{
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Financial Distress</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
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       "      <th>x73</th>\n",
       "      <th>x74</th>\n",
       "      <th>x75</th>\n",
       "      <th>x76</th>\n",
       "      <th>x77</th>\n",
       "      <th>x78</th>\n",
       "      <th>x79</th>\n",
       "      <th>x81</th>\n",
       "      <th>x82</th>\n",
       "      <th>x83</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.2810</td>\n",
       "      <td>0.022934</td>\n",
       "      <td>0.87454</td>\n",
       "      <td>1.21640</td>\n",
       "      <td>0.060940</td>\n",
       "      <td>0.188270</td>\n",
       "      <td>0.52510</td>\n",
       "      <td>0.018854</td>\n",
       "      <td>0.182790</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>85.437</td>\n",
       "      <td>27.07</td>\n",
       "      <td>26.102</td>\n",
       "      <td>16.000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.060390</td>\n",
       "      <td>30</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.2700</td>\n",
       "      <td>0.006454</td>\n",
       "      <td>0.82067</td>\n",
       "      <td>1.00490</td>\n",
       "      <td>-0.014080</td>\n",
       "      <td>0.181040</td>\n",
       "      <td>0.62288</td>\n",
       "      <td>0.006423</td>\n",
       "      <td>0.035991</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>107.090</td>\n",
       "      <td>31.31</td>\n",
       "      <td>30.194</td>\n",
       "      <td>17.000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.010636</td>\n",
       "      <td>31</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0529</td>\n",
       "      <td>-0.059379</td>\n",
       "      <td>0.92242</td>\n",
       "      <td>0.72926</td>\n",
       "      <td>0.020476</td>\n",
       "      <td>0.044865</td>\n",
       "      <td>0.43292</td>\n",
       "      <td>-0.081423</td>\n",
       "      <td>-0.765400</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>120.870</td>\n",
       "      <td>36.07</td>\n",
       "      <td>35.273</td>\n",
       "      <td>17.000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.455970</td>\n",
       "      <td>32</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1131</td>\n",
       "      <td>-0.015229</td>\n",
       "      <td>0.85888</td>\n",
       "      <td>0.80974</td>\n",
       "      <td>0.076037</td>\n",
       "      <td>0.091033</td>\n",
       "      <td>0.67546</td>\n",
       "      <td>-0.018807</td>\n",
       "      <td>-0.107910</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>54.806</td>\n",
       "      <td>39.80</td>\n",
       "      <td>38.377</td>\n",
       "      <td>17.167</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-0.325390</td>\n",
       "      <td>33</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0623</td>\n",
       "      <td>0.107020</td>\n",
       "      <td>0.81460</td>\n",
       "      <td>0.83593</td>\n",
       "      <td>0.199960</td>\n",
       "      <td>0.047800</td>\n",
       "      <td>0.74200</td>\n",
       "      <td>0.128030</td>\n",
       "      <td>0.577250</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>85.437</td>\n",
       "      <td>27.07</td>\n",
       "      <td>26.102</td>\n",
       "      <td>16.000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.251000</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>3667</th>\n",
       "      <td>0</td>\n",
       "      <td>2.2605</td>\n",
       "      <td>0.202890</td>\n",
       "      <td>0.16037</td>\n",
       "      <td>0.18588</td>\n",
       "      <td>0.175970</td>\n",
       "      <td>0.198400</td>\n",
       "      <td>2.22360</td>\n",
       "      <td>1.091500</td>\n",
       "      <td>0.241640</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>100.000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.000</td>\n",
       "      <td>17.125</td>\n",
       "      <td>14.5</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.436380</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>3668</th>\n",
       "      <td>0</td>\n",
       "      <td>1.9615</td>\n",
       "      <td>0.216440</td>\n",
       "      <td>0.20095</td>\n",
       "      <td>0.21642</td>\n",
       "      <td>0.203590</td>\n",
       "      <td>0.189870</td>\n",
       "      <td>1.93820</td>\n",
       "      <td>1.000100</td>\n",
       "      <td>0.270870</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>91.500</td>\n",
       "      <td>130.50</td>\n",
       "      <td>132.400</td>\n",
       "      <td>20.000</td>\n",
       "      <td>14.5</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0.438020</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
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       "      <th>3669</th>\n",
       "      <td>0</td>\n",
       "      <td>1.7099</td>\n",
       "      <td>0.207970</td>\n",
       "      <td>0.26136</td>\n",
       "      <td>0.21399</td>\n",
       "      <td>0.193670</td>\n",
       "      <td>0.183890</td>\n",
       "      <td>1.68980</td>\n",
       "      <td>0.971860</td>\n",
       "      <td>0.281560</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>87.100</td>\n",
       "      <td>175.90</td>\n",
       "      <td>178.100</td>\n",
       "      <td>20.000</td>\n",
       "      <td>14.5</td>\n",
       "      <td>-20.2</td>\n",
       "      <td>0.482410</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
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       "      <th>3670</th>\n",
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       "      <td>1.5590</td>\n",
       "      <td>0.185450</td>\n",
       "      <td>0.30728</td>\n",
       "      <td>0.19307</td>\n",
       "      <td>0.172140</td>\n",
       "      <td>0.170680</td>\n",
       "      <td>1.53890</td>\n",
       "      <td>0.960570</td>\n",
       "      <td>0.267720</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>92.900</td>\n",
       "      <td>203.20</td>\n",
       "      <td>204.500</td>\n",
       "      <td>22.000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.500770</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
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       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>0</td>\n",
       "      <td>1.6148</td>\n",
       "      <td>0.176760</td>\n",
       "      <td>0.36369</td>\n",
       "      <td>0.18442</td>\n",
       "      <td>0.169550</td>\n",
       "      <td>0.197860</td>\n",
       "      <td>1.58420</td>\n",
       "      <td>0.958450</td>\n",
       "      <td>0.277780</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>91.700</td>\n",
       "      <td>227.50</td>\n",
       "      <td>214.500</td>\n",
       "      <td>21.000</td>\n",
       "      <td>20.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.611030</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3672 rows × 83 columns</p>\n",
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      ],
      "text/plain": [
       "      Financial Distress      x1        x2       x3       x4        x5  \\\n",
       "0                      0  1.2810  0.022934  0.87454  1.21640  0.060940   \n",
       "1                      0  1.2700  0.006454  0.82067  1.00490 -0.014080   \n",
       "2                      0  1.0529 -0.059379  0.92242  0.72926  0.020476   \n",
       "3                      1  1.1131 -0.015229  0.85888  0.80974  0.076037   \n",
       "4                      0  1.0623  0.107020  0.81460  0.83593  0.199960   \n",
       "...                  ...     ...       ...      ...      ...       ...   \n",
       "3667                   0  2.2605  0.202890  0.16037  0.18588  0.175970   \n",
       "3668                   0  1.9615  0.216440  0.20095  0.21642  0.203590   \n",
       "3669                   0  1.7099  0.207970  0.26136  0.21399  0.193670   \n",
       "3670                   0  1.5590  0.185450  0.30728  0.19307  0.172140   \n",
       "3671                   0  1.6148  0.176760  0.36369  0.18442  0.169550   \n",
       "\n",
       "            x6       x7        x8        x9  ...   x73      x74     x75  \\\n",
       "0     0.188270  0.52510  0.018854  0.182790  ...  36.0   85.437   27.07   \n",
       "1     0.181040  0.62288  0.006423  0.035991  ...  36.0  107.090   31.31   \n",
       "2     0.044865  0.43292 -0.081423 -0.765400  ...  35.0  120.870   36.07   \n",
       "3     0.091033  0.67546 -0.018807 -0.107910  ...  33.0   54.806   39.80   \n",
       "4     0.047800  0.74200  0.128030  0.577250  ...  36.0   85.437   27.07   \n",
       "...        ...      ...       ...       ...  ...   ...      ...     ...   \n",
       "3667  0.198400  2.22360  1.091500  0.241640  ...  22.0  100.000  100.00   \n",
       "3668  0.189870  1.93820  1.000100  0.270870  ...  28.0   91.500  130.50   \n",
       "3669  0.183890  1.68980  0.971860  0.281560  ...  32.0   87.100  175.90   \n",
       "3670  0.170680  1.53890  0.960570  0.267720  ...  30.0   92.900  203.20   \n",
       "3671  0.197860  1.58420  0.958450  0.277780  ...  29.0   91.700  227.50   \n",
       "\n",
       "          x76     x77   x78   x79       x81  x82  x83  \n",
       "0      26.102  16.000  16.0   0.2  0.060390   30   49  \n",
       "1      30.194  17.000  16.0   0.4  0.010636   31   50  \n",
       "2      35.273  17.000  15.0  -0.2 -0.455970   32   51  \n",
       "3      38.377  17.167  16.0   5.6 -0.325390   33   52  \n",
       "4      26.102  16.000  16.0   0.2  1.251000    7   27  \n",
       "...       ...     ...   ...   ...       ...  ...  ...  \n",
       "3667  100.000  17.125  14.5  -7.0  0.436380    4   41  \n",
       "3668  132.400  20.000  14.5 -16.0  0.438020    5   42  \n",
       "3669  178.100  20.000  14.5 -20.2  0.482410    6   43  \n",
       "3670  204.500  22.000  22.0   6.4  0.500770    7   44  \n",
       "3671  214.500  21.000  20.5   8.6  0.611030    8   45  \n",
       "\n",
       "[3672 rows x 83 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "def get_class(row):\n",
    "    return 0 if row[\"Financial Distress\"] > -0.5 else 1\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"data-distress/FinancialDistress.csv\").drop(\n",
    "    [\"Company\", \"Time\", \"x80\"], axis=1\n",
    ")\n",
    "df[\"Financial Distress\"] = df.apply(get_class, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>x1</th>\n",
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       "      <th>x81</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.0902</td>\n",
       "      <td>0.081715</td>\n",
       "      <td>0.71056</td>\n",
       "      <td>0.93446</td>\n",
       "      <td>0.14445</td>\n",
       "      <td>0.060042</td>\n",
       "      <td>0.74048</td>\n",
       "      <td>0.087447</td>\n",
       "      <td>0.28232</td>\n",
       "      <td>0.14572</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>79.951</td>\n",
       "      <td>66.12</td>\n",
       "      <td>59.471</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>1.1152</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6711</td>\n",
       "      <td>0.445250</td>\n",
       "      <td>0.21104</td>\n",
       "      <td>0.59523</td>\n",
       "      <td>0.30998</td>\n",
       "      <td>0.133260</td>\n",
       "      <td>1.34060</td>\n",
       "      <td>0.748040</td>\n",
       "      <td>0.56436</td>\n",
       "      <td>0.48288</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>87.100</td>\n",
       "      <td>175.90</td>\n",
       "      <td>178.100</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>-20.2</td>\n",
       "      <td>1.7354</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.6321</td>\n",
       "      <td>0.375790</td>\n",
       "      <td>0.46072</td>\n",
       "      <td>0.90327</td>\n",
       "      <td>0.28563</td>\n",
       "      <td>0.287440</td>\n",
       "      <td>1.35890</td>\n",
       "      <td>0.416040</td>\n",
       "      <td>0.69684</td>\n",
       "      <td>0.45008</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>92.900</td>\n",
       "      <td>203.20</td>\n",
       "      <td>204.500</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.5809</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 82 columns</p>\n",
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   ],
   "source": [
    "from collections import Counter\n",
    "from src.utils import split_stratified_into_train_val_test\n",
    "from imblearn.over_sampling import ADASYN\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_stratified_into_train_val_test(\n",
    "    df,\n",
    "    stratify_colname=\"Financial Distress\",\n",
    "    frac_train=0.8,\n",
    "    frac_val=0,\n",
    "    frac_test=0.2,\n",
    "    random_state=random_state,\n",
    ")\n",
    "\n",
    "ada = ADASYN()\n",
    "\n",
    "X_train, y_train = ada.fit_resample(X_train, y_train)\n",
    "\n",
    "\n",
    "# print(f\"Original dataset shape {len(df[[\"Financial Distress\"]])}\")\n",
    "# X, y = reducer.fit_resample(\n",
    "#     df.drop([\"Financial Distress\"], axis=1), df[[\"Financial Distress\"]]\n",
    "# )\n",
    "# print(f\"Original dataset shape {len(y)}\")\n",
    "\n",
    "\n",
    "# X_train = pd.DataFrame(\n",
    "#     sc.fit_transform(X_train.values), columns=X_train.columns, index=X_train.index\n",
    "# )\n",
    "# X_test = pd.DataFrame(\n",
    "#     sc.fit_transform(X_test.values), columns=X_test.columns, index=X_test.index\n",
    "# )\n",
    "\n",
    "\n",
    "display(X_train.head(3))\n",
    "display(y_train.head(3))\n",
    "display(X_test.head(3))\n",
    "display(y_test.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
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      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names\n",
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       "        0.35294118, 0.15901814, 1.        , 1.        , 0.04451683,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.98984772, 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.04451683, 1.        , 1.        , 0.04451683, 1.        ,\n",
       "        0.15901814, 1.        , 0.04451683, 1.        , 1.        ,\n",
       "        1.        , 0.64285714, 1.        , 0.04451683, 1.        ,\n",
       "        0.15901814, 1.        , 1.        , 0.15901814, 1.        ,\n",
       "        1.        , 0.04451683, 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.04451683, 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.04451683, 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.98984772, 0.04451683, 0.04451683,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.04451683, 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 0.98984772,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 0.98984772, 0.04451683, 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.15901814, 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.15901814, 1.        , 1.        ,\n",
       "        1.        , 0.98984772, 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 0.15901814,\n",
       "        0.04451683, 1.        , 1.        , 0.98984772, 1.        ]),\n",
       " 'preds': array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "        1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "        1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "        1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "        0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "        1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "        1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "        1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "        1, 1, 1, 0, 0, 1, 1, 1, 1]),\n",
       " 'Precision_train': np.float64(0.9209039548022598),\n",
       " 'Precision_test': np.float64(0.015552099533437015),\n",
       " 'Recall_train': np.float64(0.9889364739471805),\n",
       " 'Recall_test': np.float64(0.45454545454545453),\n",
       " 'Accuracy_train': 0.9521777777777778,\n",
       " 'Accuracy_test': 0.12244897959183673,\n",
       " 'ROC_AUC_test': np.float64(0.23084278974882058),\n",
       " 'F1_train': np.float64(0.9537084839098262),\n",
       " 'F1_test': np.float64(0.03007518796992481),\n",
       " 'MCC_test': np.float64(-0.22309912384470268),\n",
       " 'Cohen_kappa_test': np.float64(-0.029516833411874055),\n",
       " 'Confusion_matrix': array([[ 80, 633],\n",
       "        [ 12,  10]])}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.utils import run_classification, run_regression\n",
    "from sklearn import tree\n",
    "\n",
    "\n",
    "fitted_model = tree.DecisionTreeClassifier(max_depth=7, random_state=random_state).fit(\n",
    "    X_train.values, y_train.values.ravel()\n",
    ")\n",
    "result = run_classification(\n",
    "    fitted_model, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test\n",
    ")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n",
      "450 fits failed out of a total of 1800.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "450 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\tree\\_classes.py\", line 1377, in fit\n",
      "    super()._fit(\n",
      "  File \"c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\tree\\_classes.py\", line 269, in _fit\n",
      "    raise ValueError(\n",
      "ValueError: Some value(s) of y are negative which is not allowed for Poisson regression.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [-4.40722975 -4.40722975 -4.40722975 -4.40722975 -4.40722975 -4.40722975\n",
      " -4.40722975 -4.40722975 -4.40722975 -4.02547375 -4.02547375 -4.02547375\n",
      " -4.02547375 -4.02547375 -4.02547375 -4.02547375 -4.02547375 -4.02547375\n",
      " -4.17872998 -4.17872998 -4.1794137  -4.18153603 -4.18153603 -4.18153603\n",
      " -4.18153603 -4.18153603 -4.18153603 -4.29228886 -4.27823005 -4.27378326\n",
      " -4.27592366 -4.27917056 -4.23213065 -4.22140931 -4.2203268  -4.216674\n",
      " -4.22464637 -4.33616785 -4.27369282 -4.25199473 -4.31065536 -4.27417069\n",
      " -4.24259248 -4.2377248  -4.2476017  -4.32852991 -4.31823651 -4.28553229\n",
      " -4.34177931 -4.30250957 -4.28753163 -4.28076012 -4.25271136 -4.25347712\n",
      " -4.3969495  -4.36131695 -4.32082651 -4.31057625 -4.33653274 -4.3316473\n",
      " -4.29354845 -4.30058389 -4.2945977  -4.33373406 -4.38833647 -4.35267217\n",
      " -4.33498752 -4.4013588  -4.32169037 -4.30799648 -4.30856117 -4.35205443\n",
      " -4.38832687 -4.37876963 -4.36158341 -4.33729531 -4.36291152 -4.34227116\n",
      " -4.33926356 -4.31523722 -4.31237617 -4.42139371 -4.35079669 -4.40994949\n",
      " -4.35999754 -4.37508884 -4.39768089 -4.36889387 -4.31988427 -4.30750986\n",
      "  0.2652827   0.2652827   0.2652827   0.2652827   0.2652827   0.2652827\n",
      "  0.2652827   0.2652827   0.2652827  -1.9287676  -1.9287676  -1.9287676\n",
      " -1.9287676  -1.9287676  -1.9287676  -1.9287676  -1.9287676  -1.9287676\n",
      " -1.97020055 -1.97020055 -1.97042935 -1.97042935 -1.97004014 -1.97004014\n",
      " -1.9700399  -1.99709038 -1.99715081 -2.00301099 -1.99717467 -1.9930943\n",
      " -1.99098557 -1.98702489 -1.98700995 -1.98699179 -2.01302486 -2.01501513\n",
      " -2.10894255 -2.10994722 -2.09952227 -2.08394081 -2.08202479 -2.07188081\n",
      " -2.07112545 -2.09099692 -2.09034787 -2.15392376 -2.1403913  -2.14641712\n",
      " -2.12768247 -2.11768777 -2.11183283 -2.03846712 -2.12716538 -2.1272445\n",
      " -2.16307528 -2.17486177 -2.19327604 -2.16449752 -2.08104295 -2.13803493\n",
      " -2.08495978 -2.14818606 -2.14245762 -2.18712444 -2.20432741 -2.18741809\n",
      " -2.17137267 -2.16649999 -2.09136755 -2.16386709 -2.17518238 -2.15065344\n",
      " -2.24366056 -2.14969955 -2.12174719 -2.11054372 -2.17740208 -2.15664086\n",
      " -2.16327196 -2.1757021  -2.16935629 -2.20600588 -2.21021639 -2.20224851\n",
      " -2.19087629 -2.18339126 -2.16722284 -2.16000468 -2.18148192 -2.17331625\n",
      " -4.40722975 -4.40722975 -4.40722975 -4.40722975 -4.40722975 -4.40722975\n",
      " -4.40722975 -4.40722975 -4.40722975 -4.02547375 -4.02547375 -4.02547375\n",
      " -4.02547375 -4.02547375 -4.02547375 -4.02547375 -4.02547375 -4.02547375\n",
      " -4.17872998 -4.17872998 -4.1794137  -4.18153603 -4.18153603 -4.18153603\n",
      " -4.18153603 -4.18153603 -4.18153603 -4.31301085 -4.27817322 -4.27362102\n",
      " -4.27576142 -4.27917056 -4.23213065 -4.22140931 -4.2203268  -4.216674\n",
      " -4.308967   -4.33406277 -4.27031206 -4.25187889 -4.30964117 -4.27417069\n",
      " -4.24259248 -4.23837042 -4.2476017  -4.30938294 -4.35432558 -4.28424212\n",
      " -4.3429458  -4.3011881  -4.28789821 -4.28076012 -4.25271136 -4.25347712\n",
      " -4.36334041 -4.36324461 -4.32002519 -4.3118746  -4.33707678 -4.33137066\n",
      " -4.29320914 -4.30010728 -4.2945977  -4.38237244 -4.38340433 -4.34774631\n",
      " -4.33676135 -4.40583985 -4.33138452 -4.30737588 -4.30825185 -4.35224119\n",
      " -4.38925553 -4.35711249 -4.35484652 -4.34829607 -4.3661772  -4.35261695\n",
      " -4.33890487 -4.31498202 -4.31236225 -4.43228313 -4.38839311 -4.40088779\n",
      " -4.35168819 -4.38046828 -4.39657208 -4.36830249 -4.31947573 -4.30727193\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan\n",
      "         nan         nan         nan         nan         nan         nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'absolute_error', 'max_depth': 1, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "\n",
    "parameters = {\n",
    "    \"criterion\": [\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"],\n",
    "    \"max_depth\": np.arange(1, 21).tolist()[0::2],\n",
    "    \"min_samples_split\": np.arange(2, 20).tolist()[0::2],\n",
    "}\n",
    "\n",
    "grid = model_selection.GridSearchCV(\n",
    "    tree.DecisionTreeRegressor(random_state=random_state), parameters, n_jobs=-1\n",
    ")\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'RMSE_test': 1.1620245531837428,\n",
       " 'RMAE_test': 0.793506853815132,\n",
       " 'R2_test': 0.32558767171344627}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'fitted': DecisionTreeRegressor(criterion='absolute_error', max_depth=1, random_state=9),\n",
       " 'train_preds': array([0.40003, 2.4023 , 2.4023 , ..., 0.40003, 0.40003, 0.40003]),\n",
       " 'preds': array([0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 2.4023 , 0.40003, 2.4023 , 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 2.4023 , 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 ,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 2.4023 , 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 2.4023 , 0.40003, 2.4023 , 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 2.4023 , 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 2.4023 , 2.4023 , 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        2.4023 , 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        2.4023 , 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 2.4023 , 2.4023 , 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 2.4023 , 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 2.4023 , 2.4023 , 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        0.40003, 0.40003, 2.4023 , 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 2.4023 ,\n",
       "        0.40003, 2.4023 , 0.40003, 0.40003, 2.4023 , 0.40003, 2.4023 ,\n",
       "        0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003, 0.40003,\n",
       "        2.4023 , 0.40003, 0.40003, 2.4023 , 2.4023 , 0.40003, 2.4023 ]),\n",
       " 'RMSE_train': 2.6924305509873223,\n",
       " 'RMSE_test': 1.198383084740795,\n",
       " 'RMAE_test': 0.8489844401433057,\n",
       " 'R2_test': 0.2827241118036927}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "old_metrics = {\n",
    "    \"RMSE_test\": models[\"decision_tree\"][\"RMSE_test\"],\n",
    "    \"RMAE_test\": models[\"decision_tree\"][\"RMAE_test\"],\n",
    "    \"R2_test\": models[\"decision_tree\"][\"R2_test\"],\n",
    "}\n",
    "new_metrics = run_regression(grid.best_estimator_, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)\n",
    "\n",
    "display(old_metrics)\n",
    "display(new_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- x81 <= -0.00\n",
      "|   |--- x81 <= -0.44\n",
      "|   |   |--- x4 <= 0.58\n",
      "|   |   |   |--- x58 <= -0.08\n",
      "|   |   |   |   |--- class: 0\n",
      "|   |   |   |--- x58 >  -0.08\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |--- x4 >  0.58\n",
      "|   |   |   |--- x38 <= 0.04\n",
      "|   |   |   |   |--- x81 <= -0.48\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- x81 >  -0.48\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- x38 >  0.04\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |--- x81 >  -0.44\n",
      "|   |   |--- x44 <= -0.07\n",
      "|   |   |   |--- x40 <= 0.33\n",
      "|   |   |   |   |--- x3 <= 0.72\n",
      "|   |   |   |   |   |--- x23 <= 0.17\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- x23 >  0.17\n",
      "|   |   |   |   |   |   |--- x15 <= 1.22\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- x15 >  1.22\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- x3 >  0.72\n",
      "|   |   |   |   |   |--- x41 <= 9.63\n",
      "|   |   |   |   |   |   |--- x81 <= -0.40\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- x81 >  -0.40\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- x41 >  9.63\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- x40 >  0.33\n",
      "|   |   |   |   |--- x75 <= 41.70\n",
      "|   |   |   |   |   |--- x23 <= 0.21\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- x23 >  0.21\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- x75 >  41.70\n",
      "|   |   |   |   |   |--- x10 <= -0.19\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- x10 >  -0.19\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |--- x44 >  -0.07\n",
      "|   |   |   |--- x57 <= 0.18\n",
      "|   |   |   |   |--- x7 <= 0.74\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- x7 >  0.74\n",
      "|   |   |   |   |   |--- x15 <= 3.68\n",
      "|   |   |   |   |   |   |--- x28 <= -0.12\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- x28 >  -0.12\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- x15 >  3.68\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- x57 >  0.18\n",
      "|   |   |   |   |--- x49 <= 2.63\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- x49 >  2.63\n",
      "|   |   |   |   |   |--- x71 <= 45.23\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- x71 >  45.23\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|--- x81 >  -0.00\n",
      "|   |--- x25 <= -812.33\n",
      "|   |   |--- class: 1\n",
      "|   |--- x25 >  -812.33\n",
      "|   |   |--- x36 <= 0.08\n",
      "|   |   |   |--- x47 <= 17.10\n",
      "|   |   |   |   |--- x43 <= 0.15\n",
      "|   |   |   |   |   |--- x13 <= 0.07\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- x13 >  0.07\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- x43 >  0.15\n",
      "|   |   |   |   |   |--- x75 <= 33.09\n",
      "|   |   |   |   |   |   |--- x42 <= -0.92\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- x42 >  -0.92\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- x75 >  33.09\n",
      "|   |   |   |   |   |   |--- x60 <= 0.66\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- x60 >  0.66\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- x47 >  17.10\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |--- x36 >  0.08\n",
      "|   |   |   |--- x53 <= -0.02\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- x53 >  -0.02\n",
      "|   |   |   |   |--- x46 <= 0.03\n",
      "|   |   |   |   |   |--- x46 <= 0.03\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- x46 >  0.03\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- x46 >  0.03\n",
      "|   |   |   |   |   |--- x26 <= 11.02\n",
      "|   |   |   |   |   |   |--- x79 <= -17.05\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- x79 >  -17.05\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- x26 >  11.02\n",
      "|   |   |   |   |   |   |--- x24 <= 0.82\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- x24 >  0.82\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rules = tree.export_text(\n",
    "    fitted_model,\n",
    "    feature_names=X_train.columns.values.tolist(),\n",
    ")\n",
    "print(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(\n",
    "    models[\"decision_tree\"][\"fitted\"], open(\"data-distress/vtree.model.sav\", \"wb\")\n",
    ")"
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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