04_sklearn_fitting.ipynb

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   "source": [
    "# apply Jupyter notebook style\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "from custom.styles import style_string\n",
    "\n",
    "HTML(style_string)\n"
   ]
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  {
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   "source": [
    "<div style=\"text-align:center;\">\n",
    "  <img src=\"custom/molssi_main_horizontal.png\" style=\"display: block; margin: 0 auto; max-height:200px;\">\n",
    "</div>\n",
    "\n",
    "# Introduction to Data Fitting with SciKitLearn\n",
    "\n",
    "<div class=\"overview admonition\"> \n",
    "<p class=\"admonition-title\">Overview</p>\n",
    "\n",
    "Questions:\n",
    "\n",
    "* How do I fit a model using SciKitLearn?\n",
    "\n",
    "* How can I use SciKitLearn to assess my model?\n",
    "\n",
    "* How can I use other models from the SciKitLearn library?\n",
    "\n",
    "Objectives:\n",
    "\n",
    "* Fit a linear model using SciKitLearn.\n",
    "    \n",
    "* Use train_test_split to split the data.\n",
    "\n",
    "* Try other models from SciKitLearn.\n",
    "\n",
    "</div>\n",
    "\n",
    "When people think of \"machine learning\" they usually are thinking of things like \"neural networks\".\n",
    "However, \"machine learning\" really is just fitting data with various models, neural networks being on possible model type.\n",
    "\"Machine learning\" encompasses a broad array of algorithms, many of which are simpler than neural networks and might be more appropriate for specific tasks. SciKit-Learn, a foundational Python library in data fitting and machine learning, has a large of such algorithms that cater to both regression and classification problems.\n",
    "Conveniently, SciKit-Learn also uses a particular format for its models that allows you to switch out models.\n",
    "\n",
    "In this notebook, we will see how to use scikitlearn to fit chemical data to linear models. However, the concepts outlined here will be relevant to fitting data to other models."
   ]
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  {
   "cell_type": "markdown",
   "id": "bfe50af1-d2fe-420d-b80e-899ba73576a2",
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   "source": [
    "## Data Loading and Visualization\n",
    "\n",
    "Before fitting our models, we will first load our data using pandas and visualize the linear relationships using seaborn.\n",
    "For a review of pandas and seaborn, see notebok `03_python_data_science`.\n",
    "\n",
    "For this lesson, we will be loading amino acid data from an SDF file using RDKit and pandas tools, then fitting models to molecular relationships using SciKit-Learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6ab93004-76be-4486-bf7b-b36c68793f16",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd # for dataframes\n",
    "\n",
    "import seaborn as sns # for graphs\n",
    "\n",
    "from rdkit.Chem import PandasTools\n",
    "PandasTools.RenderImagesInAllDataFrames(True) # make molecule images show up\n",
    "\n",
    "df = pd.read_csv(\"data/delaney-processed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "42d9906e-0f11-4f5b-a690-2815d7035f61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>ESOL predicted log solubility in mols per litre</th>\n",
       "      <th>Minimum Degree</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of H-Bond Donors</th>\n",
       "      <th>Number of Rings</th>\n",
       "      <th>Number of Rotatable Bonds</th>\n",
       "      <th>Polar Surface Area</th>\n",
       "      <th>measured log solubility in mols per litre</th>\n",
       "      <th>smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amigdalin</td>\n",
       "      <td>-0.974</td>\n",
       "      <td>1</td>\n",
       "      <td>457.432</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>202.32</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fenfuram</td>\n",
       "      <td>-2.885</td>\n",
       "      <td>1</td>\n",
       "      <td>201.225</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42.24</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>Cc1occc1C(=O)Nc2ccccc2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>citral</td>\n",
       "      <td>-2.579</td>\n",
       "      <td>1</td>\n",
       "      <td>152.237</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.07</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>CC(C)=CCCC(C)=CC(=O)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "  Compound ID  ESOL predicted log solubility in mols per litre  \\\n",
       "0   Amigdalin                                           -0.974   \n",
       "1    Fenfuram                                           -2.885   \n",
       "2      citral                                           -2.579   \n",
       "\n",
       "   Minimum Degree  Molecular Weight  Number of H-Bond Donors  Number of Rings  \\\n",
       "0               1           457.432                        7                3   \n",
       "1               1           201.225                        1                2   \n",
       "2               1           152.237                        0                0   \n",
       "\n",
       "   Number of Rotatable Bonds  Polar Surface Area  \\\n",
       "0                          7              202.32   \n",
       "1                          2               42.24   \n",
       "2                          4               17.07   \n",
       "\n",
       "   measured log solubility in mols per litre  \\\n",
       "0                                      -0.77   \n",
       "1                                      -3.30   \n",
       "2                                      -2.06   \n",
       "\n",
       "                                              smiles  \n",
       "0  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...  \n",
       "1                             Cc1occc1C(=O)Nc2ccccc2  \n",
       "2                               CC(C)=CCCC(C)=CC(=O)  "
      ]
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     "execution_count": 32,
     "metadata": {},
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   ],
   "source": [
    "df.head(3) # preview the first three rows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22900c5b-c109-4cda-b952-5ea71a976b6e",
   "metadata": {},
   "source": [
    "We have now loaded our solubility data using pandas.\n",
    "We can use the skills we learned in the previous lesson to use RDKit to add RDKit molecule objects to our data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd6d3123-75a1-4bff-8226-3e1b0c1164b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from rdkit import Chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7ef3512d-7f8d-424a-803f-44adeaa670ab",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>ESOL predicted log solubility in mols per litre</th>\n",
       "      <th>Minimum Degree</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of H-Bond Donors</th>\n",
       "      <th>Number of Rings</th>\n",
       "      <th>Number of Rotatable Bonds</th>\n",
       "      <th>Polar Surface Area</th>\n",
       "      <th>measured log solubility in mols per litre</th>\n",
       "      <th>smiles</th>\n",
       "      <th>mol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amigdalin</td>\n",
       "      <td>-0.974</td>\n",
       "      <td>1</td>\n",
       "      <td>457.432</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>202.32</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img 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\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fenfuram</td>\n",
       "      <td>-2.885</td>\n",
       "      <td>1</td>\n",
       "      <td>201.225</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42.24</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>Cc1occc1C(=O)Nc2ccccc2</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>citral</td>\n",
       "      <td>-2.579</td>\n",
       "      <td>1</td>\n",
       "      <td>152.237</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.07</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>CC(C)=CCCC(C)=CC(=O)</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Compound ID  ESOL predicted log solubility in mols per litre  \\\n",
       "0   Amigdalin                                           -0.974   \n",
       "1    Fenfuram                                           -2.885   \n",
       "2      citral                                           -2.579   \n",
       "\n",
       "   Minimum Degree  Molecular Weight  Number of H-Bond Donors  Number of Rings  \\\n",
       "0               1           457.432                        7                3   \n",
       "1               1           201.225                        1                2   \n",
       "2               1           152.237                        0                0   \n",
       "\n",
       "   Number of Rotatable Bonds  Polar Surface Area  \\\n",
       "0                          7              202.32   \n",
       "1                          2               42.24   \n",
       "2                          4               17.07   \n",
       "\n",
       "   measured log solubility in mols per litre  \\\n",
       "0                                      -0.77   \n",
       "1                                      -3.30   \n",
       "2                                      -2.06   \n",
       "\n",
       "                                              smiles  \\\n",
       "0  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...   \n",
       "1                             Cc1occc1C(=O)Nc2ccccc2   \n",
       "2                               CC(C)=CCCC(C)=CC(=O)   \n",
       "\n",
       "                                                mol  \n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937e5e0>  \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d150>  \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d1c0>  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mol\"] = df[\"smiles\"].apply(Chem.MolFromSmiles)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651a0f5f-9185-4a91-92f4-78fd875d2153",
   "metadata": {},
   "source": [
    "For our introduction to SciKit-Learn, we will be fitting a very simple relationship - the number of heavy atoms in a molecule and the molecular weight. These will obviously correlated, but we use it as an example to demonstrate fitting data with SciKit-Learn models.\n",
    "\n",
    "We will need to add this descriptor to our dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1afe47b-dd3c-45ec-870e-af6143673ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import Descriptors\n",
    "\n",
    "df[\"NumHeavyAtoms\"] = df[\"mol\"].apply(Descriptors.HeavyAtomCount)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e586cec-6773-4d66-bc6c-effe8007d52d",
   "metadata": {},
   "source": [
    "### Visualizing Correlation\n",
    "\n",
    "One way to visualize the relationship between different variables is to use a pandas correlation matrix.\n",
    "This is available on a dataframe using [df.corr()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html). \n",
    "This will return a dataframe that shows the correlation between each variable and every other variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f64b37c2-6963-4256-977d-28690f73583a",
   "metadata": {
    "tags": []
   },
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ESOL predicted log solubility in mols per litre</th>\n",
       "      <th>Minimum Degree</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of H-Bond Donors</th>\n",
       "      <th>Number of Rings</th>\n",
       "      <th>Number of Rotatable Bonds</th>\n",
       "      <th>Polar Surface Area</th>\n",
       "      <th>measured log solubility in mols per litre</th>\n",
       "      <th>NumHeavyAtoms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ESOL predicted log solubility in mols per litre</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.110001</td>\n",
       "      <td>-0.679967</td>\n",
       "      <td>0.282070</td>\n",
       "      <td>-0.496116</td>\n",
       "      <td>-0.249812</td>\n",
       "      <td>0.189204</td>\n",
       "      <td>0.907264</td>\n",
       "      <td>-0.592453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minimum Degree</th>\n",
       "      <td>-0.110001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.096719</td>\n",
       "      <td>-0.130664</td>\n",
       "      <td>0.246020</td>\n",
       "      <td>-0.175626</td>\n",
       "      <td>-0.189464</td>\n",
       "      <td>-0.130445</td>\n",
       "      <td>-0.024945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Molecular Weight</th>\n",
       "      <td>-0.679967</td>\n",
       "      <td>-0.096719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.254877</td>\n",
       "      <td>0.652521</td>\n",
       "      <td>0.396894</td>\n",
       "      <td>0.475440</td>\n",
       "      <td>-0.640189</td>\n",
       "      <td>0.921623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of H-Bond Donors</th>\n",
       "      <td>0.282070</td>\n",
       "      <td>-0.130664</td>\n",
       "      <td>0.254877</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.211087</td>\n",
       "      <td>0.077002</td>\n",
       "      <td>0.755932</td>\n",
       "      <td>0.209429</td>\n",
       "      <td>0.328216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Rings</th>\n",
       "      <td>-0.496116</td>\n",
       "      <td>0.246020</td>\n",
       "      <td>0.652521</td>\n",
       "      <td>0.211087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.136654</td>\n",
       "      <td>0.308606</td>\n",
       "      <td>-0.512279</td>\n",
       "      <td>0.761396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Rotatable Bonds</th>\n",
       "      <td>-0.249812</td>\n",
       "      <td>-0.175626</td>\n",
       "      <td>0.396894</td>\n",
       "      <td>0.077002</td>\n",
       "      <td>-0.136654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286723</td>\n",
       "      <td>-0.244256</td>\n",
       "      <td>0.424505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polar Surface Area</th>\n",
       "      <td>0.189204</td>\n",
       "      <td>-0.189464</td>\n",
       "      <td>0.475440</td>\n",
       "      <td>0.755932</td>\n",
       "      <td>0.308606</td>\n",
       "      <td>0.286723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123210</td>\n",
       "      <td>0.575924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>measured log solubility in mols per litre</th>\n",
       "      <td>0.907264</td>\n",
       "      <td>-0.130445</td>\n",
       "      <td>-0.640189</td>\n",
       "      <td>0.209429</td>\n",
       "      <td>-0.512279</td>\n",
       "      <td>-0.244256</td>\n",
       "      <td>0.123210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.585638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumHeavyAtoms</th>\n",
       "      <td>-0.592453</td>\n",
       "      <td>-0.024945</td>\n",
       "      <td>0.921623</td>\n",
       "      <td>0.328216</td>\n",
       "      <td>0.761396</td>\n",
       "      <td>0.424505</td>\n",
       "      <td>0.575924</td>\n",
       "      <td>-0.585638</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 ESOL predicted log solubility in mols per litre  \\\n",
       "ESOL predicted log solubility in mols per litre                                         1.000000   \n",
       "Minimum Degree                                                                         -0.110001   \n",
       "Molecular Weight                                                                       -0.679967   \n",
       "Number of H-Bond Donors                                                                 0.282070   \n",
       "Number of Rings                                                                        -0.496116   \n",
       "Number of Rotatable Bonds                                                              -0.249812   \n",
       "Polar Surface Area                                                                      0.189204   \n",
       "measured log solubility in mols per litre                                               0.907264   \n",
       "NumHeavyAtoms                                                                          -0.592453   \n",
       "\n",
       "                                                 Minimum Degree  \\\n",
       "ESOL predicted log solubility in mols per litre       -0.110001   \n",
       "Minimum Degree                                         1.000000   \n",
       "Molecular Weight                                      -0.096719   \n",
       "Number of H-Bond Donors                               -0.130664   \n",
       "Number of Rings                                        0.246020   \n",
       "Number of Rotatable Bonds                             -0.175626   \n",
       "Polar Surface Area                                    -0.189464   \n",
       "measured log solubility in mols per litre             -0.130445   \n",
       "NumHeavyAtoms                                         -0.024945   \n",
       "\n",
       "                                                 Molecular Weight  \\\n",
       "ESOL predicted log solubility in mols per litre         -0.679967   \n",
       "Minimum Degree                                          -0.096719   \n",
       "Molecular Weight                                         1.000000   \n",
       "Number of H-Bond Donors                                  0.254877   \n",
       "Number of Rings                                          0.652521   \n",
       "Number of Rotatable Bonds                                0.396894   \n",
       "Polar Surface Area                                       0.475440   \n",
       "measured log solubility in mols per litre               -0.640189   \n",
       "NumHeavyAtoms                                            0.921623   \n",
       "\n",
       "                                                 Number of H-Bond Donors  \\\n",
       "ESOL predicted log solubility in mols per litre                 0.282070   \n",
       "Minimum Degree                                                 -0.130664   \n",
       "Molecular Weight                                                0.254877   \n",
       "Number of H-Bond Donors                                         1.000000   \n",
       "Number of Rings                                                 0.211087   \n",
       "Number of Rotatable Bonds                                       0.077002   \n",
       "Polar Surface Area                                              0.755932   \n",
       "measured log solubility in mols per litre                       0.209429   \n",
       "NumHeavyAtoms                                                   0.328216   \n",
       "\n",
       "                                                 Number of Rings  \\\n",
       "ESOL predicted log solubility in mols per litre        -0.496116   \n",
       "Minimum Degree                                          0.246020   \n",
       "Molecular Weight                                        0.652521   \n",
       "Number of H-Bond Donors                                 0.211087   \n",
       "Number of Rings                                         1.000000   \n",
       "Number of Rotatable Bonds                              -0.136654   \n",
       "Polar Surface Area                                      0.308606   \n",
       "measured log solubility in mols per litre              -0.512279   \n",
       "NumHeavyAtoms                                           0.761396   \n",
       "\n",
       "                                                 Number of Rotatable Bonds  \\\n",
       "ESOL predicted log solubility in mols per litre                  -0.249812   \n",
       "Minimum Degree                                                   -0.175626   \n",
       "Molecular Weight                                                  0.396894   \n",
       "Number of H-Bond Donors                                           0.077002   \n",
       "Number of Rings                                                  -0.136654   \n",
       "Number of Rotatable Bonds                                         1.000000   \n",
       "Polar Surface Area                                                0.286723   \n",
       "measured log solubility in mols per litre                        -0.244256   \n",
       "NumHeavyAtoms                                                     0.424505   \n",
       "\n",
       "                                                 Polar Surface Area  \\\n",
       "ESOL predicted log solubility in mols per litre            0.189204   \n",
       "Minimum Degree                                            -0.189464   \n",
       "Molecular Weight                                           0.475440   \n",
       "Number of H-Bond Donors                                    0.755932   \n",
       "Number of Rings                                            0.308606   \n",
       "Number of Rotatable Bonds                                  0.286723   \n",
       "Polar Surface Area                                         1.000000   \n",
       "measured log solubility in mols per litre                  0.123210   \n",
       "NumHeavyAtoms                                              0.575924   \n",
       "\n",
       "                                                 measured log solubility in mols per litre  \\\n",
       "ESOL predicted log solubility in mols per litre                                   0.907264   \n",
       "Minimum Degree                                                                   -0.130445   \n",
       "Molecular Weight                                                                 -0.640189   \n",
       "Number of H-Bond Donors                                                           0.209429   \n",
       "Number of Rings                                                                  -0.512279   \n",
       "Number of Rotatable Bonds                                                        -0.244256   \n",
       "Polar Surface Area                                                                0.123210   \n",
       "measured log solubility in mols per litre                                         1.000000   \n",
       "NumHeavyAtoms                                                                    -0.585638   \n",
       "\n",
       "                                                 NumHeavyAtoms  \n",
       "ESOL predicted log solubility in mols per litre      -0.592453  \n",
       "Minimum Degree                                       -0.024945  \n",
       "Molecular Weight                                      0.921623  \n",
       "Number of H-Bond Donors                               0.328216  \n",
       "Number of Rings                                       0.761396  \n",
       "Number of Rotatable Bonds                             0.424505  \n",
       "Polar Surface Area                                    0.575924  \n",
       "measured log solubility in mols per litre            -0.585638  \n",
       "NumHeavyAtoms                                         1.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "561c4bca-db83-4076-a0e9-02b0ad0bbc50",
   "metadata": {},
   "source": [
    "The correlation ranges from 0 (not correlated) to 1 (correlated). The correlation of a column with itself is 1.\n",
    "We can combine this with a heatmap function in seaborn to see this visually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7cc3f54a-5e7c-4759-98ec-8a2aeb96fcdc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(numeric_only=True), cmap=\"Blues\", annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b71555fb-589f-477b-a0d9-6f9f430f471e",
   "metadata": {},
   "source": [
    "## Fitting using SciKitLearn\n",
    "\n",
    "[Scikit-learn](https://scikit-learn.org/stable/index.html) is a popular machine learning library in Python that provides a wide range of algorithms for supervised and unsupervised learning, including classification, regression, clustering, and dimensionality reduction. It is built on top of NumPy, SciPy, and matplotlib, and provides a simple and efficient API for working with data in Python. \n",
    "\n",
    "For our simple example, we will fit a linear relationship between the number of heavy atoms and the molecular weight. As a reminder, the equation for a linear relationship is $y = mx + b$.\n",
    "Molecular weight and number of heavy atoms will obviously be correlated, but is a good demonstration of how to use the scikitlearn library.\n",
    "All model fits in Scikit-Learn use [a similar API](https://scikit-learn.org/stable/developers/develop.html#apis-of-scikit-learn-objects), so learning how to do a linear fit will directly translate to other models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "69aa464a-42d1-4fc4-9018-dd1aa9c032a4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression # import the linear regression model\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c72cde20-6620-4388-8c59-45a76fa06f14",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get our x and y data\n",
    "X = df[[\"NumHeavyAtoms\"]]\n",
    "Y = df[[\"Molecular Weight\"]]\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285c547a-6839-4519-9faf-2f7791e5fd70",
   "metadata": {},
   "source": [
    "That's it! That is how we fit a SciKit Learn model to our data. \n",
    "Now, our variable `linear_model` is a trained SciKit Learn model.\n",
    "We can use it to predict new values or evaluate our model.\n",
    "\n",
    "First, we will look at the score for the model on our training data.\n",
    "This score will range from 0 to 1, with a value closer to 1 indicating a better fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f424de4c-f5fc-46ab-9720-5e15f2d82750",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8493895158261833"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.score(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0845aa02-6d93-4344-93a9-f1a5226daafd",
   "metadata": {},
   "source": [
    "Our model score is `0.84`. This indicates that heavy atom count and molecular weight have a linear relationship,\n",
    "but it may be less correlated than we expect.\n",
    "We will examine this later in the notebook.\n",
    "\n",
    "Our trained model, `linear_model` now has a method called `predict`. \n",
    "If we put in values for our dependent variable, our model will return what values our model\n",
    "will predict.\n",
    "To visualize how well our model does, we will compare our model predicted values to our\n",
    "observed values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "31949038-9265-4af8-b5e8-a006e64cc44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the model to make predictions and add it to the graph\n",
    "model_values = linear_model.predict(X)\n",
    "\n",
    "# Save our predicted values in a new column in our dataframe.\n",
    "df[\"PredictedWt\"] = model_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "90ba416d-960f-4fa2-a0a9-a201506e2b1b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>ESOL predicted log solubility in mols per litre</th>\n",
       "      <th>Minimum Degree</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of H-Bond Donors</th>\n",
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       "      <th>Number of Rotatable Bonds</th>\n",
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       "      <th>smiles</th>\n",
       "      <th>mol</th>\n",
       "      <th>NumHeavyAtoms</th>\n",
       "      <th>PredictedWt</th>\n",
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       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img 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\" alt=\"Mol\"/></div></td>\n",
       "      <td>32</td>\n",
       "      <td>461.486688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fenfuram</td>\n",
       "      <td>-2.885</td>\n",
       "      <td>1</td>\n",
       "      <td>201.225</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42.24</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>Cc1occc1C(=O)Nc2ccccc2</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "      <td>15</td>\n",
       "      <td>227.477146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>citral</td>\n",
       "      <td>-2.579</td>\n",
       "      <td>1</td>\n",
       "      <td>152.237</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.07</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>CC(C)=CCCC(C)=CC(=O)</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "      <td>11</td>\n",
       "      <td>172.416077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Compound ID  ESOL predicted log solubility in mols per litre  \\\n",
       "0   Amigdalin                                           -0.974   \n",
       "1    Fenfuram                                           -2.885   \n",
       "2      citral                                           -2.579   \n",
       "\n",
       "   Minimum Degree  Molecular Weight  Number of H-Bond Donors  Number of Rings  \\\n",
       "0               1           457.432                        7                3   \n",
       "1               1           201.225                        1                2   \n",
       "2               1           152.237                        0                0   \n",
       "\n",
       "   Number of Rotatable Bonds  Polar Surface Area  \\\n",
       "0                          7              202.32   \n",
       "1                          2               42.24   \n",
       "2                          4               17.07   \n",
       "\n",
       "   measured log solubility in mols per litre  \\\n",
       "0                                      -0.77   \n",
       "1                                      -3.30   \n",
       "2                                      -2.06   \n",
       "\n",
       "                                              smiles  \\\n",
       "0  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...   \n",
       "1                             Cc1occc1C(=O)Nc2ccccc2   \n",
       "2                               CC(C)=CCCC(C)=CC(=O)   \n",
       "\n",
       "                                                mol  NumHeavyAtoms  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937e5e0>             32   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d150>             15   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d1c0>             11   \n",
       "\n",
       "   PredictedWt  \n",
       "0   461.486688  \n",
       "1   227.477146  \n",
       "2   172.416077  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebf00ed-ca54-4750-8ef1-2ad1ce0faa90",
   "metadata": {},
   "source": [
    "We will now use seaborn to visualize our actual and predicted values.\n",
    "This plot shows the `MolWt` as the x value, with the predicted `MolWt` as the Y value.\n",
    "The `MolWt` vs itself is shown as a reference line. For a perfect model,\n",
    "all values would fall along the line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2472d904-99ae-4a1e-9f6a-825039a9f79c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Molecular Weight', ylabel='PredictedWt'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Molecular Weight\", y=\"PredictedWt\", data=df)\n",
    "sns.lineplot(x=\"Molecular Weight\", y=\"Molecular Weight\", data=df, linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66c444b5-be32-4406-b925-af4f9bb400fd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1588.301287449386\n",
      "Root Mean Squared Error: 39.853497806960256\n",
      "R2 Score: 0.8493895158261833\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Evaluate the model's performance\n",
    "mse = mean_squared_error(Y, model_values)\n",
    "rmse = math.sqrt(mse)\n",
    "r2 = r2_score(Y, model_values)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"Root Mean Squared Error:\", rmse)\n",
    "print(\"R2 Score:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b10bd43-a9cc-415f-b0bf-8e4b6535c3af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db68b3e7-4df5-490b-b032-65c2c42986f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "541be77d-77ad-46cd-8ea4-3e0bfb66e5b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b9ac7d4-0f91-433b-91b0-606b88f46391",
   "metadata": {},
   "source": [
    "<div class=\"exercise admonition\">\n",
    "<p class=\"admonition-title\">Exercise - Multilinear Regression\n",
    "\n",
    "<p> To perform multilinear regression, you only need to feed in two columns of data for X. Then, you perform the fit the same way. </p>\n",
    "    \n",
    "```python\n",
    "X = df[[\"MolWt\", \"Polar Surface Area\"]]\n",
    "```\n",
    "\n",
    "Now, you will be fitting an equation of the form $y = m_{1}x_{1} + m_{2}x_{2} +b$.  \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "78e28bbb-010b-4dcc-b322-3f144ef529ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Compound ID', 'ESOL predicted log solubility in mols per litre',\n",
       "       'Minimum Degree', 'Molecular Weight', 'Number of H-Bond Donors',\n",
       "       'Number of Rings', 'Number of Rotatable Bonds', 'Polar Surface Area',\n",
       "       'measured log solubility in mols per litre', 'smiles', 'mol',\n",
       "       'NumHeavyAtoms', 'PredictedWt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d036258d-9296-4388-a12d-04c7a5d318e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[[\"NumHeavyAtoms\", \"Polar Surface Area\"]]\n",
    "Y = df[[\"Molecular Weight\"]]\n",
    "\n",
    "multilinear_model = LinearRegression()\n",
    "multilinear_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bb5d0c38-7dc9-45cb-b2e5-62467ee34642",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the model to make predictions and add it to the graph\n",
    "model_values = multilinear_model.predict(X)\n",
    "\n",
    "# Save our predicted values in a new column in our dataframe.\n",
    "df[\"PredictedWt_MultilinearModel\"] = model_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "30537561-ba3f-43f4-83aa-451bfe8371f3",
   "metadata": {},
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound ID</th>\n",
       "      <th>ESOL predicted log solubility in mols per litre</th>\n",
       "      <th>Minimum Degree</th>\n",
       "      <th>Molecular Weight</th>\n",
       "      <th>Number of H-Bond Donors</th>\n",
       "      <th>Number of Rings</th>\n",
       "      <th>Number of Rotatable Bonds</th>\n",
       "      <th>Polar Surface Area</th>\n",
       "      <th>measured log solubility in mols per litre</th>\n",
       "      <th>smiles</th>\n",
       "      <th>mol</th>\n",
       "      <th>NumHeavyAtoms</th>\n",
       "      <th>PredictedWt</th>\n",
       "      <th>PredictedWt_MultilinearModel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amigdalin</td>\n",
       "      <td>-0.974</td>\n",
       "      <td>1</td>\n",
       "      <td>457.432</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>202.32</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img 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\" alt=\"Mol\"/></div></td>\n",
       "      <td>32</td>\n",
       "      <td>461.486688</td>\n",
       "      <td>434.551954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fenfuram</td>\n",
       "      <td>-2.885</td>\n",
       "      <td>1</td>\n",
       "      <td>201.225</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42.24</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>Cc1occc1C(=O)Nc2ccccc2</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "      <td>15</td>\n",
       "      <td>227.477146</td>\n",
       "      <td>226.923908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>citral</td>\n",
       "      <td>-2.579</td>\n",
       "      <td>1</td>\n",
       "      <td>152.237</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.07</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>CC(C)=CCCC(C)=CC(=O)</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "      <td>11</td>\n",
       "      <td>172.416077</td>\n",
       "      <td>175.065597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Picene</td>\n",
       "      <td>-6.618</td>\n",
       "      <td>2</td>\n",
       "      <td>278.354</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.87</td>\n",
       "      <td>c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "      <td>22</td>\n",
       "      <td>323.834016</td>\n",
       "      <td>338.423908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thiophene</td>\n",
       "      <td>-2.232</td>\n",
       "      <td>2</td>\n",
       "      <td>84.143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>c1ccsc1</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "      <td>5</td>\n",
       "      <td>89.824474</td>\n",
       "      <td>92.304392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Compound ID  ESOL predicted log solubility in mols per litre  \\\n",
       "0   Amigdalin                                           -0.974   \n",
       "1    Fenfuram                                           -2.885   \n",
       "2      citral                                           -2.579   \n",
       "3      Picene                                           -6.618   \n",
       "4   Thiophene                                           -2.232   \n",
       "\n",
       "   Minimum Degree  Molecular Weight  Number of H-Bond Donors  Number of Rings  \\\n",
       "0               1           457.432                        7                3   \n",
       "1               1           201.225                        1                2   \n",
       "2               1           152.237                        0                0   \n",
       "3               2           278.354                        0                5   \n",
       "4               2            84.143                        0                1   \n",
       "\n",
       "   Number of Rotatable Bonds  Polar Surface Area  \\\n",
       "0                          7              202.32   \n",
       "1                          2               42.24   \n",
       "2                          4               17.07   \n",
       "3                          0                0.00   \n",
       "4                          0                0.00   \n",
       "\n",
       "   measured log solubility in mols per litre  \\\n",
       "0                                      -0.77   \n",
       "1                                      -3.30   \n",
       "2                                      -2.06   \n",
       "3                                      -7.87   \n",
       "4                                      -1.33   \n",
       "\n",
       "                                              smiles  \\\n",
       "0  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...   \n",
       "1                             Cc1occc1C(=O)Nc2ccccc2   \n",
       "2                               CC(C)=CCCC(C)=CC(=O)   \n",
       "3                 c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43   \n",
       "4                                            c1ccsc1   \n",
       "\n",
       "                                                mol  NumHeavyAtoms  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937e5e0>             32   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d150>             15   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d1c0>             11   \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d2a0>             22   \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x7f6ef937d230>              5   \n",
       "\n",
       "   PredictedWt  PredictedWt_MultilinearModel  \n",
       "0   461.486688                    434.551954  \n",
       "1   227.477146                    226.923908  \n",
       "2   172.416077                    175.065597  \n",
       "3   323.834016                    338.423908  \n",
       "4    89.824474                     92.304392  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3205e65c-34e5-4d04-956e-ff65910978d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Molecular Weight', ylabel='PredictedWt_MultilinearModel'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Molecular Weight\", y=\"PredictedWt_MultilinearModel\", data=df)\n",
    "sns.lineplot(x=\"Molecular Weight\", y=\"Molecular Weight\", data=df, linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6bed2d8f-6a87-41d4-a081-68fcf4fd3d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1539.9673158687685\n",
      "Root Mean Squared Error: 39.24241730409543\n",
      "R2 Score: 0.8539727790391034\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Evaluate the model's performance\n",
    "mse = mean_squared_error(Y, model_values)\n",
    "rmse = math.sqrt(mse)\n",
    "r2 = r2_score(Y, model_values)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"Root Mean Squared Error:\", rmse)\n",
    "print(\"R2 Score:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d443de-9a46-4793-816a-ebfe386237c9",
   "metadata": {},
   "source": [
    "## Model Validation - Train Test Split\n",
    "\n",
    "When training a model for machine learning, it is a best practice to evaluate the model's performance on data that was not part of the training set.\n",
    "One way to achieve this is to use a method called \"train test split\".\n",
    "\n",
    "Train-test split is a widely used technique in the field of machine learning and data science to evaluate the performance of a model. It involves dividing the available data into two distinct sets: a training set and a testing set. This partitioning is essential to ensure that the model generalizes well to new, unseen data and to prevent overfitting.\n",
    "\n",
    "The primary purposes of using train-test split are:\n",
    "\n",
    "Model validation: To ensure that the model built using the training data performs well on unseen data, the testing set serves as a proxy for new data. By comparing the model's predictions with the actual outcomes in the testing set, we can gauge its predictive accuracy and robustness.\n",
    "\n",
    "Prevent overfitting: Overfitting occurs when a model learns to perform exceptionally well on the training data but fails to generalize to new data. This is usually due to the model capturing the noise or random fluctuations in the training data rather than the underlying patterns. A train-test split helps mitigate this issue by allowing us to evaluate the model's performance on a separate dataset.\n",
    "\n",
    "To perform a train-test split, the data is typically divided into approximately 70-80% for training and 20-30% for testing. This ratio can be adjusted depending on the size and characteristics of the dataset. The split should be done randomly to ensure that both sets are representative of the overall data distribution.\n",
    "\n",
    "SciKit-Learn has tools that can split your data for you. We will now use `train-test-split` and repeat our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dcba06ad-e406-4e9a-ad81-c1c2de980ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0b466b3f-ed5f-4b20-bd29-a0d958e75b51",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4d337d-74fb-40a5-8729-c037cee0480c",
   "metadata": {},
   "source": [
    "Now we perform a fit using the training data only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aba914e5-91cf-4613-af10-31cc1f807a60",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttt_model = LinearRegression()\n",
    "ttt_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426c600e-9297-444e-b991-02309dd22c13",
   "metadata": {},
   "source": [
    "After performing our fit with the training data, we use the \"test\" data to evaluate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cbf5d492-26a8-4080-8f9e-87604f3071e0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = ttt_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b43cad05-a160-42f4-a092-5c787e58177c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train = pd.DataFrame()\n",
    "df_train[\"Molecular Weight\"] = Y_test\n",
    "df_train[\"Predicted MolWt\"] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2fd3f9f4-b203-49af-95e7-5cd5fa6b361a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Molecular Weight', ylabel='Predicted MolWt'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VCWz97YrlWrcQbwY8TRxneoiIiMo4fT0fs7Yk4WJ2EfaevonRUYHwVkgd3S2qAwx6iIiIULqc9eWvGXjj+9PQGUwIUrphzZRoBjxOhEEPERE1ewUlery0IwX/O3kDADCkgz/+9VA3+Loz4HEmDHqIiKhZK9EbMX7dUVzMLoKriwgL7muPZ2Lb8HRlJ8SNzERE1Ky5ScSYEN0SLb3l+M/zffHsgLYMeJyUSDDn5DVj+fn5UCqVUKvV8PLycnR3iIionqmL9cgv1iPEVwEAMJoEFGoNUMolDu4Z1URNP78500NERM3KiSt5GLMmHn/77HeU6I0AALGLiAFPM8Cgh4iImgVBEPDv+It48KNjuJpbjCKdAZnqEkd3ixoQNzITEZHTy9PoMP/rP7DvTBYAYHRUIFZO6govN87uNCcMeoiIyKklXr6N2VuScV1dAqnYBa+O7YjH7g2DSMTNys0Ngx4iInJagiBg1Y9ncV1dgtYqBdZN7YEuLZWO7hY5CIMeIiJyWiKRCKsf7o51B9Lw8ugO8ORyVrPGjcxERORUfr2Ygw8Oplm+b+ktx4qJUQx4iDM9RETkHIwmAR8eTMO7+87BJABRLZUY0K6Fo7tFjQiDHiIiavJuFWjxj69OICEtGwAwsUdL9AzzcXCvqLFx+PLWtWvX8Nhjj0GlUkGhUKB79+5ITEy03BcEAUuXLkVwcDDkcjkGDRqE1NRUq2dotVrMnj0bfn5+cHd3x7hx43D16tWGHgoRETnAsbRsjF4Tj4S0bMglYvzzwa5YPbk73GX8fz1Zc2jQk5ubi5iYGEgkEvzwww84ffo03nnnHXh7e1varFq1CqtXr8a6detw/PhxBAYGYvjw4SgoKLC0iYuLw86dO7Ft2zYkJCSgsLAQY8eOhdFodMCoiIiooXx06AIeXf8rbhVo0S7AA9/OisFDvUIc3S1qpBxae+ull17C0aNHER8fb/e+IAgIDg5GXFwcFi5cCKB0VicgIABvv/02nnvuOajVarRo0QKff/45Hn74YQDA9evXERISgt27d2PkyJE2z9VqtdBqtZbv8/PzERISwtpbRERNzLd/XMecrcl4uFcIlo7rDLlU7OguUQNqUrW3vv32W/Tq1QsPPfQQ/P39ER0djU8//dRyPz09HZmZmRgxYoTlmkwmw8CBA3Hs2DEAQGJiIvR6vVWb4OBgdOnSxdKmvBUrVkCpVFq+QkL4vwIioqaioERv+fW4bsHYOaMf3n6wKwMeqpJDg56LFy/io48+QmRkJH766Sc8//zzmDNnDj777DMAQGZmJgAgICDA6nUBAQGWe5mZmZBKpfDx8amwTXmLFi2CWq22fF25cqWuh0ZERHXMYDThnz/9iaHvHEZWwZ2aWdGh3LBM1ePQXV4mkwm9evXC8uXLAQDR0dFITU3FRx99hCeeeMLSrvxR4YIgVHl8eGVtZDIZZDLZXfaeiIjqk1qjQ3ahDvklemgNJrz9w59IvpIHAPghJRPT+rV2aP+o6XFo0BMUFIROnTpZXevYsSO2b98OAAgMDARQOpsTFBRkaZOVlWWZ/QkMDIROp0Nubq7VbE9WVhb69etX30MgIqJ6cD2vGAu3n0T8+Wyr6wqpGG9P6or7uwU7qGfUlDl0eSsmJgZnz561unbu3DmEhYUBAMLDwxEYGIi9e/da7ut0Ohw+fNgS0PTs2RMSicSqzY0bN3Dq1CkGPURETZBao7Mb8ABA5yAvDIj0c0CvyBk4dKbnH//4B/r164fly5dj8uTJ+O233/DJJ5/gk08+AVC6rBUXF4fly5cjMjISkZGRWL58ORQKBaZOnQoAUCqVePrppzFv3jyoVCr4+vpi/vz5iIqKwrBhwxw5PCIiqoXsQp3dgAcAjl/ORXahDkqFtIF7Rc7AoUFP7969sXPnTixatAhvvPEGwsPD8d577+HRRx+1tFmwYAGKi4sxY8YM5Obmok+fPtizZw88PT0tbd599124urpi8uTJKC4uxtChQ7Fp0yaIxdzJT0TU1OSXyc6yp6CK+0QVceg5PY1FTfP8iYio7ukMJuxIuopeYT4Y9u6RCtvtnzsQbf09GrBn1Fg1qXN6iIiIACAjR4MHPz6Gl3ak4KfUzAr37QyI9IOfB5e2qHYY9BARkUPtTrmBMWvicfKqGkq5BO0DvbByUlebwGdApB/entSV+3mo1liNjYiIHKJEb8Rb/zuDz3+5DADoEeqNtVN7oKW3HACwdko0sgt1KCjRw9NNAj8PKQMeuisMeoiIqMGlZxdh1pYkpF7PBwA8N7AN5o9oD4n4zgKEUsEgh+oWgx4iImpweRodzmYWwNddincmd8Pg9v6O7hI1Awx6iIiagbIlHbzkEvi5N/wsStnyQNGhPnjvke7oFeaLQKVbg/aDmi8GPURETs5eSYcBkX5YOakrgv/aP1Pf0rIKMf/rP7BiYhQ6BpWmFo/tylIS1LCYvUVE5MQqKulw5Hw2Xtp+EmqNrt77sCPpKsatS8CJK3lY8m1qvb8fUUU400NE5MQqK+lw5Hx2vZZ00OgMWPJNKr5OvAoA6NtGhfcf6V4v70VUHQx6iIicmKNKOpy7WYCZXybhfFYhRCLghaGRmD0kEmIXUb28H1F1MOghInJiXm6SSu97VnG/Nk5dU+PBj4+hRG9CC08Z3n+kO/q1ZWV0cjwGPURETszPQ4oBkX44YmeJq75KOnQM8kKPUB+IXUR49+Hu8POQ1fl7ENUGC46CBUeJyLldzyvGS9tPWgU+5pIOQXWUvXXuZgFCfRVwk4gBlC6reUhd4cLlLKpHNf385kwPEZGTC/aW11tJB0EQsOW3DLz+3WlM7tUKyyZEAah6WY3IERj0EBE1A/VR0qGgRI9FO1Lw/ckbAIBrucXQG01WpSSIGhMGPUREVGOnrqkxa0sSLuVo4Ooiwosj2+Nv/dtwOYsaNQY9RERUbYIg4LOfL+Ot/52BzmhCS2851kyJRs8wH0d3jahKDHqIiKjacop0WL33HHRGE4Z1DMC/HuoKb1ZCpyaCQQ8REVWbn4cM/3qoGzJuazA9prWlgChRU8Cgh4iIKiQIAjYcvYRwPwWGdAgAAAzvFODgXhHVDoMeIiKyK0+jw/yvT2LfmZvwVkiwf+5AqHjQIDVhDHqIiMhG4uVczN6ShOvqEkjFLpg7vB183bl3h5o2Bj1ERGRhMgn4JP4i/vnTWRhNAlqrFFg3tQe6tFQ6umtEd41BDxERAQB0BhOe+/x3HDx7CwBwf7dgLH+gS70UJSVyBAY9REQEAJC6uiDAyw0yVxcsub8zptwTwuwsciosOAoWHCWi5stkElCkM1hmc0r0RlzO0aB9oKeDe0ZUtZp+fte4QEpGRgbsxUmCICAjI6OmjyMiIge5VaDFtI2/YcaXSTCZSv9dd5OIGfCQ06px0BMeHo5bt27ZXL99+zbCw8PrpFNERFS/jl3Ixug18Yg/n43jl27jTGa+o7tEVO9qvKdHEAS7a7yFhYVwc3Ork04REVH9MJoErD1wHmv2n4dJACL9PfDBoz3QLoCzO+T8qh30zJ07FwAgEonw6quvQqFQWO4ZjUb8+uuv6N69e513kIiI6kZWfgle2HYCP1/MAQBM7tUKr4/rArlUDABQa3TILtQhv0QPL7kEfu5SKFlXi5xItYOe5ORkAKUzPSkpKZBK7/xFkEql6NatG+bPn1/3PSQiojoxa0syfrt0GwqpGG890AUPRLey3LueV4yF208i/ny25dqASD+snNQVwd5yR3SXqM7VOHvrqaeewvvvv+9UWU7M3iKi5iD1uhov7zyFdx7qhgh/D8t1tUaHWVuTrQIeswGRflg7JZozPtQo1Vv21r///W9cvHgRGzduZGBARNQEZKpL8EPKDcv3nYOV2DWjn1XAAwDZhTq7AQ8AHDmfjexCXb32k6ihVDvoeeGFFxAZGYnQ0FBMmzYNmzZtuusU9aVLl0IkEll9BQYGWu4LgoClS5ciODgYcrkcgwYNQmpqqtUztFotZs+eDT8/P7i7u2PcuHG4evXqXfWLiKipO3g2C6PeP4I525Lxx5U8y3V7iSj5JfpKn1VQxX2ipqLaQU9eXh4OHTqEZ599FlevXsXMmTMRHh6ONm3a4JlnnsGXX36J69ev17gDnTt3xo0bNyxfKSkplnurVq3C6tWrsW7dOhw/fhyBgYEYPnw4CgoKLG3i4uKwc+dObNu2DQkJCSgsLMTYsWNhNBpr3BcioqZObzRhxQ9n8NTG48jV6NEuwBNKeeVlJLyqKDPBMhTkLGp9IrNer8cvv/yCgwcP4tChQ/j111+h1WphMBiq/YylS5di165dOHHihM09QRAQHByMuLg4LFy4EEDprE5AQADefvttPPfcc1Cr1WjRogU+//xzPPzwwwCA69evIyQkBLt378bIkSPtvq9Wq4VWq7V8n5+fj5CQEO7pIaIm7VpeMWZvSUJSRh4AYFrfMCwa3RFuEnGlr1NrdJi9NRlHuKeHmph6P5HZzGg0QqfTWQIIg8FQq8MJz58/j+DgYISHh+ORRx7BxYsXAQDp6enIzMzEiBEjLG1lMhkGDhyIY8eOAQASExOh1+ut2gQHB6NLly6WNvasWLECSqXS8hUSElLjfhMRNSb7Tt/E6PfjkZSRB083V3z0aA+8Pr5LlQEPACgVUqyc1BUDIv2srg+I9MPbk7oy4CGnUe2U9ZKSEhw7dgyHDh3CgQMH8Pvvv6NNmzYYMGAAZs2ahYEDByI4OLhGb96nTx989tlnaNeuHW7evIlly5ahX79+SE1NRWZmJgAgICDA6jUBAQG4fPkyACAzMxNSqRQ+Pj42bcyvt2fRokWWc4eAOzM9RERN1YVbhVAX69GtlRJrp/RAqEpR9YvKCPaWY+2UaGQX6lBQooenmwR+Hjynh5xLtYMeb29vBAQEYNy4cXjhhRcwcOBA+Pv739Wbjxo1yvLrqKgo9O3bF23btsXmzZtx7733ArDddFfRidA1aSOTySCTye6i50REjlf237q/9W8DTzcJHuzZClLX2k3iKxUMcsi5VftvRrdu3ZCZmYnDhw8jPj4e8fHxyMnJqdPOuLu7IyoqCufPn7dkcZWfscnKyrLM/gQGBkKn0yE3N7fCNkREzujHUzcw8aNjKNKW7qN0cRFhap/QWgc8RM1Btf92/Prrr7h9+zZWrVoFuVyOVatWISgoCF26dMGsWbPw9ddfIysr6646o9VqcebMGQQFBSE8PByBgYHYu3ev5b5Op8Phw4fRr18/AEDPnj0hkUis2ty4cQOnTp2ytCEiciZagxFLvjmF579IQnJGHjYkpDu6S0RNRo0Kjrq7u+O+++7DfffdBwAoKChAfHw89u7di7/97W8oLCysUfbW/Pnzcf/99yM0NBRZWVlYtmwZ8vPzMW3aNIhEIsTFxWH58uWIjIxEZGQkli9fDoVCgalTpwIAlEolnn76acybNw8qlQq+vr6YP38+oqKiMGzYsJoMjYio0buUXYRZW5Nw6lppRfTnBrbB84PaOrhXRE1HjausA4DJZMLx48dx6NAhHDx4EEePHkVRURHCwsJq9JyrV69iypQpyM7ORosWLXDvvffil19+sTxnwYIFKC4uxowZM5Cbm4s+ffpgz5498PS8Uw343XffhaurKyZPnozi4mIMHToUmzZtglhcdcYCEVFT8d0f17FoRwoKtQb4KCRYPbk7Bne4u32VRM1Ntc/pOX78uOVMHvMhgK1atcKgQYMwePBgDB48GK1bt67n7tYP1t4iosZs09F0LP3uNACgd2sfrJkSjSAli4AS1fTzu9pBj4uLC4KCgixBzqBBgxAREXHXHW4MGPQQUWOWlV+CMWsT8HCvEMQNi4SrmJuViYCaf35Xe3nrzJkzaN++/V11joiIquePK3noFuINAPD3csP+eQOrLBdBRJWr9n8XGPAQEdW/Yp0RC/77B8Z/cBS7y1RIZ8BDdPeqNdPj4+NT5YGAZrdv376rDhERNVfnbxZgxpdJOJ9VCJEIuHJb4+guETmVagU97733Xj13g4io+RIEAV8nXsVr35xCid6EFp4yvP9wd/SL8Kv6xURUbdUKeqZNm1bf/SAiapaKtAa8uusUdiRfAwD0j/TD6snd0cKTpXKI6lqtzukxGo3YtWsXzpw5A5FIhE6dOmHcuHE8G4eIqIZ+u3QbO5KvwUUEzB3eDjMGRcDFpXrbCYioZmoc9KSlpWH06NG4du0a2rdvD0EQcO7cOYSEhOB///sf2rbl6aBERNU1uL0/5g1vh3vCfdGnjcrR3SFyajU+7GHOnDlo27Ytrly5gqSkJCQnJyMjIwPh4eGYM2dOffSRiMhpFJTo8fLOFNxQF1uuzR4ayYCHqAHUeKbn8OHD+OWXX+Dr62u5plKpsHLlSsTExNRp54iInMmpa2rM2pKESzkapN8qwpa/9al2ZiwR3b0aBz0ymQwFBQU21wsLCyGVSuukU0REzkQQBHz+y2Us+/4MdEYTgpVumD+yPQMeogZW4+WtsWPH4tlnn8Wvv/4KQRAgCAJ++eUXPP/88xg3blx99JGIqMlSF+sxc0sSXvsmFTqjCcM6BmD3C/3RM8yn/t5To8OFrEIkZ+Tiwq1CqDW6ensvoqakxjM9a9aswbRp09C3b19IJKUnhBoMBowbNw7vv/9+nXeQiKipSs8uwhMbfsWV28WQiEVYeF8HPB0bXq8zPNfzirFw+0nEn8+2XBsQ6YeVk7oi2JtFSql5q3bB0fLOnz+PP//8E4IgoFOnTk26+CgLjhI1X2qNDtmFOuSX6OEll8DPXQqlom6W6ot1Rkz44CiKdAasm9oD3f+qpVVf1BodZm1Ntgp4zAZE+mHtlOg6GxtRY1BvBUfLi4yMRGRkZG1fTkTkcPUxK6Iu1sNT5goXFxHkUjE+faIXlAoJlPL6r52VXaizG/AAwJHz2cgu1DHooWat2kHPG2+8Ua12r732Wq07Q0TUUNQanU3AA5QGBy9tP1mrWZGkjFzM3pKMqX1CMXNw6ex3qEpRZ32uSn6JvtL7BVXcJ3J21Q56li5diuDgYPj7+6OiFTGRSMSgh4iahLuZFSm/JOarkOA/v1/FP386C4NJwPakq3imfzhkrg17Sn1Vldg9WamdmrlqBz333XcfDh48iF69emH69OkYM2YMy04QUZNV21kRe0tivgoJbmtK24/tGoQVE6MaPOABAD8PKQZE+uFIBXt6/Dy4tEXNW7VT1nfv3o2LFy+iT58+ePHFF9GqVSssXLgQZ8+erc/+ERHVi9rMilS0JHZbo4dIBLwypiPWTol22IyKUiHFykldMSDSujr7gEg/vD2pK/fzULNXo43MQUFBWLRoERYtWoQjR45g48aN6N27N6KiorBv3z7I5UyHJKKmoTazIpUtiQlCaR0tRx84GOwtx9op0cgu1KGgRA9PNwn8POouI42oKat19lbv3r1x6dIlnD59GsnJydDr9Qx6iKjJMM+KvLT9pFXgU9msSFPZKKxUMMghsqfGQc/PP/+MDRs24D//+Q/atWuHp556ClOnTuX5NkTU5NR0VuTiraJKn8eNwkSNW7WDnlWrVmHjxo3IycnBo48+ioSEBERFRdVn34iI6l11ZkWMJgHrDqTh/f3n4CZxQYneZNOGG4WJGr9qn8js4uKC0NBQjB07ttLCoqtXr66zzjUUnshM5FzsnbIMoFYnL2cVlCBu2wkcu5ADABgTFYQ8jQ5H//oeuLMkFsQyD0QNqt5OZB4wYABEIhFSU1MrbOPoDXxEROVTyhVSMTY82RsfHEhDfFrNTl5OOJ+NuK+SkV2og0IqxrIJXTCxRytLUMWNwkRNS61rbzkTzvQQOQd7taf+MTwSLTxkCPByg9ZggptEjKSMXGxISEevMB+7Jy8bTQLe3XsOHxxKgyAAHQI9sW5qD0T4ezT0kIioEg1We4uIqLEpn1KukIoxqnMQXv8+FUfT7ixHxUSosGZKNOZsTbZ78rIIwKnraggCMOWeUCy5vxPcJDyMlaipY9BDRE5BrdHhtkZndW16bDjeLBfwALB8Pz023CrNXBAEiEQiuLiI8M5D3fDLxdsY0zWo/jtPRA2i2icyExE1VtfzijFrazLyi63PyYkO8UZ8uYDH7GhaDqJDvOHpJoHeaMLKH/7Ewu0nLfdVHjIGPEROhjM9RNSklM/M8pC54rVvTiH+fDa6hXgjJkJlmcnRGmxTy8vTG4145JNfkHg5F0DpclZ0qE+9joGIHINBDxE1GfaKffaP9MO0fq1x7EIONiSkY82UaAClMzky18ons6/majDv6z+Qp9HDU+aKtx/syoCHyIlVK3vr5MmTVTWx6Nq16111yBGYvUXU+NnLzDKLiVAhOtQH6w6kQSEVY3psOKJDvOHrLsXqvefsvqaltxuu5ZUAALq2UmLdlB4IVSnqfRxEVHfqJXure/fuEIlElk1+lTEajdXrKRFRDVRW7PNoWg6mx4QDADQ6I9YdSANQmr21e05/vPbNKav6Wr7uUkvAMz0mHC+N6gBpFbNCRNT0VetveXp6Oi5evIj09HRs374d4eHh+PDDD5GcnIzk5GR8+OGHaNu2LbZv317rjqxYsQIikQhxcXGWa4IgYOnSpQgODoZcLsegQYNsDkfUarWYPXs2/Pz84O7ujnHjxuHq1au17gcRNU5VFfu0t3+nV5gPfBQSrJ0Sjf1zB2LXjH7YP3cglo3vDG+FBJ883hOv3d+JAQ9RM1GtmZ6wsDDLrx966CGsWbMGo0ePtlzr2rUrQkJC8Oqrr2LChAk17sTx48fxySef2CyNrVq1CqtXr8amTZvQrl07LFu2DMOHD8fZs2fh6ekJAIiLi8N3332Hbdu2QaVSYd68eRg7diwSExMhFvNcDSJn4VVFMU9vufX9stXStQYjivVGdP9rv05bfw/0b9eCBUKJmpkab2ROSUlBeHi4zfXw8HCcPn26xh0oLCzEo48+ik8//RTLli2zXBcEAe+99x4WL16MiRMnAgA2b96MgIAAbNmyBc899xzUajXWr1+Pzz//HMOGDQMAfPHFFwgJCcG+ffswcuRIu++p1Wqh1Wot3+fn59e430TUsPw8pBgQ6We1TGU2INIPbf09sH/uQJvSEJeyizBraxIu52jwv9n9Lft2GPAQNT81ntPt2LEjli1bhpKSEss1rVaLZcuWoWPHjjXuwMyZMzFmzBhL0GKWnp6OzMxMjBgxwnJNJpNh4MCBOHbsGAAgMTERer3eqk1wcDC6dOliaWPPihUroFQqLV8hISE17jcRNSylQoqVk7piQKSf1XXzjE6Alxva+nuge6gP2vp7QKmQ4vuT1zF2bQJOXcuHq4sI19XFDuo9ETUGNZ7p+fjjj3H//fcjJCQE3bp1AwD88ccfEIlE+P7772v0rG3btiEpKQnHjx+3uZeZmQkACAgIsLoeEBCAy5cvW9pIpVL4+PjYtDG/3p5FixZh7ty5lu/z8/MZ+BA1AcHecqydEl1lsc8SvRFvfH8aW37NAAD0bu2DNVOiEaRkFXSi5qzGQc8999yD9PR0fPHFF/jzzz8hCAIefvhhTJ06Fe7u7tV+zpUrV/DCCy9gz549cHNzq7Bd+Wyx6mSQVdVGJpNBJpNVu69E1PgIQGmRrDLUGh2SM/Kw9LtUXMrRAABmDGqLucPbwVXMzcpEzV2tDidUKBR49tln7+qNExMTkZWVhZ49e1quGY1GHDlyBOvWrcPZs2cBlM7mBAXdOQo+KyvLMvsTGBgInU6H3Nxcq9merKws9OvX7676R0SOU/7UZT/30tkce4cTmpe3BMDmXpdgLzx2bxgDHiICUMvaW59//jliY2MRHBxsWWp699138c0331T7GUOHDkVKSgpOnDhh+erVqxceffRRnDhxAm3atEFgYCD27t1reY1Op8Phw4ctAU3Pnj0hkUis2ty4cQOnTp1i0EPUyKg1OlzIKkRyRi4u3CqEulxxULOrtzWYtSUJQ1cfxgMfHsPQdw5j9tZkXL2tsZSbKOvI+WwcOncLC/970ubeqev5eGn7yQrfi4ialxrP9Hz00Ud47bXXEBcXh2XLllkOI/Tx8cF7772H8ePHV+s5np6e6NKli9U1d3d3qFQqy/W4uDgsX74ckZGRiIyMxPLly6FQKDB16lQAgFKpxNNPP4158+ZBpVLB19cX8+fPR1RUlM3GaCJynIpmaFZO6opg7zv7bK7larBwx0mbquhHzmdj0c4UdAvxxr4zWTbP/yHlBuLT7B9ceOR8NrILdTb7foio+anxTM/atWvx6aefYvHixXB1vRMz9erVCykpKXXauQULFiAuLg4zZsxAr169cO3aNezZs8dyRg9QOsM0YcIETJ48GTExMVAoFPjuu+94Rg9RI6HW6GwCHqA0GCk7C6PW6HA5R2MT8JjFn89GdIi33Xv20tjLKqjiYEMiah5qPNOTnp6O6Ohom+symQxFRUV31ZlDhw5ZfS8SibB06VIsXbq0wte4ublh7dq1WLt27V29NxHVj8rKR5Sdhcku1CGvuOanLgNApyBPnL5RUOHreCYPEQG1mOkJDw/HiRMnbK7/8MMP6NSpU130iYicSFXlI9TFelzIKkROkQ4hvpUX/AzzVeCRe0LKJ23h0XvDbM7vMRsQ6Qc/Dy5tEVEtZnpefPFFzJw5EyUlJRAEAb/99hu2bt2KFStW4N///nd99JGImrCqykeU6I2Y+FHpYaKzhkQgNkKFBDtLXDERKqw9mIY9qZkQUJqtLqA0qBna3h+D2/vjpe0nrZa6ypaiICKqcdDz1FNPwWAwYMGCBdBoNJg6dSpatmyJ999/H4888kh99JGImrDKykfERqhw7OKdAGdDQjrWTCldPi8b+MREqPBUTDhmfZkEkwD4/FUs1NddZnU4YXUOLiSi5kskCIJQ2xdnZ2fDZDLB39+/LvvU4PLz86FUKqFWq+Hl5eXo7hA5net5xTazMP0j/TCtX2vM2ZoMjc5oua6QijE9Nhyjo4JwKbsIeoMJ528VYkNCulW7/XMHoq2/R4OOg4gal5p+ftd4pmfIkCHYsWMHvL294ed3Zw09Pz8fEyZMwIEDB2r6SCJycvbKRxgFARM+OGoVyACARmfEugNpiG2rwowvkyp8JjOyiKimahz0HDp0CDqd7UFfJSUliI+Pr5NOEZHzUSqsl5ouZBXaBDxlrTt4odLnMSOLiGqq2kHPyZMnLb8+ffq0VUFPo9GIH3/8ES1btqzb3hGR06psr4+bxAUJadmWzcrlMSOLiGqj2kFP9+7dIRKJIBKJMGTIEJv7crmcZ+UQUbUpFVKsnNTVZq+PCECJ3oRWPnIsvb8zPvv5EjOyiKhOVDvoSU9PhyAIaNOmDX777Te0aNHCck8qlcLf35+nIBM1UxUVCK3qXtm9Pgf+vIm3dv8JAcDIzgFY9WA3KOUS9G7tw4wsIqoT1Q56wsLCAAAmk/0TUYnIuak1OmQVaJFXrIe7VAx3mSu85RIU6Yw1qnxevuaWea9PmxbuSLmWjx6h3pjWrzVEIpHVfSKiu1XjlPUVK1YgICAA06dPt7q+YcMG3Lp1CwsXLqzTDjYEpqwTVUyt0SFXo8eru1IQX+7snMWjO2L1nnPY96dtEdAVE6Ow+6T9QqD9I/2weExH/HgqEw/2aIlWvu4AAEEQLMEOEVFVavr5XeMyFP/3f/+HDh062Fzv3LkzPv7445o+jogaset5xdh9KhOvlAt4AOBoWg5W7P4THYLt/0Pj7ymrsPJ5/PlsLPjvSby37zwe+PAYruVqAIABDxHVqxoHPZmZmQgKCrK53qJFC9y4caNOOkVEjmeuju7vKbNbFgIA4tMqrnxeUXFQs5NX1QCAW4U6q2rrRET1pcZBT0hICI4ePWpz/ejRowgODq6TThFRw1NrdLiQVYjkjFxcuFWIPI0eiZdzqwxeKrovc638n5cAL5nl1/FpOcguZNBDRPWrxocTPvPMM4iLi4Ner7ekru/fvx8LFizAvHnz6ryDRFT/rucV22w47h/phzVTouFaxZKTUm7/kMCsAm2F5/C08JRifPeW+OTIRcs1nrBMRPWtxkHPggULcPv2bcyYMcNyMrObmxsWLlyIRYsW1XkHiah+mZex4ssFJ/Hns2ESBEyPCcewDi3QIViJ6BBvaA0muEnESMrIxenravi62x4yOCDSD4PbtcDAdi1szuGJ8HfHwvs64IVtJ6zejycsE1F9q3XB0cLCQpw5cwZyuRyRkZGQyWRVv6iRYvYWNWWVnYNTHReyCjF09eEK729+6h4Eebvh9e9ScbTM3p7YCBWWjusCL6kYMqnY5iwdTzcJXFxElv79ln4bLiLgSl6xTfHQAZF+WDslmqnpd+Fu/xw09vcjsqfeC46aeXh4oHfv3rV9ORHVAXvLUuXPwalKfhXLSj7uEpuABwAS0nLwxneplmCl7AdeVkEJZmz4FQ/2bIUHoltBqZCirb+Hpdp6+YCHJyzfnbr4c9CY34+orlQr6Jk4cSI2bdoELy8vTJw4sdK2O3bsqJOOEVHlKlqWOnI+Gy9tP1ntmROvKpaVpGIXm4Cn7HtlF+qs3mfPqRtYuCMFuRo9Tl/PR782KgQoSz8I7VVb5wnLd6eu/hw01vcjqkvVCnqUSuWd01GVynrtEBFVT3ahzuaDx8xeMFKRygp/Doj0g0ZnqPT15g3IBqMJb/3vDDYeu2S5l6vR48X/nrR7AjPVjbr6c9BY34+oLlUr6Nm4caPdXxOR41S1LFXdbChz4c/y/3uPiVBhxuAIuMsq/2fC002Cm/klmPFFIhIz8mzu25sB4H6QulNXfw4a6/sR1aVa7+khIseqalnKXjaUOdgo1OrhrZBCZzChUGuAyl2K0V2C8GS/1tAaTJC5uiD5Sh6mbzqOV8d2qnQmSOoqwpg18ZWes1N2BoD7QepWbf4cNKX3I6pL1Qp6oqOjq308fFJS0l11iIjsKz874uHmiuEd/bH3jG3dqwGRfvDzsJ45MQcbiZdzsWZKNFb9dNayV2f9tF5YtDPF7vu++f1p7J7TH699c8omLf3tSV0R5C3Hgz1DsCc1Exeziyrsf0GJvknvB2mss1NVLU+W/3PQ1N6PqC5VK+iZMGGC5dclJSX48MMP0alTJ/Tt2xcA8MsvvyA1NRUzZsyol04SNXcVzY4sm9AFAKwCH3vZUGWDjVlDIrDxaLrV5uTKTl3W6IzIL9ZZbUAu1hvhLZcg6K+ZmXkj2mF8t2CMWhNf4XM83SRNdj9IY56dMi9Plj8Pqb6y4hr6/YjqUrWCniVLllh+/cwzz2DOnDl48803bdpcuXKlbntHRJXOjryy6xT++VA3vDTKUGk2VNlgIzrEG+sOpFndr6pkhLtMYtmAvO/0Tcz/7x8IU7nj6+f6QurqAonYBcHeblXOAFQ2EwQ0zv0gTWF2qqGz4piFR01Vjff0fP311/j9999trj/22GPo1asXNmzYUCcdI6JSVc2OFJYY0Nbfo9JnlN18am9WJ/lKHmIiVHZT080Bi85gwqof/8S/E9IBAKG+AvKKdfD3dANQ+QzAG+O74FJOEeRSMWYNibA5nNCsMe4HaSqzUw2dFccsPGqKahz0yOVyJCQkIDIy0up6QkIC3Nzc6qxjRFTqbrJlzPtQDCYBG57sjaSMXCgkYpt2GxLSsWZKNABYBT7mJYv8EgOe2Hgcf1zJAwBMjwnHS6M6QFpuhqj8DIBcKkZSRh5Gr4m3BDmxESqsmRKNOVuTbQ4pNO8HaUz7Z5itROQ8ahz0xMXF4e9//zsSExNx7733Aijd07Nhwwa89tprdd5Bouauttky9vahxESoMKxjAIZ0aIEDf96yXNfojJizNRmvju2Epfd3RpHWYFmy+PnibSz47x/ILzHAy80V/3qoG0Z0DqywP+YZALVGh1lbk21mSRL+Cqqmx4ZbltnK7ge5kVeMQ+duwd9TBq3BhFyNHr+l38agdi0se4gaErOViJxHjYOel156CW3atMH777+PLVu2AAA6duyITZs2YfLkyXXeQaLmxN4MR02yZcyvVxfroDWY0C3EG4mXcy0zKqWzOH9i4X0doDWYrGZ1okO90a+tCkaTAAEARIDRJOCDg2nILzGgY5An4oZFoq2/B9Saqpd0KlsWSkjLwStjOmFYB3+r/SBqjQ6Xb2vw/cnrVn2LiVAh3M8dCqm4wWd8mK1E5DxqXXDUmbDgKDUGFWUIvT2pKwSgwmwZ8+xHRTM7T8WE2ywlfTcrBgIAncGEW4VayFzFCFK64Z2fzmLfn3cywYZ39MdTMa2xcHsKruQWW733ykld4f5XoVF7y1Dnbhbgym2NVVX2snt5ds3oh+6hPlY/g8vZRXh5V4rdvUUxESosnxCFMD/3Wvx07465ZlhlP38iangNUnA0Ly8P//3vf3Hx4kXMnz8fvr6+SEpKQkBAAFq2bFmbRxI1a5VlCC38K0OosmyZil5/1M5SEgBkFWjx9OY7CQkrJkZhY0I64tOsX98+yAsfHLpgFfCU7dfoqCAs2nHnfJ+yQdqb36UivtxsTdm9PPaWhYp0hgrrfB1Ny0FRFSUx6guzlYicQ42DnpMnT2LYsGFQKpW4dOkSnnnmGfj6+mLnzp24fPkyPvvss/roJ5FTq06GUFt/jwo/ZCt7/dG0HEyPCbe6FuqrwP65Ay0f4CZBsApezOylt5vFn8/GiyPbWzZIb0hIx5Hz2Th07hZ2n7xhFfCY+wGUBmAnr+TZXRYqspPRVZa9jK+Gwmwloqav8sM57Jg7dy6efPJJnD9/3ipba9SoUThy5Eiddo6ouahOhpBao8OFrEL8cSUXl3OKcP5mAZIzcnHhViHUxRWXgACs09QHRPrB31OGtv4e6B7qg7b+Hjh7s6DK19lzNbcY0zcdR3JG6SnPCqkY/p4ymxkjs6NpOejXRlXhIXbe8so3BSuruE9EVJkaBz3Hjx/Hc889Z3O9ZcuWyMzMrNGzPvroI3Tt2hVeXl7w8vJC37598cMPP1juC4KApUuXIjg4GHK5HIMGDUJqaqrVM7RaLWbPng0/Pz+4u7tj3LhxuHr1ak2HReRQVWUIyaVizNqajPvXJeBWoQ4v70zB8HeP4IEPj2HoO4dRoq88ODEfPmjv1NxvTlzDi1+frPR1VT33aFoONh5Nx/TY8CoDJTeJuMJ9MP6eMvSP9LN7r/9fwRoRUW3VOOhxc3NDfn6+zfWzZ8+iRYsWNXpWq1atsHLlSvz+++/4/fffMWTIEIwfP94S2KxatQqrV6/GunXrcPz4cQQGBmL48OEoKLjzv9K4uDjs3LkT27ZtQ0JCAgoLCzF27FgYjY6bBieqKXOGkD0DIv2QlJGH+PPZmB4bblVCQvHXYX8ebq7oH1FBsBDhhxaeMuyaGYMVE6OsAo6l36bihW0nUKw32p1FSb6Sh9gIld3nxkSokPzXuT1AaeATHeJdZaBU2WyNUiHF25O62vwsBkT6YRVLHBDRXapx0DN+/Hi88cYb0OtLp+NFIhEyMjLw0ksvYdKkSTV61v3334/Ro0ejXbt2aNeuHd566y14eHjgl19+gSAIeO+997B48WJMnDgRXbp0webNm6HRaCyp8mq1GuvXr8c777yDYcOGITo6Gl988QVSUlKwb9++Ct9Xq9UiPz/f6ovIkcynGdv7sH9jfBe8+f1pAKV7bMoGPGumRCM5IxePfPILpsW0Rky5ACUmQoVpMa3xyCe/IKdQi0U7UqDW3FkK6xTkBZEImDM0Et/PjrV5/7M38rH8gSib6+assA1/nc5spjWYkFWgrTSAqyrF27xpeP/cgdg1ox/2zx2ItVOimSVFRHetxhuZ//Wvf2H06NHw9/dHcXExBg4ciMzMTPTt2xdvvfVWrTtiNBrx9ddfo6ioCH379kV6ejoyMzMxYsQISxuZTIaBAwfi2LFjeO6555CYmAi9Xm/VJjg4GF26dMGxY8cwcuRIu++1YsUKvP7667XuK1F9qChD6FJOkWUDb9mlo/KzPnO2JmN6bLhl07JSLsGhc7cs2VJagwlHzmfjwq0i9AgrDTwe6tUK3UK80T7QEwAqzFAyX8/V6KAu1iP5Sp5NGjxQuienR4g3BrZrcVcFKblpmIjqQ42DHi8vLyQkJODAgQNISkqCyWRCjx49MGzYsFp1ICUlBX379kVJSQk8PDywc+dOdOrUCceOHQMABAQEWLUPCAjA5cuXAQCZmZmQSqXw8fGxaVPZ/qJFixZh7ty5lu/z8/MREhJSq/4T1SV7H/YehXdmZsouHZXPrNLojFbfr5/Wyzrz6q8juZ79/Hfs/cdA+LhLIRKJLAGP+f3N8kv0gMi6X2qNDrO3Jts9qK9/pB/a+nsgwKs0wYEp3kTU2NQo6DEYDHBzc8OJEycwZMgQDBky5K470L59e5w4cQJ5eXnYvn07pk2bhsOHD1vui0Qiq/aCINhcK6+qNjKZDDIZN0RS41BVnamyJwKXLQxa1Ybhsve7hyixeNcpAMDtIh2OXcjBmK5BNq+p6IDElZO6IthbXmlR0bcndbUEPABna4io8alR0OPq6oqwsLA63SQslUoREREBAOjVqxeOHz+O999/HwsXLgRQOpsTFHTnH+esrCzL7E9gYCB0Oh1yc3OtZnuysrLQr1+/OusjUX2pKsgwB0Rzhkbi74Pa4tf023gmtg2A6mdWtW3hjtTr+dAbBUjFLvjo0WgM7WRbO6uyAxJf+uuARKVCyoP6iKjJqvFG5ldeeQWLFi3C7du366M/EAQBWq0W4eHhCAwMxN69ey33dDodDh8+bAloevbsCYlEYtXmxo0bOHXqFIMeavSqOoX56m0NZm1NxtDVh/Hgxz9jyqe/IvFyLsJUCqyYEIW2LdwrTe92l4nRJ9wXF24VQW8U4KOQYNfMfnYDHqB6BySaKRVSq3N+GPAQUVNQ4z09a9asQVpaGoKDgxEWFgZ3d+s6OElJSdV+1ssvv4xRo0YhJCQEBQUF2LZtGw4dOoQff/wRIpEIcXFxWL58OSIjIxEZGYnly5dDoVBg6tSpAAClUomnn34a8+bNg0qlgq+vL+bPn4+oqKha7zEiuhtqjQ5ZBVrkFevhLhXDXeYKb7nEblBQWZARfz4bl29rbO7Hn8/G0m9TLbMuKx6Iwks7TloqlwPmzKrWWHcgDb+m34aLCJgeE46Zg9vCx73iZd2qDki8rdFBffk2lAop/NylljFUtCxXEzfzS5BbpCut5C53hY9CarVURkRUF2oc9IwfP77KPTXVdfPmTTz++OO4ceMGlEolunbtih9//BHDhw8HACxYsADFxcWYMWMGcnNz0adPH+zZsweennc2Xr777rtwdXXF5MmTUVxcjKFDh2LTpk0Qi8V10kei6rqeV4yF/z1pdRpxTIQKs4dEIsxXYZNyXVWQoS62f98866JUSNHKV4G3J3XF5RwN8or1kLm6IPlKHrb+moFXx3bCq9+kYsHI9ujV2rfK/ld1QGJ+sR5Pb/4dCqkYG57sjQ8OpFmNteyyXE1k5BRh0U7rIqOxESosfyAKoaqGLy5KRM6LVdbBKut099QaHWZtSbZbfiEmQoWxXYMxukug1UzIhaxCDF192Ka92fppvayKgpZVvkK5WqPDpRwNvvvjOh7pHYIWnrIaz7pUlpkVE6FCdKgP1h1Iw6whEUjOyLVbGHRApJ9lFqo6buaXYO5/Tth9VmyECu9M7s4ZHyKqUE0/v6u9p0ej0WDmzJlo2bIl/P39MXXqVGRn25+aJ2pusgt1ldab8veU4Ya65E6tLI2u0lOY+0f6WZ12XJ67zHqS9vJtDWZvTca/E9Lx26XcWi0zVXRAYvmDCMsekFhe+b0/Vckt0lX4rIS0HOQWVf9ZRERVqfby1pIlS7Bp0yY8+uijcHNzw9atW/H3v/8dX3/9dX32j6hJqGqpSmsw4WJ2EWZ8WbrnrX+kH14f1xlvjO+CV785ZbV3JyZChdfHdcay/522+6yYCBWk4tL/rwiCgE3HLmH57jPQGwW08pGjU3DtZyvNmVnX8opxKUeDVj5y7Dl90+ogwqpS5Qsq+FnYS83PLzFU+qyq7hMR1US1g54dO3Zg/fr1eOSRRwAAjz32GGJiYmA0Grl/hpq9qvbDlE8vjz+fjVe/OYV7wn0xc3AE/j6wrdWenIvZRZjaJwxag8lqJsQ866Iu1kGtkWLB9j/wU+pNAMDIzgFY9WC3u65ErlRIkV2ow4wvk2wPOLQzlvI87fwsKkrNf3lMx0qf5eVW422HREQVqva/KFeuXEH//v0t399zzz1wdXXF9evXeZoxNRsVHSRY9gDB8mIiVLiZX4Lr6hKr60fTcjA9JhzTNx3Hhid7Y9tvGZbXr5/WC7PLlJXQGkyWgGjO1mT8c1JX/P3LZFzLK4ZU7ILFYzriib5hdZZkYB5P2cMQzddbeMqw5Zk+yCvWw00iRlJGLjYkpEOjM9qtrVVZav6ojDz0j1AhvoI9PT7uTIUnorpT7aDHaDRCKrX+B8jV1RUGA6efqXmo6iDBlZO62tyPiVBh1uBIlOiNWPa/MzbP1BpM0OiMmL7pOH6Y0x8Gk4CCEj18FFL0CvOxmWUxv6dM4oIb6mKEqRRYN6UHolopazSWqk6BNu/vWfLNKTz1Vy2vs5kF+PKZe/Hm96k2KfJrpkTjq98y8Mb4Ljb7iSpLzX/z+9P435xYvLrrlNUzzdlb3MRMRHWp2tlbLi4uGDVqlFX5hu+++w5DhgyxOqtnx44ddd/LesbsLaqKWqPDrK3Jdj+8y2Ysmc/pURfroZCKIZeI8e3J6/i/wxeh0RmhkIoxPTYc0SHe0BpMCPVV4MfUTGxISMeWZ/pYZWRdzyu2KffQP0KFVQ92Q5C3HD+euoF+EX5VLq2VV1XwVn7cOUU6GE0CZK4uNqnld/rlh5WTotDSR2FzLzkjFw98eMzyffmfQRs/BbzkUuSX6JFfbICXmyt83HlODxFVraaf39We6Zk2bZrNtccee6xmvSNqoqpzWrG51lTZmQ61RofES7mWgGfNlGhsPJpuNYNjninxKrcXp2y5h9/Sc/DxkYuYP6I9ApWlwcB9XWxrZ1WluqUmzMqO58yN/AozreLTspFfYkBLO/fKBmUV/QzMQVeHQP6ng4jqT7WDno0bN9ZnP4gataqysyrKWCpboLNriDc2Hk23CRyOpuVABOCdyd1tXu/pJsGXv2XgnT3nYDQJWHswDYtGdUShVg9vhRQ6gwmFWkO1T0SubvBmT34FhyWaVfQzKLvfaXpsuN2fQUVBFxFRXWJqBFE1VLWEZC9jycw8Y3NDXWJ3jw5QeiZNYYkBAWUmOnIKtZj7nz9w+NwtAMCITgEo0Opx/7oErJkSjVU/nbUKHqpzInJtgzcANjNR5VX0Mygb+EWHeFf4M6gq6CIiuls1LjhK1BxVdpCgvYylsu5sGjZgw5O9MWtIBBRS22MeygYcv17Mweg18Th87hbcJC5Ycn8naHQG/HzhdpWzJWpNxQf63U3wppRLEBuhsnsvNkJVaaq8OfCrKnCqLOgiIrpbDHqIqqGi04oHRPrh7UldbfbxXMgqRHJGLs5lFmD3qUzcvy4Bk//vZ0zfdBzJGblYMyXaJvAxBxzH0rIx5dNfcDNfiwh/D3wzMxYDIltYspvu5kTkuwnegr3leOuBKJvAJzZChbceiKqy5pZSIYVvFbM4lQVdRER3i8tbRNVUdmNxQYkenm4S+HlY76O5nleM1745hQ5BXogO8UZmfgk6BXnh21kxuJhdBFcXFyRl5GLLr5cxPTbcstRTNuDoHe6L7iHeaNPCA2+M7wyF1BXJGbmW96jticiA9VKTVVZYpB/eGN+lyp9BmModqx7sBnWx3vIzUMol1S4yWtl5RlUFXUREd4tBD1ENlM/OKkut0eG1b07hkXtC7WZoPRUTjllbkhAd6o2nYsLh+tdBggMi/fDYvWGQS0v/OkrELvjimT5QSO/89Sy7LFWbE5HLMgdvmfkluJpbDABIvpKH0Wvi0SvMp8p9QcHe8hpXUjerKOgqO2NW1RlCRES1xSrr4Dk9VHtlP6DlUjG+P3nDbgVyhVSMV8Z0RLsAT2QVaOGtkCDIyw0Gk4D/Jl7FJ/EX8UxsOBaP6VTh+5groNdFlfPqnjtU0VjvNhgxP6v8jFlNzhAiIqq3c3qImjPzoYN5xXp4urlCJnZBrkYHrUHA0QvZ2JCQjn891M1udlLZs2le3nnKcv2ecB8YjAKSMvIAAIVaIwRBsFtKouwMyYaEdKyZEg0ANtlb5fcXVaSmqet1HYzYmzGr6RlCREQ1xaCHqArX84qx8L8nEZ9mXV7iqZhwzNmajOhQb6yZEg0XkQgleqPN6yvKtvotvXSfjkIqxoqJURjf3d7RfneU3VNUpNVj+YQo6IwmFGkNdvcXVaYmqesNFYzczRlCRETVwaCHqBJqjc4m4FFIxYgO9YFcIsbHj/WEwSQgU12MYKXcbtp2ZWfTAMCHj/bAoPb+1epPZXuKaqImqesNFYzczRlCRETVwZR1okpkF+psAp41U6KRnJGLR//9K57Y8FtpsdCUTLTyVUDlIcWKB7pYpaNXlW3lXcXZNfWhJqnrDRWM3M0ZQkRE1cGgh6gS5T/wK1qqik/LxpJvT+H7kzew+1QmNjzZ2xL41DTbquw5PxduFVZ62GBt1eTcoYYKRu7mDCEiourg8hY1W1VlI6k1OsglYnz4aA+4ScRIyshFj1CfCpeqjqblYHrMnbN3fpjTH7cKSvB14lVEtfRCyrV8m9f0L/dh3pDZS9U5dwhouLN1qpPOTkR0Nxj0ULNUVXBh735MhAqxba1nIhRSMabHhiM6xBtagwn+njLMGhKBDQnpuHS7CP/66RxSrqnhLZfg3ja++OXibavnzRwcYfneEdlL1dkj1JDBSHUDMSKi2uA5PeA5Pc1NVWfU/POhbpj/9R9IvJyL6bHh6BHqA1cXEXzdpXCTuGDcuqPQ6IxWqehll7tiIlToHuKNjUcvQaMzwkPmCpEImNavtSU4krm6IPlKHjYkpOO7WbFo6++BC1mFGLr6cIX93j93INr6e9TLz6Q6Kjpbh4jIUXhODxGAm/klyNPoIAiAAECjM8BbIYWfuxQ5RZVnI90u0iHxcq4loCm7nNU/QoX103rh6c2/V7i/52hajtW1jkGe6NNGVeGymHkjcGPPXqqrzDEiIkdh0ENOJyOnCEu/S8XUPmE2QcmASD8sGdcZCqkYGp3tmToAkKfRV7JhOQeACK+M6YgAL7dKU9HHdQvCt3/cwPFLufj7oIgK25k3AjN7iYiofjF7i5zKzfwSLNqZgk7BSrtBy5Hz2Vj6bSqmx4ZX+AwPN3Gllczj07LRLsAT7rLK/88wKirI8mu3CjK4ym4EZvYSEVH9YtBDTiW3SIejaTnoEepTcdByPhv92qjs3ouJUCErX1vl+2QVaFGkNVTaRiq+89fLx902oCm/EbgmaeRERFRzXN4ip5JfYoBCKoZCIq60nUziYpOGbS4tsXD7SWx8qnflr3d1wcGzWXCXiVGktV0m6x+hQgtPGT58tAe8FRLIJS7Vykpi9hIRUf1h0ENOxcvNFdNjw2GsIinRWy7F2inRyMwvgUZnhKtYhNwiPYwmAY/cE4oj526hf4Tqrz081mIiVPjq+BXs/zMLRpMAiVgEvfHO+/WP8MOMwRF45JNfLPuG+v81W1Od7CtuGCYiqh8Mesip+LhL0beNCj9fzEFMhMruEpf5QEClQoqCEgPe+P60Tcr5M7Ft0CvMF4DIqgzFvW18IRaJsOf0TQBAuwAP9Ivww/COARC7iOAuE+PUVTWe3nzcaqN0PCuFExE5HIMeatLsnaosl7hgQ0I61kyJBgCbgGbp/Z0tr120M8VuyrkIwIoHorDgvg54qlALV7EIhSV6rPzhLDJuawAAri4izB/RHs9+nohNRy8hJkKFF0d0wKKdp+z2lZXCiYgci0EPNVnX84ptKqD3j/TDG+NLg5o5W5MxPTYc02PCrQ4EvHJbg9e/S8XiMR2ReDkXccMiERvhB4NRgLtMDECEA2dvQms04dR1Nb4/ed3ujFGfNr44eU1t+f5oWg7ihlZeXLS6Z+1UVSKDiIhqjkEPNUlqjc4m4AFKl5Fe+yYVr4/rjBf/e9Jyjo65XETfNioYTQKejAmH3iDgo0d74N/xF/HevvOWZ8REqDBrcARcRCIMbtcC4X7uKNEbkXg5z6rNUzHhmLM12er9S4OmilXnrJ2GrL9FRNScODToWbFiBXbs2IE///wTcrkc/fr1w9tvv4327dtb2giCgNdffx2ffPIJcnNz0adPH3zwwQfo3LmzpY1Wq8X8+fOxdetWFBcXY+jQofjwww/RqlUrRwyLGkBWgdYm4DGLP5+NV8Z2xD8f7Apfdyl0RhNCfBQ4eTUPf/vsd6vNxTMGtUViRp7V682zOr3CfBHgJcOYqCC8Ob4L9EYTdAYBRVoDjIIAkUiEfz3UzVKMdENCOtylrndVnNMR9beIiJoLhwY9hw8fxsyZM9G7d28YDAYsXrwYI0aMwOnTp+Hu7g4AWLVqFVavXo1NmzahXbt2WLZsGYYPH46zZ8/C09MTABAXF4fvvvsO27Ztg0qlwrx58zB27FgkJiZCLK78f97UNOUVV7xMpJCKIRJE+PbENavsq5gIFdZMicacrcnQ6IyIP58NkyBgemy4zcnKR9Ny8Fv6bYggQo8wH3QKVgIoDUrOZBZg7YHzNnuFNjzZG94KyV0V58wurLxEBvcEcemPiGrPoUHPjz/+aPX9xo0b4e/vj8TERAwYMACCIOC9997D4sWLMXHiRADA5s2bERAQgC1btuC5556DWq3G+vXr8fnnn2PYsGEAgC+++AIhISHYt28fRo4c2eDjovrnLq04mJ0eG443v0+1STc3Byllg5yjaTmYHmP/dGa9UUD3ECVaeMisrn9wIM3u5mcXkQjrpkTf1Vk7jb3+lqNx6Y+I7kajOpFZrS7dFOrr6wsASE9PR2ZmJkaMGGFpI5PJMHDgQBw7dgwAkJiYCL1eb9UmODgYXbp0sbQpT6vVIj8/3+qLGie1RocLWYVIzsjFhVuFUGt0AAB3qSv6R9g/VblvG/vn6wClwUl0iLfVNa3B/ubjST1aYvnELlD9FfSoNTrcUJdUuqyWXVjaP6VCirb+Huge6oO2/h7Vnomo6/pbFf38mqKqlv6a8tiIqGE0mo3MgiBg7ty5iI2NRZcuXQAAmZmZAICAgACrtgEBAbh8+bKljVQqhY+Pj00b8+vLW7FiBV5//fW6HgLVMXv/q+8f6Yc3x3eBu1SMJeM6Y8m3qTbLTGKRqNLnlg9yZHbqYkW19ELPMB/8cUUNb7kUIgALtp/ElHtCK312ZTMx1VmWMdffqu2eoLKcbVaES39EdLcaTdAza9YsnDx5EgkJCTb3ROU+xIS/NpFWprI2ixYtwty5cy3f5+fnIyQkpBa9pvpS0f/q489nY/GuFIztGoz+bVUYGxWE6THhMJgEBCndoDOYIK2guKdZ2SCnf6Qfsgqsa23dE+6DfwxrjxK9ETO3JOGHMB+MigpC/PlsPNmvdaXPrmgmproBiLn+Vm33BJk544ZoLv0R0d1qFEHP7Nmz8e233+LIkSNWGVeBgYEASmdzgoLuVKzOysqyzP4EBgZCp9MhNzfXarYnKysL/fr1s/t+MpkMMpnM7j1qHCr7X715H87r35/Gq/d3xhvfpeKRe0Lx9o9/4mhaDmYNiajwNOaYCBWSr+QBAPq19cWb47vARyFB11ZK/HgqE8M6+kMkEuHg2Sz83+GL0OiMOHI+G9P+CnaSr+RV+OyKZmJqGoDURf0tZ5wVqeulPyJqfhy6p0cQBMyaNQs7duzAgQMHEB5uvaE0PDwcgYGB2Lt3r+WaTqfD4cOHLQFNz549IZFIrNrcuHEDp06dqjDoocavqv/Vaw0m7D2TBYPRhLceiMLmo+mWQGRDQjqeiglHTLk9P/0jVFhyf2d0CfbClHtCcOFWEbILSqBUSKEzmLD2QBrGf3AM49Ydxbt7z1uVkTAviVX07MpmYqoTgJRX2z1BZs44K2Je+rOnpkt/RNQ8OXSmZ+bMmdiyZQu++eYbeHp6WvbgKJVKyOVyiEQixMXFYfny5YiMjERkZCSWL18OhUKBqVOnWto+/fTTmDdvHlQqFXx9fTF//nxERUVZsrmo6anqf/XmJar84tIioWU3Lmt0RqvTmD3dXFFQYkDylTyMX5eAYv2dPT0JF3LQK1xV7fcr/2ytwYQ2fu4IUrpVGJg4IgBxxlmRulr6I6Lmy6FBz0cffQQAGDRokNX1jRs34sknnwQALFiwAMXFxZgxY4blcMI9e/ZYzugBgHfffReurq6YPHmy5XDCTZs28YyeJsK8wbdQq4f3X7MuAkr329ibISm7ROXpJsFtO1k7Gp3Rkpb+1bP34unNv9u0advCHU/2DQNQ+Qbi/pF+CFa6YdaQCGxISLd69oBIvyr3x1QVgLjLXHEhq7BOz52pyw3RjUldLP0RUfMlEgRBcHQnHC0/Px9KpRJqtRpeXl6O7k6zYt7gm3g5F+umRmNDQjoS0nKgkIqxZko0Nh0t/d6sbPmHXmE+WDslGtfyijF6je0GeLPvZ8Vg1tZkXMrRWK5Fhyjx4aM9EVRmE/H1vGKbWYSy79cj1BtP/vVrjc5omWEIqiIT6mZ+CeZ//YfdAK5/pB9GRwVh0Y4Uy7W6yrCyN57q9pmIqCmo6ec3gx4w6HEUtUaHWVuTEX8+G3HDIpF46bZlmUohFeO5gW0wuL0/RCKgRG+Ch8wVUrEIJXoTTt/IR/8IPwR6y3E5uwgv77Ktlg6UBi2tvOX46verAIB+bVVYeF8HtFYp7M4OqDU6ZBVoLZXUk6/kWWZ3gNIg5bWxneAiElU6w2CevcrV6GA0mSCTuOKfP/1p1ccBkX6YMTgC0zcdt9o/ZL5XFxlW5n5wVoSInFFNP78bRfYWNU9lN/jGRvhZin6aZ3k2Hk3Hu3vvFALtH+GHp2Jb44tfLuPZAW0BlM5maPRGxA1th1mDTTh6IccSpMREqHB/12C4ubpg/59ZeGFYOzzWJ7TS4w6UCimyC3V2l8OA0pR5F5EIbf09KnyGvfT0IR1aYOF9HZBdqEOJ3ghvuQQhPnKMfD/eJuAB6i7DSqlgkENEZMaghxym7AZfg/HOhOP02HBsLJONZVZ6ErKAbqE++OTIBcwf0R4rdp+x2sTcP8IPu2bG4MCZLHjJXRHoJUNLHwXiFwyBvJLSFRX1y56qDiC0l55+4M9b0BpMiA71sewH2j0n1m7AU533ISKimmtUZSioeSm7wddddicgiQ7xtrtUBQDxf5WR6BSstAl4Su9n4+H/+xkrf/wTnjJXDOoQgMgAz2oHPOX7ZU9lmU9VnS9UtgRGZQFPVe9DREQ1x5kechhzhtHvl3PhIXO1ZGtpDSYopGJMjw1HdIg3tAYT3CRiJGXkYkNCeumMSYi3TWV0s1yNHlKxCwy13K52N5lP1TlfyEwplzhlhhURUWPFoIfqnXkzrbpYB3eZKyQuLsgt1sHLTYIVE6Nw7EIOlv3vNF4f1xlLvkmFQnJnT0/ZwCYmQoU1U6LhKhJBo698lmT5xC54ILpVpW0qcjfnwVT3vJ8BkX7w95Tx3Jl6UJ0aZ0TUPDHooXp1Pa8YC/970qoyuTkN/PH1v6FnmA/eGF9aOPRidhHenNAZJhPw6renbJa4jqblQATgqZhwKOWVBxedg5V31e/angdT2SyR+XyhskGNUgGeO1OHnK3IKhHVLaasgynr9UWt0WHWlmSrgMcsJkJl2dTbP9IP3UK8ER3iDamLC1SeMoxeE1/hc7f8rQ985FIs333G7rNjI1RY/kAUlHJJtYKH63nFUBfrkV+sh1IugZdcclcfkPbOx+kf6YfXx3UGAKg481Avyh6BUF5dHQFARI0LU9ap0cgq0NoNSoA7RUOB0jTwF4ZGIiEtG1Etlbh8u6jS57q6uODA2Uw8FdsaJghWM0KxESq8dn9nTPzoGDoFeVX5P/zLOUV4eWeKzTPeeiAKYSr3mgzXgqcGO4YzFlklorrFoIfqTV5x9Tf1qov16NbKGwFebrihLq70df/86U8cv5QLucQFT/dvg1fGdIK6WA93mRhZ+VpM/fQXZBfqKqxibnY9r9gm4AGAhLQcLN6ZglUPdqv1jA/Px2l4zlhklYjqFoMeqjfuVaSJmzf1mq1PuIhn+rdBVoEWMREqu2nrErEIxy/lAgCK9SacvJKHh3q0wqj37S+HVfY/fHWxvsLU+IS0HKiL9dwH0oQ4Y5FVIqpbPKeH6pRao8OFrEL8ceWvNPQIP7vtyhYNNf86IS0HAV4y9AzzwewhkYiJUNm8Tm8UYD5Q2bwhOK/YtuAoUHqy86whEdAajEjOyMWFW4VQlylOml/FTBRnBpoW8yZye3gEABEBnOmhOqLW6JCZX1I6qyKXwMtNgvf2nsUrYzvhze9TKywa2j/CD6/e3wk38krw4aM9IAjA3tOZ6BrsjcWjOyIzvwRv/3AWZ28WAACGtG+Byb1D0LaFB/w9ZVAqpHYP+StbyqJs2nvZTB6vKjLAODPQtNzNUQNE1Dww6KG7Zi8tPTZChfkjO+C9fWfRq7Uv4oa1g9EkwNPNFVJXMW4VlOCLp/vAVSzCyh/O4MCftyyv7R+hQscgL5y4kovXvjkNg6lMgqEI6BzkhSK9EWdvFsJL7govN9tD/ioqZWG1z0cuQWyEyiogK9v/qtLiqfHhJnIiqgyDHrorllpT5bK0EtJyIMJZ9Ar3QdeW3jh3swABXm7IKdLBTSJGproY/dqosPgb2/N44tNyIACYNTgC7jJXeMld8UxsG7RWuSPUV45FO1OsApWhHVrgzQld8OquU5bAp7ITm837fNr6e+CtB6KwuNzzzNlb3M/TNHETORFVhEEP3ZXK0oTj07KxcFR7FJQY8L+UG1bBTUyECve09kVyRp7d1yak5WDm4Aioi/VQF+ux5NtUbHv2XrxiJ0ja/+ctAKlYOakrinVGFJTooTdVfvyUeb9OmModqx7sBnWx3jIzoLzLc3qIiKhxYtBDd6WqNGGZRIwVP/xp93Tl179LxfTY8ApnZGSuYuyfO9ASjOiNpgqzrfb/eQsvFuvRIaj0cKoLWYWV9qvsfp1gbzmDHCKiZoBBD9XatVwN3CQVp6UrpGK4QITpMeF4tE+YVdFQjc6I+LQcPPnXAYX2KOUStPX3sHz/W/rtCt9nemw4jIKA5IxceMkl8HBzxfCO/th7Jsumff9IP7iKRVBr6v+wOrVGh9tFOkAElOhNKPirHpSPQooAL7d6fW8iIrLGoIdqxXyScXSoj90zdRRSMdZP64Wl355CfLllrTVTojFnazI0OiNuFWjtPt9eirGXm+0f18qytJZN6AIAVoFPTIQK0/q1xqj349ErzKdeazJdzyvGW/87jTlD2+GN71NtTn1e/kAUQmt56jMREdUca2+Btbdq4npeMUr0Riz5pjSYMQcdW369jE7BSkSHeENrMCFcpfirNpbtclTZulsKqdgm5dycYhxULhi5mV+Cef85YbXpeNaQCCRn5Npd9hoQ6Yd/PtQN+cV6ZNzWAACSr+RZZprMbeqjJpO5DtST/Vpjg50sMqA08HlncnfO+BAR1RJrb1G9Mc/uTI8JtwQzGp0RL20/iX9P6413fvrTMtuyflovuwEPULqfZ1y3YIj+en2XYC8sHtMRcom40hTjAC83LH8gCi+XybaqKksrT6ODROyCpzf/XmGb+qjJZN7gveC+9pWe+pxbpGPQQ0TUQBj0ULWUrVP1aJ8wq3uP3BOKf/5kvVm5bF0te/w8ZPj7wDYwQYR5I9pBIq7e4eChKne8M7k7cot0yC8xQFRF+6u5xQjxVVTapj5OXjZv8C4ssT040bqdoc7fm4iI7GPQQ9VirlOlkIrRykeO9dN6QWswwU0ihpebq81sS/m6WuW18pFjaMeOtepLgJebZXakqiwtADBVkb5eHycvm+tAebhVXn/M3j4lIiKqH6y9RdVSUKy37N9Z9eOfeHrz75jxZRKmbzoOtZ0aVslX8uzWzgKAe9v4ws218mCguvw8pOhfQb0lc00vjc7Y4DWZzHWgsvK1iK3g5xAboYKPOw/RIyJqKAx6yC61RoerOUW4eKsQqdfV8JRLLKUd7JVtKG9DQjqeiglHdKi31fVgbzc8FRMOdQVFQivrz4WsQpvCoUqFFG+O72ITYJnre21ISIdSLsHKSV1tAh/zhmkAdp99N8x1oHYmXcWrYzvbBD7m7C3u5yEiajjM3gKzt8rLzCtGkd6I7IISXLhVhAAvNwR6uUFdrMfUf/9q076iDCqpWAQBpZXRPWSueLZ/OHQmARsS0vHdrFirM3gqcz2vuLTURbkikuZ0c7VGh92nMuHvKYPWYILM1cWSpdUrzMeSnaXW6GxqMml0Riyo5Nl3y3xOj0gEFOtNKCgxwMvNFT7uPKeHiOhu1fTzm0EPGPSUdTO/BNkFWggQoNEacClHYznluFhnRJHOaHXAIHDnrJzNRy/Z1OACABcRUHZbTU3SxM2p3/ZKXZR9zvW84gqra5dPfa/ps4mIqHFiyjrVWmm19D8Qn5aD72fHQAAQ4qvA2z/+aVM3q+wBgxqdEXO2JuP7WbHQGk3IK9JhxY9/4tQ1NVqr3HExuwhAaXD06thO6BHqjYvZRfCS6+DnXnlxyMpqe5VNN69Nde3qPpuIiJwDgx4CULZaemlwI5OIcfKq2qZQKADL92XrZnUPUeK/yVcxKboV7m2rwqeP98TFbA06BXkiu1CHIq0eXnIpXt11Cot2pFieVdVSUlW1vcqmm9e0unZNnk1ERE0fNzITANtZD53ehAAvtwoP1jualoPoEG8AQJ82vijRm/B/hy4gT6ODSCRCgFKOvm1VUCqkaOvvgTCVO1795pTN8teR89l4afvJCjcPe1WRTn436eb1+WwiImp8GPQ0I+YMqKTLt3HuZj4ycopw5kY+fkvPgdZgxKwhEVBIS1PJi3SGKg8Y9JC5Yun9nfDnjQIkZeTBKFQ8O1KdpSR7fTUKQoUp6QMi/eDh5lrrzCtzWnlFz66PVHYiInIcLm81E2UzoOzVy8ou1KFfWxVGdArA9E3HIXYRVXnA4OFzWfjw0EXL911beiE61Mdu25osJdnrqyAIVqny5oKii3emYF+ZgqI1ybwyp5VXtAGa+3mIiJwLs7fg/Nlb5bOUZg2JwOnrajx2bxg2JFifu9M/QoXXx3fBobM3IZdI8H3KdbtLXCp3CXKK7gQqfdv4YvXk7hVmSl3IKsTQ1Ycr7OP+uQPR1t/DbkaVQirG9Nhw9GujgptEDKVcAg83V5uAx6ymmVf2UtkZ8BARNX41/fx26PLWkSNHcP/99yM4OBgikQi7du2yui8IApYuXYrg4GDI5XIMGjQIqampVm20Wi1mz54NPz8/uLu7Y9y4cbh69WoDjqLxK7+0FB3ija6tvLExwfagwfi0HCz5NhVDOgQg1FeO2YMjbA7+U0jFyCnSQywS4Yl7w7D3HwPw8WM9Kwx4gOovJdlbBtPojFh3IA1T//0rlHIJ2vp7oLDEYDfgAewvl1XGvO+oe6gP2vp7MOAhInJSDl3eKioqQrdu3fDUU09h0qRJNvdXrVqF1atXY9OmTWjXrh2WLVuG4cOH4+zZs/D09AQAxMXF4bvvvsO2bdugUqkwb948jB07FomJiRCL66bUQVNXfmlJZzQhNsIP7+07D+DOTEp0iLelnpbRBLT0luPybQ1eG9sJBqOAIp0RCqkYP5y6ga9/v4qPH+uBHmG+1epDdZeSqrsMxswrIiKqKYcGPaNGjcKoUaPs3hMEAe+99x4WL16MiRMnAgA2b96MgIAAbNmyBc899xzUajXWr1+Pzz//HMOGDQMAfPHFFwgJCcG+ffswcuTIBhtLY1Y+S6mFhwwGY+mqpnnPzMaj6VZFQ/tH+OHNCV0w7+s/LLMm0SHe+PDRHnhxZAfMHhIJN0nNgsrqnKVT3YwqZl4REVFNNdqNzOnp6cjMzMSIESMs12QyGQYOHIhjx47hueeeQ2JiIvR6vVWb4OBgdOnSBceOHasw6NFqtdBqtZbv8/Pz628gDnIzvwS5RTrkl+jh5SbB3rkDkJlXgl8v3YbMVQyZpDToMdfTKr9vJz4tG6/sSsGyCV3w2jepKNIZ8HRsa8sSljngMe+HyS/Rw0suqfKwwarO0jEvgx35axNz2RkoH0XpXp7y7corn3lV0z4SEZFzarRBT2ZmJgAgICDA6npAQAAuX75saSOVSuHj42PTxvx6e1asWIHXX3+9jnvceGTkFGHRzhSrQCb2rw3K15M00BqM8FZIEBOhQnSIt9UMT1kJaTn4+eJtGE0CQn0ViAjwtLpfVU2s2jAvgy355hQevifUZgaq7POrs1xWH30kIqKmqdGf0yMSiay+FwTB5lp5VbVZtGgR1Gq15evKlSt10tfG4GZ+iU3AA5QGMEu+OYW/D4mA3mjCT6mZmD04osrnGU0CVO5SbHnmHnQIvLMz3nKC8/maHTZYHcHecix7IAqb7cxAlX2+ebls/9yB2DWjH/bPHYi1U6Its1H12UciImp6Gu1MT2BgIIDS2ZygoCDL9aysLMvsT2BgIHQ6HXJzc61me7KystCvX78Kny2TySCTyeqp546VW6SzChT8PKR4e1JX+HvJUFhihMEowM9ThgtZhegUpITKvfKfw8xBbfHBoQvQGqxPNqjvulWFJQZLSYzKnl/ZchlraxERUVmNdqYnPDwcgYGB2Lt3r+WaTqfD4cOHLQFNz549IZFIrNrcuHEDp06dqjTocWb5JQbLr/08pPjymXux4Wg67l97FFM+/QWj3o/H69+mYs7Qdvjuj2twcSndtGxP9xAlJvRoCcA2G6q+s6fq4vnM8CIiorIcOtNTWFiItLQ7+zXS09Nx4sQJ+Pr6IjQ0FHFxcVi+fDkiIyMRGRmJ5cuXQ6FQYOrUqQAApVKJp59+GvPmzYNKpYKvry/mz5+PqKgoSzZXc+Pldue39O1JXfHG96l2l7rMm5SL9Qa8OaELXtmVYnVmz71tfLFsQhT2nL4BwDYbqr6zp+ri+czwIiKishwa9Pz+++8YPHiw5fu5c+cCAKZNm4ZNmzZhwYIFKC4uxowZM5Cbm4s+ffpgz549ljN6AODdd9+Fq6srJk+ejOLiYgwdOhSbNm1qtmf0+LhLERuhQkJaDvy9ZBUWDE1Iy4FGb8IDH/yMe1r7Ysbgtlg4qgMKS4zwcBOjSGvA9dxirDtwwW4dqppkT9VGXTy/vvtIRERNC8tQoOmVocjKL4FGa4BRAEoMRmh0RnjLJfD3lEGpkOJyThEW70zBzMGRmPLpLxU+Z2J0S+xIvgYAGNy+BeaOaIes/NJU/uQrediQkI5eYT54e1JXu6ctX88rrjB7qrLTmaurus+vLCW9vvtIRESOU9PPbwY9aFpBz5WcIlxXFwMALtwqQoCXm+UU5Zv5JRjUrgWCvOW4mqtBQYkBo96Pr/azzefijI0KQoneWK06VPVdt6qq51cnJZ21tYiInFNNP78bbfYW2bqZX4L4tGy4uogQ4qvA/1JuWC1fxUSoEO7nDoVUjFY+CmTkFFmWuuyRil2gM5os32t0Rpy8koe/xYZXOyio6rDBu1XZ86tKSTcXHa3vPhIRUdPQaLO3yFZukQ4BXm7oEOSFdQfTbPbrHE3LwdoD53G7qPT8GaNJwKtjOyO2XMFQAAhTKbBiYmera+UP9mvsqpOSTkREZMaZniYkv8QAraF0ZqaiDcpH03JQqDNArdHB112KxTtT8FRMOGYOiUB6VhFe+zYVfduqIJe4wFshw/ppveDpJoHKXdrkln2Ykk5ERDXBoKcJ8XJzRUGJHhqtsdJ2RVojsgt1aOvvgemxbfDqrhSk3iiw3DcJAib3DsXsrcnQ6IzYP3cg2vp71Hf36xxT0omIqCYY9DRi5bOSvOQSpFzNgyqg8lOUlXIJTCYBi3ecxH8Sr+LJfq3x6L1h8FZIIXN1QfKVPMz5K+BpiNTt+ir4yZR0IiKqCQY9jVRFWUlvTuiCYp0B/SP97O5niYlQ4avjV/DfxKso1Jaezuzq4oJB7f2rLM7ZkOOoi4Kf5uKkjhgXERE1PUxZR+NLWb+ZX4L5/zlht/aU+QNdZzThlZ0pVm1iIlToGeaDNftLT7l2EQEP9QrBy6M6QKmQNnjqtlqjw6ytyXaDswGRfpbsqrp4H6akExE1P0xZb+Ku5xXjUnZRpcU2NToj2vp7YOWkrsjT6HH5tgZiF+DjwxctAQ8AmARgRKeAahXnrA8NVfCTKelERFQdDHoaEfO5M1PuCa20nTkrqaWPAjfzb2PGl0kVttUaTA7LYmJ2FRERNSY8p6cRMc+MyFwr/20pm5XkIas8Q0nm6uKwLCZmVxERUWPCoKeBqTU6XMgqRHJGLi7cKoRac+cAPfPMSPKVPMTYOVAQuJOVpDOY8O7es7iWq0F0iLfdtjERKmQVaB2WxWTOrrKH2VVERNTQuLzVgKrKZDLPjGxISMeaKdEArA8h7P/XJmZ1sQGPb/gNJ6+qMbSDP1ZMisJb/ztj9dyYCBVmD4lEa1+Fw/a7MLuKiIgaE2ZvoWGyt6qTyQQAs7cm48j5bEvxz+gQb2gNJnjLJWjr74Gky7lY8N+TKNAaoJRL8K+HumF4pwCoNTpkFWihLtZDIRXDXeoKb4WkUQQWzK4iIqL6wOytRqo6mUzmjCzzzMi6A6WZWAMi/fDG+M744GAaPvv5MgCgR6g31k7tgZZ/nXXTmDOYGnPfiIio+WDQ00Cqm8kU7C3H2inRVjMjJXojZm5JRur1fADAcwPbYP6I9pCIuSWLiIiouhj0NJCaZDKVnxm5mV+CTHUJfN2leGdyNwxu719v/SQiInJWDHoaSE3rRBmMJrj+NZMT4OWGT57oiZbeCgQq3Rqkv0RERM6G6yMNxJzJVD6F214mU1pWIcauTcAPKTcs13qG+TLgISIiuguc6WlA9vbrlM9k2pF0Fa/sOgWNzoh//nQWwzsFWGZ8iIiIqPYY9DSwijKZNDoDlnyTiq8TrwIA+rZR4f1HujPgISIiqiMMehqBczcLMPPLJJzPKoRIBLwwNBKzh0RC7CJydNeIiIicBoMeB7uhLsb4dUdRrDeihacM7z/SHf3a2i/dQERERLXHoMfBgpRyPNw7BBduFeLdh7vDz0Pm6C4RERE5JQY9DnDmRj58FFJLNtbiMR0hFongwuUsIiKiesNdsg1IEAR8+etljP/gKOZsS4bBaAIASMQuDHiIiIjqGWd6GkhBiR6LdqTg+5OlZ++4S8Uo1hvhyewsIiKiBsGgpwGcuqbGzC1JuJyjgauLCAvua49nYttwdoeIiKgBMeipR4Ig4LOfL+Ot/52BzmhCS2851kyJRs8wH0d3jYiIqNlh0FOPtAYTvvjlMnRGE4Z1DMC/HuoKbzsHExIREVH9Y9BTj9wkYnz4aA/En8/GUzGtIRJxOYuIiMhRGPTUs8gAT0QGeDq6G0RERM0eU4eIiIioWXCaoOfDDz9EeHg43Nzc0LNnT8THxzu6S0RERNSIOEXQ89VXXyEuLg6LFy9GcnIy+vfvj1GjRiEjI8PRXSMiIqJGQiQIguDoTtytPn36oEePHvjoo48s1zp27IgJEyZgxYoVVb4+Pz8fSqUSarUaXl5e9dlVIiIiqiM1/fxu8jM9Op0OiYmJGDFihNX1ESNG4NixY3Zfo9VqkZ+fb/VFREREzq3JBz3Z2dkwGo0ICAiwuh4QEIDMzEy7r1mxYgWUSqXlKyQkpCG6SkRERA7U5IMes/Jn4AiCUOG5OIsWLYJarbZ8XblypSG6SERERA7U5M/p8fPzg1gstpnVycrKspn9MZPJZJDJZA3RPSIiImokmvxMj1QqRc+ePbF3716r63v37kW/fv0c1CsiIiJqbJr8TA8AzJ07F48//jh69eqFvn374pNPPkFGRgaef/55R3eNiIiIGgmnCHoefvhh5OTk4I033sCNGzfQpUsX7N69G2FhYY7uGhERETUSTnFOz93iOT1ERERNT7M7p4eIiIioOhj0EBERUbPgFHt67pZ5hY8nMxMRETUd5s/t6u7UYdADoKCgAAB4MjMREVETVFBQAKVSWWU7bmQGYDKZcP36dXh6elZ4inN5+fn5CAkJwZUrV5x+8zPH6pw4VufEsTonjtU+QRBQUFCA4OBguLhUvWOHMz0AXFxc0KpVq1q91svLy+n/AJpxrM6JY3VOHKtz4lhtVWeGx4wbmYmIiKhZYNBDREREzQKDnlqSyWRYsmRJsyhcyrE6J47VOXGszoljrRvcyExERETNAmd6iIiIqFlg0ENERETNAoMeIiIiahYY9BAREVGzwKCnFj788EOEh4fDzc0NPXv2RHx8vKO7VGNHjhzB/fffj+DgYIhEIuzatcvqviAIWLp0KYKDgyGXyzFo0CCkpqZatdFqtZg9ezb8/Pzg7u6OcePG4erVqw04iupZsWIFevfuDU9PT/j7+2PChAk4e/asVRtnGe9HH32Erl27Wg716tu3L3744QfLfWcZZ3krVqyASCRCXFyc5ZqzjHXp0qUQiURWX4GBgZb7zjJOs2vXruGxxx6DSqWCQqFA9+7dkZiYaLnvLONt3bq1ze+rSCTCzJkzATjPOAHAYDDglVdeQXh4OORyOdq0aYM33ngDJpPJ0qbBxitQjWzbtk2QSCTCp59+Kpw+fVp44YUXBHd3d+Hy5cuO7lqN7N69W1i8eLGwfft2AYCwc+dOq/srV64UPD09he3btwspKSnCww8/LAQFBQn5+fmWNs8//7zQsmVLYe/evUJSUpIwePBgoVu3boLBYGjg0VRu5MiRwsaNG4VTp04JJ06cEMaMGSOEhoYKhYWFljbOMt5vv/1W+N///iecPXtWOHv2rPDyyy8LEolEOHXqlCAIzjPOsn777TehdevWQteuXYUXXnjBct1ZxrpkyRKhc+fOwo0bNyxfWVlZlvvOMk5BEITbt28LYWFhwpNPPin8+uuvQnp6urBv3z4hLS3N0sZZxpuVlWX1e7p3714BgHDw4EFBEJxnnIIgCMuWLRNUKpXw/fffC+np6cLXX38teHh4CO+9956lTUONl0FPDd1zzz3C888/b3WtQ4cOwksvveSgHt298kGPyWQSAgMDhZUrV1qulZSUCEqlUvj4448FQRCEvLw8QSKRCNu2bbO0uXbtmuDi4iL8+OOPDdb32sjKyhIACIcPHxYEwfnH6+PjI/z73/92ynEWFBQIkZGRwt69e4WBAwdagh5nGuuSJUuEbt262b3nTOMUBEFYuHChEBsbW+F9ZxtvWS+88ILQtm1bwWQyOd04x4wZI0yfPt3q2sSJE4XHHntMEISG/X3l8lYN6HQ6JCYmYsSIEVbXR4wYgWPHjjmoV3UvPT0dmZmZVuOUyWQYOHCgZZyJiYnQ6/VWbYKDg9GlS5dG/7NQq9UAAF9fXwDOO16j0Yht27ahqKgIffv2dcpxzpw5E2PGjMGwYcOsrjvbWM+fP4/g4GCEh4fjkUcewcWLFwE43zi//fZb9OrVCw899BD8/f0RHR2NTz/91HLf2cZrptPp8MUXX2D69OkQiURON87Y2Fjs378f586dAwD88ccfSEhIwOjRowE07O8rC47WQHZ2NoxGIwICAqyuBwQEIDMz00G9qnvmsdgb5+XLly1tpFIpfHx8bNo05p+FIAiYO3cuYmNj0aVLFwDON96UlBT07dsXJSUl8PDwwM6dO9GpUyfLPwzOMs5t27YhKSkJx48ft7nnTL+nffr0wWeffYZ27drh5s2bWLZsGfr164fU1FSnGicAXLx4ER999BHmzp2Ll19+Gb/99hvmzJkDmUyGJ554wunGa7Zr1y7k5eXhySefBOBcf34BYOHChVCr1ejQoQPEYjGMRiPeeustTJkyBUDDjpdBTy2IRCKr7wVBsLnmDGozzsb+s5g1axZOnjyJhIQEm3vOMt727dvjxIkTyMvLw/bt2zFt2jQcPnzYct8ZxnnlyhW88MIL2LNnD9zc3Cps5wxjHTVqlOXXUVFR6Nu3L9q2bYvNmzfj3nvvBeAc4wQAk8mEXr16Yfny5QCA6OhopKam4qOPPsITTzxhaecs4zVbv349Ro0aheDgYKvrzjLOr776Cl988QW2bNmCzp0748SJE4iLi0NwcDCmTZtmadcQ4+XyVg34+flBLBbbRJVZWVk2EWpTZs4MqWycgYGB0Ol0yM3NrbBNYzN79mx8++23OHjwIFq1amW57mzjlUqliIiIQK9evbBixQp069YN77//vlONMzExEVlZWejZsydcXV3h6uqKw4cPY82aNXB1dbX01RnGWp67uzuioqJw/vx5p/o9BYCgoCB06tTJ6lrHjh2RkZEBwPn+rgLA5cuXsW/fPjzzzDOWa842zhdffBEvvfQSHnnkEURFReHxxx/HP/7xD6xYsQJAw46XQU8NSKVS9OzZE3v37rW6vnfvXvTr189Bvap74eHhCAwMtBqnTqfD4cOHLePs2bMnJBKJVZsbN27g1KlTje5nIQgCZs2ahR07duDAgQMIDw+3uu9s4y1PEARotVqnGufQoUORkpKCEydOWL569eqFRx99FCdOnECbNm2cZqzlabVanDlzBkFBQU71ewoAMTExNsdJnDt3DmFhYQCc8+/qxo0b4e/vjzFjxliuOds4NRoNXFysww2xWGxJWW/Q8VZ7yzMJgnAnZX39+vXC6dOnhbi4OMHd3V24dOmSo7tWIwUFBUJycrKQnJwsABBWr14tJCcnW1LvV65cKSiVSmHHjh1CSkqKMGXKFLvpg61atRL27dsnJCUlCUOGDGmU6ZJ///vfBaVSKRw6dMgqRVSj0VjaOMt4Fy1aJBw5ckRIT08XTp48Kbz88suCi4uLsGfPHkEQnGec9pTN3hIE5xnrvHnzhEOHDgkXL14UfvnlF2Hs2LGCp6en5d8cZxmnIJQeP+Dq6iq89dZbwvnz54Uvv/xSUCgUwhdffGFp40zjNRqNQmhoqLBw4UKbe840zmnTpgktW7a0pKzv2LFD8PPzExYsWGBp01DjZdBTCx988IEQFhYmSKVSoUePHpbU56bk4MGDAgCbr2nTpgmCUJpCuGTJEiEwMFCQyWTCgAEDhJSUFKtnFBcXC7NmzRJ8fX0FuVwujB07VsjIyHDAaCpnb5wAhI0bN1raOMt4p0+fbvmz2aJFC2Ho0KGWgEcQnGec9pQPepxlrObzSiQSiRAcHCxMnDhRSE1Ntdx3lnGafffdd0KXLl0EmUwmdOjQQfjkk0+s7jvTeH/66ScBgHD27Fmbe840zvz8fOGFF14QQkNDBTc3N6FNmzbC4sWLBa1Wa2nTUOMVCYIg1GyiioiIiKjp4Z4eIiIiahYY9BAREVGzwKCHiIiImgUGPURERNQsMOghIiKiZoFBDxERETULDHqIiIioWWDQQ0RERM0Cgx4iqhOHDh2CSCRCXl5eg73n0qVL0b179wZ7v7tVm/4OGjQIcXFx9dIfouaGQQ9RM/Xkk09CJBLh+eeft7k3Y8YMiEQiPPnkkw3fsUbgpZdeQseOHa2unTlzBiKRCI8//rjV9c8//xwSiQSFhYVVPnf+/PnYv39/nfYVAEQiEXbt2lXnzyVyNgx6iJqxkJAQbNu2DcXFxZZrJSUl2Lp1K0JDQx3Ys4aj1+ttrg0ePBh//vknMjMzLdcOHTqEkJAQHDx40KrtoUOHcM8998DDw6PK9/Lw8IBKpbr7ThNRrTDoIWrGevTogdDQUOzYscNybceOHQgJCUF0dLRVW61Wizlz5sDf3x9ubm6IjY3F8ePHK33+sWPHMGDAAMjlcoSEhGDOnDkoKiqyeuaCBQsQEhICmUyGyMhIrF+/HgCwadMmeHt7Wz1v165dEIlEFb7f8ePHMXz4cPj5+UGpVGLgwIFISkqyaiMSifDxxx9j/PjxcHd3x7Jly2yeExsbC4lEgkOHDlmuHTp0CDNnzkRBQQHS0tKsrg8ePBgAoFar8eyzz8Lf3x9eXl4YMmQI/vjjD0vb8stbBoMBc+bMgbe3N1QqFRYuXIhp06ZhwoQJVv0xmUxYsGABfH19ERgYiKVLl1rutW7dGgDwwAMPQCQSWb4nIlsMeoiauaeeegobN260fL9hwwZMnz7dpt2CBQuwfft2bN68GUlJSYiIiMDIkSNx+/Ztu89NSUnByJEjMXHiRJw8eRJfffUVEhISMGvWLEubJ554Atu2bcOaNWtw5swZfPzxx9WaMalIQUEBpk2bhvj4ePzyyy+IjIzE6NGjUVBQYNVuyZIlGD9+PFJSUuyO1d3dHb1797aa1Tl8+DCGDh2KmJgYy/UrV67g4sWLGDx4MARBwJgxY5CZmYndu3cjMTERPXr0wNChQyv8Gb399tv48ssvsXHjRhw9ehT5+fl2l6k2b94Md3d3/Prrr1i1ahXeeOMN7N27FwAsgefGjRtx48aNKgNRombt7ovGE1FTNG3aNGH8+PHCrVu3BJlMJqSnpwuXLl0S3NzchFu3bgnjx48Xpk2bJgiCIBQWFgoSiUT48ssvLa/X6XRCcHCwsGrVKkEQBOHgwYMCACE3N1cQBEF4/PHHhWeffdbqPePj4wUXFxehuLhYOHv2rABA2Lt3r93+bdy4UVAqlVbXdu7cKZT9Z2vJkiVCt27dKhyjwWAQPD09he+++85yDYAQFxdX1Y9HePnll4V27doJgiAIqampgpeXl2AwGISVK1cKU6dOFQRBEDZv3izIZDJBo9EI+/fvF7y8vISSkhKr57Rt21b4v//7P7v9DQgIEP75z39a9Tc0NFQYP3685drAgQOF2NhYq2f27t1bWLhwodWYdu7cWeWYiJo7V4dGXETkcH5+fhgzZgw2b95sma3w8/OzanPhwgXo9XrExMRYrkkkEtxzzz04c+aM3ecmJiYiLS0NX375peWaIAgwmUxIT09HSkoKxGIxBg4cWGdjycrKwmuvvYYDBw7g5s2bMBqN0Gg0yMjIsGrXq1evKp81ePBgLF++HNevX8ehQ4cQGxtr6e+aNWsAlC5t3XvvvZDL5UhMTERhYaHNnp3i4mJcuHDB5vlqtRo3b97EPffcY7kmFovRs2dPmEwmq7Zdu3a1+j4oKAhZWVlVjoGIrDHoISJMnz7dsuz0wQcf2NwXBAEAbPbTCIJQ4R4bk8mE5557DnPmzLG5FxoaarUvxh4XFxfL+5rZ23Rc1pNPPolbt27hvffeQ1hYGGQyGfr27QudTmfVzt3dvdLnAEBMTAykUikOHTqEgwcPWoKzXr16Qa1W49y5czh48KAlw81kMiEoKMhqH5BZ+b1JZdn7mZYnkUhsXlM+MCKiqnFPDxHhvvvug06ng06nw8iRI23uR0REQCqVIiEhwXJNr9fj999/t0ntNuvRowdSU1MRERFh8yWVShEVFQWTyYTDhw/bfX2LFi1QUFBgtfH5xIkTlY4jPj4ec+bMwejRo9G5c2fIZDJkZ2dX4ydgSy6Xo0+fPjh06BCOHDmCQYMGAQBcXV3Rr18/fPbZZ7h06ZJlE3OPHj2QmZkJV1dXm/GWnzkDAKVSiYCAAPz222+Wa0ajEcnJyTXuq0QigdForNU4iZoTBj1EBLFYjDNnzuDMmTMQi8U2993d3fH3v/8dL774In788UecPn0af/vb36DRaPD000/bfebChQvx888/Y+bMmThx4gTOnz+Pb7/9FrNnzwZQmnU0bdo0TJ8+Hbt27UJ6ejoOHTqE//znPwCAPn36QKFQ4OWXX0ZaWhq2bNmCTZs2VTqOiIgIfP755zhz5gx+/fVXPProo5DL5bX+uQwePNiS0t+jRw/LdfMSlzkwAoBhw4ahb9++mDBhAn766SdcunQJx44dwyuvvILff//d7vNnz56NFStW4JtvvsHZs2fxwgsvIDc3t9IMNXtat26N/fv3IzMzE7m5ubUeL5GzY9BDRAAALy8veHl5VXh/5cqVmDRpEh5//HH06NEDaWlp+Omnn+Dj42O3fdeuXXH48GGcP38e/fv3R3R0NF599VUEBQVZ2nz00Ud48MEHMWPGDHTo0AF/+9vfLDM7vr6++OKLL7B7925ERUVh69atVqna9mzYsAG5ubmIjo7G448/bkmxr63BgwejoKAAMTExcHW9sxtg4MCBKCgoQL9+/SCTyQCULjnt3r0bAwYMwPTp09GuXTs88sgjuHTpEgICAuw+f+HChZgyZQqeeOIJ9O3bFx4eHhg5ciTc3Nxq1M933nkHe/futXvUABHdIRLsLSATEVGDM5lM6NixIyZPnow333zT0d0hcjrcyExE5CCXL1/Gnj17MHDgQGi1Wqxbtw7p6emYOnWqo7tG5JS4vEVE5CAuLi7YtGkTevfujZiYGKSkpGDfvn0Vbg4norvD5S0iIiJqFjjTQ0RERM0Cgx4iIiJqFhj0EBERUbPAoIeIiIiaBQY9RERE1Cww6CEiIqJmgUEPERERNQsMeoiIiKhZ+H+ncfXQ/rDQ7QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Molecular Weight\", y=\"Predicted MolWt\", data =df_train)\n",
    "sns.lineplot(x=\"Molecular Weight\", y=\"Molecular Weight\", data=df, linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b9771f8c-8442-4eba-af60-2f5328397397",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1669.386531958343\n",
      "Root Mean Squared Error: 40.85812687775032\n",
      "R2 Score: 0.8522502192506366\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Evaluate the model's performance\n",
    "mse = mean_squared_error(Y_test, y_pred)\n",
    "rmse = math.sqrt(mse)\n",
    "r2 = r2_score(Y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"Root Mean Squared Error:\", rmse)\n",
    "print(\"R2 Score:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "57bc1e70-13c4-4812-b43f-108b0e1810c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1497.129827649488\n",
      "Root Mean Squared Error: 38.69276195426592\n",
      "R2 Score: 0.8545636016223443\n"
     ]
    }
   ],
   "source": [
    "# Compare it to model performance on the training data.\n",
    "\n",
    "y_pred = ttt_model.predict(X_train)\n",
    "\n",
    "\n",
    "# Evaluate the model's performance\n",
    "mse = mean_squared_error(Y_train, y_pred)\n",
    "rmse = math.sqrt(mse)\n",
    "r2 = r2_score(Y_train, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"Root Mean Squared Error:\", rmse)\n",
    "print(\"R2 Score:\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0fd6c27-c1f1-45a9-b26f-53661f170b62",
   "metadata": {},
   "source": [
    "## The SciKit Learn Model API\n",
    "\n",
    "All scikit learn models use the same API, or interface. This means to switch from a linear model to a more sophisticated model like a [random forest model](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html), one need only change\n",
    "the model creation.\n",
    "\n",
    "For example, recall our code to fit a linear model and use it for prediction:\n",
    "\n",
    "```python\n",
    "from sklearn.linear_model import LinearRegression \n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X,Y)\n",
    "predictions = model.predict(X)\n",
    "```\n",
    "\n",
    "To do the same thing with a random forest regresso, the code would be:\n",
    "\n",
    "\n",
    "```python\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X, Y)\n",
    "predictions = model.predict()\n",
    "\n",
    "```\n",
    "\n",
    "<div class=\"exercise admonition\">\n",
    "<p class=\"admonition-title\">Exercises - Challenge</p>\n",
    "\n",
    "<p> Repeat this analysis for your the vitamins data set or the amino acids dataset provided.</p>\n",
    "\n",
    "We have provided starter code for reading an SDF file into a pandas dataframe using PandasTools. \n",
    "    If you are interested in learning more about this, see  [Supplemental Notebook: RDKit PandasTools](supplemental_PandasTools.ipynb)\n",
    "\n",
    "Do you see any differences in trends observed for the amino acid dataset compared to the ESOL dataset?\n",
    "Is the relationship between number of heavy atoms and molecular weight a better fit?\n",
    "Do you have any hypotheses why or why not?\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a7969cdc-4f69-4a08-acb5-8904b1dd8c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.Chem import PandasTools\n",
    "\n",
    "PandasTools.RenderImagesInAllDataFrames(True)\n",
    "\n",
    "df2 = PandasTools.LoadSDF(\"data/amino_acids/amino_acids-nat20.sdf\", strictParsing=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cb8ba860-2e0c-44b2-bbb3-88a7e65d70c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ROMol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ala</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arg</td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"data:image/png;base64,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\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID                                             ROMol\n",
       "0  Ala  <rdkit.Chem.rdchem.Mol object at 0x7f6ef8e75b60>\n",
       "1  Arg  <rdkit.Chem.rdchem.Mol object at 0x7f6ef8e750e0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0dfc2450-3040-443d-a963-2d87713db304",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add to data set\n",
    "df2[\"Molecular Weight\"] = df2[\"ROMol\"].apply(Descriptors.MolWt)\n",
    "df2[\"NumHeavyAtoms\"] = df2[\"ROMol\"].apply(Descriptors.HeavyAtomCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "776307ef-4810-49cc-a5e2-ac04411062d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df2[[\"Molecular Weight\"]]\n",
    "X = df2[[\"NumHeavyAtoms\"]]\n",
    "\n",
    "aa_model = LinearRegression()\n",
    "aa_model.fit(X, Y)\n",
    "\n",
    "y_pred = aa_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ec245cde-99ee-4801-9a87-b4c762673a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.DataFrame()\n",
    "df_train[\"Molecular Weight\"] = Y\n",
    "df_train[\"Predicted MolWt\"] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e430a692-9185-4116-81a8-316c8167680f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Molecular Weight', ylabel='Predicted MolWt'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Molecular Weight\", y=\"Predicted MolWt\", data =df_train)\n",
    "sns.lineplot(x=\"Molecular Weight\", y=\"Molecular Weight\", data=df2, linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c79c93-251e-4ed8-ba24-4f2fee5a6dac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8379673b-3ea3-440a-bd26-7e49c7ae6ce9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb2fdcf7-bd3f-472f-a64d-d20875ddd837",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}