Merge pull request #37 from TobyBoyne/feature/problemconfig_rng

Add random Generator to ProblemConfig

.gitignore

@@ -14,4 +14,8 @@ env
 docs/_build
 .coverage
 Makefile
-make.bat
+make.bat
+
+build/
+dist/
+*.egg-info

entmoot/__version__.py

@@ -1,4 +1,4 @@
-__version__ = "1.0.4"
+__version__ = "2.0.1"
 __author__ = "Alexander Thebelt"
 __author_email__ = "[email protected]"
 __license__ = "BSD 3-Clause License"

entmoot/models/enting.py

@@ -4,8 +4,9 @@
 from entmoot.models.uncertainty_models.distance_based_uncertainty import (
     DistanceBasedUncertainty,
 )
+from entmoot.utils import sample
+
 from entmoot.models.model_params import EntingParams
-from dataclasses import asdict
 import numpy as np
 from typing import Union
 
@@ -151,7 +152,6 @@ def add_to_gurobipy_model(self, core_model, weights: tuple = None) -> None:
         from the tree model.
         """
         from gurobipy import GRB
-        from entmoot.utils import sample
 
         # add uncertainty model part
         self.unc_model.add_to_gurobipy_model(core_model)
@@ -172,7 +172,7 @@ def add_to_gurobipy_model(self, core_model, weights: tuple = None) -> None:
             if weights is not None:
                 moo_weights = weights
             else:
-                moo_weights = sample(len(self._problem_config.obj_list), 1)[0]
+                moo_weights = sample(len(self._problem_config.obj_list), 1, self._problem_config.rng)[0]
 
             for idx, obj in enumerate(self._problem_config.obj_list):
                 core_model.addConstr(
@@ -189,7 +189,6 @@ def add_to_pyomo_model(self, core_model, weights: tuple = None) -> None:
         from the tree model.
         """
         import pyomo.environ as pyo
-        from entmoot.utils import sample
 
         # add uncertainty model part
         self.unc_model.add_to_pyomo_model(core_model)
@@ -212,7 +211,7 @@ def add_to_pyomo_model(self, core_model, weights: tuple = None) -> None:
             if weights is not None:
                 moo_weights = weights
             else:
-                moo_weights = sample(len(self._problem_config.obj_list), 1)[0]
+                moo_weights = sample(len(self._problem_config.obj_list), 1, self._problem_config.rng)[0]
 
             objectives_position_name = list(
                 enumerate([obj.name for obj in self._problem_config.obj_list])

entmoot/problem_config.py

@@ -1,20 +1,14 @@
-from typing import Tuple, List
+from typing import Tuple, List, Optional
 import numpy as np
 import random
 
 
 class ProblemConfig:
-    def __init__(self, rnd_seed: int = None):
+    def __init__(self, rnd_seed: Optional[int] = None):
         self._feat_list = []
         self._obj_list = []
         self._rnd_seed = rnd_seed
-
-        if rnd_seed is not None:
-            assert isinstance(
-                rnd_seed, int
-            ), f"Argument 'rnd_seed' needs to be an integer. Got type '{type(rnd_seed)}'."
-            np.random.seed(rnd_seed)
-            random.seed(rnd_seed)
+        self.rng = np.random.default_rng(rnd_seed)
 
     @property
     def cat_idx(self):
@@ -187,16 +181,16 @@ def get_rnd_sample_numpy(self, num_samples):
         for feat in self.feat_list:
             if feat.is_real():
                 array_list.append(
-                    np.random.uniform(low=feat.lb, high=feat.ub, size=num_samples)
+                    self.rng.uniform(low=feat.lb, high=feat.ub, size=num_samples)
                 )
             elif feat.is_cat():
                 array_list.append(
-                    np.random.randint(len(feat.cat_list), size=num_samples)
+                    self.rng.integers(0, len(feat.cat_list), size=num_samples)
                 )
             elif feat.is_int() or feat.is_bin():
                 array_list.append(
-                    np.random.random_integers(
-                        low=feat.lb, high=feat.ub, size=num_samples
+                    self.rng.integers(
+                        low=feat.lb, high=feat.ub+1, size=num_samples
                     )
                 )
         return np.squeeze(np.column_stack(array_list))
@@ -208,14 +202,14 @@ def get_rnd_sample_list(self, num_samples=1, cat_enc=False):
             sample = []
             for feat in self.feat_list:
                 if feat.is_real():
-                    sample.append(random.uniform(feat.lb, feat.ub))
+                    sample.append(self.rng.uniform(feat.lb, feat.ub))
                 elif feat.is_cat():
                     if cat_enc:
-                        sample.append(random.randrange(0, len(feat.cat_list)))
+                        sample.append(self.rng.integers(0, len(feat.cat_list)))
                     else:
-                        sample.append(random.choice(feat.cat_list))
+                        sample.append(self.rng.choice(feat.cat_list))
                 elif feat.is_int() or feat.is_bin():
-                    sample.append(random.randint(feat.lb, feat.ub))
+                    sample.append(self.rng.integers(feat.lb, feat.ub+1))
             sample_list.append(tuple(sample))
         return sample_list if len(sample_list) > 1 else sample_list[0]
 

entmoot/utils.py

@@ -63,7 +63,7 @@ def grid(dimension: int, levels: int) -> np.ndarray:
     return out / n
 
 
-def sample(dimension: int, n_samples: int = 1) -> np.ndarray:
+def sample(dimension: int, n_samples: int = 1, rng: np.random.Generator = None) -> np.ndarray:
     """Sample uniformly from the unit simplex.
 
     Args:
@@ -73,5 +73,6 @@ def sample(dimension: int, n_samples: int = 1) -> np.ndarray:
     Returns:
         array, shape=(n_samples, dimesnion): Random samples from the unit simplex.
     """
-    s = np.random.standard_exponential((n_samples, dimension))
+    if rng is None: rng = np.random.default_rng()
+    s = rng.standard_exponential((n_samples, dimension))
     return (s.T / s.sum(axis=1)).T

setup.py

@@ -17,14 +17,14 @@
     url="https://github.com/cog-imperial/entmoot",
     packages=find_packages(exclude=["tests", "docs"]),
     install_requires=[
-        "numpy",
-        "lightgbm==4.0.0",
+        "numpy<=2.0.0",
+        "lightgbm>=4.0.0",
         "gurobipy",
         "pyomo"
     ],
     setup_requires=[
-        "numpy",
-        "lightgbm==4.0.0",
+        "numpy<=2.0.0",
+        "lightgbm>=4.0.0",
         "gurobipy",
         "pyomo"
     ],