pour_pca-pls_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc987bd3",
   "metadata": {},
   "source": [
    "# Regression with POUR data\n",
    "Use absorbance data from POUR runs\n",
    "- 10 or 40 mM urea in 0.15M NaCl saline starting concentration\n",
    "- True urea concentration measured with Beckman chemical analyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19b54e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "\n",
    "# Pandas library for the pandas dataframes\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd   \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "# Set larger fontsize for all plots\n",
    "plt.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8602f2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "\n",
    "abs_data = pd.read_csv('Datasets/pour_saline_uvvis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79f04a0d",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Urea Concentration (mM)</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time (hr)</th>\n",
       "      <th>Run through (y/n)</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>...</th>\n",
       "      <th>791</th>\n",
       "      <th>792</th>\n",
       "      <th>793</th>\n",
       "      <th>794</th>\n",
       "      <th>795</th>\n",
       "      <th>796</th>\n",
       "      <th>797</th>\n",
       "      <th>798</th>\n",
       "      <th>799</th>\n",
       "      <th>800</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.00</td>\n",
       "      <td>Sept. 14, 2021</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.76</td>\n",
       "      <td>Sept. 14, 2021</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.32</td>\n",
       "      <td>Sept. 14, 2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.130</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.87</td>\n",
       "      <td>Sept. 14, 2021</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.41</td>\n",
       "      <td>Sept. 14, 2021</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.14</td>\n",
       "      <td>Nov. 16, 2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.306</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5.65</td>\n",
       "      <td>Nov. 16, 2021</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.341</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>5.65</td>\n",
       "      <td>Nov. 16, 2021</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>6.02</td>\n",
       "      <td>Nov. 16, 2021</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.328</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>6.02</td>\n",
       "      <td>Nov. 16, 2021</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.389</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 615 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Urea Concentration (mM)            Date  Time (hr)  Run through (y/n)  \\\n",
       "0                     40.00  Sept. 14, 2021          0                  0   \n",
       "1                     39.76  Sept. 14, 2021          0                  1   \n",
       "2                     39.32  Sept. 14, 2021          1                  1   \n",
       "3                     38.87  Sept. 14, 2021          2                  1   \n",
       "4                     38.41  Sept. 14, 2021          3                  1   \n",
       "..                      ...             ...        ...                ...   \n",
       "85                     6.14   Nov. 16, 2021          4                  1   \n",
       "86                     5.65   Nov. 16, 2021          5                  1   \n",
       "87                     5.65   Nov. 16, 2021          5                  1   \n",
       "88                     6.02   Nov. 16, 2021          6                  1   \n",
       "89                     6.02   Nov. 16, 2021          6                  1   \n",
       "\n",
       "      190    191    192    193    194    195  ...    791    792    793    794  \\\n",
       "0   0.000 -0.018 -0.008 -0.040 -0.029 -0.003  ...  0.000 -0.004 -0.001 -0.001   \n",
       "1   0.024  0.014  0.026  0.003  0.010  0.034  ...  0.001 -0.003  0.001  0.001   \n",
       "2   0.110  0.099  0.111  0.090  0.094  0.130  ... -0.001 -0.004 -0.001 -0.003   \n",
       "3   0.174  0.203  0.195  0.176  0.192  0.219  ...  0.000 -0.002  0.000  0.000   \n",
       "4   0.223  0.231  0.225  0.188  0.233  0.256  ...  0.000 -0.004  0.000 -0.002   \n",
       "..    ...    ...    ...    ...    ...    ...  ...    ...    ...    ...    ...   \n",
       "85  0.260  0.254  0.308  0.263  0.321  0.306  ...    NaN    NaN    NaN    NaN   \n",
       "86  0.284  0.302  0.342  0.298  0.324  0.341  ...    NaN    NaN    NaN    NaN   \n",
       "87  0.295  0.306  0.330  0.305  0.341  0.345  ...    NaN    NaN    NaN    NaN   \n",
       "88  0.305  0.307  0.341  0.304  0.352  0.328  ...    NaN    NaN    NaN    NaN   \n",
       "89  0.339  0.329  0.377  0.326  0.390  0.389  ...    NaN    NaN    NaN    NaN   \n",
       "\n",
       "      795    796    797    798    799    800  \n",
       "0  -0.002  0.000 -0.001 -0.003 -0.001 -0.002  \n",
       "1  -0.002  0.002  0.001 -0.001  0.001  0.000  \n",
       "2  -0.004 -0.002 -0.001 -0.004 -0.002 -0.002  \n",
       "3  -0.002  0.001  0.000 -0.003  0.000  0.000  \n",
       "4  -0.003  0.000 -0.001 -0.004 -0.002 -0.003  \n",
       "..    ...    ...    ...    ...    ...    ...  \n",
       "85    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "86    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "87    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "88    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "89    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "\n",
       "[90 rows x 615 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d668f946",
   "metadata": {},
   "source": [
    "#### Specify features (X) and target (y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11376a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = abs_data.iloc[:, 9:17].values\n",
    "y = abs_data[['Urea Concentration (mM)']].values.reshape(-1, 1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d381e1",
   "metadata": {},
   "source": [
    "#### Fit principal component analysis (PCA) and partial least squares (PLS) regression models to the POUR data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce7d8c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCR r-squared -0.745\n",
      "PLS r-squared -1.018\n"
     ]
    }
   ],
   "source": [
    "pcr = make_pipeline(StandardScaler(), PCA(n_components=1), LinearRegression())\n",
    "pcr.fit(X_train, y_train)\n",
    "pca = pcr.named_steps[\"pca\"]  # retrieve the PCA step of the pipeline\n",
    "\n",
    "pls = PLSRegression(n_components=1)\n",
    "pls.fit(X_train, y_train)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n",
    "axes[0].scatter(pca.transform(X_test), y_test, alpha=0.3, label=\"ground truth\")\n",
    "axes[0].scatter(\n",
    "    pca.transform(X_test), pcr.predict(X_test), alpha=0.3, label=\"predictions\"\n",
    ")\n",
    "axes[0].set(\n",
    "    xlabel=\"Projected data onto first PCA component\", ylabel=\"y\", title=\"PCR / PCA\"\n",
    ")\n",
    "axes[0].legend()\n",
    "axes[1].scatter(pls.transform(X_test), y_test, alpha=0.3, label=\"ground truth\")\n",
    "axes[1].scatter(\n",
    "    pls.transform(X_test), pls.predict(X_test), alpha=0.3, label=\"predictions\"\n",
    ")\n",
    "axes[1].set(xlabel=\"Projected data onto first PLS component\", ylabel=\"y\", title=\"PLS\")\n",
    "axes[1].legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"PCR r-squared {pcr.score(X_test, y_test):.3f}\")\n",
    "print(f\"PLS r-squared {pls.score(X_test, y_test):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45372cb7",
   "metadata": {},
   "source": [
    "#### Find best wavelength range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b02c87f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max principal components regression (PCR) score: -0.7453611513115606\n",
      "Max PCR score starting wavelength: 9\n",
      "Max PCR score stopping wavelength: 17\n",
      "Max partial least squasres (PLS) regression score: -0.8806893187356952\n",
      "Max PLS score starting wavelength: 9\n",
      "Max PLS score stopping wavelength: 14\n"
     ]
    }
   ],
   "source": [
    "# Create integer lists of starting wavelengths and stopping wavelengths to probe\n",
    "start_wl = np.linspace(5, 10, num=6)\n",
    "start_wl = [round(x) for x in start_wl]\n",
    "stop_wl = np.linspace(11, 100, num=90)\n",
    "stop_wl = [round(x) for x in stop_wl]\n",
    "\n",
    "# Initiate lists for results\n",
    "starting_wavelength = []\n",
    "stopping_wavelength = []\n",
    "pcr_r_squared = []\n",
    "pls_r_squared = []\n",
    "\n",
    "for start in start_wl:\n",
    "    for stop in stop_wl:\n",
    "        X = abs_data.iloc[:, start:stop].values\n",
    "        y = abs_data[['Urea Concentration (mM)']].values.reshape(-1, 1)\n",
    "\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4, shuffle=True)\n",
    "        \n",
    "        pcr = make_pipeline(StandardScaler(), PCA(n_components=1), LinearRegression())\n",
    "        pcr.fit(X_train, y_train)\n",
    "        pca = pcr.named_steps[\"pca\"]  # retrieve the PCA step of the pipeline\n",
    "\n",
    "        pls = PLSRegression(n_components=1)\n",
    "        pls.fit(X_train, y_train)\n",
    "        \n",
    "        starting_wavelength.append(start)\n",
    "        stopping_wavelength.append(stop)\n",
    "        pcr_r_squared.append(pcr.score(X_test, y_test))\n",
    "        pls_r_squared.append(pls.score(X_test, y_test))\n",
    "        \n",
    "# Choose the highest pcr and pls scores and report\n",
    "pcr_array = np.array(pcr_r_squared)\n",
    "pcr_max_index = pcr_array.argmax()\n",
    "\n",
    "pls_array = np.array(pls_r_squared)\n",
    "pls_max_index = pls_array.argmax()\n",
    "\n",
    "print(f'Max principal components regression (PCR) score: {pcr_r_squared[pcr_max_index]}')\n",
    "print(f'Max PCR score starting wavelength: {starting_wavelength[pcr_max_index]}')\n",
    "print(f'Max PCR score stopping wavelength: {stopping_wavelength[pcr_max_index]}')\n",
    "\n",
    "print(f'Max partial least squasres (PLS) regression score: {pls_r_squared[pls_max_index]}')\n",
    "print(f'Max PLS score starting wavelength: {starting_wavelength[pls_max_index]}')\n",
    "print(f'Max PLS score stopping wavelength: {stopping_wavelength[pls_max_index]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d995be",
   "metadata": {},
   "source": [
    "## Support Vector Regression (SVR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2835f7b7",
   "metadata": {},
   "source": [
    "### From https://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html\n",
    "\n",
    "Uses RBF - radial basis function, an ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2485acd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Generate sample data\n",
    "X = np.sort(5 * np.random.rand(40, 1), axis=0)\n",
    "y = np.sin(X).ravel()\n",
    "\n",
    "# #############################################################################\n",
    "# Add noise to targets\n",
    "y[::5] += 3 * (0.5 - np.random.rand(8))\n",
    "\n",
    "# #############################################################################\n",
    "# Fit regression model\n",
    "svr_rbf = SVR(kernel=\"rbf\", C=100, gamma=0.1, epsilon=0.1)\n",
    "svr_lin = SVR(kernel=\"linear\", C=100, gamma=\"auto\")\n",
    "svr_poly = SVR(kernel=\"poly\", C=100, gamma=\"auto\", degree=3, epsilon=0.1, coef0=1)\n",
    "\n",
    "# #############################################################################\n",
    "# Look at the results\n",
    "lw = 2\n",
    "\n",
    "svrs = [svr_rbf, svr_lin, svr_poly]\n",
    "kernel_label = [\"RBF\", \"Linear\", \"Polynomial\"]\n",
    "model_color = [\"m\", \"c\", \"g\"]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)\n",
    "for ix, svr in enumerate(svrs):\n",
    "    axes[ix].plot(\n",
    "        X,\n",
    "        svr.fit(X, y).predict(X),\n",
    "        color=model_color[ix],\n",
    "        lw=lw,\n",
    "        label=\"{} model\".format(kernel_label[ix]),\n",
    "    )\n",
    "    axes[ix].scatter(\n",
    "        X[svr.support_],\n",
    "        y[svr.support_],\n",
    "        facecolor=\"none\",\n",
    "        edgecolor=model_color[ix],\n",
    "        s=50,\n",
    "        label=\"{} support vectors\".format(kernel_label[ix]),\n",
    "    )\n",
    "    axes[ix].scatter(\n",
    "        X[np.setdiff1d(np.arange(len(X)), svr.support_)],\n",
    "        y[np.setdiff1d(np.arange(len(X)), svr.support_)],\n",
    "        facecolor=\"none\",\n",
    "        edgecolor=\"k\",\n",
    "        s=50,\n",
    "        label=\"other training data\",\n",
    "    )\n",
    "    axes[ix].legend(\n",
    "        loc=\"upper center\",\n",
    "        bbox_to_anchor=(0.5, 1.1),\n",
    "        ncol=1,\n",
    "        fancybox=True,\n",
    "        shadow=True,\n",
    "    )\n",
    "\n",
    "fig.text(0.5, 0.04, \"data\", ha=\"center\", va=\"center\")\n",
    "fig.text(0.06, 0.5, \"target\", ha=\"center\", va=\"center\", rotation=\"vertical\")\n",
    "fig.suptitle(\"Support Vector Regression\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e26ed85",
   "metadata": {},
   "source": [
    "### With POUR data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e42bfe38",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.1407824194061147\n",
      "-0.134653515466578\n",
      "-0.13823565605279042\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Define X (features) and y (targets)\n",
    "X = abs_data.iloc[:, 15:16].values\n",
    "y = abs_data[['Urea Concentration (mM)']].values.ravel()\n",
    "\n",
    "# #############################################################################\n",
    "# Scale X (features)\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "# #############################################################################\n",
    "# Fit regression model\n",
    "svr_rbf = SVR(kernel=\"rbf\", C=100, gamma=0.1, epsilon=0.1)\n",
    "svr_lin = SVR(kernel=\"linear\", C=100, gamma=\"auto\")\n",
    "svr_poly = SVR(kernel=\"poly\", C=100, gamma=\"auto\", degree=3, epsilon=0.1, coef0=1)\n",
    "\n",
    "# #############################################################################\n",
    "# Look at the results\n",
    "lw = 2\n",
    "\n",
    "svrs = [svr_rbf, svr_lin, svr_poly]\n",
    "kernel_label = [\"RBF\", \"Linear\", \"Polynomial\"]\n",
    "model_color = [\"m\", \"c\", \"g\"]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)\n",
    "for ix, svr in enumerate(svrs):\n",
    "    axes[ix].plot(\n",
    "        X,\n",
    "        svr.fit(X, y).predict(X),\n",
    "        color=model_color[ix],\n",
    "        lw=lw,\n",
    "        label=\"{} model\".format(kernel_label[ix]),\n",
    "    )\n",
    "    print(svr.score(X,y))\n",
    "    axes[ix].scatter(\n",
    "        X[svr.support_],\n",
    "        y[svr.support_],\n",
    "        facecolor=\"none\",\n",
    "        edgecolor=model_color[ix],\n",
    "        s=50,\n",
    "        label=\"{} support vectors\".format(kernel_label[ix]),\n",
    "    )\n",
    "    axes[ix].scatter(\n",
    "        X[np.setdiff1d(np.arange(len(X)), svr.support_)],\n",
    "        y[np.setdiff1d(np.arange(len(X)), svr.support_)],\n",
    "        facecolor=\"none\",\n",
    "        edgecolor=\"k\",\n",
    "        s=50,\n",
    "        label=\"other training data\",\n",
    "    )\n",
    "    axes[ix].legend(\n",
    "        loc=\"upper center\",\n",
    "        bbox_to_anchor=(0.5, 1.1),\n",
    "        ncol=1,\n",
    "        fancybox=True,\n",
    "        shadow=True,\n",
    "    )\n",
    "\n",
    "fig.text(0.5, 0.04, \"data\", ha=\"center\", va=\"center\")\n",
    "fig.text(0.06, 0.5, \"target\", ha=\"center\", va=\"center\", rotation=\"vertical\")\n",
    "fig.suptitle(\"Support Vector Regression\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13a002fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f20e2cc0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}