Add more lints to Ruff for better code quality. (#621)

* Add more lints to Ruff for better code quality.

* Add more lints.

* Avoid generator instantiation.

* Replace all instances of `np.int_` with `np.int64`.

Author: hameerabbasi

.pre-commit-config.yaml

@@ -17,7 +17,7 @@ repos:
       args: ["--autofix", "--no-ensure-ascii"]
 
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.1.9
+  rev: v0.1.11
   hooks:
     - id: ruff
       args: ["--fix"]

benchmarks/benchmark_coo.py

@@ -5,9 +5,9 @@
 
 class MatrixMultiplySuite:
     def setup(self):
-        np.random.seed(0)
-        self.x = sparse.random((100, 100), density=0.01)
-        self.y = sparse.random((100, 100), density=0.01)
+        rng = np.random.default_rng(0)
+        self.x = sparse.random((100, 100), density=0.01, random_state=rng)
+        self.y = sparse.random((100, 100), density=0.01, random_state=rng)
 
         self.x @ self.y  # Numba compilation
 
@@ -17,9 +17,9 @@ def time_matmul(self):
 
 class ElemwiseSuite:
     def setup(self):
-        np.random.seed(0)
-        self.x = sparse.random((100, 100, 100), density=0.01)
-        self.y = sparse.random((100, 100, 100), density=0.01)
+        rng = np.random.default_rng(0)
+        self.x = sparse.random((100, 100, 100), density=0.01, random_state=rng)
+        self.y = sparse.random((100, 100, 100), density=0.01, random_state=rng)
 
         self.x + self.y  # Numba compilation
 
@@ -32,9 +32,9 @@ def time_mul(self):
 
 class ElemwiseBroadcastingSuite:
     def setup(self):
-        np.random.seed(0)
-        self.x = sparse.random((100, 1, 100), density=0.01)
-        self.y = sparse.random((100, 100), density=0.01)
+        rng = np.random.default_rng(0)
+        self.x = sparse.random((100, 1, 100), density=0.01, random_state=rng)
+        self.y = sparse.random((100, 100), density=0.01, random_state=rng)
 
     def time_add(self):
         self.x + self.y
@@ -45,9 +45,9 @@ def time_mul(self):
 
 class IndexingSuite:
     def setup(self):
-        np.random.seed(0)
-        self.index = np.random.randint(0, 100, 50)
-        self.x = sparse.random((100, 100, 100), density=0.01)
+        rng = np.random.default_rng(0)
+        self.index = rng.integers(0, 100, 50)
+        self.x = sparse.random((100, 100, 100), density=0.01, random_state=rng)
 
         # Numba compilation
         self.x[5]

benchmarks/benchmark_gcxs.py

@@ -5,9 +5,9 @@
 
 class MatrixMultiplySuite:
     def setup(self):
-        np.random.seed(0)
-        self.x = sparse.random((100, 100), density=0.01, format="gcxs")
-        self.y = sparse.random((100, 100), density=0.01, format="gcxs")
+        rng = np.random.default_rng(0)
+        self.x = sparse.random((100, 100), density=0.01, format="gcxs", random_state=rng)
+        self.y = sparse.random((100, 100), density=0.01, format="gcxs", random_state=rng)
 
         self.x @ self.y  # Numba compilation
 
@@ -17,9 +17,9 @@ def time_matmul(self):
 
 class ElemwiseSuite:
     def setup(self):
-        np.random.seed(0)
-        self.x = sparse.random((100, 100, 100), density=0.01, format="gcxs")
-        self.y = sparse.random((100, 100, 100), density=0.01, format="gcxs")
+        rng = np.random.default_rng(0)
+        self.x = sparse.random((100, 100, 100), density=0.01, format="gcxs", random_state=rng)
+        self.y = sparse.random((100, 100, 100), density=0.01, format="gcxs", random_state=rng)
 
         self.x + self.y  # Numba compilation
 
@@ -32,9 +32,9 @@ def time_mul(self):
 
 class ElemwiseBroadcastingSuite:
     def setup(self):
-        np.random.seed(0)
-        self.x = sparse.random((100, 1, 100), density=0.01, format="gcxs")
-        self.y = sparse.random((100, 100), density=0.01, format="gcxs")
+        rng = np.random.default_rng(0)
+        self.x = sparse.random((100, 1, 100), density=0.01, format="gcxs", random_state=rng)
+        self.y = sparse.random((100, 100), density=0.01, format="gcxs", random_state=rng)
 
     def time_add(self):
         self.x + self.y
@@ -45,9 +45,9 @@ def time_mul(self):
 
 class IndexingSuite:
     def setup(self):
-        np.random.seed(0)
-        self.index = np.random.randint(0, 100, 50)
-        self.x = sparse.random((100, 100, 100), density=0.01, format="gcxs")
+        rng = np.random.default_rng(0)
+        self.index = rng.integers(0, 100, 50)
+        self.x = sparse.random((100, 100, 100), density=0.01, format="gcxs", random_state=rng)
 
         # Numba compilation
         self.x[5]

benchmarks/benchmark_matmul.py

@@ -7,9 +7,9 @@ class Matmul_Sparse:
     params = (["coo", "gcxs"], [0, 1, None])
 
     def setup(self, p, dens_arg):
-        np.random.seed(0)
-        self.x = sparse.random((100, 100), density=0.01, format=p)
-        self.y = sparse.random((100, 100), density=0.01, format=p)
+        rng = np.random.default_rng(0)
+        self.x = sparse.random((100, 100), density=0.01, format=p, random_state=rng)
+        self.y = sparse.random((100, 100), density=0.01, format=p, random_state=rng)
 
         if dens_arg == 0:
             self.x = self.x.todense()

benchmarks/benchmark_tensordot.py

@@ -10,9 +10,9 @@ class TensordotSuiteDenseSparse:
     """
 
     def setup(self):
-        np.random.seed(0)
-        self.n = np.random.random((100, 100))
-        self.s = sparse.random((100, 100, 100, 100), density=0.01)
+        rng = np.random.default_rng(0)
+        self.n = rng.random((100, 100))
+        self.s = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
 
     def time_dense(self):
         sparse.tensordot(self.n, self.s, axes=([0, 1], [0, 2]))
@@ -28,9 +28,9 @@ class TensordotSuiteSparseSparse:
     """
 
     def setup(self):
-        np.random.seed(0)
-        self.s1 = sparse.random((100, 100), density=0.01)
-        self.s2 = sparse.random((100, 100, 100, 100), density=0.01)
+        rng = np.random.default_rng(0)
+        self.s1 = sparse.random((100, 100), density=0.01, random_state=rng)
+        self.s2 = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
 
     def time_dense(self):
         sparse.tensordot(self.s1, self.s2, axes=([0, 1], [0, 2]), return_type=np.ndarray)
@@ -46,9 +46,9 @@ class TensordotSuiteSparseDense:
     """
 
     def setup(self):
-        np.random.seed(0)
-        self.s = sparse.random((100, 100, 100, 100), density=0.01)
-        self.n = np.random.random((100, 100))
+        rng = np.random.default_rng(0)
+        self.s = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
+        self.n = rng.random((100, 100))
 
     def time_dense(self):
         sparse.tensordot(self.s, self.n, axes=([0, 1], [0, 1]))

pyproject.toml

@@ -55,13 +55,17 @@ include = ["sparse", "sparse.*"]
 version_file = "sparse/_version.py"
 
 [tool.ruff]
-select = ["F", "E", "W", "I", "B", "UP"]
+select = ["F", "E", "W", "I", "B", "UP", "YTT", "BLE", "C4", "T10", "ISC", "ICN", "PIE", "PYI", "RSE", "RET", "SIM", "PGH", "FLY", "NPY", "PERF"]
 exclude = ["sparse/_version.py"]
 line-length = 120
 
 [tool.ruff.lint.isort.sections]
 numpy = ["numpy", "numpy.*", "scipy", "scipy.*"]
 
+[tool.ruff.format]
+quote-style = "double"
+docstring-code-format = true
+
 [tool.ruff.lint.isort]
 section-order = [
     "future",