New feat: optional dependency

pkg/meta.yaml

@@ -3,7 +3,7 @@
 
 package:
   name: pyepo
-  version: 0.4.1
+  version: 0.4.2
 
 source:
   path: ./
@@ -15,32 +15,29 @@ build:
 
 channels:
   - conda-forge
+  - defaults
   - pytorch
   - gurobi
-  - defaults
 
 requirements:
   host:
-    - python >=3.7
+    - python >=3.7,<=3.12
     - pip
-    - numpy >=1.22
-    - scipy
-    - pathos
+    - numpy
     - tqdm
-    - pyomo >=6.1.2
-    - gurobi >=9.1.2
-    - scikit-learn
-    - pytorch >=1.13.1
   run:
-    - python >=3.7
-    - numpy >=1.22
+    - python >=3.7,<=3.12
+    - numpy
     - scipy
     - pathos
     - tqdm
-    - pyomo >=6.1.2
-    - gurobi >=9.1.2
     - scikit-learn
-    - pytorch >=1.13.1
+    - pytorch >=1.13
+
+extra:
+  optional-dependencies:
+    - gurobi >=9.1.2
+    - pyomo >=6.1.2
 
 about:
   home: https://github.com/khalil-research/PyEPO

pkg/pyepo/model/grb/grbmodel.py

@@ -7,8 +7,12 @@
 from copy import copy
 
 import numpy as np
-import gurobipy as gp
-from gurobipy import GRB
+try:
+    import gurobipy as gp
+    from gurobipy import GRB
+    _HAS_GUROBI = True
+except ImportError:
+    _HAS_GUROBI = False
 
 from pyepo import EPO
 from pyepo.model.opt import optModel
@@ -24,6 +28,9 @@ class optGrbModel(optModel):
 
     def __init__(self):
         super().__init__()
+        # error
+        if not _HAS_GUROBI:
+            raise ImportError("Gurobi is not installed. Please install gurobipy to use this feature.")
         # model sense
         self._model.update()
         if self._model.modelSense == GRB.MINIMIZE:

pkg/pyepo/model/grb/knapsack.py

@@ -5,8 +5,12 @@
 """
 
 import numpy as np
-import gurobipy as gp
-from gurobipy import GRB
+try:
+    import gurobipy as gp
+    from gurobipy import GRB
+    _HAS_GUROBI = True
+except ImportError:
+    _HAS_GUROBI = False
 
 from pyepo.model.grb.grbmodel import optGrbModel
 

pkg/pyepo/model/grb/portfolio.py

@@ -5,8 +5,12 @@
 """
 
 import numpy as np
-import gurobipy as gp
-from gurobipy import GRB
+try:
+    import gurobipy as gp
+    from gurobipy import GRB
+    _HAS_GUROBI = True
+except ImportError:
+    _HAS_GUROBI = False
 
 from pyepo.model.grb.grbmodel import optGrbModel
 

pkg/pyepo/model/grb/shortestpath.py

@@ -4,8 +4,12 @@
 Shortest path problem
 """
 
-import gurobipy as gp
-from gurobipy import GRB
+try:
+    import gurobipy as gp
+    from gurobipy import GRB
+    _HAS_GUROBI = True
+except ImportError:
+    _HAS_GUROBI = False
 
 from pyepo.model.grb.grbmodel import optGrbModel
 

pkg/pyepo/model/grb/tsp.py

@@ -7,9 +7,13 @@
 from collections import defaultdict
 from itertools import combinations
 
-import gurobipy as gp
 import numpy as np
-from gurobipy import GRB
+try:
+    import gurobipy as gp
+    from gurobipy import GRB
+    _HAS_GUROBI = True
+except ImportError:
+    _HAS_GUROBI = False
 
 from pyepo.model.grb.grbmodel import optGrbModel
 

pkg/pyepo/model/omo/knapsack.py

@@ -5,10 +5,14 @@
 """
 
 import numpy as np
-from pyomo import environ as pe
 
-from pyepo import EPO
-from pyepo.model.omo.omomodel import optOmoModel
+try:
+    from pyomo import environ as pe
+    from pyepo import EPO
+    from pyepo.model.omo.omomodel import optOmoModel
+    _HAS_PYOMO = True
+except ImportError:
+    _HAS_PYOMO = False
 
 
 class knapsackModel(optOmoModel):
@@ -98,15 +102,15 @@ def relax(self):
 
 
 if __name__ == "__main__":
-    
+
     import random
     # random seed
     random.seed(42)
     # set random cost for test
     cost = [random.random() for _ in range(16)]
     weights = np.random.choice(range(300, 800), size=(2,16)) / 100
     capacity = [20, 20]
-    
+
     # solve model
     optmodel = knapsackModel(weights=weights, capacity=capacity, solver="gurobi") # init model
     optmodel = optmodel.copy()
@@ -117,7 +121,7 @@ def relax(self):
     for i in range(16):
         if sol[i] > 1e-3:
             print(i)
-            
+
     # relax
     optmodel = optmodel.relax()
     optmodel.setObj(cost) # set objective function
@@ -127,7 +131,7 @@ def relax(self):
     for i in range(16):
         if sol[i] > 1e-3:
             print(i)
-            
+
     # add constraint
     optmodel = optmodel.addConstr([weights[0,i] for i in range(16)], 10)
     optmodel.setObj(cost) # set objective function
@@ -136,4 +140,4 @@ def relax(self):
     print('Obj: {}'.format(obj))
     for i in range(16):
         if sol[i] > 1e-3:
-            print(i)
+            print(i)

pkg/pyepo/model/omo/omomodel.py

@@ -5,11 +5,15 @@
 """
 
 from copy import copy
-from pyomo import opt as po
-from pyomo import environ as pe
 
-from pyepo import EPO
-from pyepo.model.opt import optModel
+try:
+    from pyomo import opt as po
+    from pyomo import environ as pe
+    from pyepo import EPO
+    from pyepo.model.opt import optModel
+    _HAS_PYOMO = True
+except ImportError:
+    _HAS_PYOMO = False
 
 
 class optOmoModel(optModel):
@@ -27,6 +31,9 @@ def __init__(self, solver="glpk"):
             solver (str): optimization solver in the background
         """
         super().__init__()
+        # error
+        if not _HAS_PYOMO:
+            raise ImportError("Pyomo is not installed. Please install pyomo to use this feature.")
         # init obj
         if self.modelSense == EPO.MINIMIZE:
             self._model.obj = pe.Objective(sense=pe.minimize, expr=0)

pkg/pyepo/model/omo/shortestpath.py

@@ -4,9 +4,12 @@
 Shortest path problem
 """
 
-from pyomo import environ as pe
-
-from pyepo.model.omo.omomodel import optOmoModel
+try:
+    from pyomo import environ as pe
+    from pyepo.model.omo.omomodel import optOmoModel
+    _HAS_PYOMO = True
+except ImportError:
+    _HAS_PYOMO = False
 
 
 class shortestPathModel(optOmoModel):

pkg/pyepo/twostage/__init__.py

@@ -5,7 +5,4 @@
 """
 
 from pyepo.twostage.sklearnpred import sklearnPred
-try:
-    from pyepo.twostage.autosklearnpred import autoSklearnPred
-except:
-    print("Auto-Sklearn cannot be imported.")
+from pyepo.twostage.autosklearnpred import autoSklearnPred

pkg/pyepo/twostage/autosklearnpred.py

@@ -5,12 +5,17 @@
 """
 
 import numpy as np
-from autosklearn.regression import AutoSklearnRegressor
-from autosklearn.metrics import mean_squared_error
-from autosklearn.pipeline.components import data_preprocessing
+try:
+    from autosklearn.regression import AutoSklearnRegressor
+    from autosklearn.metrics import mean_squared_error
+    from autosklearn.pipeline.components import data_preprocessing
+    from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
+    from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT
+    _HAS_AUTO = True
+except ImportError:
+    _HAS_AUTO = False
 from ConfigSpace.configuration_space import ConfigurationSpace
-from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
-from autosklearn.pipeline.constants import SPARSE, DENSE, UNSIGNED_DATA, INPUT
+
 
 from pyepo.metric import makeAutoSkScorer
 
@@ -62,6 +67,9 @@ def autoSklearnPred(optmodel, seed, timelimit, metric="mse"):
     Returns:
         AutoSklearnRegressor: Auto-SKlearn multi-output regression model
     """
+    # error
+    if not _HAS_AUTO:
+        raise ImportError("Autosklearn is not installed. Please install autosklearn to use this feature.")
     # add NoPreprocessing component to auto-sklearn.
     data_preprocessing.add_preprocessor(NoPreprocessing)
     # get metrics

pkg/setup.py

@@ -17,7 +17,7 @@
     long_description=long_description,
     long_description_content_type="text/markdown",
     # version
-    version = "0.4.1",
+    version = "0.4.2",
     # Github repo
     url = "https://github.com/khalil-research/PyEPO",
     # author name
@@ -32,10 +32,13 @@
         "scipy",
         "pathos",
         "tqdm",
-        "Pyomo>=6.1.2",
-        "gurobipy>=9.1.2",
         "scikit_learn",
         "torch>=1.13.1"],
+    extras_require={
+        "pyomo": ["pyomo>=6.1.2"],
+        "gurobi": ["gurobipy>=9.1.2"],
+        "all": ["pyomo>=6.1.2", "gurobipy>=9.1.2"],
+    },
    # classifiers
     classifiers = [
         "Programming Language :: Python :: 3",