07ML_Opt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "937e5dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fa33e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hyperparameter Optimization Toolkit\n",
    "from bayes_opt import BayesianOptimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39068dbb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monomer_ID</th>\n",
       "      <th>smiles</th>\n",
       "      <th>TC</th>\n",
       "      <th>Monomer_length</th>\n",
       "      <th>MW_ratio</th>\n",
       "      <th>Mass_max</th>\n",
       "      <th>Mass_ave</th>\n",
       "      <th>K_bond_min</th>\n",
       "      <th>K_bond_ave</th>\n",
       "      <th>K_ang_ave</th>\n",
       "      <th>...</th>\n",
       "      <th>Mor19</th>\n",
       "      <th>Mor31</th>\n",
       "      <th>SMR_VSA1</th>\n",
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       "      <th>1040</th>\n",
       "      <td>PI1127</td>\n",
       "      <td>*Oc1ccc(C(C)(C)c2ccc(OC(=O)SCCCSC(*)=O)cc2)cc1</td>\n",
       "      <td>0.187003</td>\n",
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       "      <td>8.445870</td>\n",
       "      <td>182.96</td>\n",
       "      <td>358.148750</td>\n",
       "      <td>58.585646</td>\n",
       "      <td>...</td>\n",
       "      <td>1.166168</td>\n",
       "      <td>0.340033</td>\n",
       "      <td>19.432465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.395199</td>\n",
       "      <td>9.589074</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.325336</td>\n",
       "      <td>2.560220</td>\n",
       "      <td>0.834583</td>\n",
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       "      <th>759</th>\n",
       "      <td>PI808</td>\n",
       "      <td>*Oc1c(cc(cc1)C(c1ccc(cc1)C(c1cc(c(cc1)OC(=O)*)...</td>\n",
       "      <td>0.234166</td>\n",
       "      <td>16.84</td>\n",
       "      <td>0.789839</td>\n",
       "      <td>15.999</td>\n",
       "      <td>6.905483</td>\n",
       "      <td>232.52</td>\n",
       "      <td>362.025082</td>\n",
       "      <td>57.530720</td>\n",
       "      <td>...</td>\n",
       "      <td>1.140136</td>\n",
       "      <td>1.159799</td>\n",
       "      <td>14.637928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.075658</td>\n",
       "      <td>4.794537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.817257</td>\n",
       "      <td>7.008897</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>84</th>\n",
       "      <td>PI90</td>\n",
       "      <td>*C(C*)c1c(cccc1)COCCCCC</td>\n",
       "      <td>0.238572</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.137320</td>\n",
       "      <td>15.999</td>\n",
       "      <td>5.837514</td>\n",
       "      <td>218.51</td>\n",
       "      <td>342.141667</td>\n",
       "      <td>53.424955</td>\n",
       "      <td>...</td>\n",
       "      <td>0.931952</td>\n",
       "      <td>0.621401</td>\n",
       "      <td>4.736863</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.733927</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.825916</td>\n",
       "      <td>3.085222</td>\n",
       "      <td>4.769873</td>\n",
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       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>PI1132</td>\n",
       "      <td>*c1ccc(Cc2ccc(N(*)Cc3ccccc3)cc2)cc1</td>\n",
       "      <td>0.238998</td>\n",
       "      <td>10.35</td>\n",
       "      <td>0.667883</td>\n",
       "      <td>14.007</td>\n",
       "      <td>7.141132</td>\n",
       "      <td>250.32</td>\n",
       "      <td>370.125610</td>\n",
       "      <td>59.059072</td>\n",
       "      <td>...</td>\n",
       "      <td>0.332636</td>\n",
       "      <td>0.928172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.690354</td>\n",
       "      <td>5.687386</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.377741</td>\n",
       "      <td>5.591437</td>\n",
       "      <td>1.037233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>PI165</td>\n",
       "      <td>*C(C*)c1ccc(cc1)C(=O)OCC(C)C</td>\n",
       "      <td>0.237524</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.137354</td>\n",
       "      <td>15.999</td>\n",
       "      <td>6.589323</td>\n",
       "      <td>218.51</td>\n",
       "      <td>354.499687</td>\n",
       "      <td>55.940966</td>\n",
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       "      <td>0.671247</td>\n",
       "      <td>0.301739</td>\n",
       "      <td>9.531400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.189736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.487189</td>\n",
       "      <td>2.129772</td>\n",
       "      <td>0.898828</td>\n",
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       "      <th>960</th>\n",
       "      <td>PI1034</td>\n",
       "      <td>*c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(-c5ccc6c(c...</td>\n",
       "      <td>0.291517</td>\n",
       "      <td>19.40</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.999</td>\n",
       "      <td>9.175433</td>\n",
       "      <td>272.66</td>\n",
       "      <td>386.618507</td>\n",
       "      <td>66.540036</td>\n",
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       "      <td>-0.882484</td>\n",
       "      <td>0.684675</td>\n",
       "      <td>28.651875</td>\n",
       "      <td>5.316789</td>\n",
       "      <td>41.431955</td>\n",
       "      <td>5.687386</td>\n",
       "      <td>11.814359</td>\n",
       "      <td>11.383156</td>\n",
       "      <td>3.140823</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>847</th>\n",
       "      <td>PI907</td>\n",
       "      <td>*Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c...</td>\n",
       "      <td>0.165411</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.535101</td>\n",
       "      <td>32.067</td>\n",
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       "      <td>70.708243</td>\n",
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       "      <td>24.265468</td>\n",
       "      <td>-17.571201</td>\n",
       "      <td>-25.513996</td>\n",
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       "      <th>715</th>\n",
       "      <td>PI764</td>\n",
       "      <td>*C(=O)NCCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1</td>\n",
       "      <td>0.262578</td>\n",
       "      <td>15.06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.999</td>\n",
       "      <td>8.824184</td>\n",
       "      <td>232.52</td>\n",
       "      <td>392.005122</td>\n",
       "      <td>65.872290</td>\n",
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       "      <td>1.743724</td>\n",
       "      <td>0.091628</td>\n",
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       "      <th>905</th>\n",
       "      <td>PI969</td>\n",
       "      <td>*C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)...</td>\n",
       "      <td>0.227667</td>\n",
       "      <td>22.24</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.999</td>\n",
       "      <td>8.938690</td>\n",
       "      <td>250.32</td>\n",
       "      <td>381.584875</td>\n",
       "      <td>64.520630</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.349817</td>\n",
       "      <td>1.229677</td>\n",
       "      <td>23.972686</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.043749</td>\n",
       "      <td>11.374773</td>\n",
       "      <td>17.229350</td>\n",
       "      <td>9.799819</td>\n",
       "      <td>8.257228</td>\n",
       "      <td>0.067189</td>\n",
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       "      <th>235</th>\n",
       "      <td>PI241</td>\n",
       "      <td>*C(C*)O</td>\n",
       "      <td>0.438738</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.636859</td>\n",
       "      <td>15.999</td>\n",
       "      <td>6.293286</td>\n",
       "      <td>218.51</td>\n",
       "      <td>349.115714</td>\n",
       "      <td>56.354231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016872</td>\n",
       "      <td>0.126590</td>\n",
       "      <td>5.106527</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.923737</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "<p>840 rows × 56 columns</p>\n",
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      ],
      "text/plain": [
       "     monomer_ID                                             smiles        TC  \\\n",
       "1040     PI1127     *Oc1ccc(C(C)(C)c2ccc(OC(=O)SCCCSC(*)=O)cc2)cc1  0.187003   \n",
       "759       PI808  *Oc1c(cc(cc1)C(c1ccc(cc1)C(c1cc(c(cc1)OC(=O)*)...  0.234166   \n",
       "84         PI90                            *C(C*)c1c(cccc1)COCCCCC  0.238572   \n",
       "1044     PI1132                *c1ccc(Cc2ccc(N(*)Cc3ccccc3)cc2)cc1  0.238998   \n",
       "159       PI165                       *C(C*)c1ccc(cc1)C(=O)OCC(C)C  0.237524   \n",
       "...         ...                                                ...       ...   \n",
       "960      PI1034  *c1ccc(Oc2ccccc2Oc2ccc(N3C(=O)c4ccc(-c5ccc6c(c...  0.291517   \n",
       "847       PI907  *Oc1ccc(S(=O)(=O)c2ccc(Oc3ccc(C(C)(C)c4ccc(*)c...  0.165411   \n",
       "715       PI764    *C(=O)NCCNC(=O)c1ccc(N2C(=O)c3ccc(*)cc3C2=O)cc1  0.262578   \n",
       "905       PI969  *C(=O)c1ccc2c(c1)C(=O)N(c1ccc(C3(c4ccc(N5C(=O)...  0.227667   \n",
       "235       PI241                                            *C(C*)O  0.438738   \n",
       "\n",
       "      Monomer_length  MW_ratio  Mass_max   Mass_ave  K_bond_min  K_bond_ave  \\\n",
       "1040           18.44  0.927781    32.067   8.445870      182.96  358.148750   \n",
       "759            16.84  0.789839    15.999   6.905483      232.52  362.025082   \n",
       "84              2.61  0.137320    15.999   5.837514      218.51  342.141667   \n",
       "1044           10.35  0.667883    14.007   7.141132      250.32  370.125610   \n",
       "159             2.60  0.137354    15.999   6.589323      218.51  354.499687   \n",
       "...              ...       ...       ...        ...         ...         ...   \n",
       "960            19.40  1.000000    15.999   9.175433      272.66  386.618507   \n",
       "847            18.84  0.535101    32.067  10.467243      209.78  363.837179   \n",
       "715            15.06  1.000000    15.999   8.824184      232.52  392.005122   \n",
       "905            22.24  1.000000    15.999   8.938690      250.32  381.584875   \n",
       "235             2.55  0.636859    15.999   6.293286      218.51  349.115714   \n",
       "\n",
       "      K_ang_ave  ...     Mor19     Mor31   SMR_VSA1   SMR_VSA3  SlogP_VSA5  \\\n",
       "1040  58.585646  ...  1.166168  0.340033  19.432465   0.000000   31.395199   \n",
       "759   57.530720  ...  1.140136  1.159799  14.637928   0.000000   61.075658   \n",
       "84    53.424955  ...  0.931952  0.621401   4.736863   0.000000   43.733927   \n",
       "1044  59.059072  ...  0.332636  0.928172   0.000000   0.000000   16.690354   \n",
       "159   55.940966  ...  0.671247  0.301739   9.531400   0.000000   36.189736   \n",
       "...         ...  ...       ...       ...        ...        ...         ...   \n",
       "960   66.540036  ... -0.882484  0.684675  28.651875   5.316789   41.431955   \n",
       "847   70.708243  ... -0.420552  0.835887  81.159223   0.000000   36.101280   \n",
       "715   65.872290  ...  0.259140  0.368768  19.178149  10.633577   31.073966   \n",
       "905   64.520630  ... -0.349817  1.229677  23.972686   0.000000   74.043749   \n",
       "235   56.354231  ... -0.016872  0.126590   5.106527   0.000000    0.000000   \n",
       "\n",
       "      SlogP_VSA10  EState_VSA1  EState_VSA5  VSA_EState4  VSA_EState7  \n",
       "1040     9.589074     0.000000    17.325336     2.560220     0.834583  \n",
       "759      4.794537     0.000000    27.817257     7.008897     0.000000  \n",
       "84       0.000000     0.000000    24.825916     3.085222     4.769873  \n",
       "1044     5.687386     0.000000    22.377741     5.591437     1.037233  \n",
       "159      0.000000     0.000000    12.487189     2.129772     0.898828  \n",
       "...           ...          ...          ...          ...          ...  \n",
       "960      5.687386    11.814359    11.383156     3.140823     0.000000  \n",
       "847     52.684981    90.978736    24.265468   -17.571201   -25.513996  \n",
       "715     10.481923     0.000000     0.000000     1.743724     0.091628  \n",
       "905     11.374773    17.229350     9.799819     8.257228     0.067189  \n",
       "235      0.000000     0.000000     6.923737     0.000000     0.000000  \n",
       "\n",
       "[840 rows x 56 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"./dataset/\"\n",
    "file='Cor_train.pkl'\n",
    "train_df= pd.read_pickle(path+file)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a7c0bed",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monomer_ID</th>\n",
       "      <th>smiles</th>\n",
       "      <th>TC</th>\n",
       "      <th>Monomer_length</th>\n",
       "      <th>MW_ratio</th>\n",
       "      <th>Mass_max</th>\n",
       "      <th>Mass_ave</th>\n",
       "      <th>K_bond_min</th>\n",
       "      <th>K_bond_ave</th>\n",
       "      <th>K_ang_ave</th>\n",
       "      <th>...</th>\n",
       "      <th>Mor19</th>\n",
       "      <th>Mor31</th>\n",
       "      <th>SMR_VSA1</th>\n",
       "      <th>SMR_VSA3</th>\n",
       "      <th>SlogP_VSA5</th>\n",
       "      <th>SlogP_VSA10</th>\n",
       "      <th>EState_VSA1</th>\n",
       "      <th>EState_VSA5</th>\n",
       "      <th>VSA_EState4</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>PI55</td>\n",
       "      <td>*/C=C/CCCCCCCCCC*</td>\n",
       "      <td>0.336325</td>\n",
       "      <td>13.27</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.011</td>\n",
       "      <td>4.891412</td>\n",
       "      <td>232.52</td>\n",
       "      <td>334.952647</td>\n",
       "      <td>49.101667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.418080</td>\n",
       "      <td>0.826599</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.787395</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.366573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.724121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>PI790</td>\n",
       "      <td>*CC1(C)CC(*)(C)C(=O)OC1=O</td>\n",
       "      <td>0.185993</td>\n",
       "      <td>5.09</td>\n",
       "      <td>0.818005</td>\n",
       "      <td>15.999</td>\n",
       "      <td>7.341190</td>\n",
       "      <td>232.52</td>\n",
       "      <td>349.626364</td>\n",
       "      <td>58.537163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988033</td>\n",
       "      <td>0.405348</td>\n",
       "      <td>14.325937</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.268296</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.315069</td>\n",
       "      <td>0.751157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>PI375</td>\n",
       "      <td>*C(C*)OCCOC</td>\n",
       "      <td>0.251789</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0.274694</td>\n",
       "      <td>15.999</td>\n",
       "      <td>6.007824</td>\n",
       "      <td>218.51</td>\n",
       "      <td>327.684118</td>\n",
       "      <td>56.395938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.635721</td>\n",
       "      <td>0.326187</td>\n",
       "      <td>9.473726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.923737</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>PI1071</td>\n",
       "      <td>*C(=O)c1ccc2c(c1)C(=O)N(c1c(C(C)C)cc(Cc3cc(C(C...</td>\n",
       "      <td>0.204283</td>\n",
       "      <td>22.41</td>\n",
       "      <td>0.742111</td>\n",
       "      <td>15.999</td>\n",
       "      <td>7.334730</td>\n",
       "      <td>232.52</td>\n",
       "      <td>364.596105</td>\n",
       "      <td>58.595404</td>\n",
       "      <td>...</td>\n",
       "      <td>1.990247</td>\n",
       "      <td>1.472253</td>\n",
       "      <td>23.972686</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>164.232174</td>\n",
       "      <td>11.374773</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.866187</td>\n",
       "      <td>9.279165</td>\n",
       "      <td>0.674761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>PI684</td>\n",
       "      <td>*c1ccc(Cc2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4...</td>\n",
       "      <td>0.315177</td>\n",
       "      <td>16.51</td>\n",
       "      <td>0.931298</td>\n",
       "      <td>15.999</td>\n",
       "      <td>8.689638</td>\n",
       "      <td>250.32</td>\n",
       "      <td>379.708462</td>\n",
       "      <td>64.016256</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.242217</td>\n",
       "      <td>0.753504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.551078</td>\n",
       "      <td>22.253806</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.118534</td>\n",
       "      <td>34.309983</td>\n",
       "      <td>3.408797</td>\n",
       "      <td>0.778555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>PI637</td>\n",
       "      <td>*CCCCCCCCCCCNC(=O)CCCCC(=O)N*</td>\n",
       "      <td>0.299844</td>\n",
       "      <td>19.90</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.999</td>\n",
       "      <td>5.593491</td>\n",
       "      <td>232.52</td>\n",
       "      <td>346.915472</td>\n",
       "      <td>52.457706</td>\n",
       "      <td>...</td>\n",
       "      <td>2.885457</td>\n",
       "      <td>1.069552</td>\n",
       "      <td>9.589074</td>\n",
       "      <td>11.050456</td>\n",
       "      <td>83.470681</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.366573</td>\n",
       "      <td>2.841408</td>\n",
       "      <td>14.971719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>PI62</td>\n",
       "      <td>*C(CC)C(CC)*</td>\n",
       "      <td>0.250043</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>12.011</td>\n",
       "      <td>4.675667</td>\n",
       "      <td>218.51</td>\n",
       "      <td>327.447778</td>\n",
       "      <td>49.052000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.962662</td>\n",
       "      <td>0.572104</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.689118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.683286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.559537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>PI817</td>\n",
       "      <td>*Oc1c2c(ccc1)c(ccc2)OC(=O)Oc1ccc(cc1)OC(=O)*</td>\n",
       "      <td>0.241563</td>\n",
       "      <td>15.48</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.999</td>\n",
       "      <td>9.478588</td>\n",
       "      <td>357.53</td>\n",
       "      <td>395.132703</td>\n",
       "      <td>69.056379</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.376769</td>\n",
       "      <td>0.246920</td>\n",
       "      <td>28.906191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.589074</td>\n",
       "      <td>6.155366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.846514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>PI963</td>\n",
       "      <td>*c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6c...</td>\n",
       "      <td>0.244103</td>\n",
       "      <td>31.65</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.067</td>\n",
       "      <td>9.582653</td>\n",
       "      <td>209.78</td>\n",
       "      <td>390.896747</td>\n",
       "      <td>67.081307</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.409144</td>\n",
       "      <td>1.287767</td>\n",
       "      <td>41.864209</td>\n",
       "      <td>5.316789</td>\n",
       "      <td>57.353395</td>\n",
       "      <td>5.687386</td>\n",
       "      <td>33.342037</td>\n",
       "      <td>84.179560</td>\n",
       "      <td>1.269442</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>PI60</td>\n",
       "      <td>*/C=C\\CCCC*</td>\n",
       "      <td>0.263603</td>\n",
       "      <td>5.78</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.011</td>\n",
       "      <td>5.134125</td>\n",
       "      <td>232.52</td>\n",
       "      <td>342.639375</td>\n",
       "      <td>49.801267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681728</td>\n",
       "      <td>0.371082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.262465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.262465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.724028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    monomer_ID                                             smiles        TC  \\\n",
       "49        PI55                                  */C=C/CCCCCCCCCC*  0.336325   \n",
       "741      PI790                          *CC1(C)CC(*)(C)C(=O)OC1=O  0.185993   \n",
       "358      PI375                                        *C(C*)OCCOC  0.251789   \n",
       "993     PI1071  *C(=O)c1ccc2c(c1)C(=O)N(c1c(C(C)C)cc(Cc3cc(C(C...  0.204283   \n",
       "639      PI684  *c1ccc(Cc2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4...  0.315177   \n",
       "..         ...                                                ...       ...   \n",
       "604      PI637                      *CCCCCCCCCCCNC(=O)CCCCC(=O)N*  0.299844   \n",
       "56        PI62                                       *C(CC)C(CC)*  0.250043   \n",
       "768      PI817       *Oc1c2c(ccc1)c(ccc2)OC(=O)Oc1ccc(cc1)OC(=O)*  0.241563   \n",
       "899      PI963  *c1ccc(Oc2ccc(S(=O)(=O)c3ccc(Oc4ccc(N5C(=O)c6c...  0.244103   \n",
       "54        PI60                                        */C=C\\CCCC*  0.263603   \n",
       "\n",
       "     Monomer_length  MW_ratio  Mass_max  Mass_ave  K_bond_min  K_bond_ave  \\\n",
       "49            13.27  1.000000    12.011  4.891412      232.52  334.952647   \n",
       "741            5.09  0.818005    15.999  7.341190      232.52  349.626364   \n",
       "358            2.57  0.274694    15.999  6.007824      218.51  327.684118   \n",
       "993           22.41  0.742111    15.999  7.334730      232.52  364.596105   \n",
       "639           16.51  0.931298    15.999  8.689638      250.32  379.708462   \n",
       "..              ...       ...       ...       ...         ...         ...   \n",
       "604           19.90  1.000000    15.999  5.593491      232.52  346.915472   \n",
       "56             2.67  0.333333    12.011  4.675667      218.51  327.447778   \n",
       "768           15.48  1.000000    15.999  9.478588      357.53  395.132703   \n",
       "899           31.65  1.000000    32.067  9.582653      209.78  390.896747   \n",
       "54             5.78  1.000000    12.011  5.134125      232.52  342.639375   \n",
       "\n",
       "     K_ang_ave  ...     Mor19     Mor31   SMR_VSA1   SMR_VSA3  SlogP_VSA5  \\\n",
       "49   49.101667  ...  1.418080  0.826599   0.000000   0.000000   57.787395   \n",
       "741  58.537163  ...  0.988033  0.405348  14.325937   0.000000   20.268296   \n",
       "358  56.395938  ...  0.635721  0.326187   9.473726   0.000000    0.000000   \n",
       "993  58.595404  ...  1.990247  1.472253  23.972686   0.000000  164.232174   \n",
       "639  64.016256  ... -0.242217  0.753504   0.000000   9.551078   22.253806   \n",
       "..         ...  ...       ...       ...        ...        ...         ...   \n",
       "604  52.457706  ...  2.885457  1.069552   9.589074  11.050456   83.470681   \n",
       "56   49.052000  ...  0.962662  0.572104   0.000000   0.000000   26.689118   \n",
       "768  69.056379  ... -0.376769  0.246920  28.906191   0.000000    0.000000   \n",
       "899  67.081307  ... -0.409144  1.287767  41.864209   5.316789   57.353395   \n",
       "54   49.801267  ...  0.681728  0.371082   0.000000   0.000000   19.262465   \n",
       "\n",
       "     SlogP_VSA10  EState_VSA1  EState_VSA5  VSA_EState4  VSA_EState7  \n",
       "49      0.000000     0.000000    51.366573     0.000000    15.724121  \n",
       "741     0.000000     0.000000     0.000000    -0.315069     0.751157  \n",
       "358     0.000000     0.000000     6.923737     0.000000     0.000000  \n",
       "993    11.374773     0.000000    15.866187     9.279165     0.674761  \n",
       "639     0.000000    11.118534    34.309983     3.408797     0.778555  \n",
       "..           ...          ...          ...          ...          ...  \n",
       "604     0.000000     0.000000    51.366573     2.841408    14.971719  \n",
       "56      0.000000     0.000000    25.683286     0.000000     5.559537  \n",
       "768     9.589074     6.155366     0.000000     0.000000    -0.846514  \n",
       "899     5.687386    33.342037    84.179560     1.269442     0.000000  \n",
       "54      0.000000     0.000000    19.262465     0.000000     6.724028  \n",
       "\n",
       "[211 rows x 56 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file1='Cor_test.pkl'\n",
    "test_df= pd.read_pickle(path+file1)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77680d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=train_df.drop(train_df.columns[0:3], axis=1)\n",
    "y_train=train_df[\"TC\"]\n",
    "X_test=test_df.drop(test_df.columns[0:3], axis=1)\n",
    "y_test=test_df[\"TC\"]\n",
    "\n",
    "select_feature=[\n",
    "'Monomer_length',\n",
    " 'MW_ratio',\n",
    " 'Mass_max',\n",
    " 'Mass_ave',\n",
    " 'K_bond_ave',\n",
    " 'K_ang_ave',\n",
    " 'nH',\n",
    " 'AATS0d',\n",
    " 'AATS0Z',\n",
    " 'ATSC2Z',\n",
    " 'ATSC5Z',\n",
    " 'AATSC0Z',\n",
    " 'AATSC2Z',\n",
    " 'BCUTd-1h',\n",
    " 'BCUTZ-1h',\n",
    " 'nHBDon',\n",
    " 'MIC0',\n",
    " 'MIC1',\n",
    " 'Kier3',\n",
    " 'Mor02',\n",
    " 'Mor14',\n",
    " 'Mor19',\n",
    " 'SMR_VSA1',\n",
    " 'SMR_VSA3',\n",
    " 'VSA_EState4']\n",
    "\n",
    "X_selecttrain=X_train[select_feature]\n",
    "X_selecttest=X_test[select_feature]\n",
    "for i in [X_selecttrain,X_selecttest, y_train, y_test]:\n",
    "    i.index = range(i.shape[0])\n",
    "scaler.fit(X_selecttrain)\n",
    "#scaler.fit(Xtest)\n",
    "Xtrain  = scaler.transform(X_selecttrain)\n",
    "Xtest = scaler.transform(X_selecttest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb1ba557",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4928d29c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Definition of RF model for hyperparameters optimization\n",
    "def RF(n_estimator,max_depths,min_samples_split,min_samples_leaf):\n",
    "    n_estimator = int(n_estimator)\n",
    "    max_depths = int(max_depths)\n",
    "    min_samples_split=int(np.round(min_samples_split))\n",
    "    min_samples_leaf=int(np.round(min_samples_leaf))\n",
    "    rfg = RandomForestRegressor(n_estimators = n_estimator, max_features='auto',random_state=1, max_depth = max_depths,min_samples_split=min_samples_split,min_samples_leaf=min_samples_leaf)\n",
    "    rfg.fit(Xtrain,y_train.values.ravel())\n",
    "    res = rfg.predict(Xtest)\n",
    "    print(\"Training set score: %f\" % rfg.score(Xtrain,y_train))\n",
    "    print(\"Test set score: %f\" % rfg.score(Xtest,y_test))\n",
    "    error1=rfg.score(Xtrain,y_train)\n",
    "    error2=rfg.score(Xtest,y_test)\n",
    "    error=error2\n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c327ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Definition of KRR model for hyperparameters optimization\n",
    "def KRR(kernel_chose,alpha,gamma,degree,coef0):\n",
    "    if kernel_chose<=1:\n",
    "        kernel=\"rbf\"\n",
    "    elif kernel_chose>1 and kernel_chose<=2:\n",
    "        kernel='laplacian'\n",
    "    elif kernel_chose>2 and kernel_chose<=3:\n",
    "        kernel='polynomial'\n",
    "    else:\n",
    "        kernel='sigmoid'\n",
    "    alpha=round(alpha,5)\n",
    "    gamma==round(gamma,5)\n",
    "    degree=round(degree)\n",
    "    coef0=round(coef0,1)\n",
    "    model=KernelRidge(kernel=kernel,alpha=alpha,gamma=gamma,degree=degree,coef0=coef0)\n",
    "    model.fit(Xtrain,y_train.values.ravel())\n",
    "    res = model.predict(Xtest)\n",
    "    print (\"kernel_chose:%s\" %kernel)\n",
    "    print(\"Training set score: %f\" % model.score(Xtrain,y_train))\n",
    "    print(\"Test set score: %f\" % model.score(Xtest,y_test))\n",
    "    error1=model.score(Xtrain,y_train)\n",
    "    error2=model.score(Xtest,y_test)\n",
    "    error=error2\n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e519def2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Definition of MLP model for hyperparameters optimization\n",
    "def MLP(alphas,node,number_hidden_layer,solver_chose):\n",
    "    randomseed = np.random.seed(0)\n",
    "    if solver_chose<=1:\n",
    "        solver='adam'\n",
    "    elif solver_chose>1 and solver_chose<=2:\n",
    "        solver='lbfgs'\n",
    "    else:\n",
    "        solver='sgd'\n",
    "    alphas=alphas\n",
    "    nodes=int(node)\n",
    "    number_layer=int(number_hidden_layer)\n",
    "    layer_sizes=[nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes][0:number_layer:1]\n",
    "    print(layer_sizes)\n",
    "    print(alphas)\n",
    "    clf = MLPRegressor(solver=solver,alpha=alphas,\n",
    "                       hidden_layer_sizes=layer_sizes, max_iter=1000,\n",
    "                       verbose=False, tol=0.0001,random_state=randomseed)\n",
    "    clf.fit(Xtrain,y_train.values.ravel())\n",
    "    res = clf.predict(Xtest)\n",
    "    print(\"solver: %s\" % solver)\n",
    "    print(\"Training set score: %f\" % clf.score(Xtrain,y_train))\n",
    "    print(\"Test set score: %f\" % clf.score(Xtest,y_test))\n",
    "    error=clf.score(Xtest,y_test)\n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fe8f6f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | max_de... | min_sa... | min_sa... | n_esti... |\n",
      "-------------------------------------------------------------------------\n",
      "Training set score: 0.925725\n",
      "Test set score: 0.758280\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.7583  \u001b[0m | \u001b[0m 16.54   \u001b[0m | \u001b[0m 1.233   \u001b[0m | \u001b[0m 6.397   \u001b[0m | \u001b[0m 876.3   \u001b[0m |\n",
      "Training set score: 0.854306\n",
      "Test set score: 0.773610\n",
      "| \u001b[95m 2       \u001b[0m | \u001b[95m 0.7736  \u001b[0m | \u001b[95m 16.31   \u001b[0m | \u001b[95m 3.973   \u001b[0m | \u001b[95m 3.637   \u001b[0m | \u001b[95m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.874939\n",
      "Test set score: 0.773846\n",
      "| \u001b[95m 3       \u001b[0m | \u001b[95m 0.7738  \u001b[0m | \u001b[95m 14.49   \u001b[0m | \u001b[95m 3.401   \u001b[0m | \u001b[95m 6.969   \u001b[0m | \u001b[95m 1.063e+0\u001b[0m |\n",
      "Training set score: 0.806154\n",
      "Test set score: 0.758151\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 0.7582  \u001b[0m | \u001b[0m 12.02   \u001b[0m | \u001b[0m 5.622   \u001b[0m | \u001b[0m 3.476   \u001b[0m | \u001b[0m 1.573e+0\u001b[0m |\n",
      "Training set score: 0.826086\n",
      "Test set score: 0.763869\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.7639  \u001b[0m | \u001b[0m 22.81   \u001b[0m | \u001b[0m 5.448   \u001b[0m | \u001b[0m 8.772   \u001b[0m | \u001b[0m 168.5   \u001b[0m |\n",
      "Training set score: 0.907341\n",
      "Test set score: 0.768182\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.7682  \u001b[0m | \u001b[0m 17.58   \u001b[0m | \u001b[0m 1.588   \u001b[0m | \u001b[0m 5.425   \u001b[0m | \u001b[0m 202.1   \u001b[0m |\n",
      "Training set score: 0.804076\n",
      "Test set score: 0.758944\n",
      "| \u001b[0m 7       \u001b[0m | \u001b[0m 0.7589  \u001b[0m | \u001b[0m 11.91   \u001b[0m | \u001b[0m 6.371   \u001b[0m | \u001b[0m 3.808   \u001b[0m | \u001b[0m 222.8   \u001b[0m |\n",
      "Training set score: 0.851817\n",
      "Test set score: 0.772476\n",
      "| \u001b[0m 8       \u001b[0m | \u001b[0m 0.7725  \u001b[0m | \u001b[0m 13.3    \u001b[0m | \u001b[0m 4.148   \u001b[0m | \u001b[0m 5.742   \u001b[0m | \u001b[0m 411.5   \u001b[0m |\n",
      "Training set score: 0.828768\n",
      "Test set score: 0.766651\n",
      "| \u001b[0m 9       \u001b[0m | \u001b[0m 0.7667  \u001b[0m | \u001b[0m 19.61   \u001b[0m | \u001b[0m 5.348   \u001b[0m | \u001b[0m 6.042   \u001b[0m | \u001b[0m 779.9   \u001b[0m |\n",
      "Training set score: 0.807297\n",
      "Test set score: 0.759446\n",
      "| \u001b[0m 10      \u001b[0m | \u001b[0m 0.7594  \u001b[0m | \u001b[0m 21.9    \u001b[0m | \u001b[0m 6.22    \u001b[0m | \u001b[0m 3.298   \u001b[0m | \u001b[0m 1.404e+0\u001b[0m |\n",
      "Training set score: 0.881773\n",
      "Test set score: 0.774755\n",
      "| \u001b[95m 11      \u001b[0m | \u001b[95m 0.7748  \u001b[0m | \u001b[95m 14.74   \u001b[0m | \u001b[95m 2.847   \u001b[0m | \u001b[95m 6.424   \u001b[0m | \u001b[95m 1.063e+0\u001b[0m |\n",
      "Training set score: 0.916875\n",
      "Test set score: 0.772326\n",
      "| \u001b[0m 12      \u001b[0m | \u001b[0m 0.7723  \u001b[0m | \u001b[0m 21.15   \u001b[0m | \u001b[0m 1.652   \u001b[0m | \u001b[0m 2.752   \u001b[0m | \u001b[0m 1.067e+0\u001b[0m |\n",
      "Training set score: 0.917356\n",
      "Test set score: 0.754031\n",
      "| \u001b[0m 13      \u001b[0m | \u001b[0m 0.754   \u001b[0m | \u001b[0m 10.34   \u001b[0m | \u001b[0m 1.301   \u001b[0m | \u001b[0m 4.586   \u001b[0m | \u001b[0m 1.071e+0\u001b[0m |\n",
      "Training set score: 0.910272\n",
      "Test set score: 0.772771\n",
      "| \u001b[0m 14      \u001b[0m | \u001b[0m 0.7728  \u001b[0m | \u001b[0m 19.3    \u001b[0m | \u001b[0m 2.409   \u001b[0m | \u001b[0m 5.157   \u001b[0m | \u001b[0m 1.062e+0\u001b[0m |\n",
      "Training set score: 0.807080\n",
      "Test set score: 0.759744\n",
      "| \u001b[0m 15      \u001b[0m | \u001b[0m 0.7597  \u001b[0m | \u001b[0m 15.95   \u001b[0m | \u001b[0m 5.806   \u001b[0m | \u001b[0m 2.173   \u001b[0m | \u001b[0m 1.063e+0\u001b[0m |\n",
      "Training set score: 0.828950\n",
      "Test set score: 0.765973\n",
      "| \u001b[0m 16      \u001b[0m | \u001b[0m 0.766   \u001b[0m | \u001b[0m 18.91   \u001b[0m | \u001b[0m 4.704   \u001b[0m | \u001b[0m 9.713   \u001b[0m | \u001b[0m 1.063e+0\u001b[0m |\n",
      "Training set score: 0.916313\n",
      "Test set score: 0.772857\n",
      "| \u001b[0m 17      \u001b[0m | \u001b[0m 0.7729  \u001b[0m | \u001b[0m 15.8    \u001b[0m | \u001b[0m 1.6     \u001b[0m | \u001b[0m 2.728   \u001b[0m | \u001b[0m 1.245e+0\u001b[0m |\n",
      "Training set score: 0.882925\n",
      "Test set score: 0.777199\n",
      "| \u001b[95m 18      \u001b[0m | \u001b[95m 0.7772  \u001b[0m | \u001b[95m 19.28   \u001b[0m | \u001b[95m 2.989   \u001b[0m | \u001b[95m 5.817   \u001b[0m | \u001b[95m 1.244e+0\u001b[0m |\n",
      "Training set score: 0.829445\n",
      "Test set score: 0.766446\n",
      "| \u001b[0m 19      \u001b[0m | \u001b[0m 0.7664  \u001b[0m | \u001b[0m 21.82   \u001b[0m | \u001b[0m 5.07    \u001b[0m | \u001b[0m 2.925   \u001b[0m | \u001b[0m 1.245e+0\u001b[0m |\n",
      "Training set score: 0.848759\n",
      "Test set score: 0.772193\n",
      "| \u001b[0m 20      \u001b[0m | \u001b[0m 0.7722  \u001b[0m | \u001b[0m 19.28   \u001b[0m | \u001b[0m 3.997   \u001b[0m | \u001b[0m 9.477   \u001b[0m | \u001b[0m 1.244e+0\u001b[0m |\n",
      "Training set score: 0.895372\n",
      "Test set score: 0.771363\n",
      "| \u001b[0m 21      \u001b[0m | \u001b[0m 0.7714  \u001b[0m | \u001b[0m 17.75   \u001b[0m | \u001b[0m 1.836   \u001b[0m | \u001b[0m 7.134   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.882867\n",
      "Test set score: 0.777068\n",
      "| \u001b[0m 22      \u001b[0m | \u001b[0m 0.7771  \u001b[0m | \u001b[0m 21.9    \u001b[0m | \u001b[0m 2.773   \u001b[0m | \u001b[0m 5.014   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.854342\n",
      "Test set score: 0.773454\n",
      "| \u001b[0m 23      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 20.54   \u001b[0m | \u001b[0m 3.77    \u001b[0m | \u001b[0m 5.288   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.807533\n",
      "Test set score: 0.760167\n",
      "| \u001b[0m 24      \u001b[0m | \u001b[0m 0.7602  \u001b[0m | \u001b[0m 23.89   \u001b[0m | \u001b[0m 5.653   \u001b[0m | \u001b[0m 8.316   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.926866\n",
      "Test set score: 0.760673\n",
      "| \u001b[0m 25      \u001b[0m | \u001b[0m 0.7607  \u001b[0m | \u001b[0m 21.1    \u001b[0m | \u001b[0m 1.056   \u001b[0m | \u001b[0m 6.206   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.882848\n",
      "Test set score: 0.777029\n",
      "| \u001b[0m 26      \u001b[0m | \u001b[0m 0.777   \u001b[0m | \u001b[0m 17.37   \u001b[0m | \u001b[0m 2.878   \u001b[0m | \u001b[0m 4.889   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.882858\n",
      "Test set score: 0.777062\n",
      "| \u001b[0m 27      \u001b[0m | \u001b[0m 0.7771  \u001b[0m | \u001b[0m 22.13   \u001b[0m | \u001b[0m 3.037   \u001b[0m | \u001b[0m 2.835   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.882858\n",
      "Test set score: 0.777068\n",
      "| \u001b[0m 28      \u001b[0m | \u001b[0m 0.7771  \u001b[0m | \u001b[0m 18.62   \u001b[0m | \u001b[0m 3.078   \u001b[0m | \u001b[0m 4.446   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.854334\n",
      "Test set score: 0.773443\n",
      "| \u001b[0m 29      \u001b[0m | \u001b[0m 0.7734  \u001b[0m | \u001b[0m 24.95   \u001b[0m | \u001b[0m 4.491   \u001b[0m | \u001b[0m 3.507   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.807537\n",
      "Test set score: 0.760141\n",
      "| \u001b[0m 30      \u001b[0m | \u001b[0m 0.7601  \u001b[0m | \u001b[0m 17.6    \u001b[0m | \u001b[0m 6.042   \u001b[0m | \u001b[0m 5.827   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.882909\n",
      "Test set score: 0.777161\n",
      "| \u001b[0m 31      \u001b[0m | \u001b[0m 0.7772  \u001b[0m | \u001b[0m 19.38   \u001b[0m | \u001b[0m 2.886   \u001b[0m | \u001b[0m 3.305   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.829356\n",
      "Test set score: 0.766307\n",
      "| \u001b[0m 32      \u001b[0m | \u001b[0m 0.7663  \u001b[0m | \u001b[0m 18.36   \u001b[0m | \u001b[0m 5.345   \u001b[0m | \u001b[0m 2.386   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.854336\n",
      "Test set score: 0.773484\n",
      "| \u001b[0m 33      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 21.81   \u001b[0m | \u001b[0m 3.729   \u001b[0m | \u001b[0m 3.777   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.910168\n",
      "Test set score: 0.773314\n",
      "| \u001b[0m 34      \u001b[0m | \u001b[0m 0.7733  \u001b[0m | \u001b[0m 24.05   \u001b[0m | \u001b[0m 2.031   \u001b[0m | \u001b[0m 5.251   \u001b[0m | \u001b[0m 1.237e+0\u001b[0m |\n",
      "Training set score: 0.854336\n",
      "Test set score: 0.773553\n",
      "| \u001b[0m 35      \u001b[0m | \u001b[0m 0.7736  \u001b[0m | \u001b[0m 19.88   \u001b[0m | \u001b[0m 4.342   \u001b[0m | \u001b[0m 5.491   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.916985\n",
      "Test set score: 0.772465\n",
      "| \u001b[0m 36      \u001b[0m | \u001b[0m 0.7725  \u001b[0m | \u001b[0m 24.06   \u001b[0m | \u001b[0m 1.909   \u001b[0m | \u001b[0m 3.261   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.916574\n",
      "Test set score: 0.758510\n",
      "| \u001b[0m 37      \u001b[0m | \u001b[0m 0.7585  \u001b[0m | \u001b[0m 13.9    \u001b[0m | \u001b[0m 1.001   \u001b[0m | \u001b[0m 7.097   \u001b[0m | \u001b[0m 1.062e+0\u001b[0m |\n",
      "Training set score: 0.853518\n",
      "Test set score: 0.772634\n",
      "| \u001b[0m 38      \u001b[0m | \u001b[0m 0.7726  \u001b[0m | \u001b[0m 15.28   \u001b[0m | \u001b[0m 3.784   \u001b[0m | \u001b[0m 6.789   \u001b[0m | \u001b[0m 1.065e+0\u001b[0m |\n",
      "Training set score: 0.949397\n",
      "Test set score: 0.761444\n",
      "| \u001b[0m 39      \u001b[0m | \u001b[0m 0.7614  \u001b[0m | \u001b[0m 19.24   \u001b[0m | \u001b[0m 1.485   \u001b[0m | \u001b[0m 2.258   \u001b[0m | \u001b[0m 1.244e+0\u001b[0m |\n",
      "Training set score: 0.882844\n",
      "Test set score: 0.777171\n",
      "| \u001b[0m 40      \u001b[0m | \u001b[0m 0.7772  \u001b[0m | \u001b[0m 20.33   \u001b[0m | \u001b[0m 2.571   \u001b[0m | \u001b[0m 2.436   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.949256\n",
      "Test set score: 0.758824\n",
      "| \u001b[0m 41      \u001b[0m | \u001b[0m 0.7588  \u001b[0m | \u001b[0m 18.92   \u001b[0m | \u001b[0m 1.39    \u001b[0m | \u001b[0m 2.108   \u001b[0m | \u001b[0m 1.238e+0\u001b[0m |\n",
      "Training set score: 0.895164\n",
      "Test set score: 0.770838\n",
      "| \u001b[0m 42      \u001b[0m | \u001b[0m 0.7708  \u001b[0m | \u001b[0m 18.0    \u001b[0m | \u001b[0m 2.24    \u001b[0m | \u001b[0m 6.997   \u001b[0m | \u001b[0m 1.064e+0\u001b[0m |\n",
      "Training set score: 0.875942\n",
      "Test set score: 0.775677\n",
      "| \u001b[0m 43      \u001b[0m | \u001b[0m 0.7757  \u001b[0m | \u001b[0m 23.36   \u001b[0m | \u001b[0m 2.654   \u001b[0m | \u001b[0m 6.744   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.910169\n",
      "Test set score: 0.772935\n",
      "| \u001b[0m 44      \u001b[0m | \u001b[0m 0.7729  \u001b[0m | \u001b[0m 21.42   \u001b[0m | \u001b[0m 1.698   \u001b[0m | \u001b[0m 5.187   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.881223\n",
      "Test set score: 0.776355\n",
      "| \u001b[0m 45      \u001b[0m | \u001b[0m 0.7764  \u001b[0m | \u001b[0m 13.47   \u001b[0m | \u001b[0m 2.547   \u001b[0m | \u001b[0m 4.499   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.876888\n",
      "Test set score: 0.774427\n",
      "| \u001b[0m 46      \u001b[0m | \u001b[0m 0.7744  \u001b[0m | \u001b[0m 11.16   \u001b[0m | \u001b[0m 2.54    \u001b[0m | \u001b[0m 3.86    \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.872860\n",
      "Test set score: 0.773810\n",
      "| \u001b[0m 47      \u001b[0m | \u001b[0m 0.7738  \u001b[0m | \u001b[0m 12.68   \u001b[0m | \u001b[0m 2.917   \u001b[0m | \u001b[0m 6.811   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.931764\n",
      "Test set score: 0.759046\n",
      "| \u001b[0m 48      \u001b[0m | \u001b[0m 0.759   \u001b[0m | \u001b[0m 14.94   \u001b[0m | \u001b[0m 1.357   \u001b[0m | \u001b[0m 5.416   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.888697\n",
      "Test set score: 0.769949\n",
      "| \u001b[0m 49      \u001b[0m | \u001b[0m 0.7699  \u001b[0m | \u001b[0m 22.78   \u001b[0m | \u001b[0m 2.075   \u001b[0m | \u001b[0m 7.774   \u001b[0m | \u001b[0m 1.237e+0\u001b[0m |\n",
      "Training set score: 0.852100\n",
      "Test set score: 0.772156\n",
      "| \u001b[0m 50      \u001b[0m | \u001b[0m 0.7722  \u001b[0m | \u001b[0m 12.76   \u001b[0m | \u001b[0m 4.42    \u001b[0m | \u001b[0m 5.772   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.882867\n",
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      "| \u001b[0m 51      \u001b[0m | \u001b[0m 0.7771  \u001b[0m | \u001b[0m 21.09   \u001b[0m | \u001b[0m 3.294   \u001b[0m | \u001b[0m 2.309   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.829392\n",
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      "| \u001b[0m 52      \u001b[0m | \u001b[0m 0.7663  \u001b[0m | \u001b[0m 24.08   \u001b[0m | \u001b[0m 4.764   \u001b[0m | \u001b[0m 4.728   \u001b[0m | \u001b[0m 1.237e+0\u001b[0m |\n",
      "Training set score: 0.854357\n",
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      "| \u001b[0m 53      \u001b[0m | \u001b[0m 0.7736  \u001b[0m | \u001b[0m 18.65   \u001b[0m | \u001b[0m 3.747   \u001b[0m | \u001b[0m 4.388   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.919833\n",
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      "| \u001b[0m 54      \u001b[0m | \u001b[0m 0.7617  \u001b[0m | \u001b[0m 24.12   \u001b[0m | \u001b[0m 1.258   \u001b[0m | \u001b[0m 7.17    \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.882790\n",
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      "| \u001b[0m 55      \u001b[0m | \u001b[0m 0.777   \u001b[0m | \u001b[0m 17.57   \u001b[0m | \u001b[0m 3.041   \u001b[0m | \u001b[0m 5.695   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.875952\n",
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      "| \u001b[0m 56      \u001b[0m | \u001b[0m 0.7756  \u001b[0m | \u001b[0m 18.93   \u001b[0m | \u001b[0m 3.288   \u001b[0m | \u001b[0m 6.791   \u001b[0m | \u001b[0m 1.238e+0\u001b[0m |\n",
      "Training set score: 0.902812\n",
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      "| \u001b[0m 57      \u001b[0m | \u001b[0m 0.7711  \u001b[0m | \u001b[0m 18.12   \u001b[0m | \u001b[0m 2.254   \u001b[0m | \u001b[0m 6.233   \u001b[0m | \u001b[0m 1.245e+0\u001b[0m |\n",
      "Training set score: 0.923533\n",
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      "| \u001b[0m 58      \u001b[0m | \u001b[0m 0.7571  \u001b[0m | \u001b[0m 11.46   \u001b[0m | \u001b[0m 1.444   \u001b[0m | \u001b[0m 4.69    \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.905642\n",
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      "| \u001b[0m 59      \u001b[0m | \u001b[0m 0.7712  \u001b[0m | \u001b[0m 12.7    \u001b[0m | \u001b[0m 1.512   \u001b[0m | \u001b[0m 4.782   \u001b[0m | \u001b[0m 1.238e+0\u001b[0m |\n",
      "Training set score: 0.829369\n",
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      "| \u001b[0m 60      \u001b[0m | \u001b[0m 0.7664  \u001b[0m | \u001b[0m 24.3    \u001b[0m | \u001b[0m 4.755   \u001b[0m | \u001b[0m 3.365   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.854336\n",
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      "| \u001b[0m 61      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 23.88   \u001b[0m | \u001b[0m 3.546   \u001b[0m | \u001b[0m 5.464   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.854272\n",
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      "| \u001b[0m 62      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 16.18   \u001b[0m | \u001b[0m 4.099   \u001b[0m | \u001b[0m 6.68    \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.823707\n",
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      "| \u001b[0m 63      \u001b[0m | \u001b[0m 0.764   \u001b[0m | \u001b[0m 10.77   \u001b[0m | \u001b[0m 4.902   \u001b[0m | \u001b[0m 4.366   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.945654\n",
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      "| \u001b[0m 64      \u001b[0m | \u001b[0m 0.7599  \u001b[0m | \u001b[0m 21.59   \u001b[0m | \u001b[0m 1.319   \u001b[0m | \u001b[0m 2.781   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.854340\n",
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      "| \u001b[0m 65      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 20.26   \u001b[0m | \u001b[0m 3.74    \u001b[0m | \u001b[0m 4.447   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.853353\n",
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      "| \u001b[0m 66      \u001b[0m | \u001b[0m 0.7725  \u001b[0m | \u001b[0m 13.31   \u001b[0m | \u001b[0m 3.532   \u001b[0m | \u001b[0m 3.219   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.882672\n",
      "Test set score: 0.776658\n",
      "| \u001b[0m 67      \u001b[0m | \u001b[0m 0.7767  \u001b[0m | \u001b[0m 16.17   \u001b[0m | \u001b[0m 2.749   \u001b[0m | \u001b[0m 3.834   \u001b[0m | \u001b[0m 1.244e+0\u001b[0m |\n",
      "Training set score: 0.882882\n",
      "Test set score: 0.777162\n",
      "| \u001b[0m 68      \u001b[0m | \u001b[0m 0.7772  \u001b[0m | \u001b[0m 19.25   \u001b[0m | \u001b[0m 3.059   \u001b[0m | \u001b[0m 2.756   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.916830\n",
      "Test set score: 0.772642\n",
      "| \u001b[0m 69      \u001b[0m | \u001b[0m 0.7726  \u001b[0m | \u001b[0m 18.0    \u001b[0m | \u001b[0m 2.411   \u001b[0m | \u001b[0m 3.271   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.853515\n",
      "Test set score: 0.772501\n",
      "| \u001b[0m 70      \u001b[0m | \u001b[0m 0.7725  \u001b[0m | \u001b[0m 15.7    \u001b[0m | \u001b[0m 4.059   \u001b[0m | \u001b[0m 6.904   \u001b[0m | \u001b[0m 1.064e+0\u001b[0m |\n",
      "Training set score: 0.888645\n",
      "Test set score: 0.770045\n",
      "| \u001b[0m 71      \u001b[0m | \u001b[0m 0.77    \u001b[0m | \u001b[0m 18.98   \u001b[0m | \u001b[0m 2.434   \u001b[0m | \u001b[0m 7.54    \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.854323\n",
      "Test set score: 0.773368\n",
      "| \u001b[0m 72      \u001b[0m | \u001b[0m 0.7734  \u001b[0m | \u001b[0m 19.86   \u001b[0m | \u001b[0m 4.071   \u001b[0m | \u001b[0m 7.106   \u001b[0m | \u001b[0m 1.237e+0\u001b[0m |\n",
      "Training set score: 0.882910\n",
      "Test set score: 0.777106\n",
      "| \u001b[0m 73      \u001b[0m | \u001b[0m 0.7771  \u001b[0m | \u001b[0m 21.2    \u001b[0m | \u001b[0m 3.372   \u001b[0m | \u001b[0m 4.469   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.829363\n",
      "Test set score: 0.766328\n",
      "| \u001b[0m 74      \u001b[0m | \u001b[0m 0.7663  \u001b[0m | \u001b[0m 19.0    \u001b[0m | \u001b[0m 4.611   \u001b[0m | \u001b[0m 6.745   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.926551\n",
      "Test set score: 0.761114\n",
      "| \u001b[0m 75      \u001b[0m | \u001b[0m 0.7611  \u001b[0m | \u001b[0m 17.46   \u001b[0m | \u001b[0m 1.498   \u001b[0m | \u001b[0m 6.429   \u001b[0m | \u001b[0m 1.238e+0\u001b[0m |\n",
      "Training set score: 0.881928\n",
      "Test set score: 0.776101\n",
      "| \u001b[0m 76      \u001b[0m | \u001b[0m 0.7761  \u001b[0m | \u001b[0m 14.09   \u001b[0m | \u001b[0m 3.202   \u001b[0m | \u001b[0m 5.631   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.881138\n",
      "Test set score: 0.775107\n",
      "| \u001b[0m 77      \u001b[0m | \u001b[0m 0.7751  \u001b[0m | \u001b[0m 13.46   \u001b[0m | \u001b[0m 3.272   \u001b[0m | \u001b[0m 5.269   \u001b[0m | \u001b[0m 1.065e+0\u001b[0m |\n",
      "Training set score: 0.882869\n",
      "Test set score: 0.777149\n",
      "| \u001b[0m 78      \u001b[0m | \u001b[0m 0.7771  \u001b[0m | \u001b[0m 19.6    \u001b[0m | \u001b[0m 2.7     \u001b[0m | \u001b[0m 4.261   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.854414\n",
      "Test set score: 0.773661\n",
      "| \u001b[0m 79      \u001b[0m | \u001b[0m 0.7737  \u001b[0m | \u001b[0m 20.16   \u001b[0m | \u001b[0m 4.201   \u001b[0m | \u001b[0m 4.854   \u001b[0m | \u001b[0m 1.245e+0\u001b[0m |\n",
      "Training set score: 0.916905\n",
      "Test set score: 0.772696\n",
      "| \u001b[0m 80      \u001b[0m | \u001b[0m 0.7727  \u001b[0m | \u001b[0m 19.05   \u001b[0m | \u001b[0m 1.862   \u001b[0m | \u001b[0m 4.442   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.910055\n",
      "Test set score: 0.773468\n",
      "| \u001b[0m 81      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 17.38   \u001b[0m | \u001b[0m 2.398   \u001b[0m | \u001b[0m 4.54    \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.882522\n",
      "Test set score: 0.776773\n",
      "| \u001b[0m 82      \u001b[0m | \u001b[0m 0.7768  \u001b[0m | \u001b[0m 15.01   \u001b[0m | \u001b[0m 3.484   \u001b[0m | \u001b[0m 2.281   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.874161\n",
      "Test set score: 0.773554\n",
      "| \u001b[0m 83      \u001b[0m | \u001b[0m 0.7736  \u001b[0m | \u001b[0m 13.28   \u001b[0m | \u001b[0m 3.391   \u001b[0m | \u001b[0m 7.155   \u001b[0m | \u001b[0m 1.065e+0\u001b[0m |\n",
      "Training set score: 0.854396\n",
      "Test set score: 0.773624\n",
      "| \u001b[0m 84      \u001b[0m | \u001b[0m 0.7736  \u001b[0m | \u001b[0m 20.27   \u001b[0m | \u001b[0m 4.199   \u001b[0m | \u001b[0m 6.308   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.901907\n",
      "Test set score: 0.771552\n",
      "| \u001b[0m 85      \u001b[0m | \u001b[0m 0.7716  \u001b[0m | \u001b[0m 15.27   \u001b[0m | \u001b[0m 1.714   \u001b[0m | \u001b[0m 6.443   \u001b[0m | \u001b[0m 1.065e+0\u001b[0m |\n",
      "Training set score: 0.829423\n",
      "Test set score: 0.766565\n",
      "| \u001b[0m 86      \u001b[0m | \u001b[0m 0.7666  \u001b[0m | \u001b[0m 15.11   \u001b[0m | \u001b[0m 4.509   \u001b[0m | \u001b[0m 3.173   \u001b[0m | \u001b[0m 1.246e+0\u001b[0m |\n",
      "Training set score: 0.854342\n",
      "Test set score: 0.773453\n",
      "| \u001b[0m 87      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 22.96   \u001b[0m | \u001b[0m 4.224   \u001b[0m | \u001b[0m 6.966   \u001b[0m | \u001b[0m 1.238e+0\u001b[0m |\n",
      "Training set score: 0.915302\n",
      "Test set score: 0.773139\n",
      "| \u001b[0m 88      \u001b[0m | \u001b[0m 0.7731  \u001b[0m | \u001b[0m 14.16   \u001b[0m | \u001b[0m 1.907   \u001b[0m | \u001b[0m 2.355   \u001b[0m | \u001b[0m 1.243e+0\u001b[0m |\n",
      "Training set score: 0.882837\n",
      "Test set score: 0.777141\n",
      "| \u001b[0m 89      \u001b[0m | \u001b[0m 0.7771  \u001b[0m | \u001b[0m 20.38   \u001b[0m | \u001b[0m 3.449   \u001b[0m | \u001b[0m 2.69    \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.853752\n",
      "Test set score: 0.772650\n",
      "| \u001b[0m 90      \u001b[0m | \u001b[0m 0.7726  \u001b[0m | \u001b[0m 21.92   \u001b[0m | \u001b[0m 3.917   \u001b[0m | \u001b[0m 4.901   \u001b[0m | \u001b[0m 1.062e+0\u001b[0m |\n",
      "Training set score: 0.881928\n",
      "Test set score: 0.776101\n",
      "| \u001b[0m 91      \u001b[0m | \u001b[0m 0.7761  \u001b[0m | \u001b[0m 14.43   \u001b[0m | \u001b[0m 2.596   \u001b[0m | \u001b[0m 3.761   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.853353\n",
      "Test set score: 0.772463\n",
      "| \u001b[0m 92      \u001b[0m | \u001b[0m 0.7725  \u001b[0m | \u001b[0m 13.74   \u001b[0m | \u001b[0m 3.826   \u001b[0m | \u001b[0m 8.274   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.828420\n",
      "Test set score: 0.766234\n",
      "| \u001b[0m 93      \u001b[0m | \u001b[0m 0.7662  \u001b[0m | \u001b[0m 13.39   \u001b[0m | \u001b[0m 4.574   \u001b[0m | \u001b[0m 7.201   \u001b[0m | \u001b[0m 1.067e+0\u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.854372\n",
      "Test set score: 0.773790\n",
      "| \u001b[0m 94      \u001b[0m | \u001b[0m 0.7738  \u001b[0m | \u001b[0m 17.33   \u001b[0m | \u001b[0m 3.822   \u001b[0m | \u001b[0m 3.045   \u001b[0m | \u001b[0m 1.244e+0\u001b[0m |\n",
      "Training set score: 0.882686\n",
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      "| \u001b[0m 95      \u001b[0m | \u001b[0m 0.7755  \u001b[0m | \u001b[0m 21.08   \u001b[0m | \u001b[0m 3.229   \u001b[0m | \u001b[0m 3.612   \u001b[0m | \u001b[0m 1.06e+03\u001b[0m |\n",
      "Training set score: 0.828958\n",
      "Test set score: 0.765998\n",
      "| \u001b[0m 96      \u001b[0m | \u001b[0m 0.766   \u001b[0m | \u001b[0m 22.39   \u001b[0m | \u001b[0m 5.224   \u001b[0m | \u001b[0m 3.886   \u001b[0m | \u001b[0m 1.06e+03\u001b[0m |\n",
      "Training set score: 0.916879\n",
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      "| \u001b[0m 97      \u001b[0m | \u001b[0m 0.7722  \u001b[0m | \u001b[0m 21.21   \u001b[0m | \u001b[0m 1.63    \u001b[0m | \u001b[0m 2.858   \u001b[0m | \u001b[0m 1.059e+0\u001b[0m |\n",
      "Training set score: 0.882653\n",
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      "| \u001b[0m 98      \u001b[0m | \u001b[0m 0.7755  \u001b[0m | \u001b[0m 20.26   \u001b[0m | \u001b[0m 3.34    \u001b[0m | \u001b[0m 2.659   \u001b[0m | \u001b[0m 1.058e+0\u001b[0m |\n",
      "Training set score: 0.940111\n",
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      "| \u001b[0m 99      \u001b[0m | \u001b[0m 0.7626  \u001b[0m | \u001b[0m 19.68   \u001b[0m | \u001b[0m 1.004   \u001b[0m | \u001b[0m 4.409   \u001b[0m | \u001b[0m 1.06e+03\u001b[0m |\n",
      "Training set score: 0.854336\n",
      "Test set score: 0.773484\n",
      "| \u001b[0m 100     \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 22.46   \u001b[0m | \u001b[0m 3.994   \u001b[0m | \u001b[0m 6.501   \u001b[0m | \u001b[0m 1.241e+0\u001b[0m |\n",
      "Training set score: 0.828956\n",
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      "| \u001b[0m 101     \u001b[0m | \u001b[0m 0.766   \u001b[0m | \u001b[0m 20.02   \u001b[0m | \u001b[0m 4.745   \u001b[0m | \u001b[0m 3.768   \u001b[0m | \u001b[0m 1.059e+0\u001b[0m |\n",
      "Training set score: 0.846103\n",
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      "| \u001b[0m 102     \u001b[0m | \u001b[0m 0.7692  \u001b[0m | \u001b[0m 10.48   \u001b[0m | \u001b[0m 3.656   \u001b[0m | \u001b[0m 5.194   \u001b[0m | \u001b[0m 1.064e+0\u001b[0m |\n",
      "Training set score: 0.882925\n",
      "Test set score: 0.777199\n",
      "| \u001b[0m 103     \u001b[0m | \u001b[0m 0.7772  \u001b[0m | \u001b[0m 19.66   \u001b[0m | \u001b[0m 3.055   \u001b[0m | \u001b[0m 4.775   \u001b[0m | \u001b[0m 1.244e+0\u001b[0m |\n",
      "Training set score: 0.829365\n",
      "Test set score: 0.766332\n",
      "| \u001b[0m 104     \u001b[0m | \u001b[0m 0.7663  \u001b[0m | \u001b[0m 21.59   \u001b[0m | \u001b[0m 4.595   \u001b[0m | \u001b[0m 2.058   \u001b[0m | \u001b[0m 1.239e+0\u001b[0m |\n",
      "Training set score: 0.829161\n",
      "Test set score: 0.766366\n",
      "| \u001b[0m 105     \u001b[0m | \u001b[0m 0.7664  \u001b[0m | \u001b[0m 14.64   \u001b[0m | \u001b[0m 4.854   \u001b[0m | \u001b[0m 5.971   \u001b[0m | \u001b[0m 1.237e+0\u001b[0m |\n",
      "Training set score: 0.916838\n",
      "Test set score: 0.772148\n",
      "| \u001b[0m 106     \u001b[0m | \u001b[0m 0.7721  \u001b[0m | \u001b[0m 19.56   \u001b[0m | \u001b[0m 1.69    \u001b[0m | \u001b[0m 2.372   \u001b[0m | \u001b[0m 1.057e+0\u001b[0m |\n",
      "Training set score: 0.882480\n",
      "Test set score: 0.776725\n",
      "| \u001b[0m 107     \u001b[0m | \u001b[0m 0.7767  \u001b[0m | \u001b[0m 15.88   \u001b[0m | \u001b[0m 2.541   \u001b[0m | \u001b[0m 2.466   \u001b[0m | \u001b[0m 1.242e+0\u001b[0m |\n",
      "Training set score: 0.916952\n",
      "Test set score: 0.772095\n",
      "| \u001b[0m 108     \u001b[0m | \u001b[0m 0.7721  \u001b[0m | \u001b[0m 22.34   \u001b[0m | \u001b[0m 1.643   \u001b[0m | \u001b[0m 2.365   \u001b[0m | \u001b[0m 1.057e+0\u001b[0m |\n",
      "Training set score: 0.882466\n",
      "Test set score: 0.776708\n",
      "| \u001b[0m 109     \u001b[0m | \u001b[0m 0.7767  \u001b[0m | \u001b[0m 15.95   \u001b[0m | \u001b[0m 3.294   \u001b[0m | \u001b[0m 4.489   \u001b[0m | \u001b[0m 1.24e+03\u001b[0m |\n",
      "Training set score: 0.882650\n",
      "Test set score: 0.775566\n",
      "| \u001b[0m 110     \u001b[0m | \u001b[0m 0.7756  \u001b[0m | \u001b[0m 19.92   \u001b[0m | \u001b[0m 3.299   \u001b[0m | \u001b[0m 2.195   \u001b[0m | \u001b[0m 1.054e+0\u001b[0m |\n",
      "=========================================================================\n",
      "{'target': 0.7771991052382827, 'params': {'max_depths': 19.278129232569064, 'min_samples_leaf': 2.989362159767528, 'min_samples_split': 5.816558946743761, 'n_estimator': 1243.5126904380131}}\n"
     ]
    }
   ],
   "source": [
    "#Note: Since Bayesian optimization is affected by the initial random candidates, it may cause the optimization hyperparameters to vary slightly from different machines\n",
    "from bayes_opt import BayesianOptimization\n",
    "pbounds = {'n_estimator': (10, 2000),\"max_depths\":(10,25),\"min_samples_split\":(2,10),\"min_samples_leaf\":(1,10)}\n",
    "optimizer = BayesianOptimization(f=RF,pbounds=pbounds,random_state=2)\n",
    "bo=optimizer.maximize(init_points=10,n_iter=100)\n",
    "\n",
    "print(optimizer.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85d7ddfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depths': 19.278129232569064,\n",
       " 'min_samples_leaf': 2.989362159767528,\n",
       " 'min_samples_split': 5.816558946743761,\n",
       " 'n_estimator': 1243.5126904380131}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The set of hyperparameters of the RF model\n",
    "RF_par=optimizer.max.get('params')\n",
    "RF_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "95fac06d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***==========Construction of RF model==========***\n",
      "Training set score: 0.882925\n",
      "Test set score: 0.777199\n",
      "MSE:0.0008715691895641649\n",
      "RMSE:0.029522350678158487\n"
     ]
    }
   ],
   "source": [
    "#RF model\n",
    "print (\"***==========Construction of RF model==========***\")\n",
    "n_estimator = int(RF_par.get('n_estimator'))\n",
    "max_depths = int(RF_par.get('max_depths'))\n",
    "min_samples_split=int(np.round(RF_par.get('min_samples_split')))\n",
    "min_samples_leaf=int(np.round(RF_par.get('min_samples_leaf')))\n",
    "rfg = RandomForestRegressor(n_estimators = n_estimator, max_features='auto',random_state=1, max_depth = max_depths,min_samples_split=min_samples_split,min_samples_leaf=min_samples_leaf)\n",
    "rfg.fit(Xtrain,y_train.values.ravel())\n",
    "res = rfg.predict(Xtest)\n",
    "print(\"Training set score: %f\" % rfg.score(Xtrain,y_train))\n",
    "print(\"Test set score: %f\" % rfg.score(Xtest,y_test))\n",
    "mse=mean_squared_error(y_test,res)\n",
    "rmse=math.sqrt(mse)\n",
    "print('MSE:{}'.format(mse))\n",
    "print('RMSE:{}'.format(rmse))\n",
    "RF_train= rfg.predict(Xtrain)\n",
    "RF_test= rfg.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9690b8fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   |   alpha   |   coef0   |  degree   |   gamma   | kernel... |\n",
      "-------------------------------------------------------------------------------------\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.627861\n",
      "Test set score: 0.685263\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.6853  \u001b[0m | \u001b[0m 0.4366  \u001b[0m | \u001b[0m 1.104   \u001b[0m | \u001b[0m 3.649   \u001b[0m | \u001b[0m 0.00441 \u001b[0m | \u001b[0m 1.681   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.619948\n",
      "Test set score: 0.678883\n",
      "| \u001b[0m 2       \u001b[0m | \u001b[0m 0.6789  \u001b[0m | \u001b[0m 0.331   \u001b[0m | \u001b[0m 1.819   \u001b[0m | \u001b[0m 3.858   \u001b[0m | \u001b[0m 0.003067\u001b[0m | \u001b[0m 1.067   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.584257\n",
      "Test set score: 0.647400\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m 0.6474  \u001b[0m | \u001b[0m 0.6215  \u001b[0m | \u001b[0m 3.117   \u001b[0m | \u001b[0m 2.404   \u001b[0m | \u001b[0m 0.005184\u001b[0m | \u001b[0m 0.7378  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.608031\n",
      "Test set score: 0.668884\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 0.6689  \u001b[0m | \u001b[0m 0.7855  \u001b[0m | \u001b[0m 4.416   \u001b[0m | \u001b[0m 3.483   \u001b[0m | \u001b[0m 0.008481\u001b[0m | \u001b[0m 0.3186  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.486044\n",
      "Test set score: 0.557578\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.5576  \u001b[0m | \u001b[0m 0.5057  \u001b[0m | \u001b[0m 1.261   \u001b[0m | \u001b[0m 3.284   \u001b[0m | \u001b[0m 0.001056\u001b[0m | \u001b[0m 0.5086  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.550776\n",
      "Test set score: 0.626365\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.6264  \u001b[0m | \u001b[0m 0.5971  \u001b[0m | \u001b[0m 1.904   \u001b[0m | \u001b[0m 2.321   \u001b[0m | \u001b[0m 0.002281\u001b[0m | \u001b[0m 1.399   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.739035\n",
      "Test set score: 0.732542\n",
      "| \u001b[95m 7       \u001b[0m | \u001b[95m 0.7325  \u001b[0m | \u001b[95m 0.4683  \u001b[0m | \u001b[95m 1.807   \u001b[0m | \u001b[95m 3.921   \u001b[0m | \u001b[95m 0.004882\u001b[0m | \u001b[95m 2.021   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.722093\n",
      "Test set score: 0.718981\n",
      "| \u001b[0m 8       \u001b[0m | \u001b[0m 0.719   \u001b[0m | \u001b[0m 0.3875  \u001b[0m | \u001b[0m 4.175   \u001b[0m | \u001b[0m 3.74    \u001b[0m | \u001b[0m 0.001707\u001b[0m | \u001b[0m 2.803   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.791475\n",
      "Test set score: 0.740820\n",
      "| \u001b[95m 9       \u001b[0m | \u001b[95m 0.7408  \u001b[0m | \u001b[95m 0.9646  \u001b[0m | \u001b[95m 3.0     \u001b[0m | \u001b[95m 4.669   \u001b[0m | \u001b[95m 0.003482\u001b[0m | \u001b[95m 2.269   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.271897\n",
      "Test set score: 0.327748\n",
      "| \u001b[0m 10      \u001b[0m | \u001b[0m 0.3277  \u001b[0m | \u001b[0m 0.4281  \u001b[0m | \u001b[0m 2.747   \u001b[0m | \u001b[0m 4.33    \u001b[0m | \u001b[0m 0.005402\u001b[0m | \u001b[0m 3.815   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.579122\n",
      "Test set score: 0.650050\n",
      "| \u001b[0m 11      \u001b[0m | \u001b[0m 0.65    \u001b[0m | \u001b[0m 0.6752  \u001b[0m | \u001b[0m 2.848   \u001b[0m | \u001b[0m 4.06    \u001b[0m | \u001b[0m 0.003707\u001b[0m | \u001b[0m 1.673   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.525989\n",
      "Test set score: 0.604107\n",
      "| \u001b[0m 12      \u001b[0m | \u001b[0m 0.6041  \u001b[0m | \u001b[0m 0.4007  \u001b[0m | \u001b[0m 1.027   \u001b[0m | \u001b[0m 3.66    \u001b[0m | \u001b[0m 0.001112\u001b[0m | \u001b[0m 1.712   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.764594\n",
      "Test set score: 0.745403\n",
      "| \u001b[95m 13      \u001b[0m | \u001b[95m 0.7454  \u001b[0m | \u001b[95m 0.9462  \u001b[0m | \u001b[95m 3.081   \u001b[0m | \u001b[95m 4.8     \u001b[0m | \u001b[95m 0.002488\u001b[0m | \u001b[95m 2.261   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.700334\n",
      "Test set score: 0.732425\n",
      "| \u001b[0m 14      \u001b[0m | \u001b[0m 0.7324  \u001b[0m | \u001b[0m 0.2659  \u001b[0m | \u001b[0m 2.665   \u001b[0m | \u001b[0m 2.645   \u001b[0m | \u001b[0m 0.009779\u001b[0m | \u001b[0m 0.3861  \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.753151\n",
      "Test set score: 0.741878\n",
      "| \u001b[0m 15      \u001b[0m | \u001b[0m 0.7419  \u001b[0m | \u001b[0m 0.7705  \u001b[0m | \u001b[0m 3.205   \u001b[0m | \u001b[0m 4.678   \u001b[0m | \u001b[0m 0.001913\u001b[0m | \u001b[0m 2.234   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.628019\n",
      "Test set score: 0.682501\n",
      "| \u001b[0m 16      \u001b[0m | \u001b[0m 0.6825  \u001b[0m | \u001b[0m 0.6236  \u001b[0m | \u001b[0m 2.123   \u001b[0m | \u001b[0m 3.503   \u001b[0m | \u001b[0m 0.008716\u001b[0m | \u001b[0m 0.4452  \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.278147\n",
      "Test set score: 0.333372\n",
      "| \u001b[0m 17      \u001b[0m | \u001b[0m 0.3334  \u001b[0m | \u001b[0m 0.4664  \u001b[0m | \u001b[0m 1.27    \u001b[0m | \u001b[0m 2.285   \u001b[0m | \u001b[0m 0.000396\u001b[0m | \u001b[0m 3.545   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.682942\n",
      "Test set score: 0.722466\n",
      "| \u001b[0m 18      \u001b[0m | \u001b[0m 0.7225  \u001b[0m | \u001b[0m 0.5389  \u001b[0m | \u001b[0m 1.503   \u001b[0m | \u001b[0m 3.691   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.66    \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.492205\n",
      "Test set score: 0.573661\n",
      "| \u001b[0m 19      \u001b[0m | \u001b[0m 0.5737  \u001b[0m | \u001b[0m 0.7645  \u001b[0m | \u001b[0m 1.24    \u001b[0m | \u001b[0m 3.4     \u001b[0m | \u001b[0m 0.001421\u001b[0m | \u001b[0m 1.486   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.715086\n",
      "Test set score: 0.717196\n",
      "| \u001b[0m 20      \u001b[0m | \u001b[0m 0.7172  \u001b[0m | \u001b[0m 0.1913  \u001b[0m | \u001b[0m 1.789   \u001b[0m | \u001b[0m 3.469   \u001b[0m | \u001b[0m 0.004819\u001b[0m | \u001b[0m 2.294   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.756575\n",
      "Test set score: 0.756445\n",
      "| \u001b[95m 21      \u001b[0m | \u001b[95m 0.7564  \u001b[0m | \u001b[95m 0.2379  \u001b[0m | \u001b[95m 1.549   \u001b[0m | \u001b[95m 3.668   \u001b[0m | \u001b[95m 0.01    \u001b[0m | \u001b[95m 1.942   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.638033\n",
      "Test set score: 0.693383\n",
      "| \u001b[0m 22      \u001b[0m | \u001b[0m 0.6934  \u001b[0m | \u001b[0m 0.5679  \u001b[0m | \u001b[0m 3.287   \u001b[0m | \u001b[0m 3.368   \u001b[0m | \u001b[0m 0.006402\u001b[0m | \u001b[0m 1.604   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.642137\n",
      "Test set score: 0.660252\n",
      "| \u001b[0m 23      \u001b[0m | \u001b[0m 0.6603  \u001b[0m | \u001b[0m 0.3739  \u001b[0m | \u001b[0m 2.339   \u001b[0m | \u001b[0m 2.216   \u001b[0m | \u001b[0m 0.00709 \u001b[0m | \u001b[0m 2.258   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.534497\n",
      "Test set score: 0.582913\n",
      "| \u001b[0m 24      \u001b[0m | \u001b[0m 0.5829  \u001b[0m | \u001b[0m 0.8222  \u001b[0m | \u001b[0m 3.273   \u001b[0m | \u001b[0m 2.761   \u001b[0m | \u001b[0m 0.000691\u001b[0m | \u001b[0m 2.822   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.794058\n",
      "Test set score: 0.728149\n",
      "| \u001b[0m 25      \u001b[0m | \u001b[0m 0.7281  \u001b[0m | \u001b[0m 0.557   \u001b[0m | \u001b[0m 1.514   \u001b[0m | \u001b[0m 3.63    \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.136   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.639750\n",
      "Test set score: 0.653097\n",
      "| \u001b[0m 26      \u001b[0m | \u001b[0m 0.6531  \u001b[0m | \u001b[0m 0.184   \u001b[0m | \u001b[0m 2.483   \u001b[0m | \u001b[0m 2.483   \u001b[0m | \u001b[0m 0.004672\u001b[0m | \u001b[0m 2.517   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: -2.375322\n",
      "Test set score: -3.888064\n",
      "| \u001b[0m 27      \u001b[0m | \u001b[0m-3.888   \u001b[0m | \u001b[0m 0.6753  \u001b[0m | \u001b[0m 1.634   \u001b[0m | \u001b[0m 4.237   \u001b[0m | \u001b[0m 0.007982\u001b[0m | \u001b[0m 3.099   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.029731\n",
      "Test set score: 0.034067\n",
      "| \u001b[0m 28      \u001b[0m | \u001b[0m 0.03407 \u001b[0m | \u001b[0m 0.3288  \u001b[0m | \u001b[0m 4.053   \u001b[0m | \u001b[0m 4.787   \u001b[0m | \u001b[0m 0.00321 \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 29      \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.826   \u001b[0m | \u001b[0m 2.704   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.725   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.628270\n",
      "Test set score: 0.689359\n",
      "| \u001b[0m 30      \u001b[0m | \u001b[0m 0.6894  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 4.997   \u001b[0m | \u001b[0m 4.196   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.687   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.582482\n",
      "Test set score: 0.594133\n",
      "| \u001b[0m 31      \u001b[0m | \u001b[0m 0.5941  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 3.256   \u001b[0m | \u001b[0m 4.435   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.751347\n",
      "Test set score: 0.740693\n",
      "| \u001b[0m 32      \u001b[0m | \u001b[0m 0.7407  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 2.504   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.98    \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.133385\n",
      "Test set score: 0.159951\n",
      "| \u001b[0m 33      \u001b[0m | \u001b[0m 0.16    \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.358   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 3.78    \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.267803\n",
      "Test set score: 0.322303\n",
      "| \u001b[0m 34      \u001b[0m | \u001b[0m 0.3223  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 0.8653  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 35      \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.481   \u001b[0m | \u001b[0m 4.869   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.061   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.544583\n",
      "Test set score: 0.615964\n",
      "| \u001b[0m 36      \u001b[0m | \u001b[0m 0.616   \u001b[0m | \u001b[0m 0.8002  \u001b[0m | \u001b[0m 4.924   \u001b[0m | \u001b[0m 4.894   \u001b[0m | \u001b[0m 0.003893\u001b[0m | \u001b[0m 0.04564 \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.601516\n",
      "Test set score: 0.665446\n",
      "| \u001b[0m 37      \u001b[0m | \u001b[0m 0.6654  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.892   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.06195 \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kernel_chose:polynomial\n",
      "Training set score: 0.821365\n",
      "Test set score: 0.657845\n",
      "| \u001b[0m 38      \u001b[0m | \u001b[0m 0.6578  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 2.345   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.601516\n",
      "Test set score: 0.665446\n",
      "| \u001b[0m 39      \u001b[0m | \u001b[0m 0.6654  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 3.502   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.8496  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 40      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 4.831   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.601516\n",
      "Test set score: 0.665446\n",
      "| \u001b[0m 41      \u001b[0m | \u001b[0m 0.6654  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.81    \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 42      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.305   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.9986  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.794027\n",
      "Test set score: 0.732195\n",
      "| \u001b[0m 43      \u001b[0m | \u001b[0m 0.7322  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.282   \u001b[0m | \u001b[0m 3.81    \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 1.603   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.483416\n",
      "Test set score: 0.546909\n",
      "| \u001b[0m 44      \u001b[0m | \u001b[0m 0.5469  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 4.393   \u001b[0m | \u001b[0m 3.093   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 2.03    \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 45      \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 3.609   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.825   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.582482\n",
      "Test set score: 0.594133\n",
      "| \u001b[0m 46      \u001b[0m | \u001b[0m 0.5941  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.015   \u001b[0m | \u001b[0m 2.465   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.267803\n",
      "Test set score: 0.322303\n",
      "| \u001b[0m 47      \u001b[0m | \u001b[0m 0.3223  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 3.217   \u001b[0m | \u001b[0m 3.506   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.582482\n",
      "Test set score: 0.594133\n",
      "| \u001b[0m 48      \u001b[0m | \u001b[0m 0.5941  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 3.811   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 0.5583  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 49      \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.481   \u001b[0m | \u001b[0m 3.107   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.58    \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 50      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.005066\n",
      "Test set score: 0.004522\n",
      "| \u001b[0m 51      \u001b[0m | \u001b[0m 0.004522\u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 3.341   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.601516\n",
      "Test set score: 0.665446\n",
      "| \u001b[0m 52      \u001b[0m | \u001b[0m 0.6654  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.8937  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 53      \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.972   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.898734\n",
      "Test set score: 0.507367\n",
      "| \u001b[0m 54      \u001b[0m | \u001b[0m 0.5074  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 4.302   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.007   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.215575\n",
      "Test set score: 0.259788\n",
      "| \u001b[0m 55      \u001b[0m | \u001b[0m 0.2598  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 2.802   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 4.0     \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.968254\n",
      "Test set score: -3.212937\n",
      "| \u001b[0m 56      \u001b[0m | \u001b[0m-3.213   \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 3.646   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.431   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.512892\n",
      "Test set score: 0.567365\n",
      "| \u001b[0m 57      \u001b[0m | \u001b[0m 0.5674  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 3.682   \u001b[0m | \u001b[0m 3.852   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 2.729   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.633899\n",
      "Test set score: 0.685918\n",
      "| \u001b[0m 58      \u001b[0m | \u001b[0m 0.6859  \u001b[0m | \u001b[0m 0.08346 \u001b[0m | \u001b[0m 4.116   \u001b[0m | \u001b[0m 2.944   \u001b[0m | \u001b[0m 0.000960\u001b[0m | \u001b[0m 1.128   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.794027\n",
      "Test set score: 0.732195\n",
      "| \u001b[0m 59      \u001b[0m | \u001b[0m 0.7322  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 3.623   \u001b[0m | \u001b[0m 4.138   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 1.114   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.826757\n",
      "Test set score: 0.774338\n",
      "| \u001b[95m 60      \u001b[0m | \u001b[95m 0.7743  \u001b[0m | \u001b[95m 0.1014  \u001b[0m | \u001b[95m 1.707   \u001b[0m | \u001b[95m 2.361   \u001b[0m | \u001b[95m 0.009651\u001b[0m | \u001b[95m 1.621   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.675264\n",
      "Test set score: 0.717966\n",
      "| \u001b[0m 61      \u001b[0m | \u001b[0m 0.718   \u001b[0m | \u001b[0m 0.5648  \u001b[0m | \u001b[0m 3.924   \u001b[0m | \u001b[0m 3.168   \u001b[0m | \u001b[0m 0.009636\u001b[0m | \u001b[0m 1.248   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.715298\n",
      "Test set score: 0.710821\n",
      "| \u001b[0m 62      \u001b[0m | \u001b[0m 0.7108  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 3.391   \u001b[0m | \u001b[0m 3.678   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 2.521   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.596957\n",
      "Test set score: 0.661887\n",
      "| \u001b[0m 63      \u001b[0m | \u001b[0m 0.6619  \u001b[0m | \u001b[0m 0.9614  \u001b[0m | \u001b[0m 4.261   \u001b[0m | \u001b[0m 4.222   \u001b[0m | \u001b[0m 0.00903 \u001b[0m | \u001b[0m 0.9493  \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.630780\n",
      "Test set score: 0.650072\n",
      "| \u001b[0m 64      \u001b[0m | \u001b[0m 0.6501  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 4.319   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.291   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.516160\n",
      "Test set score: 0.587324\n",
      "| \u001b[0m 65      \u001b[0m | \u001b[0m 0.5873  \u001b[0m | \u001b[0m 0.6153  \u001b[0m | \u001b[0m 2.03    \u001b[0m | \u001b[0m 3.514   \u001b[0m | \u001b[0m 0.002122\u001b[0m | \u001b[0m 0.4167  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.794027\n",
      "Test set score: 0.732195\n",
      "| \u001b[0m 66      \u001b[0m | \u001b[0m 0.7322  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.648   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 1.333   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.628270\n",
      "Test set score: 0.689359\n",
      "| \u001b[0m 67      \u001b[0m | \u001b[0m 0.6894  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 2.298   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.233   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.794027\n",
      "Test set score: 0.732195\n",
      "| \u001b[0m 68      \u001b[0m | \u001b[0m 0.7322  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 3.705   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 1.844   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.334617\n",
      "Test set score: 0.401904\n",
      "| \u001b[0m 69      \u001b[0m | \u001b[0m 0.4019  \u001b[0m | \u001b[0m 0.8     \u001b[0m | \u001b[0m 1.916   \u001b[0m | \u001b[0m 3.003   \u001b[0m | \u001b[0m 0.003628\u001b[0m | \u001b[0m 3.558   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.057170\n",
      "Test set score: 0.066789\n",
      "| \u001b[0m 70      \u001b[0m | \u001b[0m 0.06679 \u001b[0m | \u001b[0m 0.1702  \u001b[0m | \u001b[0m 4.63    \u001b[0m | \u001b[0m 4.262   \u001b[0m | \u001b[0m 0.009805\u001b[0m | \u001b[0m 3.622   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 71      \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.153   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.131   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.626923\n",
      "Test set score: 0.682302\n",
      "| \u001b[0m 72      \u001b[0m | \u001b[0m 0.6823  \u001b[0m | \u001b[0m 0.6923  \u001b[0m | \u001b[0m 1.045   \u001b[0m | \u001b[0m 2.026   \u001b[0m | \u001b[0m 0.009454\u001b[0m | \u001b[0m 0.7824  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.802712\n",
      "Test set score: 0.780567\n",
      "| \u001b[95m 73      \u001b[0m | \u001b[95m 0.7806  \u001b[0m | \u001b[95m 0.02657 \u001b[0m | \u001b[95m 1.937   \u001b[0m | \u001b[95m 2.068   \u001b[0m | \u001b[95m 0.007145\u001b[0m | \u001b[95m 0.6307  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.610062\n",
      "Test set score: 0.670775\n",
      "| \u001b[0m 74      \u001b[0m | \u001b[0m 0.6708  \u001b[0m | \u001b[0m 0.8281  \u001b[0m | \u001b[0m 4.886   \u001b[0m | \u001b[0m 4.945   \u001b[0m | \u001b[0m 0.009152\u001b[0m | \u001b[0m 0.01989 \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.004746\n",
      "Test set score: 0.003894\n",
      "| \u001b[0m 75      \u001b[0m | \u001b[0m 0.003894\u001b[0m | \u001b[0m 0.1953  \u001b[0m | \u001b[0m 3.584   \u001b[0m | \u001b[0m 3.419   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 3.497   \u001b[0m |\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 76      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.757   \u001b[0m | \u001b[0m 3.659   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.6509  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.601516\n",
      "Test set score: 0.665446\n",
      "| \u001b[0m 77      \u001b[0m | \u001b[0m 0.6654  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 4.298   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.139780\n",
      "Test set score: 0.168618\n",
      "| \u001b[0m 78      \u001b[0m | \u001b[0m 0.1686  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 3.187   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 1.789   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 79      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.189   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.715775\n",
      "Test set score: 0.717930\n",
      "| \u001b[0m 80      \u001b[0m | \u001b[0m 0.7179  \u001b[0m | \u001b[0m 0.1764  \u001b[0m | \u001b[0m 1.74    \u001b[0m | \u001b[0m 3.432   \u001b[0m | \u001b[0m 0.004793\u001b[0m | \u001b[0m 2.264   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.614358\n",
      "Test set score: 0.652211\n",
      "| \u001b[0m 81      \u001b[0m | \u001b[0m 0.6522  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.213   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.601516\n",
      "Test set score: 0.665446\n",
      "| \u001b[0m 82      \u001b[0m | \u001b[0m 0.6654  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 3.882   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.815507\n",
      "Test set score: 0.773507\n",
      "| \u001b[0m 83      \u001b[0m | \u001b[0m 0.7735  \u001b[0m | \u001b[0m 0.01368 \u001b[0m | \u001b[0m 2.135   \u001b[0m | \u001b[0m 4.64    \u001b[0m | \u001b[0m 0.005939\u001b[0m | \u001b[0m 0.08083 \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.718581\n",
      "Test set score: 0.721703\n",
      "| \u001b[0m 84      \u001b[0m | \u001b[0m 0.7217  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 2.266   \u001b[0m | \u001b[0m 3.125   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.013   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 85      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 2.618   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.9968  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.628270\n",
      "Test set score: 0.689359\n",
      "| \u001b[0m 86      \u001b[0m | \u001b[0m 0.6894  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.16    \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.572896\n",
      "Test set score: 0.554271\n",
      "| \u001b[0m 87      \u001b[0m | \u001b[0m 0.5543  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.692   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 2.29    \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.747556\n",
      "Test set score: 0.763375\n",
      "| \u001b[0m 88      \u001b[0m | \u001b[0m 0.7634  \u001b[0m | \u001b[0m 0.1247  \u001b[0m | \u001b[0m 4.732   \u001b[0m | \u001b[0m 2.31    \u001b[0m | \u001b[0m 0.009557\u001b[0m | \u001b[0m 0.8464  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.601516\n",
      "Test set score: 0.665446\n",
      "| \u001b[0m 89      \u001b[0m | \u001b[0m 0.6654  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 3.669   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.665788\n",
      "Test set score: 0.671629\n",
      "| \u001b[0m 90      \u001b[0m | \u001b[0m 0.6716  \u001b[0m | \u001b[0m 0.2619  \u001b[0m | \u001b[0m 3.828   \u001b[0m | \u001b[0m 2.316   \u001b[0m | \u001b[0m 0.007577\u001b[0m | \u001b[0m 2.347   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.582482\n",
      "Test set score: 0.594133\n",
      "| \u001b[0m 91      \u001b[0m | \u001b[0m 0.5941  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.945   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.267803\n",
      "Test set score: 0.322303\n",
      "| \u001b[0m 92      \u001b[0m | \u001b[0m 0.3223  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 3.293   \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 0.9976  \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 93      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 2.846   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.673244\n",
      "Test set score: 0.717045\n",
      "| \u001b[0m 94      \u001b[0m | \u001b[0m 0.717   \u001b[0m | \u001b[0m 0.6007  \u001b[0m | \u001b[0m 3.865   \u001b[0m | \u001b[0m 4.077   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.84    \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.572890\n",
      "Test set score: 0.642486\n",
      "| \u001b[0m 95      \u001b[0m | \u001b[0m 0.6425  \u001b[0m | \u001b[0m 0.9368  \u001b[0m | \u001b[0m 2.643   \u001b[0m | \u001b[0m 4.469   \u001b[0m | \u001b[0m 0.006455\u001b[0m | \u001b[0m 0.4752  \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.565947\n",
      "Test set score: 0.638513\n",
      "| \u001b[0m 96      \u001b[0m | \u001b[0m 0.6385  \u001b[0m | \u001b[0m 0.471   \u001b[0m | \u001b[0m 2.364   \u001b[0m | \u001b[0m 4.698   \u001b[0m | \u001b[0m 0.002186\u001b[0m | \u001b[0m 1.831   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.473578\n",
      "Test set score: 0.555087\n",
      "| \u001b[0m 97      \u001b[0m | \u001b[0m 0.5551  \u001b[0m | \u001b[0m 0.6946  \u001b[0m | \u001b[0m 2.836   \u001b[0m | \u001b[0m 4.081   \u001b[0m | \u001b[0m 0.001046\u001b[0m | \u001b[0m 1.645   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 98      \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.774   \u001b[0m | \u001b[0m 3.969   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.936   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 99      \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.766   \u001b[0m | \u001b[0m 2.702   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 100     \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 4.117   \u001b[0m | \u001b[0m 3.737   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.984749\n",
      "Test set score: -2.885038\n",
      "| \u001b[0m 101     \u001b[0m | \u001b[0m-2.885   \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 3.587   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 2.802   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.890093\n",
      "Test set score: 0.769422\n",
      "| \u001b[0m 102     \u001b[0m | \u001b[0m 0.7694  \u001b[0m | \u001b[0m 0.03368 \u001b[0m | \u001b[0m 3.47    \u001b[0m | \u001b[0m 4.848   \u001b[0m | \u001b[0m 0.008005\u001b[0m | \u001b[0m 1.121   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.059262\n",
      "Test set score: 0.069289\n",
      "| \u001b[0m 103     \u001b[0m | \u001b[0m 0.06929 \u001b[0m | \u001b[0m 0.1356  \u001b[0m | \u001b[0m 4.669   \u001b[0m | \u001b[0m 2.547   \u001b[0m | \u001b[0m 0.009948\u001b[0m | \u001b[0m 3.651   \u001b[0m |\n",
      "kernel_chose:rbf\n",
      "Training set score: 0.928314\n",
      "Test set score: 0.706135\n",
      "| \u001b[0m 104     \u001b[0m | \u001b[0m 0.7061  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 4.105   \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 0.6328  \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.708871\n",
      "Test set score: 0.708965\n",
      "| \u001b[0m 105     \u001b[0m | \u001b[0m 0.709   \u001b[0m | \u001b[0m 0.5121  \u001b[0m | \u001b[0m 2.843   \u001b[0m | \u001b[0m 3.546   \u001b[0m | \u001b[0m 0.002488\u001b[0m | \u001b[0m 2.358   \u001b[0m |\n",
      "kernel_chose:laplacian\n",
      "Training set score: 0.996798\n",
      "Test set score: 0.673815\n",
      "| \u001b[0m 106     \u001b[0m | \u001b[0m 0.6738  \u001b[0m | \u001b[0m 0.001   \u001b[0m | \u001b[0m 2.666   \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 0.01    \u001b[0m | \u001b[0m 1.252   \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.022074\n",
      "Test set score: 0.024836\n",
      "| \u001b[0m 107     \u001b[0m | \u001b[0m 0.02484 \u001b[0m | \u001b[0m 0.2739  \u001b[0m | \u001b[0m 4.237   \u001b[0m | \u001b[0m 4.873   \u001b[0m | \u001b[0m 0.002351\u001b[0m | \u001b[0m 3.33    \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.834365\n",
      "Test set score: 0.670597\n",
      "| \u001b[0m 108     \u001b[0m | \u001b[0m 0.6706  \u001b[0m | \u001b[0m 0.6651  \u001b[0m | \u001b[0m 4.755   \u001b[0m | \u001b[0m 4.175   \u001b[0m | \u001b[0m 0.007331\u001b[0m | \u001b[0m 2.96    \u001b[0m |\n",
      "kernel_chose:sigmoid\n",
      "Training set score: 0.000039\n",
      "Test set score: -0.001501\n",
      "| \u001b[0m 109     \u001b[0m | \u001b[0m-0.001501\u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.0001  \u001b[0m | \u001b[0m 3.061   \u001b[0m |\n",
      "kernel_chose:polynomial\n",
      "Training set score: 0.875271\n",
      "Test set score: 0.485911\n",
      "| \u001b[0m 110     \u001b[0m | \u001b[0m 0.4859  \u001b[0m | \u001b[0m 0.04032 \u001b[0m | \u001b[0m 4.187   \u001b[0m | \u001b[0m 3.769   \u001b[0m | \u001b[0m 0.004657\u001b[0m | \u001b[0m 2.753   \u001b[0m |\n",
      "=====================================================================================\n",
      "{'target': 0.7805667152356458, 'params': {'alpha': 0.026572614164137982, 'coef0': 1.9368959359566578, 'degree': 2.0677627000798404, 'gamma': 0.0071451448056355305, 'kernel_chose': 0.6307439327705886}}\n"
     ]
    }
   ],
   "source": [
    "#Note: Since Bayesian optimization is affected by the initial random candidates, it may cause the optimization hyperparameters to vary slightly from different machines\n",
    "pbounds = {'kernel_chose':(0,4), 'alpha':(1e-3,1), 'gamma':(0.0001,0.01) ,'degree':(2,5),'coef0':(1,5) }\n",
    "optimizer_krr= BayesianOptimization(f=KRR,pbounds=pbounds,random_state=2)\n",
    "bo=optimizer_krr.maximize(init_points=10,n_iter=100)\n",
    "print(optimizer_krr.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a906e6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.026572614164137982,\n",
       " 'coef0': 1.9368959359566578,\n",
       " 'degree': 2.0677627000798404,\n",
       " 'gamma': 0.0071451448056355305,\n",
       " 'kernel_chose': 0.6307439327705886}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The set of hyperparameters of the KRR model\n",
    "KRR_par=optimizer_krr.max.get('params')\n",
    "KRR_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a267ebd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***==========Construction of KRR model==========***\n",
      "Training set score: 0.802712\n",
      "Test set score: 0.780567\n",
      "MSE: 0.000858\n",
      "RMSE: 0.029298\n"
     ]
    }
   ],
   "source": [
    "#KRR model\n",
    "print (\"***==========Construction of KRR model==========***\")\n",
    "alpha=KRR_par.get('alpha')\n",
    "coef0=KRR_par.get('coef0')\n",
    "degree=KRR_par.get('degree')\n",
    "gamma=KRR_par.get('gamma')\n",
    "kernel_chose=KRR_par.get('kernel_chose')\n",
    "if kernel_chose<=1:\n",
    "    kernel=\"rbf\"\n",
    "elif kernel_chose>1 and kernel_chose<=2:\n",
    "    kernel='laplacian'\n",
    "elif kernel_chose>2 and kernel_chose<=3:\n",
    "    kernel='polynomial'\n",
    "else:\n",
    "    kernel='sigmoid'\n",
    "alpha=round(alpha,5)\n",
    "gamma==round(gamma,5)\n",
    "degree=round(degree)\n",
    "coef0=round(coef0,1)\n",
    "model=KernelRidge(kernel=kernel,alpha=alpha,gamma=gamma,degree=degree,coef0=coef0)\n",
    "model.fit(Xtrain,y_train.values.ravel())\n",
    "print(\"Training set score: %f\" % model.score(Xtrain,y_train))\n",
    "print(\"Test set score: %f\" % model.score(Xtest,y_test))\n",
    "KRR_train = model.predict(Xtrain)\n",
    "KRR_test = model.predict(Xtest)\n",
    "mse=mean_squared_error(y_test,KRR_test)\n",
    "rmse=math.sqrt(mse)\n",
    "print(\"MSE: %f\" % mse)\n",
    "print(\"RMSE: %f\" % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a42f556",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   |  alphas   |   node    | number... | solver... |\n",
      "-------------------------------------------------------------------------\n",
      "[184]\n",
      "0.417027834482527\n",
      "solver: adam\n",
      "Training set score: 0.771051\n",
      "Test set score: 0.729970\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.73    \u001b[0m | \u001b[0m 0.417   \u001b[0m | \u001b[0m 184.7   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 0.907   \u001b[0m |\n",
      "[24]\n",
      "0.14676442325820488\n",
      "solver: lbfgs\n",
      "Training set score: 0.892809\n",
      "Test set score: 0.748949\n",
      "| \u001b[95m 2       \u001b[0m | \u001b[95m 0.7489  \u001b[0m | \u001b[95m 0.1468  \u001b[0m | \u001b[95m 24.55   \u001b[0m | \u001b[95m 1.745   \u001b[0m | \u001b[95m 1.037   \u001b[0m |\n",
      "[138, 138]\n",
      "0.39677350655592764\n",
      "solver: sgd\n",
      "Training set score: -0.374294\n",
      "Test set score: -1.146124\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m-1.146   \u001b[0m | \u001b[0m 0.3968  \u001b[0m | \u001b[0m 138.4   \u001b[0m | \u001b[0m 2.677   \u001b[0m | \u001b[0m 2.056   \u001b[0m |\n",
      "[224]\n",
      "0.20446020520902014\n",
      "solver: sgd\n",
      "Training set score: -1.390695\n",
      "Test set score: -2.091426\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m-2.091   \u001b[0m | \u001b[0m 0.2045  \u001b[0m | \u001b[0m 224.9   \u001b[0m | \u001b[0m 1.11    \u001b[0m | \u001b[0m 2.011   \u001b[0m |\n",
      "[143]\n",
      "0.41731062931910334\n",
      "solver: adam\n",
      "Training set score: 0.773860\n",
      "Test set score: 0.716884\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.7169  \u001b[0m | \u001b[0m 0.4173  \u001b[0m | \u001b[0m 143.5   \u001b[0m | \u001b[0m 1.562   \u001b[0m | \u001b[0m 0.5943  \u001b[0m |\n",
      "[247, 247]\n",
      "0.8007465612298499\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.716315\n",
      "Test set score: 0.695090\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.6951  \u001b[0m | \u001b[0m 0.8007  \u001b[0m | \u001b[0m 247.9   \u001b[0m | \u001b[0m 2.254   \u001b[0m | \u001b[0m 2.077   \u001b[0m |\n",
      "[229]\n",
      "0.8763903884045153\n",
      "solver: adam\n",
      "Training set score: 0.707334\n",
      "Test set score: 0.682860\n",
      "| \u001b[0m 7       \u001b[0m | \u001b[0m 0.6829  \u001b[0m | \u001b[0m 0.8764  \u001b[0m | \u001b[0m 229.1   \u001b[0m | \u001b[0m 1.34    \u001b[0m | \u001b[0m 0.1172  \u001b[0m |\n",
      "[224]\n",
      "0.16983872126037328\n",
      "solver: lbfgs\n",
      "Training set score: 0.910883\n",
      "Test set score: 0.791691\n",
      "| \u001b[95m 8       \u001b[0m | \u001b[95m 0.7917  \u001b[0m | \u001b[95m 0.1698  \u001b[0m | \u001b[95m 224.9   \u001b[0m | \u001b[95m 1.393   \u001b[0m | \u001b[95m 1.263   \u001b[0m |\n",
      "[136, 136, 136]\n",
      "0.9578899512552004\n",
      "solver: adam\n",
      "Training set score: 0.065588\n",
      "Test set score: 0.076796\n",
      "| \u001b[0m 9       \u001b[0m | \u001b[0m 0.0768  \u001b[0m | \u001b[0m 0.9579  \u001b[0m | \u001b[0m 137.0   \u001b[0m | \u001b[0m 3.768   \u001b[0m | \u001b[0m 0.9465  \u001b[0m |\n",
      "[213]\n",
      "0.6865040626723068\n",
      "solver: sgd\n",
      "Training set score: -1.086656\n",
      "Test set score: -1.963409\n",
      "| \u001b[0m 10      \u001b[0m | \u001b[0m-1.963   \u001b[0m | \u001b[0m 0.6865  \u001b[0m | \u001b[0m 213.8   \u001b[0m | \u001b[0m 1.073   \u001b[0m | \u001b[0m 2.25    \u001b[0m |\n",
      "[42, 42, 42]\n",
      "0.007729778785561219\n",
      "solver: sgd\n",
      "Training set score: -1.307429\n",
      "Test set score: -2.167710\n",
      "| \u001b[0m 11      \u001b[0m | \u001b[0m-2.168   \u001b[0m | \u001b[0m 0.00773 \u001b[0m | \u001b[0m 42.27   \u001b[0m | \u001b[0m 3.837   \u001b[0m | \u001b[0m 2.294   \u001b[0m |\n",
      "[106, 106, 106]\n",
      "0.4425628918822878\n",
      "solver: sgd\n",
      "Training set score: 0.626191\n",
      "Test set score: 0.567091\n",
      "| \u001b[0m 12      \u001b[0m | \u001b[0m 0.5671  \u001b[0m | \u001b[0m 0.4426  \u001b[0m | \u001b[0m 106.4   \u001b[0m | \u001b[0m 3.191   \u001b[0m | \u001b[0m 2.233   \u001b[0m |\n",
      "[57, 57, 57, 57]\n",
      "0.6233894524217247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.429268\n",
      "Test set score: 0.435139\n",
      "| \u001b[0m 13      \u001b[0m | \u001b[0m 0.4351  \u001b[0m | \u001b[0m 0.6234  \u001b[0m | \u001b[0m 57.92   \u001b[0m | \u001b[0m 4.836   \u001b[0m | \u001b[0m 2.414   \u001b[0m |\n",
      "[207, 207, 207, 207]\n",
      "0.021216061589892313\n",
      "solver: sgd\n",
      "Training set score: -0.256186\n",
      "Test set score: -0.553567\n",
      "| \u001b[0m 14      \u001b[0m | \u001b[0m-0.5536  \u001b[0m | \u001b[0m 0.02122 \u001b[0m | \u001b[0m 207.0   \u001b[0m | \u001b[0m 4.859   \u001b[0m | \u001b[0m 2.106   \u001b[0m |\n",
      "[6]\n",
      "0.36442554737551286\n",
      "solver: sgd\n",
      "Training set score: -3.472363\n",
      "Test set score: -5.611256\n",
      "| \u001b[0m 15      \u001b[0m | \u001b[0m-5.611   \u001b[0m | \u001b[0m 0.3644  \u001b[0m | \u001b[0m 6.712   \u001b[0m | \u001b[0m 1.61    \u001b[0m | \u001b[0m 2.915   \u001b[0m |\n",
      "[136, 136, 136]\n",
      "0.7288709285180422\n",
      "solver: adam\n",
      "Training set score: 0.262944\n",
      "Test set score: 0.313221\n",
      "| \u001b[0m 16      \u001b[0m | \u001b[0m 0.3132  \u001b[0m | \u001b[0m 0.7289  \u001b[0m | \u001b[0m 136.8   \u001b[0m | \u001b[0m 3.732   \u001b[0m | \u001b[0m 0.6986  \u001b[0m |\n",
      "[224]\n",
      "0.25297825858105005\n",
      "solver: adam\n",
      "Training set score: 0.830114\n",
      "Test set score: 0.721042\n",
      "| \u001b[0m 17      \u001b[0m | \u001b[0m 0.721   \u001b[0m | \u001b[0m 0.253   \u001b[0m | \u001b[0m 224.8   \u001b[0m | \u001b[0m 1.307   \u001b[0m | \u001b[0m 0.4799  \u001b[0m |\n",
      "[225, 225]\n",
      "0.014587365240961582\n",
      "solver: adam\n",
      "Training set score: 0.902500\n",
      "Test set score: 0.638191\n",
      "| \u001b[0m 18      \u001b[0m | \u001b[0m 0.6382  \u001b[0m | \u001b[0m 0.01459 \u001b[0m | \u001b[0m 225.4   \u001b[0m | \u001b[0m 2.34    \u001b[0m | \u001b[0m 0.8929  \u001b[0m |\n",
      "[224, 224]\n",
      "0.2038498922449522\n",
      "solver: lbfgs\n",
      "Training set score: 0.855261\n",
      "Test set score: 0.818107\n",
      "| \u001b[95m 19      \u001b[0m | \u001b[95m 0.8181  \u001b[0m | \u001b[95m 0.2038  \u001b[0m | \u001b[95m 224.4   \u001b[0m | \u001b[95m 2.764   \u001b[0m | \u001b[95m 1.057   \u001b[0m |\n",
      "[224, 224]\n",
      "1.0\n",
      "solver: adam\n",
      "Training set score: 0.468456\n",
      "Test set score: 0.528084\n",
      "| \u001b[0m 20      \u001b[0m | \u001b[0m 0.5281  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 224.8   \u001b[0m | \u001b[0m 2.107   \u001b[0m | \u001b[0m 0.8362  \u001b[0m |\n",
      "[24, 24, 24]\n",
      "0.24178276134571305\n",
      "solver: lbfgs\n",
      "Training set score: 0.835634\n",
      "Test set score: 0.804412\n",
      "| \u001b[0m 21      \u001b[0m | \u001b[0m 0.8044  \u001b[0m | \u001b[0m 0.2418  \u001b[0m | \u001b[0m 24.46   \u001b[0m | \u001b[0m 3.014   \u001b[0m | \u001b[0m 1.872   \u001b[0m |\n",
      "[23]\n",
      "0.8095369497794838\n",
      "solver: lbfgs\n",
      "Training set score: 0.780028\n",
      "Test set score: 0.778623\n",
      "| \u001b[0m 22      \u001b[0m | \u001b[0m 0.7786  \u001b[0m | \u001b[0m 0.8095  \u001b[0m | \u001b[0m 23.36   \u001b[0m | \u001b[0m 1.948   \u001b[0m | \u001b[0m 1.923   \u001b[0m |\n",
      "[23, 23]\n",
      "0.2941264755736885\n",
      "solver: adam\n",
      "Training set score: 0.597527\n",
      "Test set score: 0.556516\n",
      "| \u001b[0m 23      \u001b[0m | \u001b[0m 0.5565  \u001b[0m | \u001b[0m 0.2941  \u001b[0m | \u001b[0m 23.37   \u001b[0m | \u001b[0m 2.883   \u001b[0m | \u001b[0m 0.6413  \u001b[0m |\n",
      "[24]\n",
      "0.4463112494005321\n",
      "solver: sgd\n",
      "Training set score: -4.023254\n",
      "Test set score: -6.697485\n",
      "| \u001b[0m 24      \u001b[0m | \u001b[0m-6.697   \u001b[0m | \u001b[0m 0.4463  \u001b[0m | \u001b[0m 24.73   \u001b[0m | \u001b[0m 1.213   \u001b[0m | \u001b[0m 2.823   \u001b[0m |\n",
      "[25, 25]\n",
      "0.003802470473964221\n",
      "solver: adam\n",
      "Training set score: -0.454899\n",
      "Test set score: -2.538679\n",
      "| \u001b[0m 25      \u001b[0m | \u001b[0m-2.539   \u001b[0m | \u001b[0m 0.003802\u001b[0m | \u001b[0m 25.45   \u001b[0m | \u001b[0m 2.996   \u001b[0m | \u001b[0m 0.5597  \u001b[0m |\n",
      "[23, 23, 23]\n",
      "0.7802567738101787\n",
      "solver: lbfgs\n",
      "Training set score: 0.613787\n",
      "Test set score: 0.662292\n",
      "| \u001b[0m 26      \u001b[0m | \u001b[0m 0.6623  \u001b[0m | \u001b[0m 0.7803  \u001b[0m | \u001b[0m 23.3    \u001b[0m | \u001b[0m 3.215   \u001b[0m | \u001b[0m 1.876   \u001b[0m |\n",
      "[23]\n",
      "0.2543146608391432\n",
      "solver: adam\n",
      "Training set score: 0.048271\n",
      "Test set score: -0.790507\n",
      "| \u001b[0m 27      \u001b[0m | \u001b[0m-0.7905  \u001b[0m | \u001b[0m 0.2543  \u001b[0m | \u001b[0m 23.36   \u001b[0m | \u001b[0m 1.369   \u001b[0m | \u001b[0m 0.591   \u001b[0m |\n",
      "[22, 22]\n",
      "0.5618594429291637\n",
      "solver: lbfgs\n",
      "Training set score: 0.732726\n",
      "Test set score: 0.765615\n",
      "| \u001b[0m 28      \u001b[0m | \u001b[0m 0.7656  \u001b[0m | \u001b[0m 0.5619  \u001b[0m | \u001b[0m 22.16   \u001b[0m | \u001b[0m 2.49    \u001b[0m | \u001b[0m 1.649   \u001b[0m |\n",
      "[22, 22, 22]\n",
      "1e-05\n",
      "solver: lbfgs\n",
      "Training set score: 0.926453\n",
      "Test set score: 0.671466\n",
      "| \u001b[0m 29      \u001b[0m | \u001b[0m 0.6715  \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 22.27   \u001b[0m | \u001b[0m 3.775   \u001b[0m | \u001b[0m 1.246   \u001b[0m |\n",
      "[22, 22, 22]\n",
      "0.9541598855403616\n",
      "solver: adam\n",
      "Training set score: 0.317273\n",
      "Test set score: 0.363680\n",
      "| \u001b[0m 30      \u001b[0m | \u001b[0m 0.3637  \u001b[0m | \u001b[0m 0.9542  \u001b[0m | \u001b[0m 22.12   \u001b[0m | \u001b[0m 3.354   \u001b[0m | \u001b[0m 0.1571  \u001b[0m |\n",
      "[22, 22, 22]\n",
      "0.09875994060657602\n",
      "solver: sgd\n",
      "Training set score: -2.574794\n",
      "Test set score: -3.792425\n",
      "| \u001b[0m 31      \u001b[0m | \u001b[0m-3.792   \u001b[0m | \u001b[0m 0.09876 \u001b[0m | \u001b[0m 22.03   \u001b[0m | \u001b[0m 3.573   \u001b[0m | \u001b[0m 2.847   \u001b[0m |\n",
      "[23, 23, 23, 23]\n",
      "0.17641524613682902\n",
      "solver: lbfgs\n",
      "Training set score: 0.810224\n",
      "Test set score: 0.800191\n",
      "| \u001b[0m 32      \u001b[0m | \u001b[0m 0.8002  \u001b[0m | \u001b[0m 0.1764  \u001b[0m | \u001b[0m 23.36   \u001b[0m | \u001b[0m 4.626   \u001b[0m | \u001b[0m 1.01    \u001b[0m |\n",
      "[21, 21, 21, 21]\n",
      "0.32787092645703747\n",
      "solver: adam\n",
      "Training set score: 0.421775\n",
      "Test set score: 0.485024\n",
      "| \u001b[0m 33      \u001b[0m | \u001b[0m 0.485   \u001b[0m | \u001b[0m 0.3279  \u001b[0m | \u001b[0m 21.95   \u001b[0m | \u001b[0m 4.759   \u001b[0m | \u001b[0m 0.2704  \u001b[0m |\n",
      "[23, 23, 23, 23]\n",
      "1.0\n",
      "solver: adam\n",
      "Training set score: 0.013144\n",
      "Test set score: 0.013159\n",
      "| \u001b[0m 34      \u001b[0m | \u001b[0m 0.01316 \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 23.27   \u001b[0m | \u001b[0m 4.27    \u001b[0m | \u001b[0m 0.1316  \u001b[0m |\n",
      "[223]\n",
      "0.4997347989072709\n",
      "solver: adam\n",
      "Training set score: 0.778577\n",
      "Test set score: 0.727702\n",
      "| \u001b[0m 35      \u001b[0m | \u001b[0m 0.7277  \u001b[0m | \u001b[0m 0.4997  \u001b[0m | \u001b[0m 223.4   \u001b[0m | \u001b[0m 1.918   \u001b[0m | \u001b[0m 0.9428  \u001b[0m |\n",
      "[223, 223, 223]\n",
      "0.5192659302291194\n",
      "solver: adam\n",
      "Training set score: 0.419918\n",
      "Test set score: 0.491816\n",
      "| \u001b[0m 36      \u001b[0m | \u001b[0m 0.4918  \u001b[0m | \u001b[0m 0.5193  \u001b[0m | \u001b[0m 223.2   \u001b[0m | \u001b[0m 3.27    \u001b[0m | \u001b[0m 0.01377 \u001b[0m |\n",
      "[20, 20, 20]\n",
      "0.05514624444145513\n",
      "solver: adam\n",
      "Training set score: 0.454229\n",
      "Test set score: 0.307841\n",
      "| \u001b[0m 37      \u001b[0m | \u001b[0m 0.3078  \u001b[0m | \u001b[0m 0.05515 \u001b[0m | \u001b[0m 20.98   \u001b[0m | \u001b[0m 3.349   \u001b[0m | \u001b[0m 0.4082  \u001b[0m |\n",
      "[222, 222, 222]\n",
      "0.7504437480362028\n",
      "solver: lbfgs\n",
      "Training set score: 0.592059\n",
      "Test set score: 0.648898\n",
      "| \u001b[0m 38      \u001b[0m | \u001b[0m 0.6489  \u001b[0m | \u001b[0m 0.7504  \u001b[0m | \u001b[0m 222.8   \u001b[0m | \u001b[0m 3.458   \u001b[0m | \u001b[0m 1.38    \u001b[0m |\n",
      "[224, 224, 224, 224]\n",
      "0.8975711126304734\n",
      "solver: lbfgs\n",
      "Training set score: 0.500119\n",
      "Test set score: 0.574082\n",
      "| \u001b[0m 39      \u001b[0m | \u001b[0m 0.5741  \u001b[0m | \u001b[0m 0.8976  \u001b[0m | \u001b[0m 224.1   \u001b[0m | \u001b[0m 4.035   \u001b[0m | \u001b[0m 1.406   \u001b[0m |\n",
      "[227]\n",
      "0.5675989850652138\n",
      "solver: adam\n",
      "Training set score: 0.724362\n",
      "Test set score: 0.720248\n",
      "| \u001b[0m 40      \u001b[0m | \u001b[0m 0.7202  \u001b[0m | \u001b[0m 0.5676  \u001b[0m | \u001b[0m 227.5   \u001b[0m | \u001b[0m 1.537   \u001b[0m | \u001b[0m 0.246   \u001b[0m |\n",
      "[228, 228]\n",
      "0.35399604517479527\n",
      "solver: lbfgs\n",
      "Training set score: 0.788994\n",
      "Test set score: 0.793430\n",
      "| \u001b[0m 41      \u001b[0m | \u001b[0m 0.7934  \u001b[0m | \u001b[0m 0.354   \u001b[0m | \u001b[0m 228.5   \u001b[0m | \u001b[0m 2.171   \u001b[0m | \u001b[0m 1.247   \u001b[0m |\n",
      "[227, 227, 227]\n",
      "0.5947951119597682\n",
      "solver: adam\n",
      "Training set score: 0.382421\n",
      "Test set score: 0.452155\n",
      "| \u001b[0m 42      \u001b[0m | \u001b[0m 0.4522  \u001b[0m | \u001b[0m 0.5948  \u001b[0m | \u001b[0m 227.5   \u001b[0m | \u001b[0m 3.057   \u001b[0m | \u001b[0m 0.5158  \u001b[0m |\n",
      "[229, 229]\n",
      "0.7610377660441612\n",
      "solver: adam\n",
      "Training set score: 0.466478\n",
      "Test set score: 0.544472\n",
      "| \u001b[0m 43      \u001b[0m | \u001b[0m 0.5445  \u001b[0m | \u001b[0m 0.761   \u001b[0m | \u001b[0m 229.4   \u001b[0m | \u001b[0m 2.893   \u001b[0m | \u001b[0m 0.1614  \u001b[0m |\n",
      "[223, 223, 223, 223]\n",
      "1e-05\n",
      "solver: adam\n",
      "Training set score: 0.897671\n",
      "Test set score: 0.791578\n",
      "| \u001b[0m 44      \u001b[0m | \u001b[0m 0.7916  \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 223.0   \u001b[0m | \u001b[0m 4.854   \u001b[0m | \u001b[0m 0.7994  \u001b[0m |\n",
      "[229]\n",
      "1.0\n",
      "solver: lbfgs\n",
      "Training set score: 0.759884\n",
      "Test set score: 0.777868\n",
      "| \u001b[0m 45      \u001b[0m | \u001b[0m 0.7779  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 230.0   \u001b[0m | \u001b[0m 1.854   \u001b[0m | \u001b[0m 1.502   \u001b[0m |\n",
      "[223, 223, 223, 223]\n",
      "0.06381846259151616\n",
      "solver: sgd\n",
      "Training set score: -0.042665\n",
      "Test set score: -0.118332\n",
      "| \u001b[0m 46      \u001b[0m | \u001b[0m-0.1183  \u001b[0m | \u001b[0m 0.06382 \u001b[0m | \u001b[0m 223.2   \u001b[0m | \u001b[0m 4.908   \u001b[0m | \u001b[0m 2.312   \u001b[0m |\n",
      "[229]\n",
      "1e-05\n",
      "solver: lbfgs\n",
      "Training set score: 0.947788\n",
      "Test set score: 0.625175\n",
      "| \u001b[0m 47      \u001b[0m | \u001b[0m 0.6252  \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 229.3   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.416   \u001b[0m |\n",
      "[228]\n",
      "0.9614707604280228\n",
      "solver: sgd\n",
      "Training set score: 0.238994\n",
      "Test set score: -0.041583\n",
      "| \u001b[0m 48      \u001b[0m | \u001b[0m-0.04158 \u001b[0m | \u001b[0m 0.9615  \u001b[0m | \u001b[0m 228.4   \u001b[0m | \u001b[0m 1.372   \u001b[0m | \u001b[0m 2.497   \u001b[0m |\n",
      "[223, 223, 223, 223, 223]\n",
      "1.0\n",
      "solver: adam\n",
      "Training set score: -0.000647\n",
      "Test set score: -0.000288\n",
      "| \u001b[0m 49      \u001b[0m | \u001b[0m-0.000287\u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 223.9   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "[230]\n",
      "0.01713706011160208\n",
      "solver: adam\n",
      "Training set score: 0.610154\n",
      "Test set score: 0.113443\n",
      "| \u001b[0m 50      \u001b[0m | \u001b[0m 0.1134  \u001b[0m | \u001b[0m 0.01714 \u001b[0m | \u001b[0m 230.6   \u001b[0m | \u001b[0m 1.527   \u001b[0m | \u001b[0m 0.05904 \u001b[0m |\n",
      "[229, 229, 229]\n",
      "1e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: lbfgs\n",
      "Training set score: 0.948245\n",
      "Test set score: 0.631691\n",
      "| \u001b[0m 51      \u001b[0m | \u001b[0m 0.6317  \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 229.8   \u001b[0m | \u001b[0m 3.014   \u001b[0m | \u001b[0m 1.739   \u001b[0m |\n",
      "[228, 228, 228]\n",
      "0.9200626150558804\n",
      "solver: lbfgs\n",
      "Training set score: 0.567755\n",
      "Test set score: 0.628564\n",
      "| \u001b[0m 52      \u001b[0m | \u001b[0m 0.6286  \u001b[0m | \u001b[0m 0.9201  \u001b[0m | \u001b[0m 228.7   \u001b[0m | \u001b[0m 3.725   \u001b[0m | \u001b[0m 1.569   \u001b[0m |\n",
      "[221, 221, 221, 221]\n",
      "0.26843017670965225\n",
      "solver: adam\n",
      "Training set score: 0.459968\n",
      "Test set score: 0.532416\n",
      "| \u001b[0m 53      \u001b[0m | \u001b[0m 0.5324  \u001b[0m | \u001b[0m 0.2684  \u001b[0m | \u001b[0m 221.6   \u001b[0m | \u001b[0m 4.252   \u001b[0m | \u001b[0m 0.7375  \u001b[0m |\n",
      "[221, 221]\n",
      "0.0023678745799228258\n",
      "solver: adam\n",
      "Training set score: 0.851853\n",
      "Test set score: 0.492962\n",
      "| \u001b[0m 54      \u001b[0m | \u001b[0m 0.493   \u001b[0m | \u001b[0m 0.002368\u001b[0m | \u001b[0m 221.7   \u001b[0m | \u001b[0m 2.183   \u001b[0m | \u001b[0m 0.68    \u001b[0m |\n",
      "[230, 230, 230]\n",
      "1.0\n",
      "solver: lbfgs\n",
      "Training set score: 0.557484\n",
      "Test set score: 0.615466\n",
      "| \u001b[0m 55      \u001b[0m | \u001b[0m 0.6155  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 230.9   \u001b[0m | \u001b[0m 3.437   \u001b[0m | \u001b[0m 1.149   \u001b[0m |\n",
      "[230, 230]\n",
      "1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.649327\n",
      "Test set score: 0.628421\n",
      "| \u001b[0m 56      \u001b[0m | \u001b[0m 0.6284  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 230.5   \u001b[0m | \u001b[0m 2.919   \u001b[0m | \u001b[0m 2.859   \u001b[0m |\n",
      "[231, 231]\n",
      "0.09043502642683889\n",
      "solver: sgd\n",
      "Training set score: -1.003988\n",
      "Test set score: -1.645782\n",
      "| \u001b[0m 57      \u001b[0m | \u001b[0m-1.646   \u001b[0m | \u001b[0m 0.09044 \u001b[0m | \u001b[0m 231.4   \u001b[0m | \u001b[0m 2.042   \u001b[0m | \u001b[0m 2.052   \u001b[0m |\n",
      "[229, 229, 229]\n",
      "1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.546229\n",
      "Test set score: 0.601652\n",
      "| \u001b[0m 58      \u001b[0m | \u001b[0m 0.6017  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 229.1   \u001b[0m | \u001b[0m 3.298   \u001b[0m | \u001b[0m 3.0     \u001b[0m |\n",
      "[230, 230, 230, 230]\n",
      "0.8621971046534439\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.488272\n",
      "Test set score: 0.518840\n",
      "| \u001b[0m 59      \u001b[0m | \u001b[0m 0.5188  \u001b[0m | \u001b[0m 0.8622  \u001b[0m | \u001b[0m 230.2   \u001b[0m | \u001b[0m 4.591   \u001b[0m | \u001b[0m 2.79    \u001b[0m |\n",
      "[229, 229, 229, 229]\n",
      "0.2202844637509541\n",
      "solver: adam\n",
      "Training set score: 0.454574\n",
      "Test set score: 0.528752\n",
      "| \u001b[0m 60      \u001b[0m | \u001b[0m 0.5288  \u001b[0m | \u001b[0m 0.2203  \u001b[0m | \u001b[0m 229.7   \u001b[0m | \u001b[0m 4.621   \u001b[0m | \u001b[0m 0.5387  \u001b[0m |\n",
      "[228, 228, 228, 228]\n",
      "0.15506967789356288\n",
      "solver: sgd\n",
      "Training set score: 0.018550\n",
      "Test set score: -0.203028\n",
      "| \u001b[0m 61      \u001b[0m | \u001b[0m-0.203   \u001b[0m | \u001b[0m 0.1551  \u001b[0m | \u001b[0m 228.7   \u001b[0m | \u001b[0m 4.833   \u001b[0m | \u001b[0m 2.292   \u001b[0m |\n",
      "[183, 183]\n",
      "0.5071646356074743\n",
      "solver: adam\n",
      "Training set score: 0.539805\n",
      "Test set score: 0.604597\n",
      "| \u001b[0m 62      \u001b[0m | \u001b[0m 0.6046  \u001b[0m | \u001b[0m 0.5072  \u001b[0m | \u001b[0m 183.6   \u001b[0m | \u001b[0m 2.137   \u001b[0m | \u001b[0m 0.8567  \u001b[0m |\n",
      "[185, 185]\n",
      "0.6076228556728677\n",
      "solver: lbfgs\n",
      "Training set score: 0.738572\n",
      "Test set score: 0.772586\n",
      "| \u001b[0m 63      \u001b[0m | \u001b[0m 0.7726  \u001b[0m | \u001b[0m 0.6076  \u001b[0m | \u001b[0m 185.1   \u001b[0m | \u001b[0m 2.36    \u001b[0m | \u001b[0m 1.662   \u001b[0m |\n",
      "[183]\n",
      "0.3858234601296824\n",
      "solver: sgd\n",
      "Training set score: -1.817283\n",
      "Test set score: -2.840161\n",
      "| \u001b[0m 64      \u001b[0m | \u001b[0m-2.84    \u001b[0m | \u001b[0m 0.3858  \u001b[0m | \u001b[0m 183.8   \u001b[0m | \u001b[0m 1.296   \u001b[0m | \u001b[0m 2.411   \u001b[0m |\n",
      "[185, 185]\n",
      "0.8607471952947524\n",
      "solver: adam\n",
      "Training set score: 0.461508\n",
      "Test set score: 0.532181\n",
      "| \u001b[0m 65      \u001b[0m | \u001b[0m 0.5322  \u001b[0m | \u001b[0m 0.8607  \u001b[0m | \u001b[0m 185.0   \u001b[0m | \u001b[0m 2.502   \u001b[0m | \u001b[0m 0.2771  \u001b[0m |\n",
      "[186, 186]\n",
      "0.12433724468599311\n",
      "solver: adam\n",
      "Training set score: 0.787226\n",
      "Test set score: 0.774116\n",
      "| \u001b[0m 66      \u001b[0m | \u001b[0m 0.7741  \u001b[0m | \u001b[0m 0.1243  \u001b[0m | \u001b[0m 186.1   \u001b[0m | \u001b[0m 2.138   \u001b[0m | \u001b[0m 0.9085  \u001b[0m |\n",
      "[185]\n",
      "0.868606260036447\n",
      "solver: adam\n",
      "Training set score: 0.654626\n",
      "Test set score: 0.674203\n",
      "| \u001b[0m 67      \u001b[0m | \u001b[0m 0.6742  \u001b[0m | \u001b[0m 0.8686  \u001b[0m | \u001b[0m 185.6   \u001b[0m | \u001b[0m 1.241   \u001b[0m | \u001b[0m 0.1848  \u001b[0m |\n",
      "[185, 185, 185]\n",
      "0.3450976871936193\n",
      "solver: lbfgs\n",
      "Training set score: 0.808298\n",
      "Test set score: 0.800829\n",
      "| \u001b[0m 68      \u001b[0m | \u001b[0m 0.8008  \u001b[0m | \u001b[0m 0.3451  \u001b[0m | \u001b[0m 185.8   \u001b[0m | \u001b[0m 3.67    \u001b[0m | \u001b[0m 1.451   \u001b[0m |\n",
      "[184, 184, 184, 184]\n",
      "0.32133224140671657\n",
      "solver: lbfgs\n",
      "Training set score: 0.748766\n",
      "Test set score: 0.771773\n",
      "| \u001b[0m 69      \u001b[0m | \u001b[0m 0.7718  \u001b[0m | \u001b[0m 0.3213  \u001b[0m | \u001b[0m 184.2   \u001b[0m | \u001b[0m 4.143   \u001b[0m | \u001b[0m 1.396   \u001b[0m |\n",
      "[185, 185, 185]\n",
      "0.9690772666182182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.570872\n",
      "Test set score: 0.603871\n",
      "| \u001b[0m 70      \u001b[0m | \u001b[0m 0.6039  \u001b[0m | \u001b[0m 0.9691  \u001b[0m | \u001b[0m 185.0   \u001b[0m | \u001b[0m 3.917   \u001b[0m | \u001b[0m 2.355   \u001b[0m |\n",
      "[183, 183, 183]\n",
      "1e-05\n",
      "solver: adam\n",
      "Training set score: 0.793778\n",
      "Test set score: 0.541093\n",
      "| \u001b[0m 71      \u001b[0m | \u001b[0m 0.5411  \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 183.7   \u001b[0m | \u001b[0m 3.426   \u001b[0m | \u001b[0m 0.0     \u001b[0m |\n",
      "[186, 186]\n",
      "0.8359818301804225\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.656678\n",
      "Test set score: 0.614381\n",
      "| \u001b[0m 72      \u001b[0m | \u001b[0m 0.6144  \u001b[0m | \u001b[0m 0.836   \u001b[0m | \u001b[0m 186.7   \u001b[0m | \u001b[0m 2.588   \u001b[0m | \u001b[0m 2.176   \u001b[0m |\n",
      "[185, 185, 185, 185]\n",
      "0.34603021048015575\n",
      "solver: adam\n",
      "Training set score: 0.336923\n",
      "Test set score: 0.397382\n",
      "| \u001b[0m 73      \u001b[0m | \u001b[0m 0.3974  \u001b[0m | \u001b[0m 0.346   \u001b[0m | \u001b[0m 185.3   \u001b[0m | \u001b[0m 4.755   \u001b[0m | \u001b[0m 0.2579  \u001b[0m |\n",
      "[187, 187, 187]\n",
      "0.770885733072726\n",
      "solver: adam\n",
      "Training set score: 0.262741\n",
      "Test set score: 0.312202\n",
      "| \u001b[0m 74      \u001b[0m | \u001b[0m 0.3122  \u001b[0m | \u001b[0m 0.7709  \u001b[0m | \u001b[0m 187.2   \u001b[0m | \u001b[0m 3.18    \u001b[0m | \u001b[0m 0.2555  \u001b[0m |\n",
      "[231, 231, 231, 231, 231]\n",
      "1.0\n",
      "solver: lbfgs\n",
      "Training set score: 0.430005\n",
      "Test set score: 0.512153\n",
      "| \u001b[0m 75      \u001b[0m | \u001b[0m 0.5122  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 231.3   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 1.249   \u001b[0m |\n",
      "[186, 186, 186, 186]\n",
      "0.3148071783459703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.628054\n",
      "Test set score: 0.632183\n",
      "| \u001b[0m 76      \u001b[0m | \u001b[0m 0.6322  \u001b[0m | \u001b[0m 0.3148  \u001b[0m | \u001b[0m 187.0   \u001b[0m | \u001b[0m 4.573   \u001b[0m | \u001b[0m 2.693   \u001b[0m |\n",
      "[188, 188, 188]\n",
      "0.04644923249811626\n",
      "solver: lbfgs\n",
      "Training set score: 0.897820\n",
      "Test set score: 0.814093\n",
      "| \u001b[0m 77      \u001b[0m | \u001b[0m 0.8141  \u001b[0m | \u001b[0m 0.04645 \u001b[0m | \u001b[0m 188.2   \u001b[0m | \u001b[0m 3.4     \u001b[0m | \u001b[0m 1.83    \u001b[0m |\n",
      "[188, 188, 188, 188]\n",
      "0.9816106950849316\n",
      "solver: lbfgs\n",
      "Training set score: 0.519310\n",
      "Test set score: 0.583736\n",
      "| \u001b[0m 78      \u001b[0m | \u001b[0m 0.5837  \u001b[0m | \u001b[0m 0.9816  \u001b[0m | \u001b[0m 188.2   \u001b[0m | \u001b[0m 4.692   \u001b[0m | \u001b[0m 1.606   \u001b[0m |\n",
      "[188, 188, 188]\n",
      "0.9555639896259173\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.577639\n",
      "Test set score: 0.584853\n",
      "| \u001b[0m 79      \u001b[0m | \u001b[0m 0.5849  \u001b[0m | \u001b[0m 0.9556  \u001b[0m | \u001b[0m 188.5   \u001b[0m | \u001b[0m 3.308   \u001b[0m | \u001b[0m 2.918   \u001b[0m |\n",
      "[189, 189, 189, 189]\n",
      "0.13983030300144267\n",
      "solver: sgd\n",
      "Training set score: -0.422277\n",
      "Test set score: -0.691715\n",
      "| \u001b[0m 80      \u001b[0m | \u001b[0m-0.6917  \u001b[0m | \u001b[0m 0.1398  \u001b[0m | \u001b[0m 189.4   \u001b[0m | \u001b[0m 4.505   \u001b[0m | \u001b[0m 2.574   \u001b[0m |\n",
      "[188, 188]\n",
      "0.9658946494244084\n",
      "solver: lbfgs\n",
      "Training set score: 0.647274\n",
      "Test set score: 0.692564\n",
      "| \u001b[0m 81      \u001b[0m | \u001b[0m 0.6926  \u001b[0m | \u001b[0m 0.9659  \u001b[0m | \u001b[0m 188.5   \u001b[0m | \u001b[0m 2.44    \u001b[0m | \u001b[0m 1.324   \u001b[0m |\n",
      "[188]\n",
      "0.1330143580199972\n",
      "solver: sgd\n",
      "Training set score: -1.646398\n",
      "Test set score: -2.815919\n",
      "| \u001b[0m 82      \u001b[0m | \u001b[0m-2.816   \u001b[0m | \u001b[0m 0.133   \u001b[0m | \u001b[0m 188.4   \u001b[0m | \u001b[0m 1.416   \u001b[0m | \u001b[0m 2.68    \u001b[0m |\n",
      "[189, 189, 189]\n",
      "0.683192477238874\n",
      "solver: adam\n",
      "Training set score: 0.348343\n",
      "Test set score: 0.409939\n",
      "| \u001b[0m 83      \u001b[0m | \u001b[0m 0.4099  \u001b[0m | \u001b[0m 0.6832  \u001b[0m | \u001b[0m 189.1   \u001b[0m | \u001b[0m 3.395   \u001b[0m | \u001b[0m 0.04812 \u001b[0m |\n",
      "[182, 182, 182]\n",
      "1.0\n",
      "solver: adam\n",
      "Training set score: 0.223548\n",
      "Test set score: 0.264886\n",
      "| \u001b[0m 84      \u001b[0m | \u001b[0m 0.2649  \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 182.8   \u001b[0m | \u001b[0m 3.727   \u001b[0m | \u001b[0m 0.9678  \u001b[0m |\n",
      "[187, 187, 187, 187]\n",
      "1e-05\n",
      "solver: lbfgs\n",
      "Training set score: 0.881256\n",
      "Test set score: 0.693880\n",
      "| \u001b[0m 85      \u001b[0m | \u001b[0m 0.6939  \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 187.1   \u001b[0m | \u001b[0m 4.476   \u001b[0m | \u001b[0m 1.122   \u001b[0m |\n",
      "[185, 185, 185, 185]\n",
      "0.01879806874738335\n",
      "solver: sgd\n",
      "Training set score: -0.298352\n",
      "Test set score: -0.369964\n",
      "| \u001b[0m 86      \u001b[0m | \u001b[0m-0.37    \u001b[0m | \u001b[0m 0.0188  \u001b[0m | \u001b[0m 185.7   \u001b[0m | \u001b[0m 4.95    \u001b[0m | \u001b[0m 2.301   \u001b[0m |\n",
      "[187, 187, 187]\n",
      "0.9599404327013248\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.559180\n",
      "Test set score: 0.574558\n",
      "| \u001b[0m 87      \u001b[0m | \u001b[0m 0.5746  \u001b[0m | \u001b[0m 0.9599  \u001b[0m | \u001b[0m 187.3   \u001b[0m | \u001b[0m 3.892   \u001b[0m | \u001b[0m 2.207   \u001b[0m |\n",
      "[143, 143, 143]\n",
      "0.35068653663197924\n",
      "solver: adam\n",
      "Training set score: 0.507119\n",
      "Test set score: 0.574281\n",
      "| \u001b[0m 88      \u001b[0m | \u001b[0m 0.5743  \u001b[0m | \u001b[0m 0.3507  \u001b[0m | \u001b[0m 144.0   \u001b[0m | \u001b[0m 3.118   \u001b[0m | \u001b[0m 0.06983 \u001b[0m |\n",
      "[145]\n",
      "0.5684500606190303\n",
      "solver: adam\n",
      "Training set score: 0.746111\n",
      "Test set score: 0.697180\n",
      "| \u001b[0m 89      \u001b[0m | \u001b[0m 0.6972  \u001b[0m | \u001b[0m 0.5685  \u001b[0m | \u001b[0m 145.1   \u001b[0m | \u001b[0m 1.669   \u001b[0m | \u001b[0m 0.0992  \u001b[0m |\n",
      "[144]\n",
      "0.9739277649879313\n",
      "solver: lbfgs\n",
      "Training set score: 0.769002\n",
      "Test set score: 0.787371\n",
      "| \u001b[0m 90      \u001b[0m | \u001b[0m 0.7874  \u001b[0m | \u001b[0m 0.9739  \u001b[0m | \u001b[0m 144.3   \u001b[0m | \u001b[0m 1.965   \u001b[0m | \u001b[0m 1.569   \u001b[0m |\n",
      "[145, 145]\n",
      "0.060924858032444955\n",
      "solver: lbfgs\n",
      "Training set score: 0.911844\n",
      "Test set score: 0.795668\n",
      "| \u001b[0m 91      \u001b[0m | \u001b[0m 0.7957  \u001b[0m | \u001b[0m 0.06092 \u001b[0m | \u001b[0m 145.3   \u001b[0m | \u001b[0m 2.457   \u001b[0m | \u001b[0m 1.136   \u001b[0m |\n",
      "[144, 144, 144]\n",
      "0.878212634234348\n",
      "solver: lbfgs\n",
      "Training set score: 0.586441\n",
      "Test set score: 0.641536\n",
      "| \u001b[0m 92      \u001b[0m | \u001b[0m 0.6415  \u001b[0m | \u001b[0m 0.8782  \u001b[0m | \u001b[0m 144.7   \u001b[0m | \u001b[0m 3.446   \u001b[0m | \u001b[0m 1.776   \u001b[0m |\n",
      "[145]\n",
      "0.06581211465292701\n",
      "solver: lbfgs\n",
      "Training set score: 0.945562\n",
      "Test set score: 0.690411\n",
      "| \u001b[0m 93      \u001b[0m | \u001b[0m 0.6904  \u001b[0m | \u001b[0m 0.06581 \u001b[0m | \u001b[0m 146.0   \u001b[0m | \u001b[0m 1.006   \u001b[0m | \u001b[0m 1.571   \u001b[0m |\n",
      "[145, 145]\n",
      "1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solver: sgd\n",
      "Training set score: 0.631905\n",
      "Test set score: 0.626024\n",
      "| \u001b[0m 94      \u001b[0m | \u001b[0m 0.626   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 145.8   \u001b[0m | \u001b[0m 2.169   \u001b[0m | \u001b[0m 2.46    \u001b[0m |\n",
      "[146]\n",
      "0.7627447586666313\n",
      "solver: adam\n",
      "Training set score: 0.721825\n",
      "Test set score: 0.693300\n",
      "| \u001b[0m 95      \u001b[0m | \u001b[0m 0.6933  \u001b[0m | \u001b[0m 0.7627  \u001b[0m | \u001b[0m 146.8   \u001b[0m | \u001b[0m 1.836   \u001b[0m | \u001b[0m 0.667   \u001b[0m |\n",
      "[146, 146, 146]\n",
      "0.813975432399833\n",
      "solver: lbfgs\n",
      "Training set score: 0.561095\n",
      "Test set score: 0.620145\n",
      "| \u001b[0m 96      \u001b[0m | \u001b[0m 0.6201  \u001b[0m | \u001b[0m 0.814   \u001b[0m | \u001b[0m 146.7   \u001b[0m | \u001b[0m 3.41    \u001b[0m | \u001b[0m 1.032   \u001b[0m |\n",
      "[143, 143]\n",
      "0.059726337948586235\n",
      "solver: lbfgs\n",
      "Training set score: 0.923943\n",
      "Test set score: 0.723808\n",
      "| \u001b[0m 97      \u001b[0m | \u001b[0m 0.7238  \u001b[0m | \u001b[0m 0.05973 \u001b[0m | \u001b[0m 143.4   \u001b[0m | \u001b[0m 2.76    \u001b[0m | \u001b[0m 1.605   \u001b[0m |\n",
      "[147, 147]\n",
      "1e-05\n",
      "solver: sgd\n",
      "Training set score: -1.018269\n",
      "Test set score: -1.116252\n",
      "| \u001b[0m 98      \u001b[0m | \u001b[0m-1.116   \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 147.3   \u001b[0m | \u001b[0m 2.29    \u001b[0m | \u001b[0m 2.123   \u001b[0m |\n",
      "[144]\n",
      "0.051131247422057685\n",
      "solver: sgd\n",
      "Training set score: -2.512186\n",
      "Test set score: -2.662515\n",
      "| \u001b[0m 99      \u001b[0m | \u001b[0m-2.663   \u001b[0m | \u001b[0m 0.05113 \u001b[0m | \u001b[0m 144.4   \u001b[0m | \u001b[0m 1.875   \u001b[0m | \u001b[0m 2.64    \u001b[0m |\n",
      "[145, 145, 145]\n",
      "0.9256832372275536\n",
      "solver: adam\n",
      "Training set score: 0.280161\n",
      "Test set score: 0.335209\n",
      "| \u001b[0m 100     \u001b[0m | \u001b[0m 0.3352  \u001b[0m | \u001b[0m 0.9257  \u001b[0m | \u001b[0m 145.5   \u001b[0m | \u001b[0m 3.044   \u001b[0m | \u001b[0m 0.4254  \u001b[0m |\n",
      "[142, 142]\n",
      "0.5782941028388388\n",
      "solver: adam\n",
      "Training set score: 0.543942\n",
      "Test set score: 0.603886\n",
      "| \u001b[0m 101     \u001b[0m | \u001b[0m 0.6039  \u001b[0m | \u001b[0m 0.5783  \u001b[0m | \u001b[0m 142.6   \u001b[0m | \u001b[0m 2.597   \u001b[0m | \u001b[0m 0.473   \u001b[0m |\n",
      "[143, 143, 143, 143]\n",
      "0.9570130949128943\n",
      "solver: lbfgs\n",
      "Training set score: 0.527312\n",
      "Test set score: 0.591878\n",
      "| \u001b[0m 102     \u001b[0m | \u001b[0m 0.5919  \u001b[0m | \u001b[0m 0.957   \u001b[0m | \u001b[0m 143.1   \u001b[0m | \u001b[0m 4.189   \u001b[0m | \u001b[0m 1.379   \u001b[0m |\n",
      "[145]\n",
      "0.9052492604909168\n",
      "solver: lbfgs\n",
      "Training set score: 0.779208\n",
      "Test set score: 0.796590\n",
      "| \u001b[0m 103     \u001b[0m | \u001b[0m 0.7966  \u001b[0m | \u001b[0m 0.9052  \u001b[0m | \u001b[0m 145.4   \u001b[0m | \u001b[0m 1.689   \u001b[0m | \u001b[0m 1.146   \u001b[0m |\n",
      "[141, 141, 141]\n",
      "0.5842207694520758\n",
      "solver: lbfgs\n",
      "Training set score: 0.655909\n",
      "Test set score: 0.697739\n",
      "| \u001b[0m 104     \u001b[0m | \u001b[0m 0.6977  \u001b[0m | \u001b[0m 0.5842  \u001b[0m | \u001b[0m 141.9   \u001b[0m | \u001b[0m 3.467   \u001b[0m | \u001b[0m 1.679   \u001b[0m |\n",
      "[145, 145, 145, 145]\n",
      "0.33833702720763476\n",
      "solver: lbfgs\n",
      "Training set score: 0.713369\n",
      "Test set score: 0.743829\n",
      "| \u001b[0m 105     \u001b[0m | \u001b[0m 0.7438  \u001b[0m | \u001b[0m 0.3383  \u001b[0m | \u001b[0m 145.2   \u001b[0m | \u001b[0m 4.878   \u001b[0m | \u001b[0m 1.001   \u001b[0m |\n",
      "[146, 146, 146, 146, 146]\n",
      "0.24006480158726057\n",
      "solver: adam\n",
      "Training set score: 0.343152\n",
      "Test set score: 0.404255\n",
      "| \u001b[0m 106     \u001b[0m | \u001b[0m 0.4043  \u001b[0m | \u001b[0m 0.2401  \u001b[0m | \u001b[0m 146.8   \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 0.3389  \u001b[0m |\n",
      "[143, 143, 143, 143]\n",
      "0.03185280252457479\n",
      "solver: adam\n",
      "Training set score: 0.850280\n",
      "Test set score: 0.795345\n",
      "| \u001b[0m 107     \u001b[0m | \u001b[0m 0.7953  \u001b[0m | \u001b[0m 0.03185 \u001b[0m | \u001b[0m 143.6   \u001b[0m | \u001b[0m 4.695   \u001b[0m | \u001b[0m 0.3893  \u001b[0m |\n",
      "[142, 142, 142, 142]\n",
      "1e-05\n",
      "solver: adam\n",
      "Training set score: 0.794800\n",
      "Test set score: 0.647572\n",
      "| \u001b[0m 108     \u001b[0m | \u001b[0m 0.6476  \u001b[0m | \u001b[0m 1e-05   \u001b[0m | \u001b[0m 142.0   \u001b[0m | \u001b[0m 4.269   \u001b[0m | \u001b[0m 0.326   \u001b[0m |\n",
      "[144, 144, 144, 144]\n",
      "0.05218485653520622\n",
      "solver: sgd\n",
      "Training set score: -0.748744\n",
      "Test set score: -1.314594\n",
      "| \u001b[0m 109     \u001b[0m | \u001b[0m-1.315   \u001b[0m | \u001b[0m 0.05218 \u001b[0m | \u001b[0m 144.4   \u001b[0m | \u001b[0m 4.967   \u001b[0m | \u001b[0m 2.099   \u001b[0m |\n",
      "[246, 246, 246]\n",
      "0.5250322347559733\n",
      "solver: sgd\n",
      "Training set score: 0.691754\n",
      "Test set score: 0.692418\n",
      "| \u001b[0m 110     \u001b[0m | \u001b[0m 0.6924  \u001b[0m | \u001b[0m 0.525   \u001b[0m | \u001b[0m 246.9   \u001b[0m | \u001b[0m 3.522   \u001b[0m | \u001b[0m 2.032   \u001b[0m |\n",
      "=========================================================================\n",
      "{'target': 0.8181067709510259, 'params': {'alphas': 0.2038498922449522, 'node': 224.3519632429965, 'number_hidden_layer': 2.764071738001656, 'solver_chose': 1.0569318442495188}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Note: Since Bayesian optimization is affected by the initial random candidates, it may cause the optimization hyperparameters to vary slightly from different machines\n",
    "pbounds = {'alphas': (1e-5, 1),\"node\":(1,256),\"number_hidden_layer\":(1,5),'solver_chose':(0,3)}\n",
    "optimizer_MLP = BayesianOptimization(f=MLP,pbounds=pbounds,random_state=1)\n",
    "bo=optimizer_MLP.maximize(init_points=10,n_iter=100)\n",
    "print(optimizer_MLP.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "889a3136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alphas': 0.2038498922449522,\n",
       " 'node': 224.3519632429965,\n",
       " 'number_hidden_layer': 2.764071738001656,\n",
       " 'solver_chose': 1.0569318442495188}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The set of hyperparameters of the MLP model\n",
    "MLP_par=optimizer_MLP.max.get('params')\n",
    "MLP_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bd7fbddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***==========Construction of MLP model==========***\n",
      "[224, 224]\n",
      "0.2038498922449522\n",
      "Training set score: 0.857644\n",
      "Test set score: 0.823241\n",
      "MSE: 0.000691\n",
      "RMSE: 0.026296\n"
     ]
    }
   ],
   "source": [
    "#MLP model (The results differ slightly from our article because different random seed was set up)\n",
    "print (\"***==========Construction of MLP model==========***\")\n",
    "node=MLP_par.get('node')\n",
    "number_hidden_layer=MLP_par.get('number_hidden_layer')\n",
    "solver_chose=MLP_par.get('solver_chose')\n",
    "alphas=MLP_par.get('alphas')\n",
    "randomseed = np.random.seed(5)\n",
    "if solver_chose<=1:\n",
    "    solver='adam'\n",
    "elif solver_chose>1 and solver_chose<=2:\n",
    "    solver='lbfgs'\n",
    "else:\n",
    "    solver='sgd'\n",
    "alphas=alphas\n",
    "nodes=int(node)\n",
    "number_layer=int(number_hidden_layer)\n",
    "layer_sizes=[nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes,nodes][0:number_layer:1]\n",
    "print(layer_sizes)\n",
    "print(alphas)\n",
    "clf = MLPRegressor(solver=solver,alpha=alphas,\n",
    "                   hidden_layer_sizes=layer_sizes, max_iter=1000,\n",
    "                   verbose=False, tol=0.0001,random_state=randomseed)\n",
    "clf.fit(Xtrain,y_train.values.ravel())\n",
    "print(\"Training set score: %f\" % clf.score(Xtrain,y_train))\n",
    "print(\"Test set score: %f\" % clf.score(Xtest,y_test))\n",
    "MLP_train = clf.predict(Xtrain)\n",
    "MLP_test = clf.predict(Xtest)\n",
    "mse=mean_squared_error(y_test,MLP_test)\n",
    "rmse=math.sqrt(mse)\n",
    "print(\"MSE: %f\" % mse)\n",
    "print(\"RMSE: %f\" % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "576f24be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***==========Plots of RF model prediction results==========***\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (\"***==========Plots of RF model prediction results==========***\")\n",
    "plt.rc('font',family='Arial',weight='normal')\n",
    "font1 = {'family' : 'Arial',\n",
    "'weight' : 'normal',\n",
    "'size'   : 14,\n",
    "}\n",
    "font2 = {'family' : 'Arial',\n",
    "'weight' : 'normal',\n",
    "'size'   : 14,\n",
    "}\n",
    "plt.figure(figsize=(4,3.5))\n",
    "plt.plot(y_train,RF_train,color='blue',marker='o',linestyle='', markerfacecolor='b',alpha=0.6)\n",
    "plt.plot(y_test,RF_test,color='darkred',marker='o',linestyle='', markerfacecolor='deeppink',alpha=0.6)\n",
    "plt.legend(labels=[\"Training data\",\"Test data\"],loc=\"lower right\",fontsize=12)  \n",
    "title='Calculated TC (W/mK)'\n",
    "title1='Predicted TC (W/mK)'\n",
    "plt.xlabel(title,font1 )\n",
    "plt.ylabel(title1,font1 )\n",
    "plt.xlim((0, 0.55))\n",
    "plt.ylim((0, 0.55))\n",
    "plt.plot([0,0.55],[0,0.55], color='k', linewidth=1.5, linestyle='--')\n",
    "my_x_ticks = np.arange(0,0.6,0.1)\n",
    "my_y_ticks = np.arange(0,0.6,0.1)\n",
    "plt.xticks(my_x_ticks,size=14)\n",
    "plt.yticks(my_y_ticks,size=14)\n",
    "plt.tick_params(width=1.5)\n",
    "bwith = 1.5 \n",
    "TK = plt.gca()\n",
    "TK.spines['bottom'].set_linewidth(bwith)\n",
    "TK.spines['left'].set_linewidth(bwith)\n",
    "TK.spines['top'].set_linewidth(bwith)\n",
    "TK.spines['right'].set_linewidth(bwith)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6ae8d513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***==========Plots of KRR model prediction results==========***\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (\"***==========Plots of KRR model prediction results==========***\")\n",
    "plt.rc('font',family='Arial',weight='normal')\n",
    "font1 = {'family' : 'Arial',\n",
    "'weight' : 'normal',\n",
    "'size'   : 14,\n",
    "}\n",
    "font2 = {'family' : 'Arial',\n",
    "'weight' : 'normal',\n",
    "'size'   : 14,\n",
    "}\n",
    "plt.figure(figsize=(4,3.5))\n",
    "#plt.title(\"Traing dataset with r2=0.955\",font1)\n",
    "plt.plot(y_train,KRR_train,color='blue',marker='o',linestyle='', markerfacecolor='b',alpha=0.6)\n",
    "plt.plot(y_test,KRR_test,color='darkred',marker='o',linestyle='', markerfacecolor='deeppink',alpha=0.6)\n",
    "plt.legend(labels=[\"Training data\",\"Test data\"],loc=\"lower right\",fontsize=12)  \n",
    "title='Calculated TC (W/mK)'\n",
    "title1='Predicted TC (W/mK)'\n",
    "plt.xlabel(title,font1 )\n",
    "plt.ylabel(title1,font1 )\n",
    "plt.xlim((0, 0.55))\n",
    "plt.ylim((0, 0.55))\n",
    "plt.plot([0,0.55],[0,0.55], color='k', linewidth=1.5, linestyle='--')\n",
    "my_x_ticks = np.arange(0,0.6,0.1)\n",
    "my_y_ticks = np.arange(0,0.6,0.1)\n",
    "plt.xticks(my_x_ticks,size=14)\n",
    "plt.yticks(my_y_ticks,size=14)\n",
    "plt.tick_params(width=1.5)\n",
    "bwith = 1.5\n",
    "TK = plt.gca()\n",
    "TK.spines['bottom'].set_linewidth(bwith)\n",
    "TK.spines['left'].set_linewidth(bwith)\n",
    "TK.spines['top'].set_linewidth(bwith)\n",
    "TK.spines['right'].set_linewidth(bwith)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9af70cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***==========Plots of MLP model prediction results==========***\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (\"***==========Plots of MLP model prediction results==========***\")\n",
    "plt.rc('font',family='Arial',weight='normal')\n",
    "font1 = {'family' : 'Arial',\n",
    "'weight' : 'normal',\n",
    "'size'   : 14,\n",
    "}\n",
    "font2 = {'family' : 'Arial',\n",
    "'weight' : 'normal',\n",
    "'size'   : 14,\n",
    "}\n",
    "plt.figure(figsize=(4,3.5))\n",
    "#plt.title(\"Traing dataset with r2=0.955\",font1)\n",
    "plt.plot(y_train,MLP_train,color='blue',marker='o',linestyle='', markerfacecolor='b',alpha=0.6)\n",
    "plt.plot(y_test,MLP_test,color='darkred',marker='o',linestyle='', markerfacecolor='deeppink',alpha=0.6)\n",
    "plt.legend(labels=[\"Training data\",\"Test data\"],loc=\"lower right\",fontsize=12)  \n",
    "title='Calculated TC (W/mK)'\n",
    "title1='Predicted TC (W/mK)'\n",
    "plt.xlabel(title,font1 )\n",
    "plt.ylabel(title1,font1 )\n",
    "plt.xlim((0, 0.55))\n",
    "plt.ylim((0, 0.55))\n",
    "plt.plot([0,0.55],[0,0.55], color='k', linewidth=1.5, linestyle='--')\n",
    "my_x_ticks = np.arange(0,0.6,0.1)\n",
    "my_y_ticks = np.arange(0,0.6,0.1)\n",
    "plt.xticks(my_x_ticks,size=14)\n",
    "plt.yticks(my_y_ticks,size=14)\n",
    "plt.tick_params(width=1.5)\n",
    "bwith = 1.5\n",
    "TK = plt.gca()\n",
    "TK.spines['bottom'].set_linewidth(bwith)\n",
    "TK.spines['left'].set_linewidth(bwith)\n",
    "TK.spines['top'].set_linewidth(bwith)\n",
    "TK.spines['right'].set_linewidth(bwith)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cf3db90d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MLP_Opt.model']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path1=\"./pretrained_model/\"\n",
    "RF_name = 'RF_Opt.model'\n",
    "KRR_name = 'KRR_Opt.model'\n",
    "MLP_name = 'MLP_Opt.model'\n",
    "joblib.dump(rfg, path1+RF_name)\n",
    "joblib.dump(model, path1+KRR_name)\n",
    "joblib.dump(clf, path1+MLP_name)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}