Gaussian Processes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a764d92",
   "metadata": {},
   "source": [
    "# Gaussian Processes\n",
    "\n",
    "If one is ready to assume the data available is such that the variable being fit is Gaussian for any pair of samples taken, one can use fit the data and obtain a reliable uncertainty with a very small set of assumptions.\n",
    "\n",
    "For this example, we will use the dataset collected in Mauna Loa showing the amount of CO2 present in the atmosphere. This data may be downloaded from https://gml.noaa.gov/ccgg/trends/data.html\n",
    "\n",
    "If you have Linux or MacOS with standard tools installed, just run the next line of the notebook to download it with wget. If you are use Windows, download the dataset at: https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_weekly_mlo.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8337a15a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-01 15:22:49--  https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_weekly_mlo.txt\n",
      "Resolving gml.noaa.gov (gml.noaa.gov)... 140.172.200.41, 2610:20:8800:6101::29\n",
      "Connecting to gml.noaa.gov (gml.noaa.gov)|140.172.200.41|:443... connected.\n",
      "WARNING: cannot verify gml.noaa.gov's certificate, issued by ‘/C=US/O=Let's Encrypt/CN=R3’:\n",
      "  Issued certificate has expired.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 190941 (186K) [text/plain]\n",
      "Saving to: ‘co2_weekly_mlo.txt.1’\n",
      "\n",
      "100%[======================================>] 190,941      432KB/s   in 0.4s   \n",
      "\n",
      "2021-12-01 15:22:51 (432 KB/s) - ‘co2_weekly_mlo.txt.1’ saved [190941/190941]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget --no-check-certificate https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_weekly_mlo.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8131588",
   "metadata": {},
   "source": [
    "We can take a quick look at the file to see what is inside:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07ecc0d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# --------------------------------------------------------------------\n",
      "# USE OF NOAA GML DATA\n",
      "# \n",
      "# These data are made freely available to the public and the\n",
      "# scientific community in the belief that their wide dissemination\n",
      "# will lead to greater understanding and new scientific insights.\n",
      "# The availability of these data does not constitute publication\n",
      "# of the data.  NOAA relies on the ethics and integrity of the user to\n",
      "# ensure that GML receives fair credit for their work.  If the data \n",
      "# are obtained for potential use in a publication or presentation, \n",
      "# GML should be informed at the outset of the nature of this work.  \n",
      "# If the GML data are essential to the work, or if an important \n",
      "# result or conclusion depends on the GML data, co-authorship\n",
      "# may be appropriate.  This should be discussed at an early stage in\n",
      "# the work.  Manuscripts using the GML data should be sent to GML\n",
      "# for review before they are submitted for publication so we can\n",
      "# ensure that the quality and limitations of the data are accurately\n",
      "# represented.\n",
      "# \n",
      "# Contact:   Pieter Tans (303 497 6678; pieter.tans@noaa.gov)\n",
      "# \n",
      "# File Creation:  Tue Nov 30 05:00:14 2021\n",
      "# \n",
      "# RECIPROCITY\n",
      "# \n",
      "# Use of these data implies an agreement to reciprocate.\n",
      "# Laboratories making similar measurements agree to make their\n",
      "# own data available to the general public and to the scientific\n",
      "# community in an equally complete and easily accessible form.\n",
      "# Modelers are encouraged to make available to the community,\n",
      "# upon request, their own tools used in the interpretation\n",
      "# of the GML data, namely well documented model code, transport\n",
      "# fields, and additional information necessary for other\n",
      "# scientists to repeat the work and to run modified versions.\n",
      "# Model availability includes collaborative support for new\n",
      "# users of the models.\n",
      "# --------------------------------------------------------------------\n",
      "#  \n",
      "#  \n",
      "# See www.esrl.noaa.gov/gmd/ccgg/trends/ for additional details.\n",
      "#  \n",
      "# NOTE: DATA FOR THE LAST SEVERAL MONTHS ARE PRELIMINARY, ARE STILL SUBJECT\n",
      "# TO QUALITY CONTROL PROCEDURES.\n",
      "# NOTE: The week \"1 yr ago\" is exactly 365 days ago, and thus does not run from\n",
      "# Sunday through Saturday. 365 also ignores the possibility of a leap year.\n",
      "# The week \"10 yr ago\" is exactly 10*365 days +3 days (for leap years) ago.\n",
      "#  \n",
      "#      Start of week      CO2 molfrac           (-999.99 = no data)  increase\n",
      "# (yr, mon, day, decimal)    (ppm)  #days       1 yr ago  10 yr ago  since 1800\n",
      "  1974   5  19  1974.3795    333.37  5          -999.99   -999.99     50.40\n",
      "  1974   5  26  1974.3986    332.95  6          -999.99   -999.99     50.06\n",
      "  1974   6   2  1974.4178    332.35  5          -999.99   -999.99     49.60\n",
      "  1974   6   9  1974.4370    332.20  7          -999.99   -999.99     49.65\n",
      "  1974   6  16  1974.4562    332.37  7          -999.99   -999.99     50.06\n",
      "  1974   6  23  1974.4753    331.73  5          -999.99   -999.99     49.72\n",
      "  1974   6  30  1974.4945    331.68  6          -999.99   -999.99     50.02\n",
      "  1974   7   7  1974.5137    331.46  6          -999.99   -999.99     50.20\n",
      "  1974   7  14  1974.5329    330.83  5          -999.99   -999.99     50.01\n",
      "  1974   7  21  1974.5521    330.76  7          -999.99   -999.99     50.41\n",
      "  1974   7  28  1974.5712    329.80  4          -999.99   -999.99     49.97\n",
      "  1974   8   4  1974.5904    329.85  5          -999.99   -999.99     50.54\n"
     ]
    }
   ],
   "source": [
    "with open(\"co2_weekly_mlo.txt\", \"r\") as f:\n",
    "    for i, line in enumerate(f.readlines()):\n",
    "        if i > 60: # let's print only the first 60 lines only\n",
    "            break\n",
    "        print(line[:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce4d8e8",
   "metadata": {},
   "source": [
    "We start by loading the necessary Python modules. If you have not yet installed them, run the following cell to install them with pip:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44ca341e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (1.19.2)\n",
      "Requirement already satisfied: scikit-learn in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (0.24.2)\n",
      "Requirement already satisfied: pandas in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (1.3.0)\n",
      "Requirement already satisfied: matplotlib in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (3.4.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from scikit-learn) (1.0.1)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from scikit-learn) (1.6.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from pandas) (2021.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from matplotlib) (8.3.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy scikit-learn pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "300cf8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, ExpSineSquared, WhiteKernel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecd6a69",
   "metadata": {},
   "source": [
    "Let us now load the data downloaded using pandas. If you are not familiar with Pandas, take a look at the documentation for a quick introduction at https://pandas.pydata.org/docs/user_guide/10min.html\n",
    "\n",
    "We can easily parse the data file using Pandas' read_csv function. It would require some coding to do it manually. As Pandas does not detect the column names, we have to specify it manually in the names parameter below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4959a292",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"co2_weekly_mlo.txt\",\n",
    "                   delim_whitespace=True,\n",
    "                   comment='#',\n",
    "                   header=None,\n",
    "                   names=[\"year\", \"month\", \"day\", \"decimal\",\n",
    "                          \"co2\",\n",
    "                          \"ndays\",\n",
    "                          \"last_year\", \"last_decade\",\n",
    "                          \"increase\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8295e8a",
   "metadata": {},
   "source": [
    "Let's print out the dataset read first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "024fb65a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>decimal</th>\n",
       "      <th>co2</th>\n",
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       "      <th>0</th>\n",
       "      <td>1974</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1974.3795</td>\n",
       "      <td>333.37</td>\n",
       "      <td>5</td>\n",
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       "      <td>50.40</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1974</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>1974.3986</td>\n",
       "      <td>332.95</td>\n",
       "      <td>6</td>\n",
       "      <td>-999.99</td>\n",
       "      <td>-999.99</td>\n",
       "      <td>50.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1974</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1974.4178</td>\n",
       "      <td>332.35</td>\n",
       "      <td>5</td>\n",
       "      <td>-999.99</td>\n",
       "      <td>-999.99</td>\n",
       "      <td>49.60</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1974</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1974.4370</td>\n",
       "      <td>332.20</td>\n",
       "      <td>7</td>\n",
       "      <td>-999.99</td>\n",
       "      <td>-999.99</td>\n",
       "      <td>49.65</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1974.4562</td>\n",
       "      <td>332.37</td>\n",
       "      <td>7</td>\n",
       "      <td>-999.99</td>\n",
       "      <td>-999.99</td>\n",
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       "      <th>2475</th>\n",
       "      <td>2021</td>\n",
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       "      <td>24</td>\n",
       "      <td>2021.8123</td>\n",
       "      <td>413.90</td>\n",
       "      <td>6</td>\n",
       "      <td>411.63</td>\n",
       "      <td>389.48</td>\n",
       "      <td>136.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2476</th>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>2021.8315</td>\n",
       "      <td>414.17</td>\n",
       "      <td>6</td>\n",
       "      <td>411.93</td>\n",
       "      <td>389.80</td>\n",
       "      <td>136.85</td>\n",
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       "      <th>2477</th>\n",
       "      <td>2021</td>\n",
       "      <td>11</td>\n",
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       "      <td>414.97</td>\n",
       "      <td>7</td>\n",
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       "      <td>390.09</td>\n",
       "      <td>137.29</td>\n",
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      ],
      "text/plain": [
       "      year  month  day    decimal     co2  ndays  last_year  last_decade  \\\n",
       "0     1974      5   19  1974.3795  333.37      5    -999.99      -999.99   \n",
       "1     1974      5   26  1974.3986  332.95      6    -999.99      -999.99   \n",
       "2     1974      6    2  1974.4178  332.35      5    -999.99      -999.99   \n",
       "3     1974      6    9  1974.4370  332.20      7    -999.99      -999.99   \n",
       "4     1974      6   16  1974.4562  332.37      7    -999.99      -999.99   \n",
       "...    ...    ...  ...        ...     ...    ...        ...          ...   \n",
       "2475  2021     10   24  2021.8123  413.90      6     411.63       389.48   \n",
       "2476  2021     10   31  2021.8315  414.17      6     411.93       389.80   \n",
       "2477  2021     11    7  2021.8507  414.97      7     412.97       390.09   \n",
       "2478  2021     11   14  2021.8699  414.88      7     412.80       390.71   \n",
       "2479  2021     11   21  2021.8890  415.36      7     413.55       390.78   \n",
       "\n",
       "      increase  \n",
       "0        50.40  \n",
       "1        50.06  \n",
       "2        49.60  \n",
       "3        49.65  \n",
       "4        50.06  \n",
       "...        ...  \n",
       "2475    136.92  \n",
       "2476    136.85  \n",
       "2477    137.29  \n",
       "2478    136.84  \n",
       "2479    136.96  \n",
       "\n",
       "[2480 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c178424",
   "metadata": {},
   "source": [
    "We can plot this fairly easily using Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e63b38c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "data.plot(x=\"decimal\", y=\"co2\", legend=False, ax=ax)\n",
    "ax.set(xlabel=\"Time [years]\", ylabel=r\"CO$_2$ [ppm]\", title=\"Atmospheric CO$_2$ concentration in Mauna Loa\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd249fe",
   "metadata": {},
   "source": [
    "The spikes are related to missing data. We can clean those up and plot again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0837b3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.co2 > -999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "333581b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "data.plot(x=\"decimal\", y=\"co2\", legend=False, ax=ax)\n",
    "ax.set(xlabel=\"Time [years]\", ylabel=r\"CO$_2$ [ppm]\", title=\"Atmospheric CO$_2$ concentration in Mauna Loa\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62fd7f3",
   "metadata": {},
   "source": [
    "That's better.\n",
    "\n",
    "We can now try to model this behaviour with Gaussian Processes. The key parameter we must choose is the covariance matrix analytic form, which establishes how correlated two points in the time axis are.\n",
    "\n",
    "One simple assumption would be that two values of the CO2 concentration are highly correlated they happened at close by times. We use a Gaussian-based model (named here \"Radial Basis Function\", as this does not refer to a probability distribution) to establish that if two points are one year apart they are likely to be highly correlated. \n",
    "\n",
    "We also assume the data within an year from each other is highly correlated and should show a periodicity of 1 year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a31d7eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kernel with parameters given in GPML book\n",
    "\n",
    "# these hyper parameters are optimised in the GP fit\n",
    "# we provide here only a starting value\n",
    "\n",
    "# long term smooth rising trend\n",
    "k1 = 66.0**2 * RBF(length_scale=67.0)\n",
    "\n",
    "# seasonal component\n",
    "k2 = (2.4**2 * RBF(length_scale=90.0) * ExpSineSquared(length_scale=1.3, periodicity=1.0))\n",
    "\n",
    "# add some white noise\n",
    "kn = WhiteKernel(noise_level=0.19**2)\n",
    "\n",
    "kernel = k1 + k2 + kn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad8ddf0",
   "metadata": {},
   "source": [
    "The kernel hyper-parameters are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "032ada0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66**2 * RBF(length_scale=67) + 2.4**2 * RBF(length_scale=90) * ExpSineSquared(length_scale=1.3, periodicity=1) + WhiteKernel(noise_level=0.0361)\n"
     ]
    }
   ],
   "source": [
    "print(kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4412a9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/danilo/miniconda3/envs/mlmkl/lib/python3.7/site-packages/sklearn/gaussian_process/kernels.py:418: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__k1__constant_value is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "gp = GaussianProcessRegressor(kernel=kernel)\n",
    "time = data.decimal.to_numpy()[:, np.newaxis]\n",
    "co2 = data.co2.to_numpy()[:, np.newaxis]\n",
    "gp = gp.fit(time, co2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb69bb80",
   "metadata": {},
   "source": [
    "These are the hyper-parameters after the fit and the log-likelihood given the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "667db862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPML kernel: 316**2 * RBF(length_scale=81.2) + 2.36**2 * RBF(length_scale=49.3) * ExpSineSquared(length_scale=0.0522, periodicity=23) + WhiteKernel(noise_level=0.14)\n",
      "Log-marginal-likelihood: -1677.717\n"
     ]
    }
   ],
   "source": [
    "print(\"GPML kernel: %s\" % gp.kernel_)\n",
    "print(\"Log-marginal-likelihood: %.3f\" % gp.log_marginal_likelihood(gp.kernel_.theta))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4968439e",
   "metadata": {},
   "source": [
    "Now we can use the fit model to predict how the CO2 concentration will evolve at other times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef6b7762",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_time = np.linspace(time.min(), time.max()+10, 1000)[:, np.newaxis]\n",
    "co2_pred, co2_std = gp.predict(new_time, return_std=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b263358",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.scatter(time, co2, color='red', marker='.', label=\"Data\")\n",
    "ax.fill_between(new_time[:,0],\n",
    "                co2_pred[:,0] - co2_std,\n",
    "                co2_pred[:,0] + co2_std,\n",
    "                color=\"b\",\n",
    "                alpha=0.4,\n",
    "                label=\"Predictions\")\n",
    "ax.set(xlabel=\"Time [years]\", ylabel=r\"CO$_2$ [ppm]\", title=\"Atmospheric CO$_2$ concentration in Mauna Loa\")\n",
    "ax.legend(frameon=False, loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b625b695",
   "metadata": {},
   "source": [
    "Many more details on the kernel selection as well as further details in this example can be seen in the following references:\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_co2.html\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py\n",
    "\n",
    "To read more on Gaussian Processes:\n",
    "\n",
    "http://www.gaussianprocess.org/gpml/chapters/RW.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec18ae45",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}