Merge branch 'bugfix/fix_fails' into feature/agg_mode

.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/dataframe/merge/tests/test_merge_execution.py

@@ -312,11 +312,15 @@ def test_join_on(setup):
     expected4.set_index("a2", inplace=True)
     result4.set_index("a2", inplace=True)
     pd.testing.assert_frame_equal(
-        sort_dataframe_inplace(expected4, 0), sort_dataframe_inplace(result4, 0)
+        sort_dataframe_inplace(expected4, 0, kind="mergesort"),
+        sort_dataframe_inplace(result4, 0, kind="mergesort"),
     )
 
 
 def test_merge_one_chunk(setup):
+    def sort_by_col1(df):
+        return df.sort_values(by=df.columns[1], kind="mergesort")
+
     df1 = pd.DataFrame(
         {"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 5]},
         index=["a1", "a2", "a3", "a4"],
@@ -348,8 +352,8 @@ def test_merge_one_chunk(setup):
     result = jdf.execute().fetch()
 
     pd.testing.assert_frame_equal(
-        expected.sort_values(by=expected.columns[1]).reset_index(drop=True),
-        result.sort_values(by=result.columns[1]).reset_index(drop=True),
+        sort_by_col1(expected).reset_index(drop=True),
+        sort_by_col1(result).reset_index(drop=True),
     )
 
     # right have one chunk
@@ -361,8 +365,8 @@ def test_merge_one_chunk(setup):
     result = jdf.execute().fetch()
 
     pd.testing.assert_frame_equal(
-        expected.sort_values(by=expected.columns[1]).reset_index(drop=True),
-        result.sort_values(by=result.columns[1]).reset_index(drop=True),
+        sort_by_col1(expected).reset_index(drop=True),
+        sort_by_col1(result).reset_index(drop=True),
     )
 
     # left have one chunk and how="left", then one chunk tile
@@ -377,8 +381,8 @@ def test_merge_one_chunk(setup):
     result = jdf.execute().fetch()
 
     pd.testing.assert_frame_equal(
-        expected.sort_values(by=expected.columns[1]).reset_index(drop=True),
-        result.sort_values(by=result.columns[1]).reset_index(drop=True),
+        sort_by_col1(expected).reset_index(drop=True),
+        sort_by_col1(result).reset_index(drop=True),
     )
 
 
@@ -418,7 +422,8 @@ def test_broadcast_merge(setup):
     expected.set_index("key", inplace=True)
     result.set_index("key", inplace=True)
     pd.testing.assert_frame_equal(
-        sort_dataframe_inplace(expected, 0), sort_dataframe_inplace(result, 0)
+        sort_dataframe_inplace(expected, 0, kind="mergesort"),
+        sort_dataframe_inplace(result, 0, kind="mergesort"),
     )
 
     # test broadcast right and how="left"
@@ -438,8 +443,8 @@ def test_broadcast_merge(setup):
     expected.set_index("key", inplace=True)
     result.set_index("key", inplace=True)
     pd.testing.assert_frame_equal(
-        expected.sort_values(by=["key", "value_x"]),
-        result.sort_values(by=["key", "value_x"]),
+        expected.sort_values(by=["key", "value_x"], kind="mergesort"),
+        result.sort_values(by=["key", "value_x"], kind="mergesort"),
     )
 
     # test broadcast left
@@ -459,7 +464,8 @@ def test_broadcast_merge(setup):
     expected.set_index("key", inplace=True)
     result.set_index("key", inplace=True)
     pd.testing.assert_frame_equal(
-        sort_dataframe_inplace(expected, 0), sort_dataframe_inplace(result, 0)
+        sort_dataframe_inplace(expected, 0, kind="mergesort"),
+        sort_dataframe_inplace(result, 0, kind="mergesort"),
     )
 
     # test broadcast left and how="right"
@@ -479,8 +485,8 @@ def test_broadcast_merge(setup):
     expected.set_index("key", inplace=True)
     result.set_index("key", inplace=True)
     pd.testing.assert_frame_equal(
-        expected.sort_values(by=["key", "value_x"]),
-        result.sort_values(by=["key", "value_x"]),
+        expected.sort_values(by=["key", "value_x"], kind="mergesort"),
+        result.sort_values(by=["key", "value_x"], kind="mergesort"),
     )
 
 

mars/dataframe/utils.py

@@ -106,9 +106,9 @@ def hash_dtypes(dtypes, size):
     return [dtypes[index] for index in hashed_indexes]
 
 
-def sort_dataframe_inplace(df, *axis):
+def sort_dataframe_inplace(df, *axis, **kw):
     for ax in axis:
-        df.sort_index(axis=ax, inplace=True)
+        df.sort_index(axis=ax, inplace=True, **kw)
     return df
 
 

mars/learn/contrib/lightgbm/_predict.py

@@ -78,7 +78,7 @@ def __call__(self):
         elif hasattr(self.model, "classes_"):
             dtype = np.array(self.model.classes_).dtype
         else:
-            dtype = getattr(self.model, "out_dtype_", np.dtype("float"))
+            dtype = getattr(self.model, "out_dtype_", [np.dtype("float")])[0]
 
         if self.output_types[0] == OutputType.tensor:
             # tensor

mars/learn/contrib/lightgbm/_train.py

@@ -406,11 +406,11 @@ def execute(cls, ctx, op: "LGBMTrain"):
                 op.model_type == LGBMModelType.RANKER
                 or op.model_type == LGBMModelType.REGRESSOR
             ):
-                model.set_params(out_dtype_=np.dtype("float"))
+                model.set_params(out_dtype_=[np.dtype("float")])
             elif hasattr(label_val, "dtype"):
-                model.set_params(out_dtype_=label_val.dtype)
+                model.set_params(out_dtype_=[label_val.dtype])
             else:
-                model.set_params(out_dtype_=label_val.dtypes[0])
+                model.set_params(out_dtype_=[label_val.dtypes[0]])
 
             ctx[op.outputs[0].key] = pickle.dumps(model)
         finally:

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