use generator

Author: aloctavodia

preliz/internal/optimization.py

@@ -612,7 +612,7 @@ def func(x, dist, q):
 def get_weighted_rvs(target, size, rng):
     targets = [t[0] for t in target]
     weights = [t[1] for t in target]
-    target_rnd_choices = np.random.choice(len(targets), size=size, p=weights)
+    target_rnd_choices = rng.choice(len(targets), size=size, p=weights)
     samples = [target.rvs(size, random_state=rng) for target in targets]
     return np.choose(target_rnd_choices, samples)
 

preliz/tests/predictive_explorer.ipynb

@@ -97,10 +97,12 @@
    "source": [
     "%%ipytest\n",
     "\n",
+    "rng = np.random.default_rng(1241)\n",
+    "\n",
     "data = pd.DataFrame(\n",
     "    {\n",
-    "        \"y\": np.random.normal(size=117),\n",
-    "        \"x\": np.random.normal(size=117),\n",
+    "        \"y\": rng.normal(size=117),\n",
+    "        \"x\": rng.normal(size=117),\n",
     "    }\n",
     ")\n",
     "data.head()\n",

preliz/tests/test_ppe.py

@@ -9,7 +9,7 @@
 
 import preliz as pz
 
-np.random.seed(42)
+rng = np.random.default_rng(1241)
 
 
 @pytest.mark.parametrize(
@@ -20,7 +20,7 @@
             "sigma_x": 10,
             "sigma_z": 10,
             "target": pz.Normal(mu=174, sigma=20),
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": 174,
             "new_z": 2.9,
         },
@@ -29,7 +29,7 @@
             "sigma_x": [10, 10],
             "sigma_z": 10,
             "target": pz.Normal(mu=174, sigma=20),
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": [174, 174],
             "new_z": 2.9,
         },
@@ -38,7 +38,7 @@
             "sigma_x": 10,
             "sigma_z": 10,
             "target": [(pz.Normal(mu=174, sigma=20), 0.5), (pz.Normal(mu=176, sigma=19.5), 0.5)],
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": 175,
             "new_z": 2.9,
         },
@@ -51,7 +51,7 @@
                 (pz.Normal(mu=176, sigma=19.5), 0.4),
                 (pz.StudentT(mu=174, sigma=20, nu=3), 0.1),
             ],
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": [175, 175],
             "new_z": 3,
         },