Update pre-commit hook versions

.pre-commit-config.yaml

@@ -1,28 +1,28 @@
 repos:
  - repo: https://github.com/Quantco/pre-commit-mirrors-black
-   rev: 19.10b0
+   rev: 20.8b1
    hooks:
      - id: black-conda
        additional_dependencies: [flake8-docstrings, flake8-rst-docstrings]
        args:
          - --safe
          - --target-version=py36
  - repo: https://github.com/Quantco/pre-commit-mirrors-flake8
-   rev: v3.7.9
+   rev: v3.8.4
    hooks:
     - id: flake8-conda
  - repo: https://github.com/Quantco/pre-commit-mirrors-isort
-   rev: 4.3.21
+   rev: 5.7.0
    hooks:
     - id: isort-conda
       additional_dependencies: [toml]
  - repo: https://github.com/Quantco/pre-commit-mirrors-mypy
-   rev: "0.770"
+   rev: "0.812"
    hooks:
     - id: mypy-conda
       additional_dependencies:
        - python=3.8
  - repo: https://github.com/Quantco/pre-commit-mirrors-pyupgrade
-   rev: 1.26.0
+   rev: 2.10.0
    hooks:
     - id: pyupgrade-conda

src/quantcore/matrix/benchmark/main.py

@@ -98,7 +98,8 @@ def _to_standardized_mat(mat):
 
     times = pd.DataFrame(
         index=pd.MultiIndex.from_product(
-            [ops_to_run, matrices.keys()], names=["operation", "storage"],
+            [ops_to_run, matrices.keys()],
+            names=["operation", "storage"],
         ),
         columns=["memory", "time"],
     ).reset_index()

src/quantcore/matrix/categorical_matrix.py

@@ -58,7 +58,9 @@ def recover_orig(self) -> np.ndarray:
         return self.cat.categories[self.cat.codes]
 
     def _matvec_setup(
-        self, other: Union[List, np.ndarray], cols: np.ndarray = None,
+        self,
+        other: Union[List, np.ndarray],
+        cols: np.ndarray = None,
     ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
         other = np.asarray(other)
         if other.ndim > 1:

src/quantcore/matrix/dense_matrix.py

@@ -59,8 +59,8 @@ def cross_sandwich(
         L_cols: Optional[np.ndarray] = None,
         R_cols: Optional[np.ndarray] = None,
     ):
-        from .sparse_matrix import SparseMatrix
         from .categorical_matrix import CategoricalMatrix
+        from .sparse_matrix import SparseMatrix
 
         if isinstance(other, SparseMatrix) or isinstance(other, CategoricalMatrix):
             return other.cross_sandwich(self, d, rows, R_cols, L_cols).T

tests/test_matrices.py

@@ -198,7 +198,8 @@ def test_to_array_standardized_mat(mat: mx.StandardizedMatrix):
 
 @pytest.mark.parametrize("mat", get_matrices())
 @pytest.mark.parametrize(
-    "other_type", [lambda x: x, np.asarray, mx.DenseMatrix],
+    "other_type",
+    [lambda x: x, np.asarray, mx.DenseMatrix],
 )
 @pytest.mark.parametrize("cols", [None, [], [1], np.array([1])])
 @pytest.mark.parametrize("other_shape", [[], [1], [2]])
@@ -266,7 +267,8 @@ def process_mat_vec_subsets(mat, vec, mat_rows, mat_cols, vec_idxs):
 
 @pytest.mark.parametrize("mat", get_matrices())
 @pytest.mark.parametrize(
-    "other_type", [lambda x: x, np.array, mx.DenseMatrix],
+    "other_type",
+    [lambda x: x, np.array, mx.DenseMatrix],
 )
 @pytest.mark.parametrize("rows", [None, [], [2], np.arange(2)])
 @pytest.mark.parametrize("cols", [None, [], [1], np.arange(1)])
@@ -335,7 +337,8 @@ def test_cross_sandwich(
 
 @pytest.mark.parametrize("mat", get_matrices())
 @pytest.mark.parametrize(
-    "vec_type", [lambda x: x, np.array, mx.DenseMatrix],
+    "vec_type",
+    [lambda x: x, np.array, mx.DenseMatrix],
 )
 @pytest.mark.parametrize("rows", [None, [], [1], np.arange(2)])
 @pytest.mark.parametrize("cols", [None, [], [0], np.arange(1)])
@@ -391,7 +394,8 @@ def test_transpose(mat):
 
 @pytest.mark.parametrize("mat", get_matrices())
 @pytest.mark.parametrize(
-    "vec_type", [lambda x: x, np.array, mx.DenseMatrix],
+    "vec_type",
+    [lambda x: x, np.array, mx.DenseMatrix],
 )
 def test_rmatmul(mat: Union[mx.MatrixBase, mx.StandardizedMatrix], vec_type):
     vec_as_list = [3.0, -0.1, 0]

tests/test_split_matrix.py

@@ -125,7 +125,8 @@ def test_sandwich_sparse_dense(X: np.ndarray, Acols, Bcols):
 # TODO: ensure cols are in order
 @pytest.mark.parametrize("mat", [split_mat(), split_with_cat(), split_with_cat_64()])
 @pytest.mark.parametrize(
-    "cols", [None, [0], [1, 2, 3], [1, 5]],
+    "cols",
+    [None, [0], [1, 2, 3], [1, 5]],
 )
 def test_sandwich(mat: mx.SplitMatrix, cols):
     for i in range(10):