API

Here are some examples of the machine learning API applied to regression models.

from __future__ import absolute_import, division, print_function, unicode_literals

from future import standard_library

from gemseo.api import (
    configure_logger,
    create_design_space,
    create_discipline,
    create_scenario,
)
from gemseo.mlearning.api import (
    create_regression_model,
    get_regression_models,
    get_regression_options,
)

configure_logger()

standard_library.install_aliases()

Get available regression models

print(get_regression_models())

Out:

['GaussianProcessRegression', 'LinearRegression', 'MixtureOfExperts', 'PCERegression', 'PolynomialRegression', 'RBFRegression', 'RandomForestRegressor']

Get regression model options

print(get_regression_options("GaussianProcessRegression"))

Out:

{'type': 'object', 'properties': {'transformer': {'description': 'transformation strategy for data groups.\nIf None, do not transform data. Default: None.\n:type transformer: dict(str)\n'}, 'input_names': {'description': 'names of the input variables.\n:type input_names: list(str)\n'}, 'output_names': {'description': 'names of the output variables.\n:type output_names: list(str)\n'}, 'kernel': {'description': 'kernel function. If None, use a Matern(2.5).\nDefault: None.\n:type kernel: openturns.Kernel\n'}, 'alpha': {'type': 'number', 'description': 'nugget effect. Default: 1e-10.\n:type alpha: float or array\n'}, 'optimizer': {'type': 'string', 'description': "optimization algorithm. Default: 'fmin_l_bfgs_b'.\n:type optimizer: str or callable\n"}, 'n_restarts_optimizer': {'type': 'integer', 'description': 'number of restarts of the optimizer.\nDefault: 10.\n:type n_restarts_optimizer: int\n'}, 'random_state': {'description': 'the seed used to initialize the centers.\nIf None, the random number generator is the RandomState instance\nused by `np.random`\nDefault: None.\n:type random_state: int'}}, 'required': ['alpha', 'n_restarts_optimizer', 'optimizer']}

Create regression model

expressions_dict = {"y_1": "1+2*x_1+3*x_2", "y_2": "-1-2*x_1-3*x_2"}
discipline = create_discipline(
    "AnalyticDiscipline", name="func", expressions_dict=expressions_dict
)

design_space = create_design_space()
design_space.add_variable("x_1", l_b=0.0, u_b=1.0)
design_space.add_variable("x_2", l_b=0.0, u_b=1.0)

discipline.set_cache_policy(discipline.MEMORY_FULL_CACHE)
scenario = create_scenario(
    [discipline], "DisciplinaryOpt", "y_1", design_space, scenario_type="DOE"
)
scenario.execute({"algo": "fullfact", "n_samples": 9})

dataset = discipline.cache.export_to_dataset()
model = create_regression_model("LinearRegression", data=dataset)
model.learn()

print(model)

Out:

LinearRegression(fit_intercept=True, penalty_level=0.0, l2_penalty_ratio=1.0)
| based on the scikit-learn library
| built from 9 learning samples