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Scenario#

from __future__ import annotations

from gemseo import configure_logger
from gemseo import create_design_space
from gemseo import create_discipline
from gemseo import create_scenario
from gemseo import get_available_scenario_types
from gemseo import get_scenario_differentiation_modes
from gemseo import get_scenario_inputs_schema
from gemseo import get_scenario_options_schema
from gemseo import monitor_scenario

configure_logger()

<RootLogger root (INFO)>

In this example, we will discover the different functions of the API to related to scenarios, which are the GEMSEO' objects dedicated to the resolution of a problem, e.g. optimization or trade-off, associated with a list of disciplines and a design space. All classes implementing scenarios inherit from Scenario which is an abstract class. Classical concrete classes are MDOScenario and DOEScenario, respectively dedicated to optimization and trade-off problems.

Get available scenario type#

The high-level function get_available_scenario_types() can be used to get the available scenario types (MDOScenario and DOEScenario).

get_available_scenario_types()

['MDO', 'DOE']

Get scenario options schema#

The get_scenario_options_schema() function can be used to get the options of a given scenario type:

get_scenario_options_schema("MDO")

{'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'name': {'description': 'The name to be given to this scenario. If empty, use the name of the class.', 'type': 'string'}, 'maximize_objective': {'description': 'Whether to maximize the objective.', 'type': 'boolean'}, 'formulation_settings_model': {'description': 'The formulation settings as a Pydantic model. If ``None``, use ``**settings``.', 'type': 'null'}}}

Create a scenario#

The API function create_scenario() can be used to create a scenario:

  • The four first arguments are mandatory:

    • disciplines: the list of Discipline (or possibly, a single Discipline),

    • objective_name: the name of the objective function (one of the discipline outputs),

    • design_space: the DesignSpace or the file path of the design space,

    • either a formulation_name followed by its formulation_settings`; or

    • a formulation_settings_model (see Formulation Settings).

  • The other arguments are optional:

    • name: scenario name,

    • scenario_type: type of scenario, either "MDO" (default) or "DOE" ,

    • **formulation_settings: settings passed to the formulation as keyword arguments when the formulation_settings_model was not provided.

  • This function returns an instance of MDOScenario or DOEScenario.

discipline = create_discipline("AnalyticDiscipline", expressions={"y": "x1+x2"})
design_space = create_design_space()
design_space.add_variable("x1", 1, "float", 0.0, 1.0)
design_space.add_variable("x2", 1, "float", 0.0, 1.0)

scenario = create_scenario(
    discipline,
    "y",
    design_space,
    scenario_type="DOE",
    formulation_name="DisciplinaryOpt",
)
scenario.execute(algo_name="PYDOE_FULLFACT", n_samples=25)
scenario.post_process(
    post_name="ScatterPlotMatrix",
    variable_names=["x1", "x2", "y"],
    save=False,
    show=True,
)
plot scenario
    INFO - 08:36:14:
    INFO - 08:36:14: *** Start DOEScenario execution ***
    INFO - 08:36:14: DOEScenario
    INFO - 08:36:14:    Disciplines: AnalyticDiscipline
    INFO - 08:36:14:    MDO formulation: DisciplinaryOpt
    INFO - 08:36:14: Optimization problem:
    INFO - 08:36:14:    minimize y(x1, x2)
    INFO - 08:36:14:    with respect to x1, x2
    INFO - 08:36:14:    over the design space:
    INFO - 08:36:14:       +------+-------------+-------+-------------+-------+
    INFO - 08:36:14:       | Name | Lower bound | Value | Upper bound | Type  |
    INFO - 08:36:14:       +------+-------------+-------+-------------+-------+
    INFO - 08:36:14:       | x1   |      0      |  None |      1      | float |
    INFO - 08:36:14:       | x2   |      0      |  None |      1      | float |
    INFO - 08:36:14:       +------+-------------+-------+-------------+-------+
    INFO - 08:36:14: Solving optimization problem with algorithm PYDOE_FULLFACT:
    INFO - 08:36:14:      4%|▍         | 1/25 [00:00<00:00, 456.70 it/sec, obj=0]
    INFO - 08:36:14:      8%|▊         | 2/25 [00:00<00:00, 750.86 it/sec, obj=0.25]
    INFO - 08:36:14:     12%|█▏        | 3/25 [00:00<00:00, 949.44 it/sec, obj=0.5]
    INFO - 08:36:14:     16%|█▌        | 4/25 [00:00<00:00, 1114.25 it/sec, obj=0.75]
    INFO - 08:36:14:     20%|██        | 5/25 [00:00<00:00, 1251.28 it/sec, obj=1]
    INFO - 08:36:14:     24%|██▍       | 6/25 [00:00<00:00, 1355.11 it/sec, obj=0.25]
    INFO - 08:36:14:     28%|██▊       | 7/25 [00:00<00:00, 1447.60 it/sec, obj=0.5]
    INFO - 08:36:14:     32%|███▏      | 8/25 [00:00<00:00, 1519.12 it/sec, obj=0.75]
    INFO - 08:36:14:     36%|███▌      | 9/25 [00:00<00:00, 1588.08 it/sec, obj=1]
    INFO - 08:36:14:     40%|████      | 10/25 [00:00<00:00, 1644.37 it/sec, obj=1.25]
    INFO - 08:36:14:     44%|████▍     | 11/25 [00:00<00:00, 1696.41 it/sec, obj=0.5]
    INFO - 08:36:14:     48%|████▊     | 12/25 [00:00<00:00, 1733.18 it/sec, obj=0.75]
    INFO - 08:36:14:     52%|█████▏    | 13/25 [00:00<00:00, 1766.82 it/sec, obj=1]
    INFO - 08:36:14:     56%|█████▌    | 14/25 [00:00<00:00, 1802.62 it/sec, obj=1.25]
    INFO - 08:36:14:     60%|██████    | 15/25 [00:00<00:00, 1836.17 it/sec, obj=1.5]
    INFO - 08:36:14:     64%|██████▍   | 16/25 [00:00<00:00, 1861.50 it/sec, obj=0.75]
    INFO - 08:36:14:     68%|██████▊   | 17/25 [00:00<00:00, 1888.43 it/sec, obj=1]
    INFO - 08:36:14:     72%|███████▏  | 18/25 [00:00<00:00, 1913.90 it/sec, obj=1.25]
    INFO - 08:36:14:     76%|███████▌  | 19/25 [00:00<00:00, 1935.35 it/sec, obj=1.5]
    INFO - 08:36:14:     80%|████████  | 20/25 [00:00<00:00, 1954.75 it/sec, obj=1.75]
    INFO - 08:36:14:     84%|████████▍ | 21/25 [00:00<00:00, 1975.07 it/sec, obj=1]
    INFO - 08:36:14:     88%|████████▊ | 22/25 [00:00<00:00, 1986.45 it/sec, obj=1.25]
    INFO - 08:36:14:     92%|█████████▏| 23/25 [00:00<00:00, 2002.26 it/sec, obj=1.5]
    INFO - 08:36:14:     96%|█████████▌| 24/25 [00:00<00:00, 2018.47 it/sec, obj=1.75]
    INFO - 08:36:14:    100%|██████████| 25/25 [00:00<00:00, 2034.21 it/sec, obj=2]
    INFO - 08:36:14: Optimization result:
    INFO - 08:36:14:    Optimizer info:
    INFO - 08:36:14:       Status: None
    INFO - 08:36:14:       Message: None
    INFO - 08:36:14:       Number of calls to the objective function by the optimizer: 25
    INFO - 08:36:14:    Solution:
    INFO - 08:36:14:       Objective: 0.0
    INFO - 08:36:14:       Design space:
    INFO - 08:36:14:          +------+-------------+-------+-------------+-------+
    INFO - 08:36:14:          | Name | Lower bound | Value | Upper bound | Type  |
    INFO - 08:36:14:          +------+-------------+-------+-------------+-------+
    INFO - 08:36:14:          | x1   |      0      |   0   |      1      | float |
    INFO - 08:36:14:          | x2   |      0      |   0   |      1      | float |
    INFO - 08:36:14:          +------+-------------+-------+-------------+-------+
    INFO - 08:36:14: *** End DOEScenario execution (time: 0:00:00.016556) ***

<gemseo.post.scatter_plot_matrix.ScatterPlotMatrix object at 0x7f6dc8f6bb50>

get_scenario_inputs_schema(scenario)

{'$defs': {'_algo_enum': {'enum': ['CustomDOE', 'DiagonalDOE', 'MorrisDOE', 'OATDOE', 'OT_SOBOL', 'OT_RANDOM', 'OT_HASELGROVE', 'OT_REVERSE_HALTON', 'OT_HALTON', 'OT_FAURE', 'OT_MONTE_CARLO', 'OT_FACTORIAL', 'OT_COMPOSITE', 'OT_AXIAL', 'OT_OPT_LHS', 'OT_LHS', 'OT_LHSC', 'OT_FULLFACT', 'OT_SOBOL_INDICES', 'PYDOE_BBDESIGN', 'PYDOE_CCDESIGN', 'PYDOE_FF2N', 'PYDOE_FULLFACT', 'PYDOE_LHS', 'PYDOE_PBDESIGN', 'Halton', 'LHS', 'MC', 'PoissonDisk', 'Sobol'], 'title': 'algo_enum', 'type': 'string'}}, 'properties': {'algo_name': {'$ref': '#/$defs/_algo_enum', 'description': 'The name of the algorithm.'}, 'algo_settings': {'description': 'The settings for the algorithm.', 'title': 'Algo Settings', 'type': 'object'}}, 'required': ['algo_name'], 'title': 'Settings', 'type': 'object'}

Get scenario differentiation modes#

The get_scenario_differentiation_modes() can be used to get the available differentiation modes of a scenario:

get_scenario_differentiation_modes()

(<DifferentiationMethod.COMPLEX_STEP: 'complex_step'>, <DifferentiationMethod.FINITE_DIFFERENCES: 'finite_differences'>, <DifferentiationMethod.CENTERED_DIFFERENCES: 'centered_differences'>, <DifferentiationMethod.USER_GRAD: 'user'>, <DifferentiationMethod.NO_DERIVATIVE: 'no_derivative'>)

Monitor a scenario#

To monitor a scenario execution programmatically, ie get a notification when a discipline status is changed, use monitor_scenario(). The first argument is the scenario to monitor, and the second is an observer object, that is notified by its update(atom) method, which takes an ExecutionSequence as argument. This method will be called every time a discipline status changes. The atom represents a discipline's position in the process. One discipline can have multiple atoms, since one discipline can be used in multiple positions in the MDO formulation.

class Observer:
    """Observer."""

    def update(self, atom) -> None:
        """Update method.

        :param ExecutionSequence atom: atomic execution sequence.
        """
        print(atom)


scenario = create_scenario(
    discipline,
    "y",
    design_space,
    scenario_type="DOE",
    formulation_name="DisciplinaryOpt",
)
monitor_scenario(scenario, Observer())
scenario.execute(algo_name="PYDOE_FULLFACT", n_samples=25)

    INFO - 08:36:15:
    INFO - 08:36:15: *** Start DOEScenario execution ***
    INFO - 08:36:15: DOEScenario
    INFO - 08:36:15:    Disciplines: AnalyticDiscipline
    INFO - 08:36:15:    MDO formulation: DisciplinaryOpt
DOEScenario(RUNNING)
    INFO - 08:36:15: Optimization problem:
    INFO - 08:36:15:    minimize y(x1, x2)
    INFO - 08:36:15:    with respect to x1, x2
    INFO - 08:36:15:    over the design space:
    INFO - 08:36:15:       +------+-------------+-------+-------------+-------+
    INFO - 08:36:15:       | Name | Lower bound | Value | Upper bound | Type  |
    INFO - 08:36:15:       +------+-------------+-------+-------------+-------+
    INFO - 08:36:15:       | x1   |      0      |   0   |      1      | float |
    INFO - 08:36:15:       | x2   |      0      |   0   |      1      | float |
    INFO - 08:36:15:       +------+-------------+-------+-------------+-------+
    INFO - 08:36:15: Solving optimization problem with algorithm PYDOE_FULLFACT:
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:      4%|▍         | 1/25 [00:00<00:00, 1707.78 it/sec, obj=0]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:      8%|▊         | 2/25 [00:00<00:00, 1771.99 it/sec, obj=0.25]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     12%|█▏        | 3/25 [00:00<00:00, 1806.07 it/sec, obj=0.5]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     16%|█▌        | 4/25 [00:00<00:00, 1831.77 it/sec, obj=0.75]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     20%|██        | 5/25 [00:00<00:00, 1854.25 it/sec, obj=1]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     24%|██▍       | 6/25 [00:00<00:00, 1867.73 it/sec, obj=0.25]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     28%|██▊       | 7/25 [00:00<00:00, 1881.46 it/sec, obj=0.5]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     32%|███▏      | 8/25 [00:00<00:00, 1863.62 it/sec, obj=0.75]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     36%|███▌      | 9/25 [00:00<00:00, 1869.58 it/sec, obj=1]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     40%|████      | 10/25 [00:00<00:00, 1877.74 it/sec, obj=1.25]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     44%|████▍     | 11/25 [00:00<00:00, 1879.55 it/sec, obj=0.5]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     48%|████▊     | 12/25 [00:00<00:00, 1877.07 it/sec, obj=0.75]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     52%|█████▏    | 13/25 [00:00<00:00, 1878.33 it/sec, obj=1]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     56%|█████▌    | 14/25 [00:00<00:00, 1883.39 it/sec, obj=1.25]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     60%|██████    | 15/25 [00:00<00:00, 1888.87 it/sec, obj=1.5]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     64%|██████▍   | 16/25 [00:00<00:00, 1886.25 it/sec, obj=0.75]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     68%|██████▊   | 17/25 [00:00<00:00, 1889.58 it/sec, obj=1]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     72%|███████▏  | 18/25 [00:00<00:00, 1890.79 it/sec, obj=1.25]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     76%|███████▌  | 19/25 [00:00<00:00, 1890.09 it/sec, obj=1.5]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     80%|████████  | 20/25 [00:00<00:00, 1892.91 it/sec, obj=1.75]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     84%|████████▍ | 21/25 [00:00<00:00, 1893.43 it/sec, obj=1]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     88%|████████▊ | 22/25 [00:00<00:00, 1895.46 it/sec, obj=1.25]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     92%|█████████▏| 23/25 [00:00<00:00, 1894.45 it/sec, obj=1.5]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:     96%|█████████▌| 24/25 [00:00<00:00, 1893.27 it/sec, obj=1.75]
AnalyticDiscipline(RUNNING)
AnalyticDiscipline(DONE)
    INFO - 08:36:15:    100%|██████████| 25/25 [00:00<00:00, 1895.47 it/sec, obj=2]
    INFO - 08:36:15: Optimization result:
    INFO - 08:36:15:    Optimizer info:
    INFO - 08:36:15:       Status: None
    INFO - 08:36:15:       Message: None
    INFO - 08:36:15:       Number of calls to the objective function by the optimizer: 25
    INFO - 08:36:15:    Solution:
    INFO - 08:36:15:       Objective: 0.0
    INFO - 08:36:15:       Design space:
    INFO - 08:36:15:          +------+-------------+-------+-------------+-------+
    INFO - 08:36:15:          | Name | Lower bound | Value | Upper bound | Type  |
    INFO - 08:36:15:          +------+-------------+-------+-------------+-------+
    INFO - 08:36:15:          | x1   |      0      |   0   |      1      | float |
    INFO - 08:36:15:          | x2   |      0      |   0   |      1      | float |
    INFO - 08:36:15:          +------+-------------+-------+-------------+-------+
DOEScenario(DONE)
    INFO - 08:36:15: *** End DOEScenario execution (time: 0:00:00.017261) ***

Total running time of the script: (0 minutes 0.683 seconds)

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