{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# API\n\nHere are some examples of the machine learning API\napplied to regression models.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom future import standard_library\n\nfrom gemseo.api import (\n    configure_logger,\n    create_design_space,\n    create_discipline,\n    create_scenario,\n)\nfrom gemseo.mlearning.api import (\n    create_regression_model,\n    get_regression_models,\n    get_regression_options,\n)\n\nconfigure_logger()\n\nstandard_library.install_aliases()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get available regression models\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(get_regression_models())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get regression model options\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(get_regression_options(\"GaussianProcessRegression\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create regression model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "expressions_dict = {\"y_1\": \"1+2*x_1+3*x_2\", \"y_2\": \"-1-2*x_1-3*x_2\"}\ndiscipline = create_discipline(\n    \"AnalyticDiscipline\", name=\"func\", expressions_dict=expressions_dict\n)\n\ndesign_space = create_design_space()\ndesign_space.add_variable(\"x_1\", l_b=0.0, u_b=1.0)\ndesign_space.add_variable(\"x_2\", l_b=0.0, u_b=1.0)\n\ndiscipline.set_cache_policy(discipline.MEMORY_FULL_CACHE)\nscenario = create_scenario(\n    [discipline], \"DisciplinaryOpt\", \"y_1\", design_space, scenario_type=\"DOE\"\n)\nscenario.execute({\"algo\": \"fullfact\", \"n_samples\": 9})\n\ndataset = discipline.cache.export_to_dataset()\nmodel = create_regression_model(\"LinearRegression\", data=dataset)\nmodel.learn()\n\nprint(model)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
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      "file_extension": ".py",
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