concat", "diff", None]
 
         # Given a base or derived feature name,
         # returns a mapping from base feature names to coefficients
@@ -137,7 +137,7 @@ class DTreeLearner(LearnerBase):
         self._append_to_data_file(feature_vec_list, "true")
 
     def add_negative_examples(self, *args) -> None:
-        #TODO: add precondition that args should not contains np.array type 
+        # TODO: add precondition that args should not contains np.array type
         # NOTE the size of nonrepeating_samp_list and nonrepeating_fv_list can be different.
         if len(args) == 0:
             return
@@ -146,7 +146,7 @@ class DTreeLearner(LearnerBase):
         ]
         if len(nonrepeating_samp_list) == 0:
             raise ValueError(f"All negative examples {args} are repeated.")
-  
+
         fv_list = [
             tuple(self._s2f_func(samp)) for samp in nonrepeating_samp_list
         ]
@@ -268,6 +268,7 @@ def construct_sample_to_feature_func_by_concatenate(a_mat: np.ndarray, b_vec: np
     # In this case, the output dimension is two times of perception dimension
     return 2*perc_dim, sample_to_feature_vec
 
+
 def construct_sample_to_feature_func_by_diff(a_mat: np.ndarray, b_vec: np.ndarray) \
         -> Tuple[int, Callable[[np.ndarray], np.ndarray]]:
     perc_dim, state_dim = a_mat.shape
@@ -328,7 +329,7 @@ def test_sample_to_feature():
     sample = np.array([1., 2., 3., -2., -3.])
     # sample_to_feature_func will compute dBar and psiBar
     feature_vec = sample_to_feature_func(sample)
-    assert len(feature_vec ) == feature_dim, "The dimension of feature vector should match."
+    assert len(feature_vec) == feature_dim, "The dimension of feature vector should match."
     print("sample: " + str(feature_vec))
     assert np.array_equal(feature_vec, np.array([0., 0.]))