├── Conformal Predictions for Classification.ipynb
├── Conformal Predictions for Regression.ipynb
├── GalaxyProperties.csv
├── README.md
├── UQ_EAS_2024.pdf
├── spectra.csv
└── star_classification.csv


/Conformal Predictions for Regression.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "id": "d654cff0",
   7 |    "metadata": {
   8 |     "slideshow": {
   9 |      "slide_type": "slide"
  10 |     }
  11 |    },
  12 |    "outputs": [],
  13 |    "source": [
  14 |     "import numpy as np\n",
  15 |     "import pandas as pd\n",
  16 |     "import matplotlib.pyplot as plt\n",
  17 |     "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
  18 |     "from sklearn.model_selection import train_test_split, cross_validate, cross_val_predict\n",
  19 |     "from sklearn.model_selection import KFold\n",
  20 |     "\n",
  21 |     "from mapie.regression import MapieQuantileRegressor\n",
  22 |     "from mapie.metrics import regression_coverage_score"
  23 |    ]
  24 |   },
  25 |   {
  26 |    "cell_type": "markdown",
  27 |    "id": "89717b81",
  28 |    "metadata": {},
  29 |    "source": [
  30 |     "Data are some simple synthetic galaxy spectra generated a few years ago for this paper, using BAGPIPES. There are spectra with 850 wavelengths, and we want to reconstruct some physical properties: age, star formation history timescale, dust content in a simple screen model, and stellar mass. We'll use age here as it's one of the trickiest properties to predict."
  31 |    ]
  32 |   },
  33 |   {
  34 |    "cell_type": "code",
  35 |    "execution_count": 2,
  36 |    "id": "23f7937d",
  37 |    "metadata": {},
  38 |    "outputs": [],
  39 |    "source": [
  40 |     "features = pd.read_csv('spectra.csv', header=None, sep = ' ')"
  41 |    ]
  42 |   },
  43 |   {
  44 |    "cell_type": "code",
  45 |    "execution_count": 3,
  46 |    "id": "1d02605c",
  47 |    "metadata": {},
  48 |    "outputs": [
  49 |     {
  50 |      "data": {
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  67 |        "  <thead>\n",
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  69 |        "      <th></th>\n",
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  71 |        "      <th>1</th>\n",
  72 |        "      <th>2</th>\n",
  73 |        "      <th>3</th>\n",
  74 |        "      <th>4</th>\n",
  75 |        "      <th>5</th>\n",
  76 |        "      <th>6</th>\n",
  77 |        "      <th>7</th>\n",
  78 |        "      <th>8</th>\n",
  79 |        "      <th>9</th>\n",
  80 |        "      <th>...</th>\n",
  81 |        "      <th>840</th>\n",
  82 |        "      <th>841</th>\n",
  83 |        "      <th>842</th>\n",
  84 |        "      <th>843</th>\n",
  85 |        "      <th>844</th>\n",
  86 |        "      <th>845</th>\n",
  87 |        "      <th>846</th>\n",
  88 |        "      <th>847</th>\n",
  89 |        "      <th>848</th>\n",
  90 |        "      <th>849</th>\n",
  91 |        "    </tr>\n",
  92 |        "  </thead>\n",
  93 |        "  <tbody>\n",
  94 |        "    <tr>\n",
  95 |        "      <th>0</th>\n",
  96 |        "      <td>0.010532</td>\n",
  97 |        "      <td>0.011854</td>\n",
  98 |        "      <td>0.011477</td>\n",
  99 |        "      <td>0.011186</td>\n",
 100 |        "      <td>0.010723</td>\n",
 101 |        "      <td>0.007781</td>\n",
 102 |        "      <td>0.003281</td>\n",
 103 |        "      <td>0.007836</td>\n",
 104 |        "      <td>0.008751</td>\n",
 105 |        "      <td>0.012667</td>\n",
 106 |        "      <td>...</td>\n",
 107 |        "      <td>0.024035</td>\n",
 108 |        "      <td>0.023810</td>\n",
 109 |        "      <td>0.024046</td>\n",
 110 |        "      <td>0.023865</td>\n",
 111 |        "      <td>0.023751</td>\n",
 112 |        "      <td>0.023780</td>\n",
 113 |        "      <td>0.023700</td>\n",
 114 |        "      <td>0.023532</td>\n",
 115 |        "      <td>0.023523</td>\n",
 116 |        "      <td>0.023560</td>\n",
 117 |        "    </tr>\n",
 118 |        "    <tr>\n",
 119 |        "      <th>1</th>\n",
 120 |        "      <td>0.012205</td>\n",
 121 |        "      <td>0.013479</td>\n",
 122 |        "      <td>0.013350</td>\n",
 123 |        "      <td>0.013067</td>\n",
 124 |        "      <td>0.013404</td>\n",
 125 |        "      <td>0.011026</td>\n",
 126 |        "      <td>0.005107</td>\n",
 127 |        "      <td>0.011005</td>\n",
 128 |        "      <td>0.012001</td>\n",
 129 |        "      <td>0.014975</td>\n",
 130 |        "      <td>...</td>\n",
 131 |        "      <td>0.045495</td>\n",
 132 |        "      <td>0.044942</td>\n",
 133 |        "      <td>0.045321</td>\n",
 134 |        "      <td>0.044943</td>\n",
 135 |        "      <td>0.044721</td>\n",
 136 |        "      <td>0.044843</td>\n",
 137 |        "      <td>0.044598</td>\n",
 138 |        "      <td>0.043910</td>\n",
 139 |        "      <td>0.043594</td>\n",
 140 |        "      <td>0.043794</td>\n",
 141 |        "    </tr>\n",
 142 |        "    <tr>\n",
 143 |        "      <th>2</th>\n",
 144 |        "      <td>0.003660</td>\n",
 145 |        "      <td>0.004010</td>\n",
 146 |        "      <td>0.004001</td>\n",
 147 |        "      <td>0.003920</td>\n",
 148 |        "      <td>0.004109</td>\n",
 149 |        "      <td>0.003515</td>\n",
 150 |        "      <td>0.001691</td>\n",
 151 |        "      <td>0.003498</td>\n",
 152 |        "      <td>0.003792</td>\n",
 153 |        "      <td>0.004505</td>\n",
 154 |        "      <td>...</td>\n",
 155 |        "      <td>0.016169</td>\n",
 156 |        "      <td>0.015951</td>\n",
 157 |        "      <td>0.016057</td>\n",
 158 |        "      <td>0.015907</td>\n",
 159 |        "      <td>0.015811</td>\n",
 160 |        "      <td>0.015818</td>\n",
 161 |        "      <td>0.015722</td>\n",
 162 |        "      <td>0.015475</td>\n",
 163 |        "      <td>0.015348</td>\n",
 164 |        "      <td>0.015374</td>\n",
 165 |        "    </tr>\n",
 166 |        "    <tr>\n",
 167 |        "      <th>3</th>\n",
 168 |        "      <td>0.065447</td>\n",
 169 |        "      <td>0.071665</td>\n",
 170 |        "      <td>0.071253</td>\n",
 171 |        "      <td>0.069657</td>\n",
 172 |        "      <td>0.072569</td>\n",
 173 |        "      <td>0.061464</td>\n",
 174 |        "      <td>0.029246</td>\n",
 175 |        "      <td>0.060936</td>\n",
 176 |        "      <td>0.065971</td>\n",
 177 |        "      <td>0.078945</td>\n",
 178 |        "      <td>...</td>\n",
 179 |        "      <td>0.052733</td>\n",
 180 |        "      <td>0.051933</td>\n",
 181 |        "      <td>0.052260</td>\n",
 182 |        "      <td>0.051681</td>\n",
 183 |        "      <td>0.051270</td>\n",
 184 |        "      <td>0.051238</td>\n",
 185 |        "      <td>0.050767</td>\n",
 186 |        "      <td>0.049791</td>\n",
 187 |        "      <td>0.049233</td>\n",
 188 |        "      <td>0.049177</td>\n",
 189 |        "    </tr>\n",
 190 |        "    <tr>\n",
 191 |        "      <th>4</th>\n",
 192 |        "      <td>0.143073</td>\n",
 193 |        "      <td>0.156413</td>\n",
 194 |        "      <td>0.155725</td>\n",
 195 |        "      <td>0.152241</td>\n",
 196 |        "      <td>0.159254</td>\n",
 197 |        "      <td>0.135972</td>\n",
 198 |        "      <td>0.065265</td>\n",
 199 |        "      <td>0.134719</td>\n",
 200 |        "      <td>0.145695</td>\n",
 201 |        "      <td>0.172647</td>\n",
 202 |        "      <td>...</td>\n",
 203 |        "      <td>0.071784</td>\n",
 204 |        "      <td>0.070843</td>\n",
 205 |        "      <td>0.071313</td>\n",
 206 |        "      <td>0.070579</td>\n",
 207 |        "      <td>0.070022</td>\n",
 208 |        "      <td>0.069911</td>\n",
 209 |        "      <td>0.069362</td>\n",
 210 |        "      <td>0.068354</td>\n",
 211 |        "      <td>0.067859</td>\n",
 212 |        "      <td>0.067661</td>\n",
 213 |        "    </tr>\n",
 214 |        "  </tbody>\n",
 215 |        "</table>\n",
 216 |        "<p>5 rows × 850 columns</p>\n",
 217 |        "</div>"
 218 |       ],
 219 |       "text/plain": [
 220 |        "        0         1         2         3         4         5         6    \\\n",
 221 |        "0  0.010532  0.011854  0.011477  0.011186  0.010723  0.007781  0.003281   \n",
 222 |        "1  0.012205  0.013479  0.013350  0.013067  0.013404  0.011026  0.005107   \n",
 223 |        "2  0.003660  0.004010  0.004001  0.003920  0.004109  0.003515  0.001691   \n",
 224 |        "3  0.065447  0.071665  0.071253  0.069657  0.072569  0.061464  0.029246   \n",
 225 |        "4  0.143073  0.156413  0.155725  0.152241  0.159254  0.135972  0.065265   \n",
 226 |        "\n",
 227 |        "        7         8         9    ...       840       841       842       843  \\\n",
 228 |        "0  0.007836  0.008751  0.012667  ...  0.024035  0.023810  0.024046  0.023865   \n",
 229 |        "1  0.011005  0.012001  0.014975  ...  0.045495  0.044942  0.045321  0.044943   \n",
 230 |        "2  0.003498  0.003792  0.004505  ...  0.016169  0.015951  0.016057  0.015907   \n",
 231 |        "3  0.060936  0.065971  0.078945  ...  0.052733  0.051933  0.052260  0.051681   \n",
 232 |        "4  0.134719  0.145695  0.172647  ...  0.071784  0.070843  0.071313  0.070579   \n",
 233 |        "\n",
 234 |        "        844       845       846       847       848       849  \n",
 235 |        "0  0.023751  0.023780  0.023700  0.023532  0.023523  0.023560  \n",
 236 |        "1  0.044721  0.044843  0.044598  0.043910  0.043594  0.043794  \n",
 237 |        "2  0.015811  0.015818  0.015722  0.015475  0.015348  0.015374  \n",
 238 |        "3  0.051270  0.051238  0.050767  0.049791  0.049233  0.049177  \n",
 239 |        "4  0.070022  0.069911  0.069362  0.068354  0.067859  0.067661  \n",
 240 |        "\n",
 241 |        "[5 rows x 850 columns]"
 242 |       ]
 243 |      },
 244 |      "execution_count": 3,
 245 |      "metadata": {},
 246 |      "output_type": "execute_result"
 247 |     }
 248 |    ],
 249 |    "source": [
 250 |     "features.head()"
 251 |    ]
 252 |   },
 253 |   {
 254 |    "cell_type": "code",
 255 |    "execution_count": 4,
 256 |    "id": "ff8f135c",
 257 |    "metadata": {},
 258 |    "outputs": [],
 259 |    "source": [
 260 |     "target = pd.read_csv('GalaxyProperties.csv')"
 261 |    ]
 262 |   },
 263 |   {
 264 |    "cell_type": "code",
 265 |    "execution_count": 5,
 266 |    "id": "1e30bbc8",
 267 |    "metadata": {},
 268 |    "outputs": [
 269 |     {
 270 |      "data": {
 271 |       "text/html": [
 272 |        "<div>\n",
 273 |        "<style scoped>\n",
 274 |        "    .dataframe tbody tr th:only-of-type {\n",
 275 |        "        vertical-align: middle;\n",
 276 |        "    }\n",
 277 |        "\n",
 278 |        "    .dataframe tbody tr th {\n",
 279 |        "        vertical-align: top;\n",
 280 |        "    }\n",
 281 |        "\n",
 282 |        "    .dataframe thead th {\n",
 283 |        "        text-align: right;\n",
 284 |        "    }\n",
 285 |        "</style>\n",
 286 |        "<table border=\"1\" class=\"dataframe\">\n",
 287 |        "  <thead>\n",
 288 |        "    <tr style=\"text-align: right;\">\n",
 289 |        "      <th></th>\n",
 290 |        "      <th>Unnamed: 0</th>\n",
 291 |        "      <th>Dust attenuation value</th>\n",
 292 |        "      <th>Age (Gyr)</th>\n",
 293 |        "      <th>Log10(Mass/Mass_Sun)</th>\n",
 294 |        "      <th>Tau (Gyr)</th>\n",
 295 |        "    </tr>\n",
 296 |        "  </thead>\n",
 297 |        "  <tbody>\n",
 298 |        "    <tr>\n",
 299 |        "      <th>0</th>\n",
 300 |        "      <td>0</td>\n",
 301 |        "      <td>0.164869</td>\n",
 302 |        "      <td>0.203291</td>\n",
 303 |        "      <td>7.885367</td>\n",
 304 |        "      <td>0.033035</td>\n",
 305 |        "    </tr>\n",
 306 |        "    <tr>\n",
 307 |        "      <th>1</th>\n",
 308 |        "      <td>1</td>\n",
 309 |        "      <td>0.200852</td>\n",
 310 |        "      <td>0.470248</td>\n",
 311 |        "      <td>8.297678</td>\n",
 312 |        "      <td>0.087146</td>\n",
 313 |        "    </tr>\n",
 314 |        "    <tr>\n",
 315 |        "      <th>2</th>\n",
 316 |        "      <td>2</td>\n",
 317 |        "      <td>0.197863</td>\n",
 318 |        "      <td>4.033993</td>\n",
 319 |        "      <td>8.184615</td>\n",
 320 |        "      <td>1.273974</td>\n",
 321 |        "    </tr>\n",
 322 |        "    <tr>\n",
 323 |        "      <th>3</th>\n",
 324 |        "      <td>3</td>\n",
 325 |        "      <td>0.001000</td>\n",
 326 |        "      <td>0.721636</td>\n",
 327 |        "      <td>8.360110</td>\n",
 328 |        "      <td>0.242588</td>\n",
 329 |        "    </tr>\n",
 330 |        "    <tr>\n",
 331 |        "      <th>4</th>\n",
 332 |        "      <td>4</td>\n",
 333 |        "      <td>0.001000</td>\n",
 334 |        "      <td>0.699464</td>\n",
 335 |        "      <td>8.373327</td>\n",
 336 |        "      <td>0.510683</td>\n",
 337 |        "    </tr>\n",
 338 |        "  </tbody>\n",
 339 |        "</table>\n",
 340 |        "</div>"
 341 |       ],
 342 |       "text/plain": [
 343 |        "   Unnamed: 0  Dust attenuation value  Age (Gyr)  Log10(Mass/Mass_Sun)  \\\n",
 344 |        "0           0                0.164869   0.203291              7.885367   \n",
 345 |        "1           1                0.200852   0.470248              8.297678   \n",
 346 |        "2           2                0.197863   4.033993              8.184615   \n",
 347 |        "3           3                0.001000   0.721636              8.360110   \n",
 348 |        "4           4                0.001000   0.699464              8.373327   \n",
 349 |        "\n",
 350 |        "   Tau (Gyr)  \n",
 351 |        "0   0.033035  \n",
 352 |        "1   0.087146  \n",
 353 |        "2   1.273974  \n",
 354 |        "3   0.242588  \n",
 355 |        "4   0.510683  "
 356 |       ]
 357 |      },
 358 |      "execution_count": 5,
 359 |      "metadata": {},
 360 |      "output_type": "execute_result"
 361 |     }
 362 |    ],
 363 |    "source": [
 364 |     "target.head()"
 365 |    ]
 366 |   },
 367 |   {
 368 |    "cell_type": "code",
 369 |    "execution_count": 6,
 370 |    "id": "afa84ac3",
 371 |    "metadata": {},
 372 |    "outputs": [
 373 |     {
 374 |      "data": {
 375 |       "text/plain": [
 376 |        "(1000, 850)"
 377 |       ]
 378 |      },
 379 |      "execution_count": 6,
 380 |      "metadata": {},
 381 |      "output_type": "execute_result"
 382 |     }
 383 |    ],
 384 |    "source": [
 385 |     "features.shape"
 386 |    ]
 387 |   },
 388 |   {
 389 |    "cell_type": "code",
 390 |    "execution_count": 7,
 391 |    "id": "499a47da",
 392 |    "metadata": {},
 393 |    "outputs": [],
 394 |    "source": [
 395 |     "#Our goal will be to predict log(age), which empirically works better than Age\n",
 396 |     "\n",
 397 |     "log_age = np.log(target['Age (Gyr)'].values) "
 398 |    ]
 399 |   },
 400 |   {
 401 |    "cell_type": "markdown",
 402 |    "id": "c733b08f",
 403 |    "metadata": {},
 404 |    "source": [
 405 |     "We split data into into train, calibration and validation sets (using train_test_split function twice), and run a regression model that outputs point predictions:"
 406 |    ]
 407 |   },
 408 |   {
 409 |    "cell_type": "code",
 410 |    "execution_count": 8,
 411 |    "id": "43e65b4c",
 412 |    "metadata": {},
 413 |    "outputs": [
 414 |     {
 415 |      "data": {
 416 |       "text/html": [
 417 |        "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=10)</pre></div></div></div></div></div>"
 418 |       ],
 419 |       "text/plain": [
 420 |        "RandomForestRegressor(random_state=10)"
 421 |       ]
 422 |      },
 423 |      "execution_count": 8,
 424 |      "metadata": {},
 425 |      "output_type": "execute_result"
 426 |     }
 427 |    ],
 428 |    "source": [
 429 |     "X_train, X_test, y_train, y_test = train_test_split(features, log_age, test_size=0.33, random_state=10)\n",
 430 |     "\n",
 431 |     "X_calib, X_val, y_calib, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=10)\n",
 432 |     "\n",
 433 |     "n = len(y_calib)\n",
 434 |     "\n",
 435 |     "n_val = len(y_val)\n",
 436 |     "\n",
 437 |     "#Train model\n",
 438 |     "\n",
 439 |     "model = RandomForestRegressor(random_state=10)\n",
 440 |     "\n",
 441 |     "model.fit(X_train, y_train)\n"
 442 |    ]
 443 |   },
 444 |   {
 445 |    "cell_type": "markdown",
 446 |    "id": "5eb4262d",
 447 |    "metadata": {},
 448 |    "source": [
 449 |     "### Simplest version of conformal predictions - generating intervals from point predictions."
 450 |    ]
 451 |   },
 452 |   {
 453 |    "cell_type": "markdown",
 454 |    "id": "0cd2996a",
 455 |    "metadata": {},
 456 |    "source": [
 457 |     "We can predict the labels for the calibration set and visualize absolute residuals:"
 458 |    ]
 459 |   },
 460 |   {
 461 |    "cell_type": "code",
 462 |    "execution_count": 9,
 463 |    "id": "425e7500",
 464 |    "metadata": {},
 465 |    "outputs": [],
 466 |    "source": [
 467 |     "y_calib_pred = model.predict(X_calib)"
 468 |    ]
 469 |   },
 470 |   {
 471 |    "cell_type": "code",
 472 |    "execution_count": 10,
 473 |    "id": "8ba839d5",
 474 |    "metadata": {},
 475 |    "outputs": [
 476 |     {
 477 |      "data": {
 478 |       "image/png": 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KfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACScmM5iiKfG7rlyw3gAEqbKx8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJBUweNj5cqVUVZW1uWorKws9NsAACWqKL9k7JOf/GT8/ve/73w8ZMiQYrwNAFCCihIfQ4cOdbUDAOhRUT7zsX///qiuro66urr4yle+Ei+99NJJx3Z0dER7e3uXAwAYuAp+5eOSSy6JBx54ICZMmBD/+Mc/4nvf+17MmDEj9u7dG+eee2638Y2NjXHnnXcWehoMYMW8bwwAxVeWZVlWzDc4evRoXHDBBbF06dJoaGjo9nxHR0d0dHR0Pm5vb4+amppoa2uLkSNHFnw+/uLiZNywDqDv2tvbo6Kiold/fxf9rrZnn312fOpTn4r9+/f3+Hwul4tcLlfsaQAAp4mi/56Pjo6O+POf/xxVVVXFfisAoAQUPD5uu+22aG5ujpaWlvjjH/8YX/rSl6K9vT3mz59f6LcCAEpQwX/s8re//S2++tWvxmuvvRbnnXdeTJs2LXbu3Bm1tbWFfisAoAQVPD42bNhQ6C8JAAwg7u0CACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASKrov14dKJ5871Xk/jXA6cCVDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlBvLwWkm35vFAZQaVz4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACScm8XSMD9Wk4/+f6ZHLjrmiLNBAYfVz4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFJuLAd9VIo3iyvWnPO56Vqp3tDtdFi7Ysrn+ztd5kxXpfRn6MoHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkVbT4uOeee6Kuri6GDx8eU6ZMiSeffLJYbwUAlJCixMfGjRtjyZIlsWLFitizZ09ceumlUV9fH6+88kox3g4AKCFFiY/Vq1fH17/+9fjGN74RH//4x+Puu++OmpqaWLt2bTHeDgAoIQX/9erHjh2L3bt3x7Jly7qcnzNnTuzYsaPb+I6Ojujo6Oh83NbWFhER7e3thZ5aRES82/GvonxdSl++e85eek8+a5fvug30/xYU6/vLVz7rcbrMma76+8/wxNfMsuz9B2cF9ve//z2LiOwPf/hDl/Pf//73swkTJnQbf8cdd2QR4XA4HA6HYwAcra2t79sKRbuxXFlZWZfHWZZ1OxcRsXz58mhoaOh8/O6778Y///nPOPfcc3sc/0G0t7dHTU1NtLa2xsiRIwv6tQcaa5Uf65Uf69V71io/1qv3Cr1WWZbFkSNHorq6+n3HFjw+Ro8eHUOGDIlDhw51OX/48OEYO3Zst/G5XC5yuVyXcx/+8IcLPa0uRo4caVP2krXKj/XKj/XqPWuVH+vVe4Vcq4qKil6NK/gHTocNGxZTpkyJpqamLuebmppixowZhX47AKDEFOXHLg0NDXHjjTfG1KlTY/r06XHvvffGK6+8Erfccksx3g4AKCFFiY/rr78+Xn/99fjud78bBw8ejEmTJsUjjzwStbW1xXi7XsvlcnHHHXd0+zEP3Vmr/Fiv/Fiv3rNW+bFevdefa1WWZb35NzEAAIXh3i4AQFLiAwBISnwAAEmJDwAgqQEXH/fcc0/U1dXF8OHDY8qUKfHkk0+ecnxzc3NMmTIlhg8fHh/96EfjZz/7WaKZ9r981mrbtm1RVlbW7fjLX/6ScMb9Z/v27TF37tyorq6OsrKyeOihh973NYN1b+W7VoN5bzU2NsbFF18c5eXlMWbMmLj22mtj37597/u6wbq3+rJeg3V/rV27Nj796U93/gKx6dOnx+9+97tTviblvhpQ8bFx48ZYsmRJrFixIvbs2ROXXnpp1NfXxyuvvNLj+JaWlvj85z8fl156aezZsyduv/32+Na3vhWbNm1KPPP08l2rE/bt2xcHDx7sPMaPH59oxv3r6NGjMXny5FizZk2vxg/mvZXvWp0wGPdWc3NzLFy4MHbu3BlNTU1x/PjxmDNnThw9evSkrxnMe6sv63XCYNtf48aNi7vuuit27doVu3btiiuuuCK+8IUvxN69e3scn3xfFeRucqeJz372s9ktt9zS5dyFF16YLVu2rMfxS5cuzS688MIu526++eZs2rRpRZvj6SLftXriiSeyiMjeeOONBLM7vUVEtnnz5lOOGcx767/1Zq3srfccPnw4i4isubn5pGPsrff0Zr3sr/ecc8452c9//vMen0u9rwbMlY9jx47F7t27Y86cOV3Oz5kzJ3bs2NHja55++ulu4z/3uc/Frl274t///nfR5trf+rJWJ1x00UVRVVUVV155ZTzxxBPFnGZJG6x764OwtyLa2toiImLUqFEnHWNvvac363XCYN5f77zzTmzYsCGOHj0a06dP73FM6n01YOLjtddei3feeafbzevGjh3b7SZ3Jxw6dKjH8cePH4/XXnutaHPtb31Zq6qqqrj33ntj06ZN8eCDD8bEiRPjyiuvjO3bt6eYcskZrHurL+yt/8iyLBoaGmLmzJkxadKkk46zt/6jt+s1mPfX888/Hx/60Icil8vFLbfcEps3b45PfOITPY5Nva+K8uvV+1NZWVmXx1mWdTv3fuN7Oj8Q5bNWEydOjIkTJ3Y+nj59erS2tsaPfvSjuOyyy4o6z1I1mPdWPuyt/1i0aFE899xz8dRTT73vWHur9+s1mPfXxIkT49lnn40333wzNm3aFPPnz4/m5uaTBkjKfTVgrnyMHj06hgwZ0u3/uR8+fLhbzZ1QWVnZ4/ihQ4fGueeeW7S59re+rFVPpk2bFvv37y/09AaEwbq3CmWw7a3FixfHww8/HE888USMGzfulGPtrfzWqyeDZX8NGzYsPvaxj8XUqVOjsbExJk+eHP/3f//X49jU+2rAxMewYcNiypQp0dTU1OV8U1NTzJgxo8fXTJ8+vdv4rVu3xtSpU+PMM88s2lz7W1/Wqid79uyJqqqqQk9vQBise6tQBsveyrIsFi1aFA8++GA8/vjjUVdX976vGcx7qy/r1ZPBsr/+V5Zl0dHR0eNzyfdVUT7G2k82bNiQnXnmmdl9992XvfDCC9mSJUuys88+Oztw4ECWZVm2bNmy7MYbb+wc/9JLL2VnnXVW9u1vfzt74YUXsvvuuy8788wzs9/85jf99S0kk+9a/eQnP8k2b96c/fWvf83+9Kc/ZcuWLcsiItu0aVN/fQtJHTlyJNuzZ0+2Z8+eLCKy1atXZ3v27MlefvnlLMvsrf+W71oN5r31zW9+M6uoqMi2bduWHTx4sPP417/+1TnG3npPX9ZrsO6v5cuXZ9u3b89aWlqy5557Lrv99tuzM844I9u6dWuWZf2/rwZUfGRZlv30pz/Namtrs2HDhmWf+cxnuvwTrPnz52eXX355l/Hbtm3LLrroomzYsGHZ+eefn61duzbxjPtPPmv1gx/8ILvggguy4cOHZ+ecc042c+bMbMuWLf0w6/5x4p/r/e8xf/78LMvsrf+W71oN5r3V0zpFRLZu3brOMfbWe/qyXoN1f33ta1/r/O/7eeedl1155ZWd4ZFl/b+vyrLs/3+iBAAggQHzmQ8AoDSIDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKT+Hy25qnmB15N5AAAAAElFTkSuQmCC",
 479 |       "text/plain": [
 480 |        "<Figure size 640x480 with 1 Axes>"
 481 |       ]
 482 |      },
 483 |      "metadata": {},
 484 |      "output_type": "display_data"
 485 |     }
 486 |    ],
 487 |    "source": [
 488 |     "plt.hist(np.abs(y_calib_pred - y_calib), bins = 40);"
 489 |    ]
 490 |   },
 491 |   {
 492 |    "cell_type": "markdown",
 493 |    "id": "f04a9256",
 494 |    "metadata": {},
 495 |    "source": [
 496 |     "Let's say that we are interested in 80% coverage; alpha = 0.2. We find the corresponding 80% quantile of the absolute residuals, with the usual finite sample size correction:"
 497 |    ]
 498 |   },
 499 |   {
 500 |    "cell_type": "code",
 501 |    "execution_count": 11,
 502 |    "id": "d90148bc",
 503 |    "metadata": {},
 504 |    "outputs": [],
 505 |    "source": [
 506 |     "alpha = 0.2"
 507 |    ]
 508 |   },
 509 |   {
 510 |    "cell_type": "code",
 511 |    "execution_count": 12,
 512 |    "id": "990fe1e9",
 513 |    "metadata": {},
 514 |    "outputs": [],
 515 |    "source": [
 516 |     "qhat = np.quantile(np.abs(y_calib_pred - y_calib), np.ceil((n+1)*(1-alpha))/n)"
 517 |    ]
 518 |   },
 519 |   {
 520 |    "cell_type": "code",
 521 |    "execution_count": 13,
 522 |    "id": "18a2188d",
 523 |    "metadata": {},
 524 |    "outputs": [
 525 |     {
 526 |      "data": {
 527 |       "text/plain": [
 528 |        "0.7055971501774867"
 529 |       ]
 530 |      },
 531 |      "execution_count": 13,
 532 |      "metadata": {},
 533 |      "output_type": "execute_result"
 534 |     }
 535 |    ],
 536 |    "source": [
 537 |     "qhat"
 538 |    ]
 539 |   },
 540 |   {
 541 |    "cell_type": "markdown",
 542 |    "id": "bfc41046",
 543 |    "metadata": {},
 544 |    "source": [
 545 |     "We can now generate intervals by adding this quantity on either side of the point predictions:"
 546 |    ]
 547 |   },
 548 |   {
 549 |    "cell_type": "code",
 550 |    "execution_count": 14,
 551 |    "id": "1a3abc81",
 552 |    "metadata": {},
 553 |    "outputs": [],
 554 |    "source": [
 555 |     "intervals = np.hstack([(y_calib_pred - qhat).reshape(-1,1), (y_calib_pred + qhat).reshape(-1,1)])"
 556 |    ]
 557 |   },
 558 |   {
 559 |    "cell_type": "markdown",
 560 |    "id": "e47baac2",
 561 |    "metadata": {},
 562 |    "source": [
 563 |     "#### To check calibration, we ask how often the true value is found in the intervals:"
 564 |    ]
 565 |   },
 566 |   {
 567 |    "cell_type": "code",
 568 |    "execution_count": 15,
 569 |    "id": "af898466",
 570 |    "metadata": {},
 571 |    "outputs": [],
 572 |    "source": [
 573 |     "#whether true value is in interval\n",
 574 |     "\n",
 575 |     "inint = np.array([intervals[i][0] < y_calib[i] < intervals[i][1] for i in range(len(y_calib))], dtype = int)"
 576 |    ]
 577 |   },
 578 |   {
 579 |    "cell_type": "code",
 580 |    "execution_count": 16,
 581 |    "id": "37faf884",
 582 |    "metadata": {},
 583 |    "outputs": [
 584 |     {
 585 |      "data": {
 586 |       "text/plain": [
 587 |        "0.806060606060606"
 588 |       ]
 589 |      },
 590 |      "execution_count": 16,
 591 |      "metadata": {},
 592 |      "output_type": "execute_result"
 593 |     }
 594 |    ],
 595 |    "source": [
 596 |     "inint.mean() #Success! (Coverage is as expected)"
 597 |    ]
 598 |   },
 599 |   {
 600 |    "cell_type": "markdown",
 601 |    "id": "16b1d872",
 602 |    "metadata": {},
 603 |    "source": [
 604 |     "We should also check that the coverage holds on the validation set:"
 605 |    ]
 606 |   },
 607 |   {
 608 |    "cell_type": "code",
 609 |    "execution_count": 17,
 610 |    "id": "aee3d03c",
 611 |    "metadata": {},
 612 |    "outputs": [
 613 |     {
 614 |      "data": {
 615 |       "text/plain": [
 616 |        "0.8363636363636363"
 617 |       ]
 618 |      },
 619 |      "execution_count": 17,
 620 |      "metadata": {},
 621 |      "output_type": "execute_result"
 622 |     }
 623 |    ],
 624 |    "source": [
 625 |     "y_val_pred = model.predict(X_val)\n",
 626 |     "intervals_val = np.hstack([(y_val_pred - qhat).reshape(-1,1), (y_val_pred + qhat).reshape(-1,1)])\n",
 627 |     "inint_val = np.array([intervals_val[i][0] < y_val[i] < intervals_val[i][1] for i in range(len(y_val))], dtype = int)\n",
 628 |     "inint_val = np.array([intervals_val[i][0] < y_val[i] < intervals_val[i][1] for i in range(len(y_val))], dtype = int)\n",
 629 |     "inint_val.mean() #Not bad! Should also marginalize over effect of split of data etc."
 630 |    ]
 631 |   },
 632 |   {
 633 |    "cell_type": "markdown",
 634 |    "id": "2d0f0115",
 635 |    "metadata": {},
 636 |    "source": [
 637 |     "### Conformalized Quantile Regression"
 638 |    ]
 639 |   },
 640 |   {
 641 |    "cell_type": "markdown",
 642 |    "id": "bbab17ce",
 643 |    "metadata": {},
 644 |    "source": [
 645 |     "We need to start with a model that can output quantile intervals, so we use conformalization to calibrate them.\n",
 646 |     "\n",
 647 |     "Here we use GradientBoostingRegression with quantile loss.\n",
 648 |     "\n",
 649 |     "Read about quantile loss:\n",
 650 |     "\n",
 651 |     "https://towardsdatascience.com/quantile-loss-and-quantile-regression-b0689c13f54d\n",
 652 |     "\n",
 653 |     "Example partially taken from: \n",
 654 |     "\n",
 655 |     "https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html\n",
 656 |     "\n",
 657 |     "and\n",
 658 |     "\n",
 659 |     "https://mapie.readthedocs.io/en/latest/examples_regression/1-quickstart/plot_prefit.html"
 660 |    ]
 661 |   },
 662 |   {
 663 |    "cell_type": "markdown",
 664 |    "id": "4123a349",
 665 |    "metadata": {},
 666 |    "source": [
 667 |     "Usage note: check data for irregularities in target distribution, which may affect the quantiles."
 668 |    ]
 669 |   },
 670 |   {
 671 |    "cell_type": "code",
 672 |    "execution_count": 18,
 673 |    "id": "01735f74",
 674 |    "metadata": {},
 675 |    "outputs": [],
 676 |    "source": [
 677 |     "all_models = {}\n",
 678 |     "\n",
 679 |     "for alpha in [0.05, 0.1, 0.5, 0.9, 0.95]:\n",
 680 |     "    gbr = GradientBoostingRegressor(loss = \"quantile\", alpha = alpha)\n",
 681 |     "    all_models[\"q %1.2f\" % alpha] = gbr.fit(X_train, y_train)"
 682 |    ]
 683 |   },
 684 |   {
 685 |    "cell_type": "code",
 686 |    "execution_count": 19,
 687 |    "id": "789f57cf",
 688 |    "metadata": {},
 689 |    "outputs": [
 690 |     {
 691 |      "data": {
 692 |       "text/plain": [
 693 |        "{'q 0.05': GradientBoostingRegressor(alpha=0.05, loss='quantile'),\n",
 694 |        " 'q 0.10': GradientBoostingRegressor(alpha=0.1, loss='quantile'),\n",
 695 |        " 'q 0.50': GradientBoostingRegressor(alpha=0.5, loss='quantile'),\n",
 696 |        " 'q 0.90': GradientBoostingRegressor(loss='quantile'),\n",
 697 |        " 'q 0.95': GradientBoostingRegressor(alpha=0.95, loss='quantile')}"
 698 |       ]
 699 |      },
 700 |      "execution_count": 19,
 701 |      "metadata": {},
 702 |      "output_type": "execute_result"
 703 |     }
 704 |    ],
 705 |    "source": [
 706 |     "all_models"
 707 |    ]
 708 |   },
 709 |   {
 710 |    "cell_type": "markdown",
 711 |    "id": "2e1253a2",
 712 |    "metadata": {},
 713 |    "source": [
 714 |     "Let's compute the conformal scores s(x,y) using alpha = 0.1 and 0.9 as quantiles (for 80% coverage), on the calibration set:"
 715 |    ]
 716 |   },
 717 |   {
 718 |    "cell_type": "code",
 719 |    "execution_count": 20,
 720 |    "id": "7bf9c824",
 721 |    "metadata": {},
 722 |    "outputs": [],
 723 |    "source": [
 724 |     "y_pred_cal_q_hi = all_models[\"q 0.90\"].predict(X_calib) \n",
 725 |     "\n",
 726 |     "y_pred_cal_q_lo = all_models[\"q 0.10\"].predict(X_calib) "
 727 |    ]
 728 |   },
 729 |   {
 730 |    "cell_type": "code",
 731 |    "execution_count": 21,
 732 |    "id": "8388dbf4",
 733 |    "metadata": {},
 734 |    "outputs": [],
 735 |    "source": [
 736 |     "intervals = np.hstack([y_pred_cal_q_lo.reshape(-1,1), y_pred_cal_q_hi.reshape(-1,1)])"
 737 |    ]
 738 |   },
 739 |   {
 740 |    "cell_type": "markdown",
 741 |    "id": "da89db96",
 742 |    "metadata": {},
 743 |    "source": [
 744 |     "The spread in the width of the intervals shows the natural adaptivity for quantile regression:"
 745 |    ]
 746 |   },
 747 |   {
 748 |    "cell_type": "code",
 749 |    "execution_count": 22,
 750 |    "id": "286aa794",
 751 |    "metadata": {},
 752 |    "outputs": [
 753 |     {
 754 |      "data": {
 755 |       "image/png": "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",
 756 |       "text/plain": [
 757 |        "<Figure size 640x480 with 1 Axes>"
 758 |       ]
 759 |      },
 760 |      "metadata": {},
 761 |      "output_type": "display_data"
 762 |     }
 763 |    ],
 764 |    "source": [
 765 |     "plt.hist(y_pred_cal_q_hi-y_pred_cal_q_lo, bins = 100);"
 766 |    ]
 767 |   },
 768 |   {
 769 |    "cell_type": "markdown",
 770 |    "id": "97473afa",
 771 |    "metadata": {},
 772 |    "source": [
 773 |     "This below is just a visualization of true values vs predicted intervals:"
 774 |    ]
 775 |   },
 776 |   {
 777 |    "cell_type": "code",
 778 |    "execution_count": 23,
 779 |    "id": "4ce486b9",
 780 |    "metadata": {},
 781 |    "outputs": [
 782 |     {
 783 |      "data": {
 784 |       "image/png": 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",
 785 |       "text/plain": [
 786 |        "<Figure size 1000x1000 with 1 Axes>"
 787 |       ]
 788 |      },
 789 |      "metadata": {},
 790 |      "output_type": "display_data"
 791 |     }
 792 |    ],
 793 |    "source": [
 794 |     "fig = plt.figure(figsize=(10, 10))\n",
 795 |     "plt.scatter(range(len(y_calib)), y_calib, c ='k', s = 10, label = \"True value\")\n",
 796 |     "plt.fill_between(\n",
 797 |     "    range(len(y_calib)), y_pred_cal_q_lo, y_pred_cal_q_hi, step = 'mid', alpha=0.4, label=\"Predicted 80% interval\"\n",
 798 |     ")\n",
 799 |     "plt.xlabel(\"Object\")\n",
 800 |     "plt.ylabel(\"Intervals\")\n",
 801 |     "plt.legend(loc=\"upper left\")\n",
 802 |     "plt.show()"
 803 |    ]
 804 |   },
 805 |   {
 806 |    "cell_type": "markdown",
 807 |    "id": "08c0a783",
 808 |    "metadata": {},
 809 |    "source": [
 810 |     "#### More formally, we can ask: what fraction of true values lies in the 80% region?"
 811 |    ]
 812 |   },
 813 |   {
 814 |    "cell_type": "code",
 815 |    "execution_count": 24,
 816 |    "id": "f20da335",
 817 |    "metadata": {},
 818 |    "outputs": [],
 819 |    "source": [
 820 |     "inint = np.array([intervals[i][0] < y_calib[i] < intervals[i][1] for i in range(len(y_calib))], dtype = int)"
 821 |    ]
 822 |   },
 823 |   {
 824 |    "cell_type": "code",
 825 |    "execution_count": 25,
 826 |    "id": "e285167c",
 827 |    "metadata": {},
 828 |    "outputs": [
 829 |     {
 830 |      "data": {
 831 |       "text/plain": [
 832 |        "0.7393939393939394"
 833 |       ]
 834 |      },
 835 |      "execution_count": 25,
 836 |      "metadata": {},
 837 |      "output_type": "execute_result"
 838 |     }
 839 |    ],
 840 |    "source": [
 841 |     "inint.mean() #Target: 0.80"
 842 |    ]
 843 |   },
 844 |   {
 845 |    "cell_type": "markdown",
 846 |    "id": "800bc111",
 847 |    "metadata": {},
 848 |    "source": [
 849 |     "### Calibration:"
 850 |    ]
 851 |   },
 852 |   {
 853 |    "cell_type": "markdown",
 854 |    "id": "ab0517aa",
 855 |    "metadata": {},
 856 |    "source": [
 857 |     "These below are the conformal scores for conformal quantile regression (for 80% coverage, i.e. 10/90 quantiles):"
 858 |    ]
 859 |   },
 860 |   {
 861 |    "cell_type": "code",
 862 |    "execution_count": 26,
 863 |    "id": "2420518a",
 864 |    "metadata": {},
 865 |    "outputs": [],
 866 |    "source": [
 867 |     "s10_90 = np.array([max(y_pred_cal_q_lo[i] - y_calib[i], y_calib[i] - y_pred_cal_q_hi[i]) for i in range(len(y_calib))])"
 868 |    ]
 869 |   },
 870 |   {
 871 |    "cell_type": "code",
 872 |    "execution_count": 27,
 873 |    "id": "e685fa4e",
 874 |    "metadata": {},
 875 |    "outputs": [
 876 |     {
 877 |      "data": {
 878 |       "image/png": "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",
 879 |       "text/plain": [
 880 |        "<Figure size 640x480 with 1 Axes>"
 881 |       ]
 882 |      },
 883 |      "metadata": {},
 884 |      "output_type": "display_data"
 885 |     }
 886 |    ],
 887 |    "source": [
 888 |     "#We can take a look at the scores distribution:\n",
 889 |     "\n",
 890 |     "plt.hist(s10_90, bins = 100);"
 891 |    ]
 892 |   },
 893 |   {
 894 |    "cell_type": "code",
 895 |    "execution_count": 28,
 896 |    "id": "bc2cbe26",
 897 |    "metadata": {},
 898 |    "outputs": [],
 899 |    "source": [
 900 |     "# Get the score quantile for alpha = 0.2 (corresponding to 80% coverage, i.e. quantiles 0.1 and 0.9)\n",
 901 |     "\n",
 902 |     "alpha = 0.2\n",
 903 |     "\n",
 904 |     "correction = np.quantile(s10_90, np.ceil((n+1)*(1-alpha))/n, method='higher')"
 905 |    ]
 906 |   },
 907 |   {
 908 |    "cell_type": "markdown",
 909 |    "id": "13fff0a1",
 910 |    "metadata": {},
 911 |    "source": [
 912 |     "Now we build the new intervals by applying the correction:"
 913 |    ]
 914 |   },
 915 |   {
 916 |    "cell_type": "code",
 917 |    "execution_count": 29,
 918 |    "id": "cf199e77",
 919 |    "metadata": {},
 920 |    "outputs": [],
 921 |    "source": [
 922 |     "intervals_10_90 = np.hstack([(intervals[:,0] - correction).reshape(-1,1), (intervals[:,1] + correction).reshape(-1,1)])"
 923 |    ]
 924 |   },
 925 |   {
 926 |    "cell_type": "code",
 927 |    "execution_count": 30,
 928 |    "id": "d0ed377c",
 929 |    "metadata": {},
 930 |    "outputs": [],
 931 |    "source": [
 932 |     "inint_10_90 = np.array([intervals_10_90[i][0] < y_calib[i] < intervals_10_90[i][1] for i in range(len(y_calib))], dtype = int)"
 933 |    ]
 934 |   },
 935 |   {
 936 |    "cell_type": "code",
 937 |    "execution_count": 31,
 938 |    "id": "a5f30fda",
 939 |    "metadata": {},
 940 |    "outputs": [
 941 |     {
 942 |      "data": {
 943 |       "text/plain": [
 944 |        "0.806060606060606"
 945 |       ]
 946 |      },
 947 |      "execution_count": 31,
 948 |      "metadata": {},
 949 |      "output_type": "execute_result"
 950 |     }
 951 |    ],
 952 |    "source": [
 953 |     "inint_10_90.mean() #Success!"
 954 |    ]
 955 |   },
 956 |   {
 957 |    "cell_type": "markdown",
 958 |    "id": "368eb34e",
 959 |    "metadata": {},
 960 |    "source": [
 961 |     "Finally, we can apply the same correction to the validation set:"
 962 |    ]
 963 |   },
 964 |   {
 965 |    "cell_type": "code",
 966 |    "execution_count": 32,
 967 |    "id": "6e7c0e16",
 968 |    "metadata": {
 969 |     "slideshow": {
 970 |      "slide_type": "slide"
 971 |     }
 972 |    },
 973 |    "outputs": [
 974 |     {
 975 |      "data": {
 976 |       "text/plain": [
 977 |        "0.8606060606060606"
 978 |       ]
 979 |      },
 980 |      "execution_count": 32,
 981 |      "metadata": {},
 982 |      "output_type": "execute_result"
 983 |     }
 984 |    ],
 985 |    "source": [
 986 |     "y_pred_val_q_hi = all_models[\"q 0.90\"].predict(X_val) \n",
 987 |     "\n",
 988 |     "y_pred_val_q_lo = all_models[\"q 0.10\"].predict(X_val) \n",
 989 |     "\n",
 990 |     "intervals = np.hstack([y_pred_val_q_lo.reshape(-1,1), y_pred_val_q_hi.reshape(-1,1)]) # original\n",
 991 |     "\n",
 992 |     "intervals_10_90 = np.hstack([(intervals[:,0] - correction).reshape(-1,1), (intervals[:,1] + correction).reshape(-1,1)])\n",
 993 |     "\n",
 994 |     "inint_10_90 = np.array([intervals_10_90[i][0] < y_val[i] < intervals_10_90[i][1] for i in range(len(y_val))], dtype = int)\n",
 995 |     "\n",
 996 |     "inint_10_90.mean() #Target: 0.80"
 997 |    ]
 998 |   },
 999 |   {
1000 |    "cell_type": "markdown",
1001 |    "id": "6aa776aa",
1002 |    "metadata": {
1003 |     "slideshow": {
1004 |      "slide_type": "slide"
1005 |     }
1006 |    },
1007 |    "source": [
1008 |     "#### We can also do the same thing with MAPIE:"
1009 |    ]
1010 |   },
1011 |   {
1012 |    "cell_type": "code",
1013 |    "execution_count": 33,
1014 |    "id": "8c1dfe71",
1015 |    "metadata": {},
1016 |    "outputs": [
1017 |     {
1018 |      "name": "stdout",
1019 |      "output_type": "stream",
1020 |      "text": [
1021 |       "Coverage on calibration set: 0.81\n",
1022 |       "Coverage on validation set: 0.86\n"
1023 |      ]
1024 |     },
1025 |     {
1026 |      "name": "stderr",
1027 |      "output_type": "stream",
1028 |      "text": [
1029 |       "/Users/vivi/anaconda3/lib/python3.11/site-packages/mapie/regression/quantile_regression.py:201: UserWarning: WARNING: The alpha that is set needs to be the same as the alpha of your prefitted model in the following order [alpha/2, 1 - alpha/2, 0.5]\n",
1030 |       "  warnings.warn(\n",
1031 |       "/Users/vivi/anaconda3/lib/python3.11/site-packages/mapie/utils.py:598: UserWarning: WARNING: The predictions are ill-sorted.\n",
1032 |       "  warnings.warn(\n",
1033 |       "/Users/vivi/anaconda3/lib/python3.11/site-packages/mapie/utils.py:598: UserWarning: WARNING: The predictions are ill-sorted.\n",
1034 |       "  warnings.warn(\n",
1035 |       "/Users/vivi/anaconda3/lib/python3.11/site-packages/mapie/utils.py:598: UserWarning: WARNING: The predictions are ill-sorted.\n",
1036 |       "  warnings.warn(\n",
1037 |       "/Users/vivi/anaconda3/lib/python3.11/site-packages/mapie/utils.py:598: UserWarning: WARNING: The predictions are ill-sorted.\n",
1038 |       "  warnings.warn(\n",
1039 |       "/Users/vivi/anaconda3/lib/python3.11/site-packages/mapie/utils.py:598: UserWarning: WARNING: The predictions are ill-sorted.\n",
1040 |       "  warnings.warn(\n",
1041 |       "/Users/vivi/anaconda3/lib/python3.11/site-packages/mapie/utils.py:598: UserWarning: WARNING: The predictions are ill-sorted.\n",
1042 |       "  warnings.warn(\n"
1043 |      ]
1044 |     }
1045 |    ],
1046 |    "source": [
1047 |     "# Calibrate uncertainties on calibration set\n",
1048 |     "mapie_cqr = MapieQuantileRegressor([all_models[\"q 0.10\"], all_models[\"q 0.90\"], all_models[\"q 0.50\"]], cv=\"prefit\", alpha=0.2)\n",
1049 |     "\n",
1050 |     "mapie_cqr.fit(X_calib, y_calib)\n",
1051 |     "\n",
1052 |     "# Evaluate prediction and coverage level on calibration set\n",
1053 |     "y_pred_cqr, y_pis_cqr = mapie_cqr.predict(X_calib)\n",
1054 |     "coverage_cqr = regression_coverage_score(\n",
1055 |     "    y_calib,\n",
1056 |     "    y_pis_cqr[:, 0, 0],\n",
1057 |     "    y_pis_cqr[:, 1, 0]\n",
1058 |     ")\n",
1059 |     "\n",
1060 |     "print('Coverage on calibration set:', '%.2f' % coverage_cqr)\n",
1061 |     "\n",
1062 |     "# Evaluate prediction and coverage level on testing set\n",
1063 |     "y_pred_cqr, y_pis_cqr = mapie_cqr.predict(X_val)\n",
1064 |     "coverage_cqr = regression_coverage_score(\n",
1065 |     "    y_val,\n",
1066 |     "    y_pis_cqr[:, 0, 0],\n",
1067 |     "    y_pis_cqr[:, 1, 0]\n",
1068 |     ")\n",
1069 |     "\n",
1070 |     "print('Coverage on validation set:', '%.2f' %  coverage_cqr) #Very similar"
1071 |    ]
1072 |   },
1073 |   {
1074 |    "cell_type": "code",
1075 |    "execution_count": null,
1076 |    "id": "3af15ef0",
1077 |    "metadata": {},
1078 |    "outputs": [],
1079 |    "source": []
1080 |   }
1081 |  ],
1082 |  "metadata": {
1083 |   "kernelspec": {
1084 |    "display_name": "Python 3 (ipykernel)",
1085 |    "language": "python",
1086 |    "name": "python3"
1087 |   },
1088 |   "language_info": {
1089 |    "codemirror_mode": {
1090 |     "name": "ipython",
1091 |     "version": 3
1092 |    },
1093 |    "file_extension": ".py",
1094 |    "mimetype": "text/x-python",
1095 |    "name": "python",
1096 |    "nbconvert_exporter": "python",
1097 |    "pygments_lexer": "ipython3",
1098 |    "version": "3.11.6"
1099 |   }
1100 |  },
1101 |  "nbformat": 4,
1102 |  "nbformat_minor": 5
1103 | }
1104 | 


--------------------------------------------------------------------------------
/GalaxyProperties.csv:
--------------------------------------------------------------------------------
   1 | ,Dust attenuation value,Age (Gyr),Log10(Mass/Mass_Sun),Tau (Gyr)
   2 | 0,0.1648691903421710,0.2032911081660820,7.885366660500210,0.0330354477246532
   3 | 1,0.2008517572931340,0.4702480533753310,8.297677735406540,0.0871459775662365
   4 | 2,0.19786299417290600,4.033993194275280,8.184614779714330,1.2739737806583300
   5 | 3,0.001,0.7216364022206550,8.36011019137045,0.2425883215430400
   6 | 4,0.001,0.6994639566523550,8.373326782804570,0.5106830458053310
   7 | 5,0.08059182449752020,0.8265444287294270,7.629000986652260,0.6060364290600060
   8 | 6,0.4488191139005980,4.5293594528719800,10.993512669618200,0.6579691106823900
   9 | 7,0.001,0.3981584007117890,7.565025588307170,0.08957940934760400
  10 | 8,0.7598801007501800,0.7151506232289460,9.535678814103540,1.0226443013904000
  11 | 9,0.5421368754818960,0.4082256848043900,8.290803195318120,8.622035548936560
  12 | 10,0.001,3.9912821969369700,8.095127823175760,1.9137152251379200
  13 | 11,0.001,0.2540591649973680,7.977726295022080,0.10497383884862300
  14 | 12,0.1496828406898800,0.38720704121299600,8.580421182203520,0.14988660353936500
  15 | 13,0.06734090809871090,0.6604782432029590,8.29021278831279,0.4018216912906010
  16 | 14,0.021387970727951900,0.17918517942967400,7.5050030154061500,0.7989655630139390
  17 | 15,0.06231830804775270,0.10995177361995000,7.452544667776010,0.018187704809690000
  18 | 16,0.5364141901723020,0.08367746881107470,9.3296145668632,0.028059519146752900
  19 | 17,0.001,0.5317102334820480,8.143469117967830,0.4441444193574780
  20 | 18,0.12045045021922300,1.0512810189573600,8.036798621101120,0.371374257957743
  21 | 19,0.14692303099362400,0.5590167599684700,7.914209178471170,0.14947774844554900
  22 | 20,0.11184457273461600,1.0276707091584800,8.746104478528490,1.2482332284959100
  23 | 21,0.6156284473732840,1.3031837475223800,9.584178751622300,1.1175227470934000
  24 | 22,0.03667933131736740,0.11580784674620900,7.3147022183547100,0.017195471422045000
  25 | 23,0.20083671055312300,0.07880241622106040,7.396342776556810,0.01676536708544950
  26 | 24,0.027468747432541000,0.21882764771198200,8.640863682189290,0.07999113189126000
  27 | 25,0.9179741334469990,0.5880904744956540,8.994206312516370,0.8331645257032560
  28 | 26,0.001,0.5512681257098740,7.464687760638810,0.1442464320904800
  29 | 27,0.001,0.46585186281358200,7.761070129131670,0.1131173343618720
  30 | 28,0.6433002628712390,1.9758216693051800,10.491762603004700,0.3122854517230830
  31 | 29,0.10029978807981900,0.5813173196684770,8.263955578565730,0.22107348986056400
  32 | 30,0.12255071392489500,0.9584821767588680,8.590689580361410,0.5358532350833650
  33 | 31,0.2499109116097010,1.196421565924720,8.643371542270530,0.6987868165027740
  34 | 32,0.001,0.4863710555571170,7.700011286854750,0.16642601427018800
  35 | 33,0.001,0.5082273882663090,8.061049598602910,0.28578235210216900
  36 | 34,0.001,0.5918727260620770,7.152665106657330,0.3275243437559210
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  39 | 37,0.09773806560495690,0.9676306636878600,9.578560868184000,0.3573512202821470
  40 | 38,0.016122967209459200,1.287655694448810,8.35165944575191,0.3734056371159530
  41 | 39,0.3974097319527410,0.8122560588411880,8.519069268546340,1.1326539489083100
  42 | 40,0.1277136190772220,0.3033101133937790,7.899404268047360,0.060370150040504000
  43 | 41,0.08165807475294380,0.3253016209970600,8.648855325543660,0.1169273958981370
  44 | 42,0.12951989169871300,1.7116697211402600,8.024827286516640,0.28364420962316200
  45 | 43,0.03877371432832370,0.5987528257032480,8.41218164799717,0.15900920560503300
  46 | 44,1.2234620427638400,0.1722868833790730,8.294373883678670,0.1499729904002330
  47 | 45,0.02584938926277770,1.2345053524790100,8.383273299960570,0.09666567490734730
  48 | 46,0.19333981203577300,0.5843321410440580,8.559203138995680,0.27167133636055700
  49 | 47,0.16484674963230400,1.0180401976446800,9.261502262251460,0.3808471710818340
  50 | 48,2.3605146006249500,0.15473978791141700,10.756307173294900,0.010667627793461000
  51 | 49,0.6686020836512260,0.5289153181521880,9.716402883665760,0.5831187060274370
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  53 | 51,0.08629785758593010,0.6414958662694900,8.507101900487160,0.2759649005593100
  54 | 52,0.06391058585974870,0.5300633064486770,7.9198806456572700,0.27916437190899300
  55 | 53,0.001,0.4990835823151290,8.147649514054960,0.1380870874446530
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  58 | 56,0.001,0.6339227920160520,8.29882094429432,0.2801026011978000
  59 | 57,0.05490313892782590,0.9748562189616610,8.11643642236828,0.9885793411662630
  60 | 58,0.0115161208701687,0.7963784768355130,7.697491611141990,0.16481158913951600
  61 | 59,0.3273740860333190,2.4053198450802300,9.744910575717550,0.2475391253055610
  62 | 60,0.296168964926799,0.7721526088989840,9.230687178409920,0.7141963301613520
  63 | 61,0.001,0.7560763320308970,8.533541179219420,0.259356563593018
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  65 | 63,0.0894180798452512,2.1211995271915900,9.288442250017850,0.7181974913785310
  66 | 64,0.001,0.50969275404311,7.146801165553130,0.7345688652961750
  67 | 65,0.8450335873579440,0.9939335878541940,9.880157202161310,1.5318214183798000
  68 | 66,0.001,0.8144446277633250,8.416594396846890,0.4237736717195790
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  70 | 68,0.15641696346693900,0.1862898339169290,8.705259396161360,0.2545732123701310
  71 | 69,0.001,0.5682550455240100,8.14788223059271,0.4633355207720400
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  73 | 71,0.034320036165399000,0.8197996418345010,7.734074257935100,0.4034166113767100
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  78 | 76,0.2422078833057420,0.7793396444937160,8.164920776830290,0.19585369699062800
  79 | 77,0.12741599393860400,1.4563912722387000,9.204922527223380,0.24135907152127300
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  81 | 79,0.025197619683052200,4.530510554134520,8.832494174594740,2.3271367414929100
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  86 | 84,0.386054560638332,0.21161364738061900,9.658705298585730,0.11286255309916400
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  94 | 92,0.06297487332000100,3.9719614263570800,8.528173813582650,8.712544989230810
  95 | 93,0.17967118226135500,0.5030802532652160,8.090008243627760,0.1126208721291820
  96 | 94,0.3140112671564760,0.4021493046271590,8.70183355913865,0.1316639283940730
  97 | 95,0.17951359358621700,1.2179263446615800,8.180022672813640,0.593199394670708
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 100 | 98,0.001,2.385265437163860,7.915695087883230,0.2205475825625960
 101 | 99,0.17039091753439100,0.4471969171232270,7.928066162021970,0.22182236383560000
 102 | 100,0.001,1.7688459904038600,8.766471764965250,0.3841520706570330
 103 | 101,0.5986587344113180,0.6105271227320120,8.484320465403420,0.15200326737789800
 104 | 102,0.2412927799994020,0.3594710039606970,8.369476926984990,0.11808595677169900
 105 | 103,0.001,0.7204661638098700,8.830811231698640,0.6290688830577310
 106 | 104,0.1327937796231550,0.5411367324567400,8.077987146527850,0.11566854316588500
 107 | 105,0.001,1.5870572433225900,8.357437683898620,0.9713230101681530
 108 | 106,0.001,0.12412084743227200,7.551912827806440,0.04341710893197140
 109 | 107,0.10858689699718600,1.5083974873131600,8.859708753209080,0.7700331244273280
 110 | 108,0.001,0.5341613552678510,7.548799952728780,0.28336059992813900
 111 | 109,0.7021438295314150,0.2046897022792770,8.935321384134800,0.011941927887617600
 112 | 110,0.03820363363701580,0.34411476607742500,7.677602315109000,0.09991320551586720
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 115 | 113,0.06791937409572330,0.2782155135083180,8.315417666837190,0.06896677916838400
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 117 | 115,0.14306402191924300,1.2766873522728000,8.809524837699060,0.4816281051124610
 118 | 116,0.3723557256000500,2.2150298156955500,10.36732629065720,0.36940609851265500
 119 | 117,0.2554788152347880,1.6153327464005300,9.07156723113481,0.6095852461707660
 120 | 118,0.059032846878887700,0.3778029814911600,7.705999173026090,0.1423883782746830
 121 | 119,0.001,0.21344936249083300,7.7627644799294400,0.06120141422017860
 122 | 120,0.001,0.31151580668866700,7.465684891093250,0.1125261971357890
 123 | 121,0.056149491273069800,0.5695616571625280,8.364138918990450,0.12828498355871100
 124 | 122,0.05260260477386390,0.34342430876070400,7.487245159669480,0.07203060193594070
 125 | 123,0.14009118603619300,1.1314380705610800,9.102078090253590,0.4008102373770700
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 127 | 125,0.06758287239602950,1.2488461753872100,7.487765393067180,0.4828787052575440
 128 | 126,0.5339680984670080,0.9904999187960230,9.401441183344120,0.5568754489503780
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 130 | 128,0.0507333450679002,0.9384400278929950,8.949535831655490,0.35159020959148400
 131 | 129,0.001,0.35286655872949500,8.315973320673290,0.0809172234529951
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 698 | 696,0.27688696871596100,0.9080322882450900,9.458434611052970,0.3992078471684880
 699 | 697,0.053143263974602600,0.19630202777195900,8.482197288411680,0.04844502106439690
 700 | 698,0.001,0.3752936178673780,7.246927600030050,0.19799659027491800
 701 | 699,0.040689424622873900,1.9289521020806700,7.804061386464260,0.8013386013860850
 702 | 700,0.0405879732701441,2.649980662870250,9.345971616350440,1.0202408589794400
 703 | 701,0.001,0.5055692925069440,8.048857337128410,0.16764088740841200
 704 | 702,0.40032402949141700,0.8570704834941330,10.299105826345300,0.12898436857465400
 705 | 703,0.09057889052539170,1.1369075140148600,9.075517870303720,1.837658606237230
 706 | 704,0.9584146802364250,1.7740979960228400,9.566099498726040,0.4847461938079180
 707 | 705,0.4260884615810630,0.7559951959827100,9.3104582291642,0.6900991453629240
 708 | 706,0.001,0.8840120805670090,8.846224597870620,0.3599209143258550
 709 | 707,0.001,1.8775954551378000,8.775747317328760,0.7552853102669410
 710 | 708,1.197275703161260,0.07733186013149130,9.748431208635170,0.010176055935843400
 711 | 709,0.001,0.8742489961423430,8.133539210169730,0.5598175523191320
 712 | 710,0.03521536695319570,0.3519571589239600,8.250762758521570,0.1549447571475510
 713 | 711,0.001,0.25414433257661100,7.539677706830930,0.7916793866843700
 714 | 712,0.17707480811938900,0.527613416681065,9.0769776284541,0.13762004331363600
 715 | 713,0.001,2.086893468052480,8.593153272604610,8.104004538011040
 716 | 714,0.001,1.4023742715562500,9.032283797526930,0.496303903016218
 717 | 715,0.09218858750636760,0.20552110224195700,7.575624939034220,0.08373626060738820
 718 | 716,0.5720712355516510,1.5589469120611700,10.736205637568800,0.17154993504693000
 719 | 717,0.1519177326941040,1.188464665678130,8.758679974922360,0.1552892481120410
 720 | 718,0.16265235350405800,0.10571643319451300,7.572194440474110,0.030824791194687600
 721 | 719,0.9046617740394310,0.26767867644101600,9.559363456510600,0.1914964776928080
 722 | 720,0.013638550957427400,0.918995266063874,8.195843764610360,0.7247841129683400
 723 | 721,0.001,1.1109529498483900,8.37623162716707,0.5517824903963080
 724 | 722,0.001,2.4875890155142200,8.3693007321187,1.4515718143362500
 725 | 723,0.04521839572914920,0.8634661825870900,8.277515291731290,0.2670900522576810
 726 | 724,0.001,0.6273809604229600,7.743859024191880,0.4816732824169880
 727 | 725,0.001,4.041753331321540,8.38379292575179,0.011689164930041800
 728 | 726,0.3136553408395830,0.20062719525455100,9.2306130002141,0.05815531480239230
 729 | 727,0.001,0.2823875645438030,8.345815661628750,0.11374640135893100
 730 | 728,0.1535950308488370,0.5154508863476410,7.707146544238490,0.2503964434851650
 731 | 729,0.010306962500866900,1.1725922356844800,8.347948123066410,0.266442836159483
 732 | 730,0.3130975193941950,0.35278028685892200,9.180179752676450,0.16753421203530200
 733 | 731,0.6451284393919630,0.3734764675133560,8.320582992551900,0.0727264427752638
 734 | 732,0.7051344679503950,0.7327765375676160,8.483550893258080,0.17814592678378600
 735 | 733,0.011094525210138600,0.7495604576757590,7.969730643682640,0.24706017343693200
 736 | 734,0.07135037131334980,1.6944689922680500,9.652757068542220,2.459698257378530
 737 | 735,0.4032332975988830,0.4000037822431480,8.974145619615140,0.1535935576224230
 738 | 736,0.001,0.41058895657529600,8.38862631662786,0.21144767068979600
 739 | 737,0.24705732678089200,0.9375832681207120,9.21344105253162,0.5037715374671180
 740 | 738,0.06729781379872570,0.3404433844916250,7.47417312744864,0.13646299051085100
 741 | 739,0.09765682825750200,0.3170265687940850,8.042770825943870,0.09212055512334490
 742 | 740,0.001,0.7176719386489700,7.914841468372950,0.3316309735529360
 743 | 741,1.494174621703530,0.08922230009635560,8.51301878007556,0.37592431137377600
 744 | 742,0.001,0.6423975238563010,7.80793119811882,0.023977890048486500
 745 | 743,0.3709494943152050,0.38661711207385900,8.552014986664470,0.30434888716741600
 746 | 744,0.023703829290238600,0.6556147378961160,8.160742206827580,0.17834209314051400
 747 | 745,0.2054877558274760,0.4562022751767090,9.242012449247870,0.18048015423119700
 748 | 746,0.001,1.589207202838980,8.497351871046200,0.8727464021883900
 749 | 747,0.1646289637483460,2.527579614482720,8.320570142416630,0.0570082130783829
 750 | 748,0.001,0.8781671538656810,8.646965381243880,0.4833951073139250
 751 | 749,0.06403962695491700,1.7138747249876100,8.653730250625580,2.163054571914060
 752 | 750,0.001,0.5079055932529140,7.626339592375100,0.10530399503842500
 753 | 751,0.25177762058265200,0.797560587023176,9.624463012793070,0.20675199157460600
 754 | 752,0.001,1.6595313864051500,8.068796400900050,0.5825157506214210
 755 | 753,0.8468184348524760,0.08238438528319,8.343652307325920,0.010409033164152400
 756 | 754,0.02030901830019710,0.3320270632634480,8.266054676488300,0.1678335109779520
 757 | 755,0.0753882826054028,1.614975472950840,8.322192333705430,0.6518976661847220
 758 | 756,0.001,0.8328370352784920,7.792281745284690,0.1151616447539190
 759 | 757,0.001,0.23086104575193900,8.61224533119873,0.1793587942658680
 760 | 758,0.0551081656116094,2.918705474201010,7.94971237504894,0.8730532345258410
 761 | 759,0.18872748573077200,3.3913254716652500,9.219462468583490,0.5645165060536170
 762 | 760,0.1607285581527520,0.3385688696222330,9.093987583566680,0.1212062506066950
 763 | 761,0.001,0.7566013276974210,8.291409017364560,3.0543173816269600
 764 | 762,0.08855100033360640,1.9671361504323900,8.866943829266040,0.914510772525144
 765 | 763,1.2421971469528300,0.06670813266011740,8.187738550910630,0.02756074485628600
 766 | 764,0.001,1.6203744753213500,9.374126854916900,0.4309080731748420
 767 | 765,0.001,3.7852792786452700,9.099724605425740,1.7198660431076700
 768 | 766,0.001,0.09646406155655690,7.6427372961888100,0.01289191444459760
 769 | 767,0.0061370622615800100,0.17790727743571900,8.057746256476370,0.10404415173585900
 770 | 768,0.14587794385641400,0.9938491706586470,8.329724215293120,0.3757736538694940
 771 | 769,0.8160715306842370,0.19352755042398600,9.110346184339940,0.011244780623174900
 772 | 770,0.7532543514592350,0.09625318399746870,9.455000583133270,0.01720846880879390
 773 | 771,0.035575308904760400,1.2035402910417000,7.986190059142230,0.7974469318269880
 774 | 772,0.02779354526100830,0.5222259276567420,8.637827468954950,0.2454529556845490
 775 | 773,0.0286627019200556,3.0698835256047400,9.037362830916790,5.582347328707320
 776 | 774,0.001,0.5144842581474300,8.017122954888830,0.2942999914365280
 777 | 775,0.001,0.7347997434693770,8.136293847271550,0.3897181732034070
 778 | 776,0.03706552729070800,0.20867332911434400,8.183571964184310,0.0896091540538893
 779 | 777,0.5142896108651140,0.7365760565418260,8.387343523422990,3.5079749559664900
 780 | 778,0.2955218572708430,0.5841452017464740,8.27775807919734,0.13440470333350700
 781 | 779,0.001,1.4516540393386600,7.777898802318380,0.4323217514969840
 782 | 780,0.8161570778949160,0.2967396818227050,8.383034327256840,9.90963286598362
 783 | 781,0.1526945411156650,0.5672854830398930,8.743037752604370,0.3034528231999650
 784 | 782,0.09910090521742040,0.840642445310615,6.9820060918621900,1.2349600199379100
 785 | 783,0.19691550900837700,0.5932845987907940,7.810841576548710,0.5509286691725080
 786 | 784,1.418673239964650,0.9455692963057070,10.473275710221,0.2138013702964890
 787 | 785,0.03714954312071680,0.3022324060787580,8.177924788152330,0.09266847059522700
 788 | 786,0.001,0.41703002685173100,8.323069699975980,0.08657367982026830
 789 | 787,0.001,0.6608994850072290,8.418292317585030,0.2617354514416470
 790 | 788,0.001,0.12598714757467700,8.300536605246280,0.0719087699323686
 791 | 789,0.1370056901121730,1.2210606345336100,8.151438339876920,1.0229611240669100
 792 | 790,0.14042231746285900,0.6708277119870660,8.482757642418900,0.9355360201160850
 793 | 791,0.02666366016792540,0.947205023639884,8.314775732894350,0.4851396620507440
 794 | 792,0.001,0.3728620426407740,7.819576712396470,0.12001284851523900
 795 | 793,0.001,0.5906127781995640,8.07069238291811,0.4827240900228260
 796 | 794,0.001,0.5168529011812230,7.739177917142090,0.16619739311962100
 797 | 795,1.853762650629860,0.09897605473180540,10.514345288943700,0.01350384002467890
 798 | 796,0.001,0.3672229390961720,8.238837254943300,0.2317141250500170
 799 | 797,0.417148262881506,0.9524361167951540,10.588943327915500,0.09090513286582120
 800 | 798,0.014645018710859800,0.13307354535942800,7.272220671166980,0.010166599231119800
 801 | 799,0.020999319971135200,1.5842179192873800,8.74878322538631,0.3490375799625710
 802 | 800,0.001,1.7832158681810000,8.207512695864190,0.06596060657024360
 803 | 801,0.021287632656456100,2.5315561076117700,7.705120900793930,7.16538614974618
 804 | 802,0.001,1.5919344411305,7.773010422876470,2.7890576500743400
 805 | 803,0.001,3.5373645203256200,8.643261568296500,8.988738831520980
 806 | 804,0.09963337380487160,0.3363719418470290,8.286742056205190,0.052257252992248500
 807 | 805,0.07745260942135390,0.21856365102954100,7.4266579697002200,0.06480571828472760
 808 | 806,0.001,0.7119646968611320,7.998185060977140,0.26358693510730400
 809 | 807,0.001,1.8824645832789300,8.628606561443430,1.1713314422259200
 810 | 808,0.001,0.5714191680441810,8.654936955627180,0.16018386288552300
 811 | 809,0.5501863619099400,0.22250681503359800,8.76088577487587,0.0440453873308721
 812 | 810,0.19112085178553300,1.3443301277804300,9.04819760217334,0.7050395321792350
 813 | 811,0.024629382202749100,0.3261139938112440,7.967990637359320,0.168395415086725
 814 | 812,0.4613555588898600,0.522302694526637,8.062710049921550,0.28517859959224400
 815 | 813,0.7913655014419880,0.2544556461800080,9.009017087824210,0.01530282021397780
 816 | 814,0.001,0.5597520209087380,8.053777965379840,0.12124840189130800
 817 | 815,0.001,0.3120509701765560,7.997679481949250,0.09801892877604390
 818 | 816,0.001,2.175143402298330,7.844760193232280,5.106403224736200
 819 | 817,0.001,2.371747767322200,8.452643574494510,0.7880112594177370
 820 | 818,0.11887183372509500,1.001382285564470,8.915721137476830,0.5969844194135840
 821 | 819,0.1055043681426390,0.08095119146488970,7.299743084915510,0.010716066052313400
 822 | 820,0.4264566482393920,1.438132070367640,9.86110071353374,9.528510577091700
 823 | 821,0.5627626177189010,0.7076896620542730,8.135914443576340,0.08062542289061250
 824 | 822,2.8333146151593100,0.08355009209736040,10.069337497021000,0.010012890616460200
 825 | 823,0.008830189356403290,0.3798822361309920,7.403437104658460,0.054528585012589600
 826 | 824,0.3907138092602740,2.7767389299460200,10.351287828691700,0.4453742704725100
 827 | 825,0.11857544355023500,0.16840234067697000,9.383570498501040,0.05031075243901150
 828 | 826,0.001,0.09709463892037480,8.61220182088153,0.011662502259560600
 829 | 827,0.001,0.08658577186879110,7.4136801411821600,0.03631550631888640
 830 | 828,0.001,0.9292178378536970,7.7069577790141600,0.05435477200682810
 831 | 829,0.27187263599308700,0.8926406092943300,9.224544671633010,0.5048138000749920
 832 | 830,0.001,2.5398391237478700,9.48259334691916,0.9877775934947530
 833 | 831,0.001,0.4116154099805510,8.479795528557130,0.1235079309324880
 834 | 832,0.024717368536835400,0.546412982040136,8.123368212228280,0.08788600554635610
 835 | 833,0.001,0.982262907245562,7.6784407549463500,0.2971822740830910
 836 | 834,0.004765014915524060,0.2315805980899440,7.65470588061522,0.09828677523477000
 837 | 835,0.043200004304214800,0.5546743609805990,8.359740281980720,0.1182056426309730
 838 | 836,0.13158725177126100,0.3054901720446900,8.084749119807100,0.13824314601305000
 839 | 837,0.18974153529287000,1.2166814127360400,8.06001938311789,0.011763449620426000
 840 | 838,0.14897821916334100,0.3721007729693350,7.332221371445820,0.460807252180786
 841 | 839,0.05782845048120000,0.3292817551686690,7.460304978136870,0.13306354333250400
 842 | 840,0.001,0.1110031820852230,6.552172948336060,0.012967766161836200
 843 | 841,0.001,0.1067139932504960,6.74513489972033,0.011501467415817100
 844 | 842,1.0333731136741000,0.6557022785982990,9.960346888172740,1.9863985029518900
 845 | 843,0.001,0.9422413235619110,7.393098778411010,0.5693972008201050
 846 | 844,0.001,0.3041887338475320,7.845891985496960,0.224399075458582
 847 | 845,0.02489717489638740,0.4021134436209280,7.688388757855840,0.10367527776323300
 848 | 846,0.001,1.2641364741678200,8.80245776808159,0.3360923312646890
 849 | 847,0.6366825621755590,1.3739387163357700,9.807401028674150,0.3093681719308270
 850 | 848,0.14831078722196100,0.1360608715050920,7.687602432060790,0.09701237323896980
 851 | 849,0.06731424691070820,0.521890471446948,7.930040530484660,0.2649001419242710
 852 | 850,0.14286898418494800,4.156940506921810,8.814117396215960,4.657727754580990
 853 | 851,0.09318466218348330,0.5936229581572880,8.45730244254113,0.15083018469275800
 854 | 852,0.001,0.6192139887709020,7.291106224253060,0.3429497696671170
 855 | 853,0.0003875176646367440,0.3811837575041050,8.00581815892476,0.08967927430064710
 856 | 854,1.5655220884188800,0.06203909783546390,8.636582732149790,0.23776825987235300
 857 | 855,0.11327697005699200,0.3291634674173710,7.3255602519732000,0.17941630503663000
 858 | 856,0.1412283632968510,1.5586634092958200,9.324943409635420,0.7083572515348170
 859 | 857,0.03560137740180740,1.3693774174669800,7.73696844571289,0.3411181210154220
 860 | 858,0.5524163254570890,0.3262454378232400,9.524890868314690,0.13023533808893000
 861 | 859,0.27221032879463000,0.6702286409563820,8.218858229894760,0.1860309566116280
 862 | 860,0.035247535414980000,0.8968494056640580,7.498060504740890,0.16763214586724100
 863 | 861,0.0969596771265278,0.4622045458499690,7.914175830650490,0.14468416189768700
 864 | 862,0.001,0.2699813094785270,7.3657536738244700,8.696198882647040
 865 | 863,0.6341683795759740,1.83096776415317,10.46589847168940,0.4248876928667870
 866 | 864,0.001,0.6353358121022060,7.254206923487720,0.1131578406865020
 867 | 865,0.001,0.7186958399475660,7.983416913240970,0.2747288743578260
 868 | 866,0.11793888343323200,1.8544280824613700,8.10248112069009,0.3771047575154620
 869 | 867,0.001,0.5602165637265470,7.51018998889783,0.15108937176917600
 870 | 868,0.001,0.08314278940479350,7.599964785186350,0.028597512087823800
 871 | 869,0.033838578455126900,0.41818437689570900,8.378083511724470,0.3663348402479560
 872 | 870,0.001,0.2773169732326680,7.550377316304380,0.10060014412661100
 873 | 871,0.001,0.6750822904651920,8.359314293013920,0.2087684200430440
 874 | 872,0.1365429514302610,1.1612178283872700,9.05777678589554,8.725338826708990
 875 | 873,0.055893402978332300,0.7283832847492530,7.824171765016690,0.17451302613734200
 876 | 874,0.17174273345572600,0.3958217371168810,7.6992372788582700,0.3594034556193750
 877 | 875,0.09371571691155270,0.9063253477074220,8.717486457591930,0.5954486121981560
 878 | 876,0.08767404984285690,0.3282429330373590,7.689298386027680,0.27136705558842800
 879 | 877,0.031062486425215,1.0227047010259100,7.747883825845340,0.6352134434777280
 880 | 878,0.001,0.5158505162337440,8.097904832166170,0.1475779181676120
 881 | 879,0.3981644823369160,1.030217334612920,9.25660038613345,2.3276238525056800
 882 | 880,0.2755414641350890,0.9263551443113690,8.708889710985280,0.2948994770428530
 883 | 881,0.001,0.28252676406632400,8.354010682839100,0.11146189292625500
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 969 | 967,0.001,0.4702379618773810,8.01250140699319,0.1170140213893400
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 973 | 971,0.051020483467630000,0.6175594438686860,8.543542094038110,0.14134764335845400
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 975 | 973,0.001,0.4973737406116210,8.271139100095410,0.15144551332621800
 976 | 974,0.17394374367929400,0.3739525063441270,8.700607105653510,0.13684750160233600
 977 | 975,0.8957013416716050,0.08635245993255050,9.741763731217230,0.02396027657496160
 978 | 976,0.09267192628907160,0.41010884537554900,7.727840647427600,0.04459287589970310
 979 | 977,0.9844025066606420,0.8322039274403220,8.769276405303180,0.24091539299341700
 980 | 978,0.001,2.3470790507310300,7.899619598165070,0.11359269378299800
 981 | 979,0.0573651884701479,1.1310077704915200,8.814888528554160,0.395283949283863
 982 | 980,0.001,0.6573288123594340,7.749397058601950,0.1612023897835870
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 984 | 982,0.2237891585546370,0.8241511190772550,8.758968225343010,0.27215717996474400
 985 | 983,0.06550624387388810,0.539531403550613,8.357908393243870,0.10841542443155000
 986 | 984,0.34593910222815800,0.3348474055746610,7.790232613700890,0.14099368670648500
 987 | 985,2.0842911148187400,0.07025293509358770,10.64911916707610,0.010464988565055800
 988 | 986,0.22932627090024400,1.2406640914694600,10.047913458883100,0.20696056892926400
 989 | 987,0.7285267205714310,0.2765016518047500,9.539645469501930,0.12565156489148900
 990 | 988,0.9473150374783820,0.443906658380688,7.791185494651330,0.3478594727825480
 991 | 989,0.016790787570834100,0.6485709328519410,9.011247976760030,0.3782922316203000
 992 | 990,0.001,0.16989013708058900,7.651111716949750,0.06393655583304400
 993 | 991,0.001,0.5197096444908130,8.420501344101840,0.0992658225683428
 994 | 992,0.1773798400454860,0.3859594960080610,7.955708993321020,0.17790321843348800
 995 | 993,0.001,0.8547755212290780,7.390141415638260,0.9143252433385100
 996 | 994,0.1317593703244900,0.8949521816446090,8.858568543630080,1.0972780214636000
 997 | 995,0.32290518838563800,2.0182315277010300,8.987870343954720,2.0699163048753400
 998 | 996,0.8707943730386650,0.07513383603097700,7.607796379135360,0.06284536071398010
 999 | 997,0.001,0.8496398125233580,8.415094356770380,0.9186425782821580
1000 | 998,0.2321632614147260,0.586723514482263,8.286424584286280,0.16827301926601200
1001 | 999,0.4569837880209070,0.6122130392603940,8.276157478547620,3.5239734410065200


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/README.md:
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1 | # IntroConformalPredictions
2 | A short introduction to Conformal Predictions, with a few examples for classification and regression from the Astrophysical domain, and slides.
3 | 
4 | Presented at the EAS 2024 in Padova.
5 | 


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/UQ_EAS_2024.pdf:
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https://raw.githubusercontent.com/vacquaviva/IntroConformalPredictions/121fb0d00556bad3d3133e8581e3f201746bfc0c/UQ_EAS_2024.pdf


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