3_Training.ipynb

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   "source": [
    "# Part III: Training a model\n",
    "\n",
    "Authors: Jordi Bolibar & Facundo Sapienza"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfd3031-11c4-452d-ac19-c35a7268ebfd",
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   "source": [
    "In this notebook, we will focus on how to train different machine learning models with the dataset we have prepared. For now, we will forget about cross-validation and test, and we will just focus on the specificities of each method to familiarize ourselves with classic machine learning approaches. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce4e517f-76cf-440e-beea-0c0441fc0db7",
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d310fd91-71b7-4f55-aa5d-f9cd60f629a6",
   "metadata": {
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   "source": [
    "## 3.1 Setup\n",
    "\n",
    "At this point, `df_all` has all the data we will need to train, validate and test the model. However, before moving forward to training a machine learning model with this data, we need to separate an part of the dataset for testing the model and be sure we are not overfitting the model. \n",
    "\n",
    "For testing, we can use different strategies:\n",
    "1. Separate all the data for some given set of glaciers and use all the rest for training/validation\n",
    "1. Separate all the data for some given set of glaciers at period (2010-2020) and use all the rest for training/validation\n",
    "1. Separate all the data for some given set of glaciers at the period 2010-2020 and use all the measurements for rest of the glaciers and the other period 2000-2010 for training/validation.\n",
    "\n",
    "Following the reasoning of the previous notebook, since this dataset is very rich in the *spatial* domain, we are going to split it in that dimension instead of the *temporal* one. By doing so, we will attempt to predict glacier changes for glaciers in Scandinavia for which we have not \"seen\" any observations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f724bfc2-bcb8-4a41-ac7c-28deead14a26",
   "metadata": {
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   "source": [
    "We read the previously stored dataset with the feature matrix and the target data."
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PDD</th>\n",
       "      <th>area</th>\n",
       "      <th>icecap</th>\n",
       "      <th>lat</th>\n",
       "      <th>prcp_01</th>\n",
       "      <th>prcp_02</th>\n",
       "      <th>prcp_03</th>\n",
       "      <th>prcp_04</th>\n",
       "      <th>prcp_05</th>\n",
       "      <th>prcp_06</th>\n",
       "      <th>...</th>\n",
       "      <th>temp_09</th>\n",
       "      <th>temp_10</th>\n",
       "      <th>temp_11</th>\n",
       "      <th>temp_12</th>\n",
       "      <th>zmax</th>\n",
       "      <th>zmed</th>\n",
       "      <th>zmin</th>\n",
       "      <th>dmdtda</th>\n",
       "      <th>err_dmdtda</th>\n",
       "      <th>target_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rgi_id</th>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.00001</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>39.787001</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.930</td>\n",
       "      <td>59.556398</td>\n",
       "      <td>42.393607</td>\n",
       "      <td>49.464068</td>\n",
       "      <td>40.707838</td>\n",
       "      <td>28.460900</td>\n",
       "      <td>32.946557</td>\n",
       "      <td>...</td>\n",
       "      <td>8.765555</td>\n",
       "      <td>13.037779</td>\n",
       "      <td>10.950000</td>\n",
       "      <td>6.391112</td>\n",
       "      <td>250.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>-0.458772</td>\n",
       "      <td>0.449120</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>41.833601</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.930</td>\n",
       "      <td>57.685775</td>\n",
       "      <td>45.119089</td>\n",
       "      <td>49.848962</td>\n",
       "      <td>37.706105</td>\n",
       "      <td>33.120270</td>\n",
       "      <td>41.740137</td>\n",
       "      <td>...</td>\n",
       "      <td>8.942221</td>\n",
       "      <td>13.034446</td>\n",
       "      <td>11.120000</td>\n",
       "      <td>7.227779</td>\n",
       "      <td>250.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>-0.356250</td>\n",
       "      <td>0.435293</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.00002</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>44.907200</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.870</td>\n",
       "      <td>62.082497</td>\n",
       "      <td>43.677873</td>\n",
       "      <td>49.432192</td>\n",
       "      <td>41.313322</td>\n",
       "      <td>28.519503</td>\n",
       "      <td>32.153049</td>\n",
       "      <td>...</td>\n",
       "      <td>9.274814</td>\n",
       "      <td>13.517038</td>\n",
       "      <td>11.384073</td>\n",
       "      <td>6.784445</td>\n",
       "      <td>246.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>-0.458772</td>\n",
       "      <td>0.449120</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>46.980223</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.870</td>\n",
       "      <td>59.345685</td>\n",
       "      <td>45.845166</td>\n",
       "      <td>49.957571</td>\n",
       "      <td>37.644466</td>\n",
       "      <td>32.577409</td>\n",
       "      <td>40.674079</td>\n",
       "      <td>...</td>\n",
       "      <td>9.423703</td>\n",
       "      <td>13.538149</td>\n",
       "      <td>11.500740</td>\n",
       "      <td>7.632223</td>\n",
       "      <td>246.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>-0.356250</td>\n",
       "      <td>0.435293</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>RGI60-08.00003</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>35.337201</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.820</td>\n",
       "      <td>60.485810</td>\n",
       "      <td>45.600863</td>\n",
       "      <td>55.059464</td>\n",
       "      <td>46.842254</td>\n",
       "      <td>33.796713</td>\n",
       "      <td>38.831589</td>\n",
       "      <td>...</td>\n",
       "      <td>7.059999</td>\n",
       "      <td>11.433335</td>\n",
       "      <td>9.346666</td>\n",
       "      <td>4.700001</td>\n",
       "      <td>749.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>-0.458772</td>\n",
       "      <td>0.449120</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>...</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>RGI60-08.03415</th>\n",
       "      <th>2010-2020</th>\n",
       "      <td>34.757735</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.729</td>\n",
       "      <td>131.108065</td>\n",
       "      <td>107.707591</td>\n",
       "      <td>144.610990</td>\n",
       "      <td>98.574383</td>\n",
       "      <td>92.859806</td>\n",
       "      <td>111.586080</td>\n",
       "      <td>...</td>\n",
       "      <td>4.860000</td>\n",
       "      <td>8.513335</td>\n",
       "      <td>7.726667</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.534500</td>\n",
       "      <td>0.575100</td>\n",
       "      <td>6829.0</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.03416</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>66.325377</td>\n",
       "      <td>14.152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.006</td>\n",
       "      <td>160.244277</td>\n",
       "      <td>183.438569</td>\n",
       "      <td>186.103358</td>\n",
       "      <td>190.792902</td>\n",
       "      <td>139.485696</td>\n",
       "      <td>115.362394</td>\n",
       "      <td>...</td>\n",
       "      <td>6.778889</td>\n",
       "      <td>9.997777</td>\n",
       "      <td>9.253333</td>\n",
       "      <td>5.060001</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>-0.737400</td>\n",
       "      <td>0.251600</td>\n",
       "      <td>6830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>66.505066</td>\n",
       "      <td>14.152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.006</td>\n",
       "      <td>174.868147</td>\n",
       "      <td>146.277851</td>\n",
       "      <td>205.123464</td>\n",
       "      <td>136.355639</td>\n",
       "      <td>129.459209</td>\n",
       "      <td>140.041785</td>\n",
       "      <td>...</td>\n",
       "      <td>6.448889</td>\n",
       "      <td>9.764443</td>\n",
       "      <td>9.126666</td>\n",
       "      <td>5.783334</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>-0.649400</td>\n",
       "      <td>0.240600</td>\n",
       "      <td>6831.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.03417</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>53.358777</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.923</td>\n",
       "      <td>152.880332</td>\n",
       "      <td>173.554018</td>\n",
       "      <td>177.555157</td>\n",
       "      <td>183.980329</td>\n",
       "      <td>133.112843</td>\n",
       "      <td>112.064028</td>\n",
       "      <td>...</td>\n",
       "      <td>7.277778</td>\n",
       "      <td>10.505554</td>\n",
       "      <td>9.549999</td>\n",
       "      <td>5.363334</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>-0.616100</td>\n",
       "      <td>0.524300</td>\n",
       "      <td>6832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>53.116888</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.923</td>\n",
       "      <td>165.529468</td>\n",
       "      <td>138.168895</td>\n",
       "      <td>196.729779</td>\n",
       "      <td>132.081722</td>\n",
       "      <td>123.916238</td>\n",
       "      <td>134.381284</td>\n",
       "      <td>...</td>\n",
       "      <td>6.947778</td>\n",
       "      <td>10.288887</td>\n",
       "      <td>9.436666</td>\n",
       "      <td>6.070001</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.496600</td>\n",
       "      <td>6833.0</td>\n",
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       "                                PDD    area  icecap     lat     prcp_01  \\\n",
       "rgi_id         period                                                     \n",
       "RGI60-08.00001 2000-2010  39.787001   0.030     0.0  67.930   59.556398   \n",
       "               2010-2020  41.833601   0.030     0.0  67.930   57.685775   \n",
       "RGI60-08.00002 2000-2010  44.907200   0.030     0.0  67.870   62.082497   \n",
       "               2010-2020  46.980223   0.030     0.0  67.870   59.345685   \n",
       "RGI60-08.00003 2000-2010  35.337201   0.020     0.0  67.820   60.485810   \n",
       "...                             ...     ...     ...     ...         ...   \n",
       "RGI60-08.03415 2010-2020  34.757735   0.056     0.0  66.729  131.108065   \n",
       "RGI60-08.03416 2000-2010  66.325377  14.152     0.0  66.006  160.244277   \n",
       "               2010-2020  66.505066  14.152     0.0  66.006  174.868147   \n",
       "RGI60-08.03417 2000-2010  53.358777   0.150     0.0  65.923  152.880332   \n",
       "               2010-2020  53.116888   0.150     0.0  65.923  165.529468   \n",
       "\n",
       "                             prcp_02     prcp_03     prcp_04     prcp_05  \\\n",
       "rgi_id         period                                                      \n",
       "RGI60-08.00001 2000-2010   42.393607   49.464068   40.707838   28.460900   \n",
       "               2010-2020   45.119089   49.848962   37.706105   33.120270   \n",
       "RGI60-08.00002 2000-2010   43.677873   49.432192   41.313322   28.519503   \n",
       "               2010-2020   45.845166   49.957571   37.644466   32.577409   \n",
       "RGI60-08.00003 2000-2010   45.600863   55.059464   46.842254   33.796713   \n",
       "...                              ...         ...         ...         ...   \n",
       "RGI60-08.03415 2010-2020  107.707591  144.610990   98.574383   92.859806   \n",
       "RGI60-08.03416 2000-2010  183.438569  186.103358  190.792902  139.485696   \n",
       "               2010-2020  146.277851  205.123464  136.355639  129.459209   \n",
       "RGI60-08.03417 2000-2010  173.554018  177.555157  183.980329  133.112843   \n",
       "               2010-2020  138.168895  196.729779  132.081722  123.916238   \n",
       "\n",
       "                             prcp_06  ...   temp_09    temp_10    temp_11  \\\n",
       "rgi_id         period                 ...                                   \n",
       "RGI60-08.00001 2000-2010   32.946557  ...  8.765555  13.037779  10.950000   \n",
       "               2010-2020   41.740137  ...  8.942221  13.034446  11.120000   \n",
       "RGI60-08.00002 2000-2010   32.153049  ...  9.274814  13.517038  11.384073   \n",
       "               2010-2020   40.674079  ...  9.423703  13.538149  11.500740   \n",
       "RGI60-08.00003 2000-2010   38.831589  ...  7.059999  11.433335   9.346666   \n",
       "...                              ...  ...       ...        ...        ...   \n",
       "RGI60-08.03415 2010-2020  111.586080  ...  4.860000   8.513335   7.726667   \n",
       "RGI60-08.03416 2000-2010  115.362394  ...  6.778889   9.997777   9.253333   \n",
       "               2010-2020  140.041785  ...  6.448889   9.764443   9.126666   \n",
       "RGI60-08.03417 2000-2010  112.064028  ...  7.277778  10.505554   9.549999   \n",
       "               2010-2020  134.381284  ...  6.947778  10.288887   9.436666   \n",
       "\n",
       "                           temp_12    zmax    zmed    zmin    dmdtda  \\\n",
       "rgi_id         period                                                  \n",
       "RGI60-08.00001 2000-2010  6.391112   250.0   242.0   235.0 -0.458772   \n",
       "               2010-2020  7.227779   250.0   242.0   235.0 -0.356250   \n",
       "RGI60-08.00002 2000-2010  6.784445   246.0   239.0   228.0 -0.458772   \n",
       "               2010-2020  7.632223   246.0   239.0   228.0 -0.356250   \n",
       "RGI60-08.00003 2000-2010  4.700001   749.0   726.0   701.0 -0.458772   \n",
       "...                            ...     ...     ...     ...       ...   \n",
       "RGI60-08.03415 2010-2020  4.133333  1330.0  1314.0  1289.0  0.534500   \n",
       "RGI60-08.03416 2000-2010  5.060001  1811.0  1581.0   890.0 -0.737400   \n",
       "               2010-2020  5.783334  1811.0  1581.0   890.0 -0.649400   \n",
       "RGI60-08.03417 2000-2010  5.363334  1318.0  1186.0  1090.0 -0.616100   \n",
       "               2010-2020  6.070001  1318.0  1186.0  1090.0  0.389200   \n",
       "\n",
       "                          err_dmdtda  target_id  \n",
       "rgi_id         period                            \n",
       "RGI60-08.00001 2000-2010    0.449120        0.0  \n",
       "               2010-2020    0.435293        1.0  \n",
       "RGI60-08.00002 2000-2010    0.449120        2.0  \n",
       "               2010-2020    0.435293        3.0  \n",
       "RGI60-08.00003 2000-2010    0.449120        4.0  \n",
       "...                              ...        ...  \n",
       "RGI60-08.03415 2010-2020    0.575100     6829.0  \n",
       "RGI60-08.03416 2000-2010    0.251600     6830.0  \n",
       "               2010-2020    0.240600     6831.0  \n",
       "RGI60-08.03417 2000-2010    0.524300     6832.0  \n",
       "               2010-2020    0.496600     6833.0  \n",
       "\n",
       "[6834 rows x 37 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.read_csv('Data/df_all.csv', index_col=['rgi_id', 'period'])\n",
    "df_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1257726-1db5-4922-a665-219146967cdb",
   "metadata": {},
   "source": [
    "### Separation into train and test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c27145-f04d-45b8-9ca2-8ca0994a5da2",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Now we need to split our dataset into train and test. We are going to keep 70% of glaciers for training and 30% for test. It is very important NOT to use the test dataset until the very end of our training, once we are sure we have found the best hyperparameters for our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aac8dfc9-9280-4e86-9c1b-20e596efea91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2050, 37), (4784, 37))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get an array of all unique RGI-ids in the dataset\n",
    "glaciers_ids_all = np.unique(df_all.index.get_level_values(0).values)\n",
    "\n",
    "# Split the dataset into training and test datasets by splitting randomly on RGI-id, such that each glacier \n",
    "# is either in the training dataset or in the test dataset\n",
    "glacier_train, glacier_test = train_test_split(glaciers_ids_all, \n",
    "                                               train_size=0.3,\n",
    "                                               test_size=0.7)\n",
    "\n",
    "df_train_all = df_all.loc[glacier_train]\n",
    "df_test = df_all.loc[glacier_test]\n",
    "df_train_all.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324747a8-88a2-40ef-8bd1-826f2732ee50",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "We save the test dataset into a file in order to put it aside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c588fe84-8e04-48e4-9bea-c09a6cf494cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.to_csv('Data/df_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c973f9e-4750-4f8f-ae01-53a1be1a72df",
   "metadata": {},
   "source": [
    "### Separation into validation and train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e3b200-afab-493a-9b34-332ac45643b8",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Now we are going to train the models based on the train dataset, which will have to be split again into train and validation. As a reminder, the train dataset will be given to the machine learning models in order to fit the data, and the validation dataset will be set aside, in order to tune the hyperparameters of the model so they best fit the out-of-sample validation dataset. This is done to avoid overfitting and to select a set of model hyperparameters that best generalize unseen data. \n",
    "\n",
    "A simple and naive way of doing so, is doing a simple split of 80% for train, and 20% for validation. Bear in mind that here the new train dataset is just a subset of the previous train dataset. From the original dataset, we have split things in the following way:\n",
    "\n",
    "Full dataset:\n",
    "* Test dataset -> set aside\n",
    "* Train dataset -> split further into train/validation\n",
    "    + Validation dataset -> used for hyperparameter selection\n",
    "    + Train dataset -> actual train dataset fed to the different machine learning models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22031b51-8328-49a5-93c6-c95b4123d38a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "For now, we will just split the dataset into 80% train, 20% validation, using the same principle as before.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fbd6182-b6b2-4990-b058-bedef65a48d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1640, 37), (410, 37))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glaciers_ids = np.unique(df_train_all.index.get_level_values(0).values)\n",
    "\n",
    "train_dataset, validation_dataset = train_test_split(glaciers_ids, \n",
    "                                               train_size=0.8,\n",
    "                                               test_size=0.2)\n",
    "\n",
    "df_train = df_all.loc[train_dataset]\n",
    "df_validation = df_all.loc[validation_dataset]\n",
    "df_train.shape, df_validation.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14191f85-2e57-48a3-b476-914081281377",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "And now we need to separate the feature matrix ($X$) from the target data ($Y$), since they need to be fed separately to the models. This will give us 4 different datasets:\n",
    "- Training datasets:\n",
    "    - `X_train`\n",
    "    - `y_train`\n",
    "- Validation datasets:\n",
    "    - `X_validation`\n",
    "    - `y_validation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3074979-3f96-4440-9e99-6b49785d6af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop data that will not be used as features in our model\n",
    "features_to_drop = ['dmdtda', 'err_dmdtda', 'target_id']\n",
    "\n",
    "df_train_X = df_train.drop(features_to_drop, axis=1)\n",
    "df_train_y = df_train[['dmdtda']]\n",
    "\n",
    "X_train, y_train = df_train_X.values, df_train_y.values\n",
    "\n",
    "# And the same for validation set\n",
    "df_validation_X = df_validation.drop(features_to_drop, axis=1)\n",
    "df_validation_y = df_validation[['dmdtda']]\n",
    "\n",
    "X_validation, y_validation = df_validation_X.values, df_validation_y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "05afd7db-73c4-4b1e-8a09-167feec81fe0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PDD</th>\n",
       "      <th>area</th>\n",
       "      <th>icecap</th>\n",
       "      <th>lat</th>\n",
       "      <th>prcp_01</th>\n",
       "      <th>prcp_02</th>\n",
       "      <th>prcp_03</th>\n",
       "      <th>prcp_04</th>\n",
       "      <th>prcp_05</th>\n",
       "      <th>prcp_06</th>\n",
       "      <th>...</th>\n",
       "      <th>temp_06</th>\n",
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       "      <th>temp_09</th>\n",
       "      <th>temp_10</th>\n",
       "      <th>temp_11</th>\n",
       "      <th>temp_12</th>\n",
       "      <th>zmax</th>\n",
       "      <th>zmed</th>\n",
       "      <th>zmin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rgi_id</th>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.00698</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>36.859865</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.019</td>\n",
       "      <td>160.136730</td>\n",
       "      <td>134.720016</td>\n",
       "      <td>138.560079</td>\n",
       "      <td>138.715662</td>\n",
       "      <td>92.721910</td>\n",
       "      <td>81.761859</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.398148</td>\n",
       "      <td>-2.391111</td>\n",
       "      <td>1.798148</td>\n",
       "      <td>5.845184</td>\n",
       "      <td>8.626296</td>\n",
       "      <td>8.341111</td>\n",
       "      <td>4.724073</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>1562.0</td>\n",
       "      <td>1546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>35.741613</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.019</td>\n",
       "      <td>146.957346</td>\n",
       "      <td>119.129998</td>\n",
       "      <td>143.251826</td>\n",
       "      <td>107.723619</td>\n",
       "      <td>83.752954</td>\n",
       "      <td>80.672697</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.933703</td>\n",
       "      <td>-2.751111</td>\n",
       "      <td>1.640370</td>\n",
       "      <td>5.696295</td>\n",
       "      <td>8.384074</td>\n",
       "      <td>7.958889</td>\n",
       "      <td>4.795185</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>1562.0</td>\n",
       "      <td>1546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.02136</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>36.473586</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.397</td>\n",
       "      <td>109.365379</td>\n",
       "      <td>100.223427</td>\n",
       "      <td>86.439753</td>\n",
       "      <td>100.567858</td>\n",
       "      <td>65.197893</td>\n",
       "      <td>62.713331</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.429259</td>\n",
       "      <td>-3.505185</td>\n",
       "      <td>1.178519</td>\n",
       "      <td>5.669630</td>\n",
       "      <td>8.351112</td>\n",
       "      <td>7.546667</td>\n",
       "      <td>3.139630</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>1779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>35.741971</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.397</td>\n",
       "      <td>103.669366</td>\n",
       "      <td>85.851592</td>\n",
       "      <td>89.651551</td>\n",
       "      <td>76.308727</td>\n",
       "      <td>58.687947</td>\n",
       "      <td>65.867721</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.951481</td>\n",
       "      <td>-3.864074</td>\n",
       "      <td>1.139630</td>\n",
       "      <td>5.541852</td>\n",
       "      <td>8.135556</td>\n",
       "      <td>7.204444</td>\n",
       "      <td>3.325185</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>1779.0</td>\n",
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       "    <tr>\n",
       "      <th>RGI60-08.00736</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>47.308400</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.209</td>\n",
       "      <td>57.041137</td>\n",
       "      <td>53.807009</td>\n",
       "      <td>58.235379</td>\n",
       "      <td>59.451758</td>\n",
       "      <td>56.611135</td>\n",
       "      <td>40.908633</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-3.738890</td>\n",
       "      <td>1.325555</td>\n",
       "      <td>6.161111</td>\n",
       "      <td>10.128889</td>\n",
       "      <td>8.710000</td>\n",
       "      <td>4.033333</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1095.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\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>RGI60-08.02565</th>\n",
       "      <th>2010-2020</th>\n",
       "      <td>41.247779</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.769</td>\n",
       "      <td>77.741749</td>\n",
       "      <td>68.360854</td>\n",
       "      <td>94.047913</td>\n",
       "      <td>63.774671</td>\n",
       "      <td>67.404128</td>\n",
       "      <td>77.244195</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.364444</td>\n",
       "      <td>-2.406667</td>\n",
       "      <td>2.385556</td>\n",
       "      <td>5.781112</td>\n",
       "      <td>9.641111</td>\n",
       "      <td>8.665556</td>\n",
       "      <td>5.683333</td>\n",
       "      <td>914.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.02630</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>49.043926</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.999</td>\n",
       "      <td>84.540911</td>\n",
       "      <td>87.018715</td>\n",
       "      <td>91.849535</td>\n",
       "      <td>87.174794</td>\n",
       "      <td>79.419542</td>\n",
       "      <td>57.797309</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.893334</td>\n",
       "      <td>-2.493703</td>\n",
       "      <td>2.398518</td>\n",
       "      <td>7.295555</td>\n",
       "      <td>10.928519</td>\n",
       "      <td>9.841852</td>\n",
       "      <td>5.013333</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>49.672045</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.999</td>\n",
       "      <td>90.700416</td>\n",
       "      <td>76.601139</td>\n",
       "      <td>95.705978</td>\n",
       "      <td>64.986857</td>\n",
       "      <td>67.871304</td>\n",
       "      <td>83.119995</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.427778</td>\n",
       "      <td>-2.618148</td>\n",
       "      <td>2.809629</td>\n",
       "      <td>6.998889</td>\n",
       "      <td>10.779630</td>\n",
       "      <td>9.661852</td>\n",
       "      <td>5.996667</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RGI60-08.01337</th>\n",
       "      <th>2000-2010</th>\n",
       "      <td>35.408415</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.521</td>\n",
       "      <td>112.086137</td>\n",
       "      <td>110.928466</td>\n",
       "      <td>109.900997</td>\n",
       "      <td>118.965862</td>\n",
       "      <td>75.534210</td>\n",
       "      <td>70.490720</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.344444</td>\n",
       "      <td>-2.611481</td>\n",
       "      <td>1.964444</td>\n",
       "      <td>6.085186</td>\n",
       "      <td>8.783703</td>\n",
       "      <td>8.028889</td>\n",
       "      <td>3.742223</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>1523.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2020</th>\n",
       "      <td>34.674771</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.521</td>\n",
       "      <td>109.026369</td>\n",
       "      <td>94.922972</td>\n",
       "      <td>114.258607</td>\n",
       "      <td>90.068325</td>\n",
       "      <td>67.157398</td>\n",
       "      <td>75.501891</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.914444</td>\n",
       "      <td>-2.994815</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>5.927408</td>\n",
       "      <td>8.523703</td>\n",
       "      <td>7.731111</td>\n",
       "      <td>3.951111</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>1523.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1640 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                PDD   area  icecap     lat     prcp_01  \\\n",
       "rgi_id         period                                                    \n",
       "RGI60-08.00698 2000-2010  36.859865  0.113     0.0  60.019  160.136730   \n",
       "               2010-2020  35.741613  0.113     0.0  60.019  146.957346   \n",
       "RGI60-08.02136 2000-2010  36.473586  0.077     0.0  61.397  109.365379   \n",
       "               2010-2020  35.741971  0.077     0.0  61.397  103.669366   \n",
       "RGI60-08.00736 2000-2010  47.308400  0.662     0.0  69.209   57.041137   \n",
       "...                             ...    ...     ...     ...         ...   \n",
       "RGI60-08.02565 2010-2020  41.247779  0.018     0.0  69.769   77.741749   \n",
       "RGI60-08.02630 2000-2010  49.043926  0.201     0.0  67.999   84.540911   \n",
       "               2010-2020  49.672045  0.201     0.0  67.999   90.700416   \n",
       "RGI60-08.01337 2000-2010  35.408415  0.678     0.0  61.521  112.086137   \n",
       "               2010-2020  34.674771  0.678     0.0  61.521  109.026369   \n",
       "\n",
       "                             prcp_02     prcp_03     prcp_04    prcp_05  \\\n",
       "rgi_id         period                                                     \n",
       "RGI60-08.00698 2000-2010  134.720016  138.560079  138.715662  92.721910   \n",
       "               2010-2020  119.129998  143.251826  107.723619  83.752954   \n",
       "RGI60-08.02136 2000-2010  100.223427   86.439753  100.567858  65.197893   \n",
       "               2010-2020   85.851592   89.651551   76.308727  58.687947   \n",
       "RGI60-08.00736 2000-2010   53.807009   58.235379   59.451758  56.611135   \n",
       "...                              ...         ...         ...        ...   \n",
       "RGI60-08.02565 2010-2020   68.360854   94.047913   63.774671  67.404128   \n",
       "RGI60-08.02630 2000-2010   87.018715   91.849535   87.174794  79.419542   \n",
       "               2010-2020   76.601139   95.705978   64.986857  67.871304   \n",
       "RGI60-08.01337 2000-2010  110.928466  109.900997  118.965862  75.534210   \n",
       "               2010-2020   94.922972  114.258607   90.068325  67.157398   \n",
       "\n",
       "                            prcp_06  ...   temp_06   temp_07   temp_08  \\\n",
       "rgi_id         period                ...                                 \n",
       "RGI60-08.00698 2000-2010  81.761859  ... -6.398148 -2.391111  1.798148   \n",
       "               2010-2020  80.672697  ... -5.933703 -2.751111  1.640370   \n",
       "RGI60-08.02136 2000-2010  62.713331  ... -8.429259 -3.505185  1.178519   \n",
       "               2010-2020  65.867721  ... -7.951481 -3.864074  1.139630   \n",
       "RGI60-08.00736 2000-2010  40.908633  ... -9.000000 -3.738890  1.325555   \n",
       "...                             ...  ...       ...       ...       ...   \n",
       "RGI60-08.02565 2010-2020  77.244195  ... -6.364444 -2.406667  2.385556   \n",
       "RGI60-08.02630 2000-2010  57.797309  ... -7.893334 -2.493703  2.398518   \n",
       "               2010-2020  83.119995  ... -7.427778 -2.618148  2.809629   \n",
       "RGI60-08.01337 2000-2010  70.490720  ... -7.344444 -2.611481  1.964444   \n",
       "               2010-2020  75.501891  ... -6.914444 -2.994815  1.900000   \n",
       "\n",
       "                           temp_09    temp_10   temp_11   temp_12    zmax  \\\n",
       "rgi_id         period                                                       \n",
       "RGI60-08.00698 2000-2010  5.845184   8.626296  8.341111  4.724073  1570.0   \n",
       "               2010-2020  5.696295   8.384074  7.958889  4.795185  1570.0   \n",
       "RGI60-08.02136 2000-2010  5.669630   8.351112  7.546667  3.139630  1898.0   \n",
       "               2010-2020  5.541852   8.135556  7.204444  3.325185  1898.0   \n",
       "RGI60-08.00736 2000-2010  6.161111  10.128889  8.710000  4.033333  1367.0   \n",
       "...                            ...        ...       ...       ...     ...   \n",
       "RGI60-08.02565 2010-2020  5.781112   9.641111  8.665556  5.683333   914.0   \n",
       "RGI60-08.02630 2000-2010  7.295555  10.928519  9.841852  5.013333  1155.0   \n",
       "               2010-2020  6.998889  10.779630  9.661852  5.996667  1155.0   \n",
       "RGI60-08.01337 2000-2010  6.085186   8.783703  8.028889  3.742223  1667.0   \n",
       "               2010-2020  5.927408   8.523703  7.731111  3.951111  1667.0   \n",
       "\n",
       "                            zmed    zmin  \n",
       "rgi_id         period                     \n",
       "RGI60-08.00698 2000-2010  1562.0  1546.0  \n",
       "               2010-2020  1562.0  1546.0  \n",
       "RGI60-08.02136 2000-2010  1829.0  1779.0  \n",
       "               2010-2020  1829.0  1779.0  \n",
       "RGI60-08.00736 2000-2010  1237.0  1095.0  \n",
       "...                          ...     ...  \n",
       "RGI60-08.02565 2010-2020   886.0   839.0  \n",
       "RGI60-08.02630 2000-2010  1115.0  1065.0  \n",
       "               2010-2020  1115.0  1065.0  \n",
       "RGI60-08.01337 2000-2010  1597.0  1523.0  \n",
       "               2010-2020  1597.0  1523.0  \n",
       "\n",
       "[1640 rows x 34 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89bab83f-5a5c-4b73-8364-7ff2550b247a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## 3.2 Training a model \n",
    "\n",
    "The `sklearn` package includes multiple regression models we can try on the dataset. One of the advantages of using `sklearn` is that the syntax and logic is shared among different algorithms, making easy to try and compare different models. The [sklearn documentation](https://scikit-learn.org/stable/auto_examples/index.html#examples) includes multiple of these examples that are worth exploring. \n",
    "\n",
    "Some regression methods to explore include\n",
    "1. Classical continuous regression approaches: linear regression, logistic regression, support vector machines.\n",
    "1. Tree-based regression: decision trees, random forest, ensemble methods based on boosting.\n",
    "1. Neural networks.\n",
    "\n",
    "The following plot function will be useful in order to report the performance of the different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "034abd1f-bbe3-46a8-9612-4758874a2f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_prediction(y1, y2, n_toplot=10**10):\n",
    "    \n",
    "    from scipy.stats import gaussian_kde\n",
    "    from sklearn.metrics import r2_score, mean_squared_error\n",
    "    \n",
    "    idxs = np.arange(len(y1))\n",
    "    np.random.shuffle(idxs)\n",
    "    \n",
    "    y_expected = y1.reshape(-1)[idxs[:n_toplot]]\n",
    "    y_predicted = y2.reshape(-1)[idxs[:n_toplot]]\n",
    "\n",
    "    xy = np.vstack([y_expected, y_predicted])\n",
    "    z = gaussian_kde(xy)(xy)\n",
    "    # Sort the points by density, so that the densest points are plotted last\n",
    "    idx = z.argsort()\n",
    "    y_plt, ann_plt, z = y_expected[idx], y_predicted[idx], z[idx]\n",
    "    \n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.title(\"Model Evaluation\", fontsize=17)\n",
    "    plt.ylabel('Modeled SMB (m.w.e)', fontsize=16)\n",
    "    plt.xlabel('Reference SMB (m.w.e)', fontsize=16)\n",
    "    sc = plt.scatter(y_plt, ann_plt, c=z, s=20)\n",
    "    plt.clim(0,0.4)\n",
    "    plt.tick_params(labelsize=14)\n",
    "    plt.colorbar(sc) \n",
    "    lineStart = -2.5\n",
    "    lineEnd = 1.5\n",
    "    plt.plot([lineStart, lineEnd], [lineStart, lineEnd], 'k-')\n",
    "    plt.axvline(0.0, ls='-.', c='k')\n",
    "    plt.axhline(0.0, ls='-.', c='k')\n",
    "    plt.xlim(lineStart, lineEnd)\n",
    "    plt.ylim(lineStart, lineEnd)\n",
    "    plt.gca().set_box_aspect(1)\n",
    "    \n",
    "    textstr = '\\n'.join((\n",
    "    r'$RMSE=%.2f$' % (mean_squared_error(y_expected, y_predicted), ),\n",
    "    r'$R^2=%.2f$' % (r2_score(y_expected, y_predicted), )))\n",
    "    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "    # place a text box in upper left in axes coords\n",
    "    plt.text(0.05, 0.95, textstr, transform=plt.gca().transAxes, fontsize=14,\n",
    "            verticalalignment='top', bbox=props)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9c59ff-2c11-4a5b-82b4-49cf2585cafa",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### 3.2.1. Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640b5ee8-9c17-4942-b6c5-318e3d4cbae6",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Linear regression is the most basic type of regression we can use in our dataset. Therefore, it represents the baseline model on which to compare the rest. In this section we will use `ElasticNet`, a linear regression with combined $L_1$ and $L_2$ regularization terms.\n",
    "\n",
    "```{note}\n",
    "If you would like to better understand linear regression and regularization, we highly recommend taking a look at [this wonderful Notebook](https://machine-learning-in-glaciology-workshop.github.io/Lecture-materials/Stats/linear_regression.html) that Ellie Abrahams prepared for this workshop. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57cfdfb9-56ad-4927-9c3e-456c069556a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import ElasticNet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebc50b4-6c08-4c9f-8f7f-bc35c72b32b1",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "For linear regression and many other methods, we will scale the data to have zero mean and unit variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac950941-d906-440e-a0de-970cc65c2fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
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       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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       "    /* Redefinition of color scheme for dark theme */\n",
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       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
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       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
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       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
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       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d2362ff-0d51-400c-a9d5-20880b0f0780",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_validation_scaled = scaler.transform(X_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a006f8ef-3186-494b-a45d-d82eb6065d05",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "ElasticNet is a linear regression model that includes the classical ordinary least square error estimator (`alpha=0.0`), Lasso (`l1_ratio=1`) and Ridge (`l1_ratio=0`) regression in one single method. \n",
    "\n",
    "Here, we are going to fit an `ElasticNet` model to our train dataset, with the following hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83032493-72ef-40be-8a95-b87844a10278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNet(alpha=0.001, l1_ratio=0.8, random_state=666)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.001, l1_ratio=0.8, random_state=666)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "ElasticNet(alpha=0.001, l1_ratio=0.8, random_state=666)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "reg_linear = ElasticNet(alpha=0.001,\n",
    "                        l1_ratio=0.8,\n",
    "                        fit_intercept=True, \n",
    "                        random_state=666)\n",
    "reg_linear.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f853177b-d13d-4fcf-a937-b6cff4177b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(y_train, reg_linear.predict(X_train_scaled), n_toplot=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f622d6-db48-4c32-ba5a-4b9ae4995c36",
   "metadata": {},
   "source": [
    "We can see that performance is not great, since we haven't done any hyperparameter tuning and selection. We will leave that for the following notebook, and here we'll just focus on having an overview of all the methods and their particular hyperparameters. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491ae1fc-67e1-434d-89df-285bff0c7979",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### 3.2.2. Decision Tree Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdaad195-a4ef-47e0-bf23-6c6d21af51b9",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Decision trees are non-parametric supervised learning methods. The objective of this method is to generate a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation.\n",
    "\n",
    "For more details, you can read the [`sklearn` documentation](https://scikit-learn.org/stable/modules/tree.html#tree). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0bae097f-bec0-4149-8af0-dd247db6313e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fb9bfd-7e6e-4c10-ad67-5c8d8a43d460",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "For now, we will just fill some of the hyperparameters to see how it behaves. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb8e40d0-edf4-4e7c-9be9-e3c39be02a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.6 ms\n",
      "Wall time: 32.1 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=5, min_samples_split=5, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(max_depth=5, min_samples_split=5, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=5, min_samples_split=5, random_state=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "reg_tree = DecisionTreeRegressor(random_state=0, \n",
    "                                 max_depth=5, \n",
    "                                 min_samples_split=5)\n",
    "reg_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e99c00f-e19f-451e-8122-03837369462f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(y_train, reg_tree.predict(X_train), n_toplot=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1e70f2-1c9e-42a1-aa05-6db250b6075a",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Again, we can see performance is not good at all, since we still need to do a proper search of hyperparameters. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01e6d97-d4e6-43a1-ba72-863fd8726289",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### 3.2.3. Ensemble of trees\n",
    "\n",
    "We can try examples of models that combine slow learners. These models tend to work very well and be quite general. However, tuning the different hyperparameters of the model can be tricky and something that will be explored in detail in the next notebook. \n",
    "\n",
    "These methods are [particularly powerful for tabular data](https://arxiv.org/abs/2207.08815), the exact type of data that we have here in this project. We will be using two of the best tree ensemble methods: Random Forest and Gradient Boosting. Here you can find an example based on [Gradient boosting regression](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "285c868a-3705-479b-9fcb-8dc7379d3422",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble\n",
    "from sklearn.inspection import permutation_importance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d6aa44-48ab-4bff-b3eb-0bd1d53e15a5",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "As you can see, here we start having a greater amount of hyperparameters. Therefore, doing a good hyperparameter selection will be crucial for this methods to correctly fit the data in validation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f356587a-0f9b-4a08-98ed-fc657dd3c0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = 'random forest'\n",
    "model = 'gradient boosting'\n",
    "\n",
    "if model == 'gradient boosting':\n",
    "\n",
    "    params = {\n",
    "        \"n_estimators\": 300,\n",
    "        \"max_depth\": 4,\n",
    "        \"min_samples_split\": 5,\n",
    "        \"learning_rate\": 0.1,\n",
    "        \"loss\": 'huber', #\"squared_error\",\n",
    "        \"validation_fraction\": 0.2\n",
    "    }\n",
    "    reg_ensemble = ensemble.GradientBoostingRegressor(**params)\n",
    "    \n",
    "elif model == 'random forest':\n",
    "    \n",
    "    params = {\n",
    "        \"n_estimators\": 300,\n",
    "        \"max_depth\": 4,\n",
    "        \"min_samples_split\": 5,\n",
    "        \"criterion\": \"absolute_error\",\n",
    "        'max_features': 10,\n",
    "    }\n",
    "    reg_ensemble = ensemble.RandomForestRegressor(**params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "242d36ac-f984-4052-b207-ddd1d48e5cbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kasj\\AppData\\Local\\anaconda3\\envs\\finse_modelling\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)  # TODO: Is this still required?\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 4.42 s\n",
      "Wall time: 7.06 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(loss=&#x27;huber&#x27;, max_depth=4, min_samples_split=5,\n",
       "                          n_estimators=300, validation_fraction=0.2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(loss=&#x27;huber&#x27;, max_depth=4, min_samples_split=5,\n",
       "                          n_estimators=300, validation_fraction=0.2)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(loss='huber', max_depth=4, min_samples_split=5,\n",
       "                          n_estimators=300, validation_fraction=0.2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "reg_ensemble.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "23269987-cd0e-4511-9f9d-32c338df7d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(y_train, reg_ensemble.predict(X_train), n_toplot=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f1b8a2-3b13-46f2-b88a-32e17b0743f0",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "For this case, the default results are not that bad, but they could be improved with a hyperparameter search. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32567500-a1e8-4aca-8f46-147cc539d856",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Another interesting aspect of tree-based methods is their interpretability. It is rather easy to extract the feature importance and permutation importance, which can give us important clues on the contribution of each feature (and therefore different forcing variables) to determine glacier-wide mass balance in Scandinavia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e57d543e-714e-4de1-a658-830f6cee330f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = reg_ensemble.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + 0.5\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align=\"center\")\n",
    "plt.yticks(pos, np.array(df_validation_X.columns)[sorted_idx])\n",
    "plt.title(\"Feature Importance (MDI)\")\n",
    "\n",
    "result = permutation_importance(\n",
    "    reg_ensemble, X_validation, y_validation, n_repeats=10, random_state=42, n_jobs=2\n",
    ")\n",
    "sorted_idx = result.importances_mean.argsort()\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.boxplot(\n",
    "    result.importances[sorted_idx].T,\n",
    "    vert=False,\n",
    "    tick_labels=np.array(df_validation_X.columns)[sorted_idx],\n",
    ")\n",
    "plt.title(\"Permutation Importance (validation set)\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5330a8-ebb4-4a85-bd1e-0eff617e16cc",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "### 3.2.4. Neural Network\n",
    "\n",
    "Neural networks are some of the most powerful machine learning methods available. Nonetheless, they are much harder to calibrate, due to they huge number of hyperparameters, architecture choices and the fact that they also have parameters (weights) that need to be trained in an iterative fashion using gradient descent. \n",
    "\n",
    "In this case, we will use the simplest of neural networks, a classic Multilayer Perceptron with a small architecture with two hidden layers. We will still use `sklearn` for this, although for more serious modelling we highly recommend using more advanced dedicated libraries such as JAX, PyTorch or Keras/Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "475d1d5a-bf54-433a-a221-7669dc5a4639",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfe3873-9603-4bcc-a751-f5e059bf8797",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "These are some of the hyperparameters that `sklearn` lets us choose for the neural network. We use a small `(50,30,20,10)` architecture, a `ReLu` activation function, an LBFGS optimizer (generally better than ADAM for small networks), a batch size of 100, 200 epochs of training, and we shuffle the batches to improve the learning. \n",
    "\n",
    "Note that `sklearn` also allows us to choose a fraction of the dataset to be used for validation to set up early stopping (i.e. stopping the training once we see that the loss is no longer decrease for a given number of epochs). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0ec6db66-0a5a-4a97-9d0b-6a77fef5ccb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_nn = MLPRegressor(hidden_layer_sizes=(50,30,20,10),\n",
    "                      activation='relu',\n",
    "                      solver='lbfgs', \n",
    "                      batch_size=100, \n",
    "                      max_iter=200,\n",
    "                      learning_rate='adaptive', \n",
    "                      shuffle=True, \n",
    "                      validation_fraction=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a03c7713-d15f-4ba0-bc93-223830f7fa4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kasj\\AppData\\Local\\anaconda3\\envs\\finse_modelling\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1631: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 54s\n",
      "Wall time: 13.2 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kasj\\AppData\\Local\\anaconda3\\envs\\finse_modelling\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:545: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPRegressor(batch_size=100, hidden_layer_sizes=(50, 30, 20, 10),\n",
       "             learning_rate=&#x27;adaptive&#x27;, solver=&#x27;lbfgs&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPRegressor.html\">?<span>Documentation for MLPRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPRegressor(batch_size=100, hidden_layer_sizes=(50, 30, 20, 10),\n",
       "             learning_rate=&#x27;adaptive&#x27;, solver=&#x27;lbfgs&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPRegressor(batch_size=100, hidden_layer_sizes=(50, 30, 20, 10),\n",
       "             learning_rate='adaptive', solver='lbfgs')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "reg_nn.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a8b86419-0d79-4f94-89c0-5ef70921ca1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(y_train, reg_nn.predict(X_train_scaled), n_toplot=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b9098f-533d-4cf9-b2ac-b5dea2c1e7cf",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Same as for the tree-based methods, we can see that this neural network, even with minor hyperparameter tuning, is already quite capable of modelling the variability of glacier-wide mass balance for the training dataset. Now we need to make sure that it is also capable of correctly extrapolating to \"unseen\" glaciers from the test dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3620b47e-405c-4975-acb2-33c339b8d095",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## [ **Exercice 2** ]\n",
    "<img src=\"Figures/eye_logo.png\" width=\"75\"/>\n",
    "\n",
    "Choose 2 or 3 different ML models, and play with the hyperparameters in order to improve the performance **in validation** by looking at the scatter plot. You just need to reproduce the same workflow, but using the validation dataset for visualizing the results instead of the training one. \n",
    "\n",
    "This is just a rough way to have a best first estimate of good hyperparameters. In the next notebook we will see how to correctly look for the best possible hyperparameters by using cross-validation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ab5a9b7-f75a-4f0a-bef3-0ee523bece45",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
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