[WIP] Implement VOCS interface

.github/workflows/unix-noax.yml

@@ -31,6 +31,7 @@ jobs:
         conda install numpy pandas pytorch cpuonly -c pytorch
         conda install -c conda-forge mpi4py mpich
         pip install .[test]
+        pip install git+https://github.com/campa-consortium/generator_standard.git@obs_type
         pip uninstall --yes ax-platform # Run without Ax
     - shell: bash -l {0}
       name: Run unit tests without Ax

.github/workflows/unix-openmpi.yml

@@ -31,6 +31,7 @@ jobs:
         conda install numpy pandas pytorch cpuonly -c pytorch
         conda install -c conda-forge mpi4py openmpi=5.*
         pip install .[test]
+        pip install git+https://github.com/campa-consortium/generator_standard.git@obs_type
     - shell: bash -l {0}
       name: Run unit tests with openMPI
       run: |

.github/workflows/unix.yml

@@ -31,6 +31,7 @@ jobs:
         conda install numpy pandas pytorch cpuonly -c pytorch
         conda install -c conda-forge mpi4py mpich
         pip install .[test]
+        pip install git+https://github.com/campa-consortium/generator_standard.git@obs_type
     - shell: bash -l {0}
       name: Run unit tests with MPICH
       run: |

examples/astra/run_optimization_serial_ASTRA.py

@@ -9,34 +9,32 @@
 https://optimas.readthedocs.io/en/latest/index.html
 """
 
-from optimas.core import VaryingParameter, Objective, Parameter
 from optimas.generators import AxSingleFidelityGenerator
 from optimas.evaluators import TemplateEvaluator
 from optimas.explorations import Exploration
+from generator_standard.vocs import VOCS
 from analysis_script import analyze_simulation
 
-# Create varying parameters and objectives.
+# Create VOCS object.
 # name of parameter, lower bound of values to be explored,
 # upper bound of values to be explored
-var_1 = VaryingParameter("RF_phase", -2.5, 2.5)
-var_2 = VaryingParameter("B_sol", 0.12, 0.38)
-# Objectives that will be minimized:
-obj_1 = Objective("bunch_length", minimize=True)
-obj_2 = Objective("emittance", minimize=True)
-# Additional example parameters that will be analyzed but are not used for the
-# optimization:
-em_x = Parameter("emittance_x")
-em_y = Parameter("emittance_y")
+vocs = VOCS(
+    variables={
+        "RF_phase": [-2.5, 2.5],
+        "B_sol": [0.12, 0.38],
+    },
+    objectives={
+        "bunch_length": "MINIMIZE",
+        "emittance": "MINIMIZE",
+    },
+    observables=["emittance_x", "emittance_y"],
+)
 
 # Create generator.
 # Pick the generator to be used, here Single-fidelity Bayesian optimization.
-# The analyzed_parameters are parameters that are calculated for each
-# simulation but not used for the optimization.
 gen = AxSingleFidelityGenerator(
-    varying_parameters=[var_1, var_2],
-    objectives=[obj_1, obj_2],
+    vocs=vocs,
     n_init=8,
-    analyzed_parameters=[em_x, em_y],
 )
 
 

examples/dummy/run_example.py

@@ -1,6 +1,6 @@
 """Basic example of parallel Bayesian optimization with Ax."""
 
-from optimas.core import VaryingParameter, Objective
+from generator_standard.vocs import VOCS
 from optimas.generators import AxSingleFidelityGenerator
 from optimas.evaluators import TemplateEvaluator
 from optimas.explorations import Exploration
@@ -10,16 +10,16 @@ def analyze_simulation(simulation_directory, output_params):
     """Analyze the simulation output.
 
     This method analyzes the output generated by the simulation to
-    obtain the value of the optimization objective and other analyzed
-    parameters, if specified. The value of these parameters has to be
-    given to the `output_params` dictionary.
+    obtain the value of the optimization objective and other observables.
+    The value of these parameters has to be given to the
+    `output_params` dictionary.
 
     Parameters
     ----------
     simulation_directory : str
         Path to the simulation folder where the output was generated.
     output_params : dict
-        Dictionary where the value of the objectives and analyzed parameters
+        Dictionary where the value of the objectives and observables
         will be stored. There is one entry per parameter, where the key
         is the name of the parameter given by the user.
 
@@ -37,16 +37,18 @@ def analyze_simulation(simulation_directory, output_params):
     return output_params
 
 
-# Create varying parameters and objectives.
-var_1 = VaryingParameter("x0", 0.0, 15.0)
-var_2 = VaryingParameter("x1", 0.0, 15.0)
-obj = Objective("f", minimize=True)
+# Create VOCS object defining variables, objectives.
+vocs = VOCS(
+    variables={
+        "x0": [0.0, 15.0],
+        "x1": [0.0, 15.0],
+    },
+    objectives={"f": "MINIMIZE"},
+)
 
 
 # Create generator.
-gen = AxSingleFidelityGenerator(
-    varying_parameters=[var_1, var_2], objectives=[obj], n_init=2
-)
+gen = AxSingleFidelityGenerator(vocs=vocs, n_init=2)
 
 
 # Create evaluator.

examples/dummy_grid_sampling/run_example.py

@@ -1,6 +1,6 @@
 """Basic example of parallel grid sampling with simulations."""
 
-from optimas.core import VaryingParameter, Objective
+from generator_standard.vocs import VOCS
 from optimas.generators import GridSamplingGenerator
 from optimas.evaluators import TemplateEvaluator
 from optimas.explorations import Exploration
@@ -10,16 +10,16 @@ def analyze_simulation(simulation_directory, output_params):
     """Analyze the simulation output.
 
     This method analyzes the output generated by the simulation to
-    obtain the value of the optimization objective and other analyzed
-    parameters, if specified. The value of these parameters has to be
-    given to the `output_params` dictionary.
+    obtain the value of the optimization objective and other observables.
+    The value of these parameters has to be given to the
+    `output_params` dictionary.
 
     Parameters
     ----------
     simulation_directory : str
         Path to the simulation folder where the output was generated.
     output_params : dict
-        Dictionary where the value of the objectives and analyzed parameters
+        Dictionary where the value of the objectives and observables
         will be stored. There is one entry per parameter, where the key
         is the name of the parameter given by the user.
 
@@ -37,16 +37,18 @@ def analyze_simulation(simulation_directory, output_params):
     return output_params
 
 
-# Create varying parameters and objectives.
-var_1 = VaryingParameter("x0", 0.0, 15.0)
-var_2 = VaryingParameter("x1", 0.0, 15.0)
-obj = Objective("f")
+# Create VOCS object defining variables, objectives.
+vocs = VOCS(
+    variables={
+        "x0": [0.0, 15.0],
+        "x1": [0.0, 15.0],
+    },
+    objectives={"f": "MAXIMIZE"},
+)
 
 
 # Create generator.
-gen = GridSamplingGenerator(
-    varying_parameters=[var_1, var_2], objectives=[obj], n_steps=[5, 7]
-)
+gen = GridSamplingGenerator(vocs=vocs, n_steps=[5, 7])
 
 
 # Create evaluator.

examples/dummy_line_sampling/run_example.py

@@ -1,6 +1,6 @@
 """Basic example of parallel line sampling with simulations."""
 
-from optimas.core import VaryingParameter, Objective
+from generator_standard.vocs import VOCS, ContinuousVariable
 from optimas.generators import LineSamplingGenerator
 from optimas.evaluators import TemplateEvaluator
 from optimas.explorations import Exploration
@@ -10,16 +10,16 @@ def analyze_simulation(simulation_directory, output_params):
     """Analyze the simulation output.
 
     This method analyzes the output generated by the simulation to
-    obtain the value of the optimization objective and other analyzed
-    parameters, if specified. The value of these parameters has to be
-    given to the `output_params` dictionary.
+    obtain the value of the optimization objective and other observables.
+    The value of these parameters has to be given to the
+    `output_params` dictionary.
 
     Parameters
     ----------
     simulation_directory : str
         Path to the simulation folder where the output was generated.
     output_params : dict
-        Dictionary where the value of the objectives and analyzed parameters
+        Dictionary where the value of the objectives and observables
         will be stored. There is one entry per parameter, where the key
         is the name of the parameter given by the user.
 
@@ -37,16 +37,18 @@ def analyze_simulation(simulation_directory, output_params):
     return output_params
 
 
-# Create varying parameters and objectives.
-var_1 = VaryingParameter("x0", 0.0, 15.0, default_value=5.0)
-var_2 = VaryingParameter("x1", 0.0, 15.0, default_value=6.0)
-obj = Objective("f")
+# Create VOCS object defining variables, objectives.
+vocs = VOCS(
+    variables={
+        "x0": ContinuousVariable(domain=[0.0, 15.0], default_value=5.0),
+        "x1": ContinuousVariable(domain=[0.0, 15.0], default_value=6.0),
+    },
+    objectives={"f": "MAXIMIZE"},
+)
 
 
 # Create generator.
-gen = LineSamplingGenerator(
-    varying_parameters=[var_1, var_2], objectives=[obj], n_steps=[5, 7]
-)
+gen = LineSamplingGenerator(vocs=vocs, n_steps=[5, 7])
 
 
 # Create evaluator.

examples/dummy_mf/run_example.py

@@ -1,6 +1,6 @@
 """Basic example of parallel multi-fidelity Bayesian optimization with Ax."""
 
-from optimas.core import VaryingParameter, Objective
+from generator_standard.vocs import VOCS
 from optimas.generators import AxMultiFidelityGenerator
 from optimas.evaluators import TemplateEvaluator
 from optimas.explorations import Exploration
@@ -10,16 +10,16 @@ def analyze_simulation(simulation_directory, output_params):
     """Analyze the simulation output.
 
     This method analyzes the output generated by the simulation to
-    obtain the value of the optimization objective and other analyzed
-    parameters, if specified. The value of these parameters has to be
-    given to the `output_params` dictionary.
+    obtain the value of the optimization objective and other observables.
+    The value of these parameters has to be given to the
+    `output_params` dictionary.
 
     Parameters
     ----------
     simulation_directory : str
         Path to the simulation folder where the output was generated.
     output_params : dict
-        Dictionary where the value of the objectives and analyzed parameters
+        Dictionary where the value of the objectives and observables
         will be stored. There is one entry per parameter, where the key
         is the name of the parameter given by the user.
 
@@ -37,22 +37,24 @@ def analyze_simulation(simulation_directory, output_params):
     return output_params
 
 
-# Create varying parameters (including fidelity) and objectives.
-var_1 = VaryingParameter("x0", 0.0, 15.0)
-var_2 = VaryingParameter("x1", 0.0, 15.0)
-res = VaryingParameter(
-    "resolution", 1.0, 8.0, is_fidelity=True, fidelity_target_value=8.0
+# Create VOCS object defining variables, objectives.
+vocs = VOCS(
+    variables={
+        "x0": [0.0, 15.0],
+        "x1": [0.0, 15.0],
+        "resolution": [1.0, 8.0],
+    },
+    objectives={"f": "MINIMIZE"},
 )
-obj = Objective("f", minimize=True)
 
 
 # Create generator.
 gen = AxMultiFidelityGenerator(
-    varying_parameters=[var_1, var_2, res],
-    objectives=[obj],
+    vocs=vocs,
     n_init=4,
     fidel_cost_intercept=2.0,
 )
+gen.set_fidelity_param("resolution", fidelity_target_value=8.0)
 
 
 # Create evaluator.

examples/dummy_mt/run_example.py

@@ -1,6 +1,7 @@
 """Basic example of parallel multitask Bayesian optimization with Ax."""
 
-from optimas.core import VaryingParameter, Objective, Task
+from generator_standard.vocs import VOCS
+from optimas.core import Task
 from optimas.generators import AxMultitaskGenerator
 from optimas.evaluators import TemplateEvaluator, MultitaskEvaluator
 from optimas.explorations import Exploration
@@ -10,16 +11,16 @@ def analyze_simulation(simulation_directory, output_params):
     """Analyze the simulation output.
 
     This method analyzes the output generated by the simulation to
-    obtain the value of the optimization objective and other analyzed
-    parameters, if specified. The value of these parameters has to be
-    given to the `output_params` dictionary.
+    obtain the value of the optimization objective and other observables.
+    The value of these parameters has to be given to the
+    `output_params` dictionary.
 
     Parameters
     ----------
     simulation_directory : str
         Path to the simulation folder where the output was generated.
     output_params : dict
-        Dictionary where the value of the objectives and analyzed parameters
+        Dictionary where the value of the objectives and observables
         will be stored. There is one entry per parameter, where the key
         is the name of the parameter given by the user.
 
@@ -37,10 +38,15 @@ def analyze_simulation(simulation_directory, output_params):
     return output_params
 
 
-# Create varying parameters and objectives.
-var_1 = VaryingParameter("x0", 0.0, 15.0)
-var_2 = VaryingParameter("x1", 0.0, 15.0)
-obj = Objective("f", minimize=True)
+# Create VOCS object defining variables, objectives.
+vocs = VOCS(
+    variables={
+        "x0": [0.0, 15.0],
+        "x1": [0.0, 15.0],
+        "trial_type": {"cheap_model", "expensive_model"},
+    },
+    objectives={"f": "MINIMIZE"},
+)
 
 
 # Create tasks.
@@ -50,8 +56,7 @@ def analyze_simulation(simulation_directory, output_params):
 
 # Create generator.
 gen = AxMultitaskGenerator(
-    varying_parameters=[var_1, var_2],
-    objectives=[obj],
+    vocs=vocs,
     lofi_task=lofi_task,
     hifi_task=hifi_task,
 )