Cleaned Up Repo + Files

.gitignore

@@ -2,6 +2,9 @@
 experiments/
 test/test_.*.py
 
+# VS Code environment
+.vscode/
+
 # MacOS
 .DS_Store
 

.readthedocs.yaml

@@ -9,6 +9,5 @@ build:
   tools:
     python: miniconda-latest
 
-
 conda:
   environment: environment.yml

demo/demo_data_farming_model.py

@@ -6,7 +6,10 @@
 
 import sys
 import os.path as o
-sys.path.append(o.abspath(o.join(o.dirname(sys.modules[__name__].__file__), ".."))) # type:ignore
+
+sys.path.append(
+    o.abspath(o.join(o.dirname(sys.modules[__name__].__file__), ".."))
+)  # type:ignore
 
 
 from simopt.data_farming_base import DataFarmingExperiment
@@ -15,7 +18,12 @@
 model_name = "CNTNEWS"
 
 # Specify the names of the model factors (in order) that will be varied.
-factor_headers = ["purchase_price", "sales_price", "salvage_price", "order_quantity"]
+factor_headers = [
+    "purchase_price",
+    "sales_price",
+    "salvage_price",
+    "order_quantity",
+]
 
 # If creating the design, provide the name of a .txt file containing
 # the following:
@@ -52,12 +60,13 @@
 # No code beyond this point needs to be edited.
 
 # Create DataFarmingExperiment object.
-myexperiment = DataFarmingExperiment(model_name=model_name,
-                                     factor_settings_filename=factor_settings_filename,
-                                     factor_headers=factor_headers,
-                                     design_filepath=design_filename,
-                                     model_fixed_factors={}
-                                     )
+myexperiment = DataFarmingExperiment(
+    model_name=model_name,
+    factor_settings_filename=factor_settings_filename,
+    factor_headers=factor_headers,
+    design_filepath=design_filename,
+    model_fixed_factors={},
+)
 
 # Run replications and print results to file.
 myexperiment.run(n_reps=n_reps, crn_across_design_pts=crn_across_design_pts)

demo/demo_data_farming_over_solver_and_problem.py

@@ -6,11 +6,15 @@
 
 import sys
 import os.path as o
-sys.path.append(o.abspath(o.join(o.dirname(sys.modules[__name__].__file__), ".."))) # type:ignore
+
+sys.path.append(
+    o.abspath(o.join(o.dirname(sys.modules[__name__].__file__), ".."))
+)  # type:ignore
 
 from simopt.experiment_base import create_design, ProblemsSolvers
 
-def main():
+
+def main() -> None:
     # Specify the name of the solver as it appears in directory.py
     solver_name = "ASTRODF"
     # Specify the name of the problem as it appears in directory.py
@@ -19,13 +23,13 @@ def main():
     model_name = "CNTNEWS"
 
     # Specify the names of the sovler factors (in order) that will be varied.
-    solver_factor_headers = ["eta_1", "eta_2", "lambda_min" ]
+    solver_factor_headers = ["eta_1", "eta_2", "lambda_min"]
     # Specify the names of the model factors (in order) that will be varied.
     model_factor_headers = ["purchase_price", "sales_price", "order_quantity"]
 
     # OPTIONAL: factors chosen for cross design
     # factor name followed by list containing factor values to cross design over
-    solver_cross_design_factors = {'crn_across_solns': [True, False]} 
+    solver_cross_design_factors = {"crn_across_solns": [True, False]}
     # model_cross_design_factors = {}
 
     # OPTIONAL: Provide additional overrides for default factors.
@@ -65,38 +69,41 @@ def main():
     crn_across_init_opt = True  # Default
 
     # Create DataFarmingExperiment object for sovler design
-    solver_design_list = create_design(name=solver_name,
-                    factor_headers=solver_factor_headers,
-                    factor_settings_filename=solver_factor_settings_filename,
-                    n_stacks=solver_n_stacks,
-                    fixed_factors=solver_fixed_factors, #optional
-                    cross_design_factors= solver_cross_design_factors, #optional
-                    )
+    solver_design_list = create_design(
+        name=solver_name,
+        factor_headers=solver_factor_headers,
+        factor_settings_filename=solver_factor_settings_filename,
+        n_stacks=solver_n_stacks,
+        fixed_factors=solver_fixed_factors,  # optional
+        cross_design_factors=solver_cross_design_factors,  # optional
+    )
     # Create DataFarmingExperiment object for model design
-    model_design_list = create_design(name=model_name,
-                    factor_headers=model_factor_headers,
-                    factor_settings_filename=model_factor_settings_filename,
-                    n_stacks=problem_n_stacks,
-                    fixed_factors=model_fixed_factors, #optional
-                    #cross_design_factors=model_cross_design_factors, #optional
-                    )
+    model_design_list = create_design(
+        name=model_name,
+        factor_headers=model_factor_headers,
+        factor_settings_filename=model_factor_settings_filename,
+        n_stacks=problem_n_stacks,
+        fixed_factors=model_fixed_factors,  # optional
+        # cross_design_factors=model_cross_design_factors, #optional
+    )
 
     # create solver name list for ProblemsSolvers (do not edit)
     solver_names = []
-    for i in range(len(solver_design_list)): 
+    for _ in range(len(solver_design_list)):
         solver_names.append(solver_name)
-        
+
     # create proble name list for ProblemsSolvers (do not edit)
     problem_names = []
-    for i in range(len(model_design_list)): 
+    for _ in range(len(model_design_list)):
         problem_names.append(problem_name)
 
     # Create ProblemsSovlers experiment with solver and model design
-    experiment = ProblemsSolvers(solver_factors = solver_design_list,
-                                problem_factors = model_design_list,
-                                solver_names = solver_names,
-                                problem_names = problem_names
-                                )
+    experiment = ProblemsSolvers(
+        solver_factors=solver_design_list,
+        problem_factors=model_design_list,
+        solver_names=solver_names,
+        problem_names=problem_names,
+    )
 
     # check compatibility of selected solvers and problems
     experiment.check_compatibility()
@@ -105,16 +112,21 @@ def main():
     experiment.run(n_macroreps)
 
     # Postprocess the experimental results from each design point.
-    experiment.post_replicate(n_postreps=n_postreps,
-                            crn_across_budget=crn_across_budget,
-                            crn_across_macroreps=crn_across_macroreps)
-    experiment.post_normalize(n_postreps_init_opt=n_postreps_init_opt,
-                            crn_across_init_opt=crn_across_init_opt)
+    experiment.post_replicate(
+        n_postreps=n_postreps,
+        crn_across_budget=crn_across_budget,
+        crn_across_macroreps=crn_across_macroreps,
+    )
+    experiment.post_normalize(
+        n_postreps_init_opt=n_postreps_init_opt,
+        crn_across_init_opt=crn_across_init_opt,
+    )
 
     # Record and log results
     experiment.record_group_experiment_results()
     experiment.log_group_experiment_results()
     experiment.report_group_statistics()
 
+
 if __name__ == "__main__":
-    main()
+    main()

demo/demo_data_farming_problem.py

@@ -6,25 +6,28 @@
 
 import sys
 import os.path as o
-sys.path.append(o.abspath(o.join(o.dirname(sys.modules[__name__].__file__), "..")))
 
-from simopt.experiment_base import create_design, ProblemsSolvers # type:ignore
+sys.path.append(
+    o.abspath(o.join(o.dirname(sys.modules[__name__].__file__), ".."))
+)
 
-def main():
+from simopt.experiment_base import create_design, ProblemsSolvers  # type:ignore
+
+
+def main() -> None:
     # Specify the name of the problem as it appears in directory.py
     problem_name = "CNTNEWS-1"
     # Specify the name of the model as it appears in directory.py
     model_name = "CNTNEWS"
     # Specify the name of the solver as it appears in directory.py
-    solver_names = ["ASTRODF","RNDSRCH"]
+    solver_names = ["ASTRODF", "RNDSRCH"]
 
     # Specify the names of the model factors (in order) that will be varied.
     model_factor_headers = ["purchase_price", "sales_price", "order_quantity"]
 
     # OPTIONAL: factors chosen for cross design
     # factor name followed by list containing factor values to cross design over
-    #model_cross_design_factors = {}
-
+    # model_cross_design_factors = {}
 
     # OPTIONAL: Provide additional overrides for model default factors.
     # If empty, default factor settings are used.
@@ -34,10 +37,10 @@ def main():
     # If empty, default factor settings are used.
     # list of dictionaries that provide fixed factors for problems when you don't want to use the default values
     # if you want to use all default values use empty dictionary, order must match problem names
-    solver_fixed_factors = [{"eta_1": .5, "eta_2": .4}, {"sample_size": 15}]
+    solver_fixed_factors = [{"eta_1": 0.5, "eta_2": 0.4}, {"sample_size": 15}]
 
     # uncomment this version to run w/ only default solver factors
-    #sp;ver_fixed_factors = [{},{}]
+    # sp;ver_fixed_factors = [{},{}]
 
     # Provide the name of a file  .txt locatated in the datafarming_experiments folder containing
     # the following:
@@ -52,7 +55,6 @@ def main():
     # Specify the number stacks to use for ruby design creation
     problem_n_stacks = 1
 
-
     # Specify a common number of macroreplications of each unique solver/problem combination
     # i.e., the number of runs at each design point.
     n_macroreps = 3
@@ -70,26 +72,27 @@ def main():
     crn_across_init_opt = True  # Default
 
     # Create DataFarmingExperiment object for model design
-    model_design_list = create_design(name=model_name,
-                    factor_headers=model_factor_headers,
-                    factor_settings_filename=model_factor_settings_filename,
-                    n_stacks=problem_n_stacks,
-                    fixed_factors=model_fixed_factors, #optional
-                    #cross_design_factors=model_cross_design_factors, #optional
-                    )
-
-        
+    model_design_list = create_design(
+        name=model_name,
+        factor_headers=model_factor_headers,
+        factor_settings_filename=model_factor_settings_filename,
+        n_stacks=problem_n_stacks,
+        fixed_factors=model_fixed_factors,  # optional
+        # cross_design_factors=model_cross_design_factors, #optional
+    )
+
     # create proble name list for ProblemsSolvers (do not edit)
     problem_names = []
-    for i in range(len(model_design_list)): 
+    for _ in range(len(model_design_list)):
         problem_names.append(problem_name)
 
     # Create ProblemsSovlers experiment with solver and model design
-    experiment = ProblemsSolvers(solver_factors = solver_fixed_factors,
-                                problem_factors = model_design_list,
-                                solver_names = solver_names,
-                                problem_names = problem_names
-                                )
+    experiment = ProblemsSolvers(
+        solver_factors=solver_fixed_factors,
+        problem_factors=model_design_list,
+        solver_names=solver_names,
+        problem_names=problem_names,
+    )
 
     # check compatibility of selected solvers and problems
     experiment.check_compatibility()
@@ -98,16 +101,21 @@ def main():
     experiment.run(n_macroreps)
 
     # Postprocess the experimental results from each design point.
-    experiment.post_replicate(n_postreps=n_postreps,
-                            crn_across_budget=crn_across_budget,
-                            crn_across_macroreps=crn_across_macroreps)
-    experiment.post_normalize(n_postreps_init_opt=n_postreps_init_opt,
-                            crn_across_init_opt=crn_across_init_opt)
+    experiment.post_replicate(
+        n_postreps=n_postreps,
+        crn_across_budget=crn_across_budget,
+        crn_across_macroreps=crn_across_macroreps,
+    )
+    experiment.post_normalize(
+        n_postreps_init_opt=n_postreps_init_opt,
+        crn_across_init_opt=crn_across_init_opt,
+    )
 
     # Record and log results
     experiment.record_group_experiment_results()
     experiment.log_group_experiment_results()
     experiment.report_group_statistics()
 
+
 if __name__ == "__main__":
-    main()
+    main()