Enable ruff NPY002 rule (#11336)

machine_learning/decision_tree.py

@@ -187,7 +187,8 @@ def main():
     tree = DecisionTree(depth=10, min_leaf_size=10)
     tree.train(x, y)
 
-    test_cases = (np.random.rand(10) * 2) - 1
+    rng = np.random.default_rng()
+    test_cases = (rng.random(10) * 2) - 1
     predictions = np.array([tree.predict(x) for x in test_cases])
     avg_error = np.mean((predictions - test_cases) ** 2)
 

machine_learning/k_means_clust.py

@@ -55,12 +55,12 @@ TAG = "K-MEANS-CLUST/ "
 
 def get_initial_centroids(data, k, seed=None):
     """Randomly choose k data points as initial centroids"""
-    if seed is not None:  # useful for obtaining consistent results
-        np.random.seed(seed)
+    # useful for obtaining consistent results
+    rng = np.random.default_rng(seed)
     n = data.shape[0]  # number of data points
 
     # Pick K indices from range [0, N).
-    rand_indices = np.random.randint(0, n, k)
+    rand_indices = rng.integers(0, n, k)
 
     # Keep centroids as dense format, as many entries will be nonzero due to averaging.
     # As long as at least one document in a cluster contains a word,

machine_learning/sequential_minimum_optimization.py

@@ -289,12 +289,13 @@ class SmoSVM:
             if cmd is None:
                 return
 
-        for i2 in np.roll(self.unbound, np.random.choice(self.length)):
+        rng = np.random.default_rng()
+        for i2 in np.roll(self.unbound, rng.choice(self.length)):
             cmd = yield i1, i2
             if cmd is None:
                 return
 
-        for i2 in np.roll(self._all_samples, np.random.choice(self.length)):
+        for i2 in np.roll(self._all_samples, rng.choice(self.length)):
             cmd = yield i1, i2
             if cmd is None:
                 return