debug macos test reproducibility issue

Author: marcofavorito

.github/workflows/test.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.9, "3.10", "3.11", "3.12"]
+        python-version: ["3.12"]
 
     timeout-minutes: 30
 

black_it/calibrator.py

@@ -379,12 +379,16 @@ def calibrate(self, n_batches: int) -> tuple[NDArray, NDArray]:
                     self.params_samp,
                     self.losses_samp,
                 )
+                print(f"Parameters sampled by {method.__class__}")
+                print(new_params)
 
                 t_eval = time.time()
 
                 # simulate an ensemble of models for different parameters
 
                 new_simulated_data = self.simulate_model(new_params)
+                print(f"New simulated data: ")
+                print(new_simulated_data)
 
                 new_losses = []
 

black_it/utils/seedable.py

@@ -75,7 +75,9 @@ def random_generator(self) -> np.random.Generator:
 
     def _get_random_seed(self) -> int:
         """Get new random seed from the current random generator."""
-        return get_random_seed(self.__random_generator)
+        seed = get_random_seed(self.__random_generator)
+        print("Generated seed: ", seed)
+        return seed
 
 
 def get_random_seed(random_generator: np.random.Generator) -> int:

examples/main.py

@@ -16,6 +16,10 @@
 # along with this program. If not, see <http://www.gnu.org/licenses/>.
 
 """This is a simple example showing the main features of the library."""
+import sys
+
+import numpy as np
+
 import models.simple_models as md
 
 from black_it.calibrator import Calibrator
@@ -24,6 +28,8 @@
 from black_it.samplers.halton import HaltonSampler
 from black_it.samplers.random_forest import RandomForestSampler
 
+np.set_printoptions(threshold=sys.maxsize)
+
 if __name__ == "__main__":
     true_params = [0.20, 0.20, 0.75]
     bounds = [
@@ -44,6 +50,7 @@
     N = 2000
     seed = 1
     real_data = model(true_params, N, seed)
+    print("Real data:", real_data)
 
     # define a loss
     loss = MethodOfMomentsLoss()