Lectures/Lecture17/3dasm_Lecture17.ipynb

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    "<img src=../figures/Brown_logo.svg width=50%>\n",
    "\n",
    "## Data-Driven Design & Analyses of Structures & Materials (3dasm)\n",
    "\n",
    "## Lecture 17\n",
    "\n",
    "### Martin van der Schelling | <a href = \"mailto: m.p.vanderschelling@tudelft.nl\">m.p.vanderschelling@tudelft.nl</a>  | Doctoral Candidate\n",
    "\n",
    "### Miguel A. Bessa | <a href = \"mailto: miguel_bessa@brown.edu\">miguel_bessa@brown.edu</a>  | Associate Professor"
   ]
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    "**What:** A lecture of the \"3dasm\" course\n",
    "\n",
    "**Where:** This notebook comes from this [repository](https://github.com/bessagroup/3dasm_course)\n",
    "\n",
    "**Reference for entire course:** Murphy, Kevin P. *Probabilistic machine learning: an introduction*. MIT press, 2022. Available online [here](https://probml.github.io/pml-book/book1.html)\n",
    "\n",
    "**How:** We try to follow Murphy's book closely, but the sequence of Chapters and Sections is different. The intention is to use notebooks as an introduction to the topic and Murphy's book as a resource.\n",
    "* If working offline: Go through this notebook and read the book.\n",
    "* If attending class in person: listen to me (!) but also go through the notebook in your laptop at the same time. Read the book.\n",
    "* If attending lectures remotely: listen to me (!) via Zoom and (ideally) use two screens where you have the notebook open in 1 screen and you see the lectures on the other. Read the book."
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    "## **OPTION 1**. Run this notebook **locally in your computer**:\n",
    "1. Confirm that you have the '3dasm' mamba (or conda) environment (see Lecture 1).\n",
    "2. Go to the 3dasm_course folder in your computer and pull the last updates of the [repository](https://github.com/bessagroup/3dasm_course):\n",
    "```\n",
    "git pull\n",
    "```\n",
    "    - Note: if you can't pull the repo due to conflicts (and you can't handle these conflicts), use this command (with **caution**!) and your repo becomes the same as the one online:\n",
    "```\n",
    "git reset --hard origin/main\n",
    "```\n",
    "3. Open command window and load jupyter notebook (it will open in your internet browser):\n",
    "```\n",
    "jupyter notebook\n",
    "```\n",
    "5. Open notebook of this Lecture and choose the '3dasm' kernel."
   ]
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    "## **OPTION 2**. Use **Google's Colab** (no installation required, but times out if idle):\n",
    "\n",
    "1. go to https://colab.research.google.com\n",
    "2. login\n",
    "3. File > Open notebook\n",
    "4. click on Github (no need to login or authorize anything)\n",
    "5. paste the git link: https://github.com/bessagroup/3dasm_course\n",
    "6. click search and then click on the notebook for this Lecture."
   ]
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    "# Basic plotting tools needed in Python.\n",
    "\n",
    "import matplotlib.pyplot as plt # import plotting tools to create figures\n",
    "import numpy as np # import numpy to handle a lot of things!\n",
    "\n",
    "%config InlineBackend.figure_format = \"retina\" # render higher resolution images in the notebook\n",
    "plt.rcParams[\"figure.figsize\"] = (8,4) # rescale figure size appropriately for slides\n",
    "\n",
    "# To limit the number of rows to show in a dataframe, for presentation purposes:\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.max_rows', 10)"
   ]
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   "source": [
    "## Outline for today\n",
    "\n",
    "* Introducing a Python framework for data-driven design and analysis of structures and materials: `f3dasm`\n",
    "\n",
    "**Reading material**: This notebook"
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    "### Installing `f3dasm`\n",
    "\n",
    "You can install `f3dasm` with pip:\n",
    "\n",
    "_Make sure you install the correct version (1.5.4)_\n",
    "\n",
    "You can check this by running:\n",
    "\n",
    "```\n",
    "python -m pip list\n",
    "```"
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    "try:\n",
    "    import f3dasm\n",
    "except ModuleNotFoundError: # If f3dasm is not found in current environment, install the correct version from pip\n",
    "    %pip install f3dasm==1.5.4 --quiet\n",
    "    import f3dasm"
   ]
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    "Optionally, it is also possible to install from source:\n",
    "\n",
    "```\n",
    "git clone https://github.com/bessagroup/f3dasm\n",
    "pip install -e .\n",
    "```\n",
    "\n",
    "For more installation instruction you can check the [installation documentation](https://github.com/bessagroup/f3dasm)"
   ]
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    "### `f3dasm`: streamlining your data-driven process!\n",
    "\n",
    "The **f**ramework for **d**ata-**d**riven **d**esign and **a**nalysis of **s**tructures and **m**aterials (`f3dasm`) aims to generalize this workflow with interfaces (templates of code that you have to fill in)\n",
    "\n",
    "\n",
    "<center><img src=\"../figures/f3dasm_carstoppingdistance_line.svg\" title=\"f3dasm Car stopping distance\" width=\"70%\"></center>\n",
    "\n",
    "Today, we will illustrate these modules by recreating the car stopping distance problem:"
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    "### The car stopping distance problem (again!)\n",
    "\n",
    "<img src=\"../figures/reaction-braking-stopping.svg\" title=\"Car stopping distance\" width=\"50%\" align=\"right\">\n",
    "\n",
    "Imagine we want to predict $\\color{red}y$ for a given ${\\color{green}x}$ but that **we had no idea** that this problem is governed by:\n",
    "\n",
    "${\\color{red}y} = {\\color{blue}z} x + 0.1 {\\color{green}x}^2$\n",
    "\n",
    "- ${\\color{red}y}$ is the **output**: the car stopping distance\n",
    "- ${\\color{green}x}$ is the **input**: the car velocity\n",
    "- ${\\color{blue}z}$ is a hidden variable: an <a title=\"random variable\">rv</a> $z$ representing the driver's reaction time (in seconds)\n",
    "\n",
    "where $z \\sim \\mathcal{N}(\\mu_z=1.5,\\sigma_z^2=0.5^2)$"
   ]
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    "### Module `design`: Creating our design-of-experiments with the `Domain` class\n",
    "\n",
    "<img src=\"../figures/f3dasm_carstopping_design.svg\" title=\"f3dasm Car stopping distance design\" width=\"22%\" align=\"right\">\n",
    "\n",
    "The design-of-experiment aims to describe the variability on the **input** in order to control the **output** of the experiment.\n",
    "\n",
    "In order to create a design-of-experiment, we import the `Domain` class from the `f3dasm.design` module:"
   ]
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     "output_type": "stream",
     "text": [
      "Domain(space={}, output_space={})\n"
     ]
    }
   ],
   "source": [
    "from f3dasm.design import Domain\n",
    "\n",
    "domain = Domain()\n",
    "print(domain)"
   ]
  },
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    "Currently the domain is empty; but we can add the car velocity $x$ as an **input parameter**. <br>\n",
    "Since it is a continuous variable, we use the `add_float` method.\n",
    "\n",
    "Optionally, we can give lower and upper bounds to this variable:"
   ]
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   "execution_count": 4,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Domain(space={'x': _ContinuousParameter(lower_bound=3.0, upper_bound=83.0, log=False)}, output_space={})\n"
     ]
    }
   ],
   "source": [
    "domain.add_float(name='x', low=3.0, high=83.0)\n",
    "print(domain)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"../figures/car_velocity_empty.svg\" title=\"Car stopping distance\" width=\"40%\"></center>"
   ]
  },
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   "metadata": {
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   "source": [
    "Let's create the **input data** for our problem..."
   ]
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     "name": "stdout",
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     "text": [
      "Let's see the Data_x vector:\n",
      " [ 3.   5.5  8.  10.5 13.  15.5 18.  20.5 23.  25.5 28.  30.5 33.  35.5\n",
      " 38.  40.5 43.  45.5 48.  50.5 53.  55.5 58.  60.5 63.  65.5 68.  70.5\n",
      " 73.  75.5 78.  80.5 83. ]\n"
     ]
    }
   ],
   "source": [
    "N = 33 # number of points to generate data\n",
    "Data_x = np.linspace(3, 83, N) # generate a dataset with N points for velocities x between 3 and 83 m/s\n",
    "print(\"Let's see the Data_x vector:\\n\", Data_x)"
   ]
  },
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   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "source": [
    "<center><img src=\"../figures/car_velocities.svg\" title=\"Car stopping distance\" width=\"40%\"></center>"
   ]
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 328,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Optionally: instead of importing a schematic, create the above figure with matplotlib directly:\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_position('zero')\n",
    "ax.get_yaxis().set_visible(False)\n",
    "ax.set_xlabel('car velocity ($x$) [m/s]')\n",
    "ax.set_xticks(np.linspace(3.0, 83.0, 6))\n",
    "\n",
    "ax.scatter(Data_x, np.zeros(len(Data_x)), s=20, c='red', marker='x' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We combine both the **domain** and the **input data** in the `f3dasm.ExperimentData` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from f3dasm import ExperimentData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>jobs</th>\n",
       "      <th>input</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>open</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>open</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>open</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>open</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>open</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>open</td>\n",
       "      <td>75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>open</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>open</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>open</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    jobs input\n",
       "             x\n",
       "0   open   3.0\n",
       "1   open   5.5\n",
       "2   open   8.0\n",
       "3   open  10.5\n",
       "4   open  13.0\n",
       "..   ...   ...\n",
       "28  open  73.0\n",
       "29  open  75.5\n",
       "30  open  78.0\n",
       "31  open  80.5\n",
       "32  open  83.0\n",
       "\n",
       "[33 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experimentdata = ExperimentData(input_data=Data_x, domain=domain)\n",
    "experimentdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This object is the central piece of the data-driven process with `f3dasm`!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Module `datageneration`: Measure the car stopping distance\n",
    "\n",
    "<!-- <img src=\"../figures/reaction-braking-stopping.svg\" title=\"Car stopping distance\" width=\"25%\" align=\"right\"> -->\n",
    "\n",
    "<img src=\"../figures/f3dasm_carstopping_datageneration.svg\" title=\"f3dasm Car stopping distance datageneration\" width=\"22%\" align=\"right\">\n",
    "\n",
    "We can *create* the data from the governing equation.\n",
    "\n",
    "${\\color{red}y} = {\\color{blue}z} x + 0.1 {\\color{green}x}^2$\n",
    "\n",
    "- ${\\color{red}y}$ is the **output**: the car stopping distance\n",
    "- ${\\color{green}x}$ is the **input**: the car velocity\n",
    "- ${\\color{blue}z}$ is a hidden variable: an <a title=\"random variable\">rv</a> $z$ representing the driver's reaction time (in seconds)\n",
    "\n",
    "where $z \\sim \\mathcal{N}(\\mu_z=1.5,\\sigma_z^2=0.5^2)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We create the function `compute_distance` with the governing equation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.stats import norm # import the normal dist, as we learned before!\n",
    "\n",
    "# Define our car stopping distance function\n",
    "def compute_distance(x):\n",
    "    z = norm.rvs(1.5, 0.5, size=1) # randomly draw 1 sample from the normal dist.\n",
    "    y = z*x + 0.1*x**2 # compute the stopping distance\n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"../figures/car_stoppingdistance.svg\" title=\"Car stopping distance\" width=\"40%\" align=\"right\">\n",
    "\n",
    "We can evaluate our input data points $x$ to retrieve the stopping distance $y$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "experimentdata.evaluate(compute_distance, output_names=['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>jobs</th>\n",
       "      <th>input</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>finished</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.839821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>finished</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.525718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>finished</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.340673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>finished</td>\n",
       "      <td>10.5</td>\n",
       "      <td>22.191317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>finished</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.415758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>finished</td>\n",
       "      <td>73.0</td>\n",
       "      <td>701.546303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>finished</td>\n",
       "      <td>75.5</td>\n",
       "      <td>675.58216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>finished</td>\n",
       "      <td>78.0</td>\n",
       "      <td>676.834469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>finished</td>\n",
       "      <td>80.5</td>\n",
       "      <td>698.154607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>finished</td>\n",
       "      <td>83.0</td>\n",
       "      <td>832.753617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        jobs input      output\n",
       "                 x           y\n",
       "0   finished   3.0    5.839821\n",
       "1   finished   5.5    8.525718\n",
       "2   finished   8.0   18.340673\n",
       "3   finished  10.5   22.191317\n",
       "4   finished  13.0   23.415758\n",
       "..       ...   ...         ...\n",
       "28  finished  73.0  701.546303\n",
       "29  finished  75.5   675.58216\n",
       "30  finished  78.0  676.834469\n",
       "31  finished  80.5  698.154607\n",
       "32  finished  83.0  832.753617\n",
       "\n",
       "[33 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experimentdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can extract the car stopping distance $y$ as a numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# to_numpy extracts both the input and output data as numpy arrays:\n",
    "_, Data_y = experimentdata.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'car velocity ($x$) [m/s]')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 373,
       "width": 697
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(Data_x, Data_y, color='r', marker='x', label='data')\n",
    "ax.legend()\n",
    "ax.set_ylabel('car stopping distance ($y$) [m]')\n",
    "ax.set_xlabel('car velocity ($x$) [m/s]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Module `machine learning`: Fit a polynomial model to the data!\n",
    "\n",
    "<img src=\"../figures/f3dasm_carstopping_machinelearning.svg\" title=\"f3dasm Car stopping distance machine learning\" width=\"22%\" align=\"right\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures # For Polynomial fit\n",
    "from sklearn.linear_model import LinearRegression # For Least Squares\n",
    "from sklearn.pipeline import make_pipeline # to link different objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Creating the polynomial model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "degree = 1\n",
    "poly_model = make_pipeline(PolynomialFeatures(degree), LinearRegression())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Fitting the model with the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;polynomialfeatures&#x27;, PolynomialFeatures(degree=1)),\n",
       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;polynomialfeatures&#x27;, PolynomialFeatures(degree=1)),\n",
       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=1)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=1)),\n",
       "                ('linearregression', LinearRegression())])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_model.fit(Data_x.reshape(-1, 1), Data_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now, instead of using the governing equation (`compute_distance`), we can use the model to predict the car stopping distance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def compute_distance_with_model(x):\n",
    "    y_pred = poly_model.predict(np.array(x).reshape(-1, 1)) # We need to reshape the array\n",
    "    return float(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n",
      "/tmp/ipykernel_101759/3260301381.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  return float(y_pred)\n"
     ]
    }
   ],
   "source": [
    "experimentdata.mark_all('open') # Mark all experiments 'open' to evaluate them again\n",
    "experimentdata.evaluate(compute_distance_with_model, output_names=['y_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>jobs</th>\n",
       "      <th>input</th>\n",
       "      <th colspan=\"2\" halign=\"left\">output</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>finished</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.839821</td>\n",
       "      <td>-90.767218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>finished</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.525718</td>\n",
       "      <td>-65.864369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>finished</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.340673</td>\n",
       "      <td>-40.961519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>finished</td>\n",
       "      <td>10.5</td>\n",
       "      <td>22.191317</td>\n",
       "      <td>-16.058669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>finished</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.415758</td>\n",
       "      <td>8.844181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>finished</td>\n",
       "      <td>73.0</td>\n",
       "      <td>701.546303</td>\n",
       "      <td>606.512579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>finished</td>\n",
       "      <td>75.5</td>\n",
       "      <td>675.58216</td>\n",
       "      <td>631.415429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>finished</td>\n",
       "      <td>78.0</td>\n",
       "      <td>676.834469</td>\n",
       "      <td>656.318279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>finished</td>\n",
       "      <td>80.5</td>\n",
       "      <td>698.154607</td>\n",
       "      <td>681.221129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>finished</td>\n",
       "      <td>83.0</td>\n",
       "      <td>832.753617</td>\n",
       "      <td>706.123979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        jobs input      output            \n",
       "                 x           y      y_pred\n",
       "0   finished   3.0    5.839821  -90.767218\n",
       "1   finished   5.5    8.525718  -65.864369\n",
       "2   finished   8.0   18.340673  -40.961519\n",
       "3   finished  10.5   22.191317  -16.058669\n",
       "4   finished  13.0   23.415758    8.844181\n",
       "..       ...   ...         ...         ...\n",
       "28  finished  73.0  701.546303  606.512579\n",
       "29  finished  75.5   675.58216  631.415429\n",
       "30  finished  78.0  676.834469  656.318279\n",
       "31  finished  80.5  698.154607  681.221129\n",
       "32  finished  83.0  832.753617  706.123979\n",
       "\n",
       "[33 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experimentdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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r2Llzp44dO2Y9Vt7vW1ncfffd1kDfXiuN48ePa9myZZLM701oaKjb1zh58qT1cZ06dVyaExwcrP/3//6fJOnw4cN69NFHNWrUKBUUFKh3796aPXu2goJK/mtJ3bp17dbhac5aP1x33XWSzKG/o3UaFhamFi1aSJIOHDhgd8yRI0e0efNmSfbD3IYNG1ofu9ImBQAAVF6EuQAAAD7A9m73znbeOZOTk6PXXntN119/vWrUqKGmTZuqbdu2at++vdq3b68OHTpYxzraKWthCanKokuXLtYQy7ID19bixYt19uxZScXD440bN1ofjxw5UiaTyemX5fXY7mAsyfbt262P7e28tGV73HZeebxv5SUsLEz33nuvJGnevHnKyckpdHz27NnKy8uTJD3wwAOlusZvv/1mfexqmCuZA/kbbrhBkjRjxgxdvHhRbdu21eeff+5wJ3NRttf79ddfXb52WVl2FdtjCc/r1avn9PthGZednW33uGVXbmRkpDp16lTseM+ePdW8eXNJ5p3CXbp00Wuvvaa0tDSnfXgBAEDlQ5gLAADgA2zDVdtdeK7KyMhQ+/bt9dxzz2nbtm12b9Rl68KFC06PuxPUOWMJF9PS0qw3zrKwBLz16tXTLbfcUujYiRMnSnW98+fPuzzWNphs0KCB07GRkZF255X1fStv48aNk2QODRcuXFjomGVHZ+fOndW+fftSnd/SLkAqeQ3ZMplM1tokc8uEZcuWOW0NUpTt9cLCwlyeV1bVq1d3eMyyo9jZGNtxjv6/tNy8b9CgQTKZTMWOV6lSRYsXL1br1q0lSRs2bNBzzz2nHj16qHbt2ho4cKA++eSTEv+/BwAA/o8wFwAAwMfYC3NKMnr0aB08eFAmk0kPPPCAVqxYocOHD+vixYsyDEOGYRQKemxbLtjjqOWAuyw7bg3D0Jw5c6zPnz592vqR/+HDh6tKlSqF5tnW+vHHH+unn35y6evvf/97qeos6Xte0vfLlXN4ww033KCOHTtKKvxx/B9++EE7d+6UVPpduZJ09dVXWx/bhtol2bt3rxISEqy/zsnJcXlHrr3r2dbh7y5cuKDvvvtOkv0WCxZt2rTRTz/9pM8++0wPPPCAtT/vhQsX9NVXX2nUqFHq2rVrqf8hBAAA+AfCXAAAAB9g6fkqSUePHnVr7u7du7V27VpJ0rPPPqsZM2aof//+aty4caHA7PTp0+VTrBtatGihLl26SCrcamHBggXWj4fb689rueGYZA5J27Vr59JXo0aNXK7NtgdrSe0Zjh8/bndeWd43T7HcCC01NdXao9US7IaFhWnkyJGlPrdtiOrqejpx4oRuvfVWnTp1yvq+5uTk6JVXXnHr2rbXq0xh7jfffKMLFy6oWrVquvnmm52ODQ4O1h133KEZM2Zo3759Onr0qGbMmGEN8Ddt2qTx48d7o2wAAFBBCHMBAAB8wI033mh9vHr1arfm7tixw/rY2c2abPvQepMlrN2xY4e2bdsmSfrkk08kSTExMYqLiys2x7a/74oVKzxSV7t27ayPf/jhB6dj169fb3deWd43V5Rmt++9996r6tWryzAMJScn68KFC/r0008lSXfeeadq1apV6nps2zPs2bOnxPE5OTkaNGiQDhw4oPDwcK1YsUJ33HGHJOn999/XoUOHXL625Xo1atSw9o+tDCwtFvr166caNWq4Nbdhw4Z64IEHlJ6ebl2LS5YscasFBgAA8C+EuQAAAD7g+uuvV3R0tCTpww8/1Llz51yea7mpleS8Z+y0adNKX2AZjBgxwtq24eOPP1ZmZqY1+Bw1apTdwPLaa69VmzZtJEmffvqpW6Gfqzp27Gjt2ZqcnOyw32h2drbmzZsnyfxRd9veuGV531xh26P20qVLLs2pWbOmhg8fLsn8uhYsWKAzZ85Ikh588MEy1dOpUydrv9oNGzY4HZuXl6dhw4Zp48aNCgkJ0bx583TjjTcqMTFRJpNJly5dUmJiosvXtlyvW7duCgkJKf2L8CGGYWjp0qWSnLdYKEmVKlXUp08fSebv+++//14e5QEAAB9EmAsAAOADgoKC9PTTT0uSMjMzdf/99zu8S31BQUGhj/S3aNHC+jg5OdnunKlTp+q///1v+RXshvr161s/Pj5nzhx98sknKigokHTlBmn2vPDCC5Kkixcv6s4779TJkycdjr106ZLee+89Xbx40eW6qlatam1JsGPHDrvBomEYevzxx603Onv88ccLHS/L++YK2+B4//79Ls+zvK5ffvlFEydOlCQ1a9ZMN910k1vXLyo0NNTaNsN2t7I9Dz/8sLUv8tSpUzVw4EBJ0nXXXae77rpLknm9urLD99KlS9Zd3b169Sp1/b5m06ZN1jUxePBgh+PWrFmjffv2OTx++fJlpaamSpLCw8MrVRsKAABQGGEuAACAj3jsscfUv39/SdJnn32m9u3b6+2339b333+vLVu2aNmyZUpISFCrVq00ffp067wOHTpYP/o/depU3XvvvVq6dKk2b96szz//XMOGDdOjjz6qHj16VMjrkqT77rtPknT48GG99tpr1rotu2/tGTlypMaMGSPJHHq1adNGL7zwgr7++mtt3bpV33//vVJSUjRu3DhFRUXpscceK7RL2RUvvfSS9SP7L7/8su68804tWbJEmzdv1sKFC9WvXz+lpKRIkuLi4vTQQw8VO0dp3zdXdOjQwbo798UXX9SKFSu0Z88e7du3T/v27XP4cfoePXqodevWkq70Ax47dmy53KRt0KBBksxhbnZ2tt0xkyZN0owZM6x1W8Jl2+NBQUHKz8/Xiy++WOI1V69erdzc3ELXrwyWLFkiyXzjusaNGzsc9+2336ply5a66aab9Oabb2r58uXavHmzvv/+e82cOVO9evXS5s2bJZmD/MqycxkAANhhAAAAwGfk5OQYd999tyHJ6VdCQkKheVu2bDHq1KnjcHz79u2No0ePOpxvGIaRkJBgPV7esrOzjerVqxeqafLkySXOy8vLMyZOnGgEBweX+D2pUaOGcf78+WLnmDlzpnXMwYMHix0/ePCg0apVK6fn7tGjh/Hrr786rLO075sr9U2cONHh+VauXOmwpsmTJ1vHBQUFGYcOHXI41h2ZmZnW9yM5ObnY8Q8//NB63TFjxjg8z4gRIwxJhslkMrZs2eL0mvHx8YYko2XLlqWue8yYMYYko2nTpiWOdfX/BVfP2adPH0OS0adPn0LP33jjjYYk48UXX3S5Hmdfd955p3HhwgWn53K2FgEAgO9jZy4AAIAPqV69uubPn6/vvvtOo0ePVrNmzRQWFqaIiAi1atVKd955pz755BPrR/stbrjhBm3dulUPP/ywmjZtqipVqqhu3brq0qWLJk+erPXr1xf6yL63hYeHa8iQIdZfBwUFOb1Zm0VwcLD+8Y9/aOfOnZowYYI6dOigOnXqKDg4WBEREWrbtq1GjRql5ORkHTt2zNrP1R0xMTH68ccf9e9//1t9+vTRVVddpSpVqqhBgwa69dZbNXv2bK1evVp169Z1eI7Svm+ueP311/XBBx+oV69eqlu3rrX/cElGjx5tfdy/f39rb9+yatSokfW9/Pjjjwsd+/LLL/Xwww9Lkm6++WZ98MEHDs+TkJCg4OBgGYah559/3uG4ixcv6rPPPpMkPfroo2Ut32ccOXJEW7ZskVRyv9yJEyfqyy+/1F/+8hd169ZNTZo0UbVq1VStWjXFxMTonnvu0dKlS7Vw4cJCfZYBAEDlYzIMw6joIgAAAACUr2+//dbaq3ju3LnWm6KVh3Xr1ikuLk7BwcHat2+fYmJiyu3cRX300UcaPXq06tatq4yMDEVERJTqPPHx8UpOTlbTpk2VkZFRvkWWwvvvv6+HH35YkZGROnr0aLm0wHCF5ToJCQmaNGmSV64JAADKDztzAQAAgEroP//5jyTpqquuKrQrujx069ZNAwcOVH5+vrUHsicUFBTo1VdflSQ99dRTpQ5ybeXm5mr79u3WL0svXm+z9MsdNGiQR4PcnJycQq8XAAD4NzrjAwAAAJVMRkaG5s+fL8l847OqVauW+zX+8Y9/aMWKFZo5c6aef/55NWnSpNyvMX/+fO3atUvR0dH685//XC7nPHr0qNq3b2/99cGDBz26s9iRXr16qWPHjuUetBe1YcMG9e3b16PXAAAA3kOYCwAAAFQCR44c0fnz53Xw4EE988wzys3NVbVq1cotBC2qffv2mjVrlvbt26dDhw55JMzNz89XQkKC+vXrV6p+yL5s4sSJFV0CAADwQ/TMBQAAACqBm266SampqYWee+ONN0p10zUAAAD4JnbmAgAAAJVI9erVFRsbqz//+c8aM2ZMRZcDAACAcsTOXAAAAAAAAADwA0EVXQAAAAAAAAAAoGSEuQAAAAAAAADgBwhzAQAAAAAAAMAPEOYCAAAAAAAAgB8gzAUAAAAAAAAAP0CYCwAAAAAAAAB+gDAXAAAAAAAAAPwAYS4AAAAAAAAA+AHCXAAAAAAAAADwA4S5AAAAAAAAAOAHCHMBAAAAAAAAwA8Q5gIAAAAAAACAHyDMBQAAAAAAAAA/QJgLAAAAAAAAAH6AMBcAAAAAAAAA/ABhLgAAAAAAAAD4AcJcAAAAAAAAAPADhLkAAAAAAAAA4AcIcwEAAAAAAADADxDmAgAAAAAAAIAf+P9vnixIswLryAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 373,
       "width": 697
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting the ExperimentData object to numpy arrays for plotting\n",
    "_, out_data = experimentdata.to_numpy()\n",
    "\n",
    "# Plotting the data and the model prediction\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(Data_x, Data_y, marker='x', color='red', label='data')\n",
    "ax.plot(Data_x, out_data[:, 1], label=f'polynomial model with degree {degree}')\n",
    "ax.legend()\n",
    "ax.set_ylabel('car stopping distance ($y$) [m]')\n",
    "ax.set_xlabel('car velocity ($x$) [m/s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Saving the `ExperimentData`\n",
    "\n",
    "The `ExperimentData` object can be saved to your computer is easy with the `store` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "experimentdata.store('./your_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "This will create an `experiment_data` folder in the `Lecture17/your_data` directory:\n",
    "\n",
    "```\n",
    "your_data/\n",
    "└── experiment_data/\n",
    "     ├── domain.pkl\n",
    "     ├── input.csv\n",
    "     ├── output.csv\n",
    "     └── jobs.pkl    \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Loading the `ExperimentData` from a folder is done like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>jobs</th>\n",
       "      <th>input</th>\n",
       "      <th colspan=\"2\" halign=\"left\">output</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>finished</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.839821</td>\n",
       "      <td>-90.767218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>finished</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.525718</td>\n",
       "      <td>-65.864369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>finished</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.340673</td>\n",
       "      <td>-40.961519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>finished</td>\n",
       "      <td>10.5</td>\n",
       "      <td>22.191317</td>\n",
       "      <td>-16.058669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>finished</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.415758</td>\n",
       "      <td>8.844181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>finished</td>\n",
       "      <td>73.0</td>\n",
       "      <td>701.546303</td>\n",
       "      <td>606.512579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>finished</td>\n",
       "      <td>75.5</td>\n",
       "      <td>675.58216</td>\n",
       "      <td>631.415429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>finished</td>\n",
       "      <td>78.0</td>\n",
       "      <td>676.834469</td>\n",
       "      <td>656.318279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>finished</td>\n",
       "      <td>80.5</td>\n",
       "      <td>698.154607</td>\n",
       "      <td>681.221129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>finished</td>\n",
       "      <td>83.0</td>\n",
       "      <td>832.753617</td>\n",
       "      <td>706.123979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        jobs input      output            \n",
       "                 x           y      y_pred\n",
       "0   finished   3.0    5.839821  -90.767218\n",
       "1   finished   5.5    8.525718  -65.864369\n",
       "2   finished   8.0   18.340673  -40.961519\n",
       "3   finished  10.5   22.191317  -16.058669\n",
       "4   finished  13.0   23.415758    8.844181\n",
       "..       ...   ...         ...         ...\n",
       "28  finished  73.0  701.546303  606.512579\n",
       "29  finished  75.5   675.58216  631.415429\n",
       "30  finished  78.0  676.834469  656.318279\n",
       "31  finished  80.5  698.154607  681.221129\n",
       "32  finished  83.0  832.753617  706.123979\n",
       "\n",
       "[33 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ExperimentData.from_file('./your_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### See you next class\n",
    "\n",
    "Have fun!"
   ]
  }
 ],
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