qdscreen/main.py

• 13 E501: line too long (126 > 120 characters) (and 10 similar)

  • Line too long (126 > 120 characters):

    22 # https://stackoverflow.com/questions/20892799/using-pandas-calculate-cram%C3%A9rs-coefficient-matrix (+ corrected version)

  • Line too long (121 > 120 characters):

    159 """A numpy array containing the indices of all parent nodes, or a pandas DataFrame containing 'idx' and 'name'"""

  • Line too long (123 > 120 characters):

    311 indices (`False`). By default if the parent is identified with `parent_idx` indices will be returned (`False`),

  • Line too long (158 > 120 characters):

    343 # type: (...) -> Union[Iterable[Tuple[int, int]], Iterable[Tuple[str, str]], Tuple[Iterable[int], Iterable[int]], Tuple[Iterable[str], Iterable[str]]]

  • Line too long (134 > 120 characters):

    638 A_noredundancy = remove_redundancies(A_orig, selection_order=None if is_strict else X_stats.list_vars_by_entropy_order(desc=True))

  • Line too long (140 > 120 characters):

    653 # forestAdjMatList <- FromHToDeterForestAdjMat(H = H, criterion = criterionNlevels, removePerfectMatchingCol = removePerfectMatchingCol)

  • Line too long (135 > 120 characters):

    668 # print(paste("Multiple trees in the forest (", length(rootsFNames),") Root graph will be computed.", sep = "", collapse = ""))

  • Line too long (156 > 120 characters):

    676 # # removePerfectlyMatchingCol useless here because either pmc are in the same trees (but only one is the root), either they were deleted earlier on

  • Line too long (149 > 120 characters):

    677 # G_R <- SotABNsl(data = dataRoots, method = method, score = score, hyperparamList, removePerfectMatchingCol = removePerfectMatchingCol, ...)

  • Line too long (124 > 120 characters):

    835 self._Hcond_rel = pd.DataFrame(Hrel_array, index=list(self.Hcond.columns), columns=list(self.Hcond.columns))

  • Line too long (154 > 120 characters):

    1111 # type: (...) -> Union[Iterable[Tuple[int, int]], Iterable[Tuple[str, str]], Tuple[Iterable[int], Iterable[int]], Tuple[Iterable[str], Iterable[str]]]

  • Line too long (154 > 120 characters):

    1145 # type: (...) -> Union[Iterable[Tuple[int, int]], Iterable[Tuple[str, str]], Tuple[Iterable[int], Iterable[int]], Tuple[Iterable[str], Iterable[str]]]

  • Line too long (123 > 120 characters):

    1196 "'categorical'): found dtypes %r. This is not supported when `non_categorical_mode` is set to "
• 4 E731: do not assign a lambda expression, use a def
  • 380 get_children = lambda node: self.get_children(parent_name=node)
  • 382 get_children = lambda node: self.get_children(parent_idx=node)
  • 500 walk_arcs = lambda n: self.walk_arcs(parent_name=n)
  • 502 walk_arcs = lambda n: self.walk_arcs(parent_idx=n)
• 3 E128: continuation line under-indented for visual indent
  • 1196 "'categorical'): found dtypes %r. This is not supported when `non_categorical_mode` is set to "
  • 1197 "`'strict'`" % df_or_array.dtypes[~is_categorical_dtype].to_dict())
  • 1200 "'categorical'): found dtypes %r" % df_or_array.dtypes[~is_categorical_dtype].to_dict())
• 2 E741: ambiguous variable name 'l' (and 1 similar)

  • Ambiguous variable name 'l':

    387 for l, i, j in _walk(child, level+1):

  • Ambiguous variable name 'I':

    1002 I = np.ones((n_vars, ), dtype=bool)
• 5 S101: Use of assert detected. The enclosed code will be removed when compiling to optimised byte code.
  • 609 assert X.shape[1] > 0, "Internal error: no columns remain in dataset after preprocessing."
  • 772 assert np.all(self._H >= 0)
  • 810 assert np.all(self._Hcond >= 0)
  • 838 assert np.all(self._Hcond_rel >= 0.)
  • 839 assert np.all(self._Hcond_rel <= 1.)
• 1 T001: print found.
  • 469 print("\n".join(self.get_arcs_str_list(names=names)))