Linear regression

We want to approximate a discipline with two inputs and two outputs:

  • \(y_1=1+2x_1+3x_2\)

  • \(y_2=-1-2x_1-3x_2\)

over the unit hypercube \([0,1]\times[0,1]\).

Import

from __future__ import division, unicode_literals

from numpy import array

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

configure_logger()

Out:

<RootLogger root (INFO)>

Create the discipline to learn

We can implement this analytic discipline by means of the AnalyticDiscipline class.

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
)

Create the input sampling space

We create the input sampling space by adding the variables one by one.

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)

Create the learning set

We can build a learning set by means of a DOEScenario with a full factorial design of experiments. The number of samples can be equal to 9 for example.

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})

Out:

    INFO - 14:41:04:
    INFO - 14:41:04: *** Start DOE Scenario execution ***
    INFO - 14:41:04: DOEScenario
    INFO - 14:41:04:    Disciplines: func
    INFO - 14:41:04:    MDOFormulation: DisciplinaryOpt
    INFO - 14:41:04:    Algorithm: fullfact
    INFO - 14:41:04: Optimization problem:
    INFO - 14:41:04:    Minimize: y_1(x_1, x_2)
    INFO - 14:41:04:    With respect to: x_1, x_2
    INFO - 14:41:04: Full factorial design required. Number of samples along each direction for a design vector of size 2 with 9 samples: 3
    INFO - 14:41:04: Final number of samples for DOE = 9 vs 9 requested
    INFO - 14:41:04: DOE sampling:   0%|          | 0/9 [00:00<?, ?it]
    INFO - 14:41:04: DOE sampling: 100%|██████████| 9/9 [00:00<00:00, 577.66 it/sec, obj=6]
    INFO - 14:41:04: Optimization result:
    INFO - 14:41:04: Objective value = 1.0
    INFO - 14:41:04: The result is feasible.
    INFO - 14:41:04: Status: None
    INFO - 14:41:04: Optimizer message: None
    INFO - 14:41:04: Number of calls to the objective function by the optimizer: 9
    INFO - 14:41:04: Design space:
    INFO - 14:41:04: +------+-------------+-------+-------------+-------+
    INFO - 14:41:04: | name | lower_bound | value | upper_bound | type  |
    INFO - 14:41:04: +------+-------------+-------+-------------+-------+
    INFO - 14:41:04: | x_1  |      0      |   0   |      1      | float |
    INFO - 14:41:04: | x_2  |      0      |   0   |      1      | float |
    INFO - 14:41:04: +------+-------------+-------+-------------+-------+
    INFO - 14:41:04: *** DOE Scenario run terminated ***

{'eval_jac': False, 'algo': 'fullfact', 'n_samples': 9}

Create the regression model

Then, we build the linear regression model from the discipline cache and displays this model.

dataset = discipline.cache.export_to_dataset()
model = create_regression_model("LinearRegression", data=dataset, transformer=None)
model.learn()
print(model)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/stable/lib/python3.8/site-packages/sklearn/linear_model/_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.
  warnings.warn(
LinearRegression(fit_intercept=True, l2_penalty_ratio=1.0, penalty_level=0.0)
   based on the scikit-learn library
   built from 9 learning samples

Predict output

Once it is built, we can use it for prediction.

input_value = {"x_1": array([1.0]), "x_2": array([2.0])}
output_value = model.predict(input_value)
print(output_value)

Out:

{'y_1': array([9.]), 'y_2': array([-9.])}

Predict jacobian

We can also use it to predict the jacobian of the discipline.

jacobian_value = model.predict_jacobian(input_value)
print(jacobian_value)

Out:

{'y_1': {'x_1': array([[2.]]), 'x_2': array([[3.]])}, 'y_2': {'x_1': array([[-2.]]), 'x_2': array([[-3.]])}}

Get intercept

In addition, it is possible to access the intercept of the model, either directly or by means of a method returning either a dictionary (default option) or an array.

print(model.intercept)
print(model.get_intercept())

Out:

[ 1. -1.]
{'y_1': [0.9999999999999987], 'y_2': [-0.9999999999999987]}

Get coefficients

In addition, it is possible to access the coefficients of the model, either directly or by means of a method returning either a dictionary (default option) or an array.

print(model.coefficients)
print(model.get_coefficients())

Out:

[[ 2.  3.]
 [-2. -3.]]
{'y_1': [{'x_1': [2.000000000000001], 'x_2': [3.0000000000000018]}], 'y_2': [{'x_1': [-2.000000000000001], 'x_2': [-3.0000000000000018]}]}

Total running time of the script: ( 0 minutes 0.076 seconds)

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