Fix typos & asv & numexpr

Author: wjsi

.github/workflows/benchmark-ci.yml

@@ -57,7 +57,7 @@ jobs:
           git fetch upstream
           git merge upstream/master
           asv machine --yes
-          asv continuous -e -f 1.1 --strict upstream/master HEAD
+          asv continuous -e -f 1.1 upstream/master HEAD
         if: ${{ steps.build.outcome == 'success' }}
 
       - name: Publish benchmarks artifact

mars/learn/linear_model/_base.py

@@ -301,7 +301,7 @@ def fit(self, X, y, sample_weight=None):
                 self.coef_.execute()
             except LinAlgError:
                 # TODO: implement linalg.lstsq first
-                raise NotImplementedError("Does not support sigular matrix!")
+                raise NotImplementedError("Does not support singular matrix!")
 
         if y.ndim == 1:
             self.coef_ = mt.ravel(self.coef_)

mars/learn/linear_model/tests/test_base.py

@@ -53,7 +53,7 @@ def test_linear_regression(setup):
     assert_array_almost_equal(reg.predict(X), model.predict(X))
 
     # Regular model fitting, #samples <= 2, # features < 2
-    error_msg = re.escape("Does not support sigular matrix!")
+    error_msg = re.escape("Does not support singular matrix!")
 
     X = [[1], [2]]
     Y = [1, 2]
@@ -69,7 +69,7 @@ def test_linear_regression(setup):
     assert_array_almost_equal(reg.predict(X), model.predict(X))
 
     # Extra case #1: singular matrix, degenerate input
-    error_msg = re.escape("Does not support sigular matrix!")
+    error_msg = re.escape("Does not support singular matrix!")
 
     X = [[1]]
     Y = [0]

mars/learn/metrics/pairwise/pairwise.py

@@ -72,7 +72,7 @@
     "precomputed": None,  # HACK: precomputed is always allowed, never called
 }
 
-# These distances recquire boolean tensors, when using mars.tensor.spatial.distance
+# These distances require boolean tensors, when using mars.tensor.spatial.distance
 PAIRWISE_BOOLEAN_FUNCTIONS = [
     "dice",
     "jaccard",

mars/tensor/base/tile.py

@@ -30,7 +30,7 @@ def tile(A, reps):
     behavior, promote `A` to d-dimensions manually before calling this
     function.
 
-    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
+    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by prepending 1's to it.
     Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
     (1, 1, 2, 2).
 

mars/tensor/utils.py

@@ -774,7 +774,7 @@ def fetch_corner_data(tensor, session=None):
     # the tensor must have been executed,
     # thus the size could not be NaN
     if tensor.size > threshold:
-        # two edges for each exis
+        # two edges for each axis
         indices_iter = list(itertools.product(*(range(2) for _ in range(tensor.ndim))))
         corners = np.empty(shape=(2,) * tensor.ndim, dtype=object)
         shape = [0 for _ in range(tensor.ndim)]

setup.cfg

@@ -30,7 +30,7 @@ install_requires =
     pandas>=1.0.0,<2.0.0
     scipy>=1.0.0
     scikit-learn>=0.20
-    numexpr>=2.6.4,!=2.8.5
+    numexpr>=2.6.4,!=2.8.5,!=2.8.6
     cloudpickle>=1.5.0
     pyyaml>=5.1
     psutil>=5.9.0