train_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import root_mean_squared_error, r2_score, mean_absolute_error\n",
    "from tqdm.auto import tqdm\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol_df = pd.read_csv('./data/with_features.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into training and test sets (80% for training and 20% for testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(sol_df,random_state=42,shuffle=True, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'SMILES', 'LogS', 'mol'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['exactmw', 'amw', 'lipinskiHBA', 'lipinskiHBD', 'NumRotatableBonds',\n",
       "       'NumHBD', 'NumHBA', 'NumHeavyAtoms', 'NumAtoms', 'NumHeteroatoms',\n",
       "       'NumAmideBonds', 'FractionCSP3', 'NumRings', 'NumAromaticRings',\n",
       "       'NumAliphaticRings', 'NumSaturatedRings', 'NumHeterocycles',\n",
       "       'NumAromaticHeterocycles', 'NumSaturatedHeterocycles',\n",
       "       'NumAliphaticHeterocycles', 'NumSpiroAtoms', 'NumBridgeheadAtoms',\n",
       "       'labuteASA', 'tpsa', 'CrippenClogP', 'CrippenMR', 'chi0v', 'chi1v',\n",
       "       'chi2v', 'chi3v', 'chi4v', 'chi0n', 'chi1n', 'chi2n', 'chi3n', 'chi4n',\n",
       "       'hallKierAlpha', 'kappa1', 'kappa2', 'kappa3', 'Phi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train[train.columns[4:]]\n",
    "train_y = train['LogS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exactmw</th>\n",
       "      <th>amw</th>\n",
       "      <th>lipinskiHBA</th>\n",
       "      <th>lipinskiHBD</th>\n",
       "      <th>NumRotatableBonds</th>\n",
       "      <th>NumHBD</th>\n",
       "      <th>NumHBA</th>\n",
       "      <th>NumHeavyAtoms</th>\n",
       "      <th>NumAtoms</th>\n",
       "      <th>NumHeteroatoms</th>\n",
       "      <th>...</th>\n",
       "      <th>chi0n</th>\n",
       "      <th>chi1n</th>\n",
       "      <th>chi2n</th>\n",
       "      <th>chi3n</th>\n",
       "      <th>chi4n</th>\n",
       "      <th>hallKierAlpha</th>\n",
       "      <th>kappa1</th>\n",
       "      <th>kappa2</th>\n",
       "      <th>kappa3</th>\n",
       "      <th>Phi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>284.104859</td>\n",
       "      <td>284.311</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.690953</td>\n",
       "      <td>6.647840</td>\n",
       "      <td>2.776341</td>\n",
       "      <td>2.776341</td>\n",
       "      <td>1.709633</td>\n",
       "      <td>-2.620000</td>\n",
       "      <td>14.782147</td>\n",
       "      <td>7.239263</td>\n",
       "      <td>4.454376</td>\n",
       "      <td>5.095802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>308.104859</td>\n",
       "      <td>308.333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.652568</td>\n",
       "      <td>7.367289</td>\n",
       "      <td>3.861854</td>\n",
       "      <td>3.861854</td>\n",
       "      <td>2.814558</td>\n",
       "      <td>-2.880000</td>\n",
       "      <td>15.032579</td>\n",
       "      <td>6.084897</td>\n",
       "      <td>2.878886</td>\n",
       "      <td>3.977030</td>\n",
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       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>216.060656</td>\n",
       "      <td>216.312</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.167930</td>\n",
       "      <td>4.768581</td>\n",
       "      <td>2.352651</td>\n",
       "      <td>2.352651</td>\n",
       "      <td>1.507781</td>\n",
       "      <td>-1.423117</td>\n",
       "      <td>10.106872</td>\n",
       "      <td>3.981045</td>\n",
       "      <td>1.991959</td>\n",
       "      <td>2.682395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>130.062994</td>\n",
       "      <td>130.143</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.385031</td>\n",
       "      <td>2.616705</td>\n",
       "      <td>0.672905</td>\n",
       "      <td>0.672905</td>\n",
       "      <td>0.261541</td>\n",
       "      <td>-0.830000</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>4.089266</td>\n",
       "      <td>3.727880</td>\n",
       "      <td>3.712145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5628</th>\n",
       "      <td>156.089878</td>\n",
       "      <td>156.185</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.487525</td>\n",
       "      <td>3.725041</td>\n",
       "      <td>2.041618</td>\n",
       "      <td>2.041618</td>\n",
       "      <td>1.382411</td>\n",
       "      <td>-1.060000</td>\n",
       "      <td>8.040604</td>\n",
       "      <td>3.365968</td>\n",
       "      <td>1.500478</td>\n",
       "      <td>2.460401</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>283.133907</td>\n",
       "      <td>283.799</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.072396</td>\n",
       "      <td>6.472696</td>\n",
       "      <td>2.796352</td>\n",
       "      <td>2.796352</td>\n",
       "      <td>1.944493</td>\n",
       "      <td>-1.060000</td>\n",
       "      <td>15.995741</td>\n",
       "      <td>7.510031</td>\n",
       "      <td>4.514582</td>\n",
       "      <td>6.322554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>294.078661</td>\n",
       "      <td>294.336</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.991522</td>\n",
       "      <td>5.685774</td>\n",
       "      <td>2.452918</td>\n",
       "      <td>2.452918</td>\n",
       "      <td>1.540724</td>\n",
       "      <td>-2.350000</td>\n",
       "      <td>14.067454</td>\n",
       "      <td>5.333996</td>\n",
       "      <td>3.150817</td>\n",
       "      <td>3.751787</td>\n",
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       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>128.058577</td>\n",
       "      <td>128.131</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.910175</td>\n",
       "      <td>2.804320</td>\n",
       "      <td>1.401695</td>\n",
       "      <td>1.401695</td>\n",
       "      <td>0.927291</td>\n",
       "      <td>-1.060000</td>\n",
       "      <td>6.065945</td>\n",
       "      <td>2.478333</td>\n",
       "      <td>1.011451</td>\n",
       "      <td>1.670381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>408.008593</td>\n",
       "      <td>408.413</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.381710</td>\n",
       "      <td>7.280968</td>\n",
       "      <td>3.639442</td>\n",
       "      <td>3.639442</td>\n",
       "      <td>2.338042</td>\n",
       "      <td>-2.860000</td>\n",
       "      <td>18.917024</td>\n",
       "      <td>6.701778</td>\n",
       "      <td>4.113435</td>\n",
       "      <td>4.695470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>178.056469</td>\n",
       "      <td>178.260</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.112077</td>\n",
       "      <td>3.590739</td>\n",
       "      <td>1.540850</td>\n",
       "      <td>1.540850</td>\n",
       "      <td>0.878015</td>\n",
       "      <td>-1.180000</td>\n",
       "      <td>8.912421</td>\n",
       "      <td>3.999747</td>\n",
       "      <td>2.155108</td>\n",
       "      <td>2.970620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7913 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         exactmw      amw  lipinskiHBA  lipinskiHBD  NumRotatableBonds  \\\n",
       "252   284.104859  284.311          4.0          0.0                5.0   \n",
       "696   308.104859  308.333          4.0          1.0                4.0   \n",
       "473   216.060656  216.312          2.0          2.0                2.0   \n",
       "3080  130.062994  130.143          3.0          1.0                3.0   \n",
       "5628  156.089878  156.185          4.0          2.0                0.0   \n",
       "...          ...      ...          ...          ...                ...   \n",
       "5734  283.133907  283.799          3.0          0.0                6.0   \n",
       "5191  294.078661  294.336          7.0          3.0                4.0   \n",
       "5390  128.058577  128.131          4.0          2.0                0.0   \n",
       "860   408.008593  408.413          9.0          3.0                4.0   \n",
       "7270  178.056469  178.260          2.0          0.0                2.0   \n",
       "\n",
       "      NumHBD  NumHBA  NumHeavyAtoms  NumAtoms  NumHeteroatoms  ...      chi0n  \\\n",
       "252      0.0     4.0           21.0      37.0             4.0  ...  11.690953   \n",
       "696      1.0     4.0           23.0      39.0             4.0  ...  12.652568   \n",
       "473      2.0     2.0           15.0      27.0             3.0  ...   8.167930   \n",
       "3080     1.0     3.0            9.0      19.0             3.0  ...   5.385031   \n",
       "5628     2.0     4.0           11.0      23.0             4.0  ...   6.487525   \n",
       "...      ...     ...            ...       ...             ...  ...        ...   \n",
       "5734     0.0     2.0           19.0      41.0             4.0  ...  12.072396   \n",
       "5191     2.0     6.0           20.0      34.0             8.0  ...  10.991522   \n",
       "5390     2.0     4.0            9.0      17.0             4.0  ...   4.910175   \n",
       "860      3.0     7.0           27.0      39.0            11.0  ...  13.381710   \n",
       "7270     0.0     3.0           12.0      22.0             3.0  ...   7.112077   \n",
       "\n",
       "         chi1n     chi2n     chi3n     chi4n  hallKierAlpha     kappa1  \\\n",
       "252   6.647840  2.776341  2.776341  1.709633      -2.620000  14.782147   \n",
       "696   7.367289  3.861854  3.861854  2.814558      -2.880000  15.032579   \n",
       "473   4.768581  2.352651  2.352651  1.507781      -1.423117  10.106872   \n",
       "3080  2.616705  0.672905  0.672905  0.261541      -0.830000   8.170000   \n",
       "5628  3.725041  2.041618  2.041618  1.382411      -1.060000   8.040604   \n",
       "...        ...       ...       ...       ...            ...        ...   \n",
       "5734  6.472696  2.796352  2.796352  1.944493      -1.060000  15.995741   \n",
       "5191  5.685774  2.452918  2.452918  1.540724      -2.350000  14.067454   \n",
       "5390  2.804320  1.401695  1.401695  0.927291      -1.060000   6.065945   \n",
       "860   7.280968  3.639442  3.639442  2.338042      -2.860000  18.917024   \n",
       "7270  3.590739  1.540850  1.540850  0.878015      -1.180000   8.912421   \n",
       "\n",
       "        kappa2    kappa3       Phi  \n",
       "252   7.239263  4.454376  5.095802  \n",
       "696   6.084897  2.878886  3.977030  \n",
       "473   3.981045  1.991959  2.682395  \n",
       "3080  4.089266  3.727880  3.712145  \n",
       "5628  3.365968  1.500478  2.460401  \n",
       "...        ...       ...       ...  \n",
       "5734  7.510031  4.514582  6.322554  \n",
       "5191  5.333996  3.150817  3.751787  \n",
       "5390  2.478333  1.011451  1.670381  \n",
       "860   6.701778  4.113435  4.695470  \n",
       "7270  3.999747  2.155108  2.970620  \n",
       "\n",
       "[7913 rows x 41 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "252    -4.567303\n",
       "696    -3.062509\n",
       "473    -2.733021\n",
       "3080   -0.960256\n",
       "5628   -1.591600\n",
       "          ...   \n",
       "5734   -3.682200\n",
       "5191   -2.543000\n",
       "5390   -1.993700\n",
       "860    -1.376747\n",
       "7270   -4.130000\n",
       "Name: LogS, Length: 7913, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = test[test.columns[4:]]\n",
    "test_y = test['LogS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1979, 41)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1979,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to compare the distribution of the training and test LogS values. Since we split it randomly, we see that both distributions are somewhat similar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14e020770>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(5,5))\n",
    "plt.hist([train_y,test_y], bins=10, alpha=0.5, label=['train','test'])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "StandardScaler basically divides each row by its mean to normalize the data. Different columns have have different ranges and normalizing the data ensures that columns with large values do not dominate. It also reduces computational overhead and also helps the model to converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scale_x = StandardScaler()\n",
    "scale_y = StandardScaler()\n",
    "\n",
    "train_X = scale_x.fit_transform(train_X)\n",
    "test_X = scale_x.transform(test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the scale_x and scale_y objects are used to normalize features or x and the target y, they are then used fit to the training data and simply used to transform the test data. This is done to maintain the 'unseen nature' of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.230936  ,  0.22914806,  0.04226449, ...,  0.31100776,\n",
       "        -0.04856108,  0.1085883 ],\n",
       "       [ 0.39107257,  0.38913044,  0.04226449, ...,  0.0830988 ,\n",
       "        -0.0537098 , -0.12703618],\n",
       "       [-0.22307921, -0.22371356, -0.53987461, ..., -0.33226903,\n",
       "        -0.05660829, -0.39969926],\n",
       "       ...,\n",
       "       [-0.8102605 , -0.81098383,  0.04226449, ..., -0.62895286,\n",
       "        -0.0598126 , -0.61283939],\n",
       "       [ 1.05766595,  1.055646  ,  1.49761223, ...,  0.20489094,\n",
       "        -0.04967528,  0.02427438],\n",
       "       [-0.47665672, -0.47713333, -0.53987461, ..., -0.32857666,\n",
       "        -0.05607511, -0.33899621]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.1377989 ,  1.13834108,  1.20654268, ...,  0.30156793,\n",
       "        -0.05110354,  0.1033298 ],\n",
       "       [ 0.12452995,  0.12291715, -0.24880506, ...,  0.02304222,\n",
       "        -0.05133857, -0.10472665],\n",
       "       [-1.01103972, -1.01112497,  0.04226449, ..., -0.93560674,\n",
       "         2.55380472, -0.77486436],\n",
       "       ...,\n",
       "       [ 0.05032153,  0.04903968,  0.62440359, ..., -0.11792549,\n",
       "        -0.04896153, -0.1275635 ],\n",
       "       [-0.5967861 , -0.59759629, -0.53987461, ..., -0.64217335,\n",
       "        -0.05993202, -0.66937607],\n",
       "       [-0.6096087 , -0.61045642, -0.53987461, ...,  0.3774109 ,\n",
       "        -0.03904627,  0.55398379]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = scale_y.fit_transform(train_y.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.70695506],\n",
       "       [-0.07405467],\n",
       "       [ 0.06452452],\n",
       "       ...,\n",
       "       [ 0.37547489],\n",
       "       [ 0.63495878],\n",
       "       [-0.52303011]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our case we already had the test set. In certain situations, the model needs to be trained on new data which is truly unseen. Hence it is essential to save the scaler variables as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/scale_y.pkl']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "dump(scale_x,'./models/scale_x.pkl')\n",
    "dump(scale_y,'./models/scale_y.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initializing our Multi-Layer-Perceptron (neural network) with some 'hyperparameters'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPRegressor(hidden_layer_sizes=[64, 128], activation='relu', solver='adam', alpha=0.1, \n",
    "                   batch_size='auto', learning_rate='adaptive', learning_rate_init=0.01, \n",
    "                   max_iter=100, random_state=42, n_iter_no_change=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fitting the model to our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate=&#x27;adaptive&#x27;,\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPRegressor.html\">?<span>Documentation for MLPRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate=&#x27;adaptive&#x27;,\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate='adaptive',\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(train_X, train_y.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predicting the LogS values of our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_y = mlp.predict(test_X)\n",
    "predicted_y= scale_y.inverse_transform(predicted_y.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Predicted LogS')]"
      ]
     },
     "execution_count": 23,
     "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": [
    "ax = sns.scatterplot(x=test_y,y=predicted_y.flatten())\n",
    "ax.set(xlabel=\"Experimental LogS\")\n",
    "ax.set(ylabel=\"Predicted LogS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "always use multiple error metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7300666923416147\n"
     ]
    }
   ],
   "source": [
    "og_r2 = r2_score(test_y,predicted_y)\n",
    "print(og_r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8473057156580447\n"
     ]
    }
   ],
   "source": [
    "og_mae = mean_absolute_error(test_y,predicted_y)\n",
    "print(og_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2198619320960034\n"
     ]
    }
   ],
   "source": [
    "og_rmse = root_mean_squared_error(test_y,predicted_y) #root mean squared error \n",
    "print(og_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross Validation and Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may need to select optimal hyperparameters to improve our model's performance - Hyperparameter tuning. We might also need to test multiple algorithms, like MLP vs DecisionTrees, etc. In order to truly let our test set be unseen, we need to further divide our model into train and validation sets. For each set of hyperparameters, or for each algorithm, we then evaluate our model on the validation set and compare. Then we use the best model to predict our test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1624: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/che596/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128],\n",
       "                                    learning_rate=&#x27;adaptive&#x27;,\n",
       "                                    learning_rate_init=0.01, max_iter=100,\n",
       "                                    random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;alpha&#x27;: [0.1, 0.01, 0.001],\n",
       "                         &#x27;solver&#x27;: [&#x27;adam&#x27;, &#x27;sgd&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128],\n",
       "                                    learning_rate=&#x27;adaptive&#x27;,\n",
       "                                    learning_rate_init=0.01, max_iter=100,\n",
       "                                    random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;alpha&#x27;: [0.1, 0.01, 0.001],\n",
       "                         &#x27;solver&#x27;: [&#x27;adam&#x27;, &#x27;sgd&#x27;]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: MLPRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate=&#x27;adaptive&#x27;,\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MLPRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPRegressor.html\">?<span>Documentation for MLPRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate=&#x27;adaptive&#x27;,\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128],\n",
       "                                    learning_rate='adaptive',\n",
       "                                    learning_rate_init=0.01, max_iter=100,\n",
       "                                    random_state=42),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'alpha': [0.1, 0.01, 0.001],\n",
       "                         'solver': ['adam', 'sgd']})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# Here I am trying Grid Search where I have to provide the exact values of hyperparameters that I want to try\n",
    "# 3 values for regularization, and 2 values of the optimier or solver, \n",
    "# the total number of times the model will be intialized and run will be 6\n",
    "# This is a simple example, in real life you can try more values of the hyperparameters\n",
    "\n",
    "parameters = {'alpha':[0.1, 0.01, 0.001], 'solver':['adam','sgd']}\n",
    "grid = GridSearchCV(mlp, parameters, cv=5, n_jobs=-1)\n",
    "grid.fit(train_X, train_y.reshape(-1,1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results of Grid Search with Cross Validation. The splits indicate that the algorithm did 5-Fold Cross Validation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_fit_time [3.63559546 6.504316   5.24445281 6.69122415 6.32632699 5.73893023]\n",
      "std_fit_time [0.70870316 0.14169946 0.77630448 0.11656492 0.36757011 0.58129924]\n",
      "mean_score_time [0.00483041 0.00798521 0.00681396 0.00892935 0.00969882 0.00415177]\n",
      "std_score_time [0.00059793 0.00461418 0.00264233 0.00294013 0.00749969 0.00094768]\n",
      "param_alpha [0.1 0.1 0.01 0.01 0.001 0.001]\n",
      "param_solver ['adam' 'sgd' 'adam' 'sgd' 'adam' 'sgd']\n",
      "params [{'alpha': 0.1, 'solver': 'adam'}, {'alpha': 0.1, 'solver': 'sgd'}, {'alpha': 0.01, 'solver': 'adam'}, {'alpha': 0.01, 'solver': 'sgd'}, {'alpha': 0.001, 'solver': 'adam'}, {'alpha': 0.001, 'solver': 'sgd'}]\n",
      "split0_test_score [0.73327567 0.7431295  0.74175774 0.73931927 0.75634352 0.74350682]\n",
      "split1_test_score [0.72163157 0.71460151 0.73468436 0.71603145 0.69896043 0.71134788]\n",
      "split2_test_score [0.73165958 0.73611368 0.71779853 0.73255172 0.71289802 0.73500112]\n",
      "split3_test_score [0.72574009 0.73661704 0.72562965 0.73614208 0.73679119 0.737915  ]\n",
      "split4_test_score [0.75428359 0.74765283 0.75663677 0.72410984 0.75609221 0.73763194]\n",
      "mean_test_score [0.7333181  0.73562291 0.73530141 0.72963087 0.73221707 0.73308055]\n",
      "std_test_score [0.01128101 0.01135074 0.0133952  0.00848785 0.02303154 0.0112142 ]\n",
      "rank_test_score [3 1 2 6 5 4]\n"
     ]
    }
   ],
   "source": [
    "for i in grid.cv_results_.keys():\n",
    "    print(i,grid.cv_results_[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model from grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate=&#x27;adaptive&#x27;,\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42,\n",
       "             solver=&#x27;sgd&#x27;)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPRegressor.html\">?<span>Documentation for MLPRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate=&#x27;adaptive&#x27;,\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42,\n",
       "             solver=&#x27;sgd&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPRegressor(alpha=0.1, hidden_layer_sizes=[64, 128], learning_rate='adaptive',\n",
       "             learning_rate_init=0.01, max_iter=100, random_state=42,\n",
       "             solver='sgd')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using that to predict the solubility of our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_pred_y=grid.best_estimator_.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_pred_y = scale_y.inverse_transform(cv_pred_y.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Predicted LogS')]"
      ]
     },
     "execution_count": 32,
     "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": [
    "ax = sns.scatterplot(x=test_y,y=cv_pred_y.flatten())\n",
    "ax.set(xlabel=\"Experimental LogS\")\n",
    "ax.set(ylabel=\"Predicted LogS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old R2: 0.7300666923416147 \n",
      " New R2: 0.7506247049186345\n"
     ]
    }
   ],
   "source": [
    "new_r2 = r2_score(test_y,cv_pred_y)\n",
    "print(f'Old R2: {og_r2} \\n New R2: {new_r2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old MAE: 0.8473057156580447 \n",
      " New MAE: 0.8074002775112946\n"
     ]
    }
   ],
   "source": [
    "new_mae = mean_absolute_error(test_y,cv_pred_y)\n",
    "print(f'Old MAE: {og_mae} \\n New MAE: {new_mae}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old RMSE: 1.2198619320960034 \n",
      " New RMSE: 1.1724900175487034\n"
     ]
    }
   ],
   "source": [
    "new_rmse = root_mean_squared_error(test_y,cv_pred_y) #root mean squared error \n",
    "print(f'Old RMSE: {og_rmse} \\n New RMSE: {new_rmse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('./data/train.csv',index=False)\n",
    "test.to_csv('./data/test.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/model.joblib']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "dump(grid.best_estimator_, './models/model.joblib')"
   ]
  }
 ],
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