Fig-03-04-feature_importance.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 4: Feature importance\n",
    "\n",
    "This notebook reproduces Figure 4. It also includes boxplots showing the distributions of the most importance features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_extracted_data = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from string import ascii_uppercase\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import load\n",
    "\n",
    "from lib.check_files import (\n",
    "    check_prepared_data,\n",
    ")\n",
    "from lib.feature_importance import (\n",
    "    calculate_importances,\n",
    "    get_ranks,\n",
    "    plot_correlations,\n",
    "    plot_importances,\n",
    ")\n",
    "from lib.misc import format_feature_name\n",
    "from lib.pu import (\n",
    "    COLUMNS_TO_DROP,\n",
    "    CORRELATED_COLUMNS,\n",
    "    PRESERVATION_COLUMNS,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "colour = True\n",
    "\n",
    "colours = {\n",
    "    \"All\": \"green\",\n",
    "    \"NA\": \"red\",\n",
    "    \"SA\": \"blue\",\n",
    "}\n",
    "boxprops = {\n",
    "    \"All\": {\"hatch\": \"x\"},\n",
    "    \"NA\": {\"hatch\": \"\\\\\"},\n",
    "    \"SA\": {\"hatch\": \"/\"},\n",
    "}\n",
    "for key, value in boxprops.items():\n",
    "    if colour:\n",
    "        col = colours[key]\n",
    "    else:\n",
    "        col = \"none\"\n",
    "    value[\"facecolor\"] = col"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load PU classifiers and training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_extracted_data:\n",
    "    data_dir = \"extracted_data\"\n",
    "else:\n",
    "    data_dir = \"prepared_data\"\n",
    "    check_prepared_data(data_dir, verbose=True)\n",
    "training_filename = os.path.join(data_dir, \"training_data.csv\")\n",
    "outputs_dir = \"outputs\"\n",
    "\n",
    "figures_dir = \"figures\"\n",
    "os.makedirs(figures_dir, exist_ok=True)\n",
    "supplement_dir = \"supplementary_figures\"\n",
    "os.makedirs(supplement_dir, exist_ok=True)\n",
    "\n",
    "regions = (\"All\", \"NA\", \"SA\")\n",
    "models = {}\n",
    "for region in regions:\n",
    "    if region == \"All\":\n",
    "        classifier_filename = \"pu_classifier.joblib\"\n",
    "    else:\n",
    "        classifier_filename = f\"pu_classifier_{region}.joblib\"\n",
    "    classifier_filename = os.path.join(\n",
    "        outputs_dir,\n",
    "        classifier_filename,\n",
    "    )\n",
    "    models[region] = load(classifier_filename)\n",
    "\n",
    "df_training = pd.read_csv(training_filename)\n",
    "importances_basename = os.path.join(figures_dir, \"Fig-03-feature_importances\")\n",
    "boxplots_filename = os.path.join(figures_dir, \"Fig-04-feature_boxplots\")\n",
    "correlation_filename = os.path.join(supplement_dir, \"feature_importance_correlation\")\n",
    "\n",
    "to_drop = COLUMNS_TO_DROP | CORRELATED_COLUMNS | PRESERVATION_COLUMNS\n",
    "to_drop.add(\"label\")\n",
    "feature_names = sorted(set(df_training.columns).difference(to_drop))\n",
    "feature_names = np.array(feature_names, dtype=\"object\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>All: importance (relative)</th>\n",
       "      <th>All: importance (absolute)</th>\n",
       "      <th>NA: importance (relative)</th>\n",
       "      <th>NA: importance (absolute)</th>\n",
       "      <th>SA: importance (relative)</th>\n",
       "      <th>SA: importance (absolute)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crustal_thickness_mean (m)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.156960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_to_trench (km)</th>\n",
       "      <td>0.359673</td>\n",
       "      <td>0.067863</td>\n",
       "      <td>0.278981</td>\n",
       "      <td>0.054435</td>\n",
       "      <td>0.662303</td>\n",
       "      <td>0.103955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convergence_rate_orthogonal (cm/yr)</th>\n",
       "      <td>0.351202</td>\n",
       "      <td>0.066265</td>\n",
       "      <td>0.266122</td>\n",
       "      <td>0.051926</td>\n",
       "      <td>0.268803</td>\n",
       "      <td>0.042191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trench_velocity_orthogonal (cm/yr)</th>\n",
       "      <td>0.309414</td>\n",
       "      <td>0.058381</td>\n",
       "      <td>0.477813</td>\n",
       "      <td>0.093232</td>\n",
       "      <td>0.155889</td>\n",
       "      <td>0.024468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seafloor_spreading_rate (km/Myr)</th>\n",
       "      <td>0.297252</td>\n",
       "      <td>0.056086</td>\n",
       "      <td>0.273516</td>\n",
       "      <td>0.053369</td>\n",
       "      <td>0.214918</td>\n",
       "      <td>0.033734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducted_carbonates_volume (m)</th>\n",
       "      <td>0.219912</td>\n",
       "      <td>0.041493</td>\n",
       "      <td>0.118902</td>\n",
       "      <td>0.023200</td>\n",
       "      <td>0.399737</td>\n",
       "      <td>0.062743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seafloor_age (Ma)</th>\n",
       "      <td>0.184110</td>\n",
       "      <td>0.034738</td>\n",
       "      <td>0.279399</td>\n",
       "      <td>0.054517</td>\n",
       "      <td>0.163110</td>\n",
       "      <td>0.025602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducting_plate_absolute_velocity_orthogonal (cm/yr)</th>\n",
       "      <td>0.176010</td>\n",
       "      <td>0.033210</td>\n",
       "      <td>0.178054</td>\n",
       "      <td>0.034742</td>\n",
       "      <td>0.199097</td>\n",
       "      <td>0.031250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trench_velocity_obliquity (degrees)</th>\n",
       "      <td>0.170294</td>\n",
       "      <td>0.032131</td>\n",
       "      <td>0.157590</td>\n",
       "      <td>0.030749</td>\n",
       "      <td>0.334611</td>\n",
       "      <td>0.052521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducted_sediment_volume (m)</th>\n",
       "      <td>0.168269</td>\n",
       "      <td>0.031749</td>\n",
       "      <td>0.142447</td>\n",
       "      <td>0.027795</td>\n",
       "      <td>0.102050</td>\n",
       "      <td>0.016018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbonate_thickness (m)</th>\n",
       "      <td>0.164037</td>\n",
       "      <td>0.030951</td>\n",
       "      <td>0.134097</td>\n",
       "      <td>0.026165</td>\n",
       "      <td>0.284102</td>\n",
       "      <td>0.044593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnetic_anomaly_std (nT)</th>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.030467</td>\n",
       "      <td>0.206152</td>\n",
       "      <td>0.040225</td>\n",
       "      <td>0.109819</td>\n",
       "      <td>0.017237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convergence_obliquity (degrees)</th>\n",
       "      <td>0.159407</td>\n",
       "      <td>0.030077</td>\n",
       "      <td>0.173206</td>\n",
       "      <td>0.033796</td>\n",
       "      <td>0.212094</td>\n",
       "      <td>0.033290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trench_velocity_parallel (cm/yr)</th>\n",
       "      <td>0.157328</td>\n",
       "      <td>0.029685</td>\n",
       "      <td>0.136085</td>\n",
       "      <td>0.026553</td>\n",
       "      <td>0.336822</td>\n",
       "      <td>0.052868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slab_flux (m^2/yr)</th>\n",
       "      <td>0.156898</td>\n",
       "      <td>0.029604</td>\n",
       "      <td>0.116042</td>\n",
       "      <td>0.022642</td>\n",
       "      <td>0.453220</td>\n",
       "      <td>0.071137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co2_volume (m^3/m^2)</th>\n",
       "      <td>0.154391</td>\n",
       "      <td>0.029131</td>\n",
       "      <td>0.172947</td>\n",
       "      <td>0.033746</td>\n",
       "      <td>0.193867</td>\n",
       "      <td>0.030429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducting_plate_absolute_obliquity (degrees)</th>\n",
       "      <td>0.142740</td>\n",
       "      <td>0.026932</td>\n",
       "      <td>0.128964</td>\n",
       "      <td>0.025164</td>\n",
       "      <td>0.168662</td>\n",
       "      <td>0.026473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crustal_thickness_std (m)</th>\n",
       "      <td>0.140992</td>\n",
       "      <td>0.026602</td>\n",
       "      <td>0.096003</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.254302</td>\n",
       "      <td>0.039915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducted_plate_volume (m)</th>\n",
       "      <td>0.140501</td>\n",
       "      <td>0.026510</td>\n",
       "      <td>0.149017</td>\n",
       "      <td>0.029077</td>\n",
       "      <td>0.104624</td>\n",
       "      <td>0.016422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_to_trench_edge (degrees)</th>\n",
       "      <td>0.138710</td>\n",
       "      <td>0.026172</td>\n",
       "      <td>0.126495</td>\n",
       "      <td>0.024682</td>\n",
       "      <td>0.139731</td>\n",
       "      <td>0.021932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sediment_thickness (m)</th>\n",
       "      <td>0.137942</td>\n",
       "      <td>0.026027</td>\n",
       "      <td>0.130143</td>\n",
       "      <td>0.025394</td>\n",
       "      <td>0.116259</td>\n",
       "      <td>0.018248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducting_plate_absolute_velocity_parallel (cm/yr)</th>\n",
       "      <td>0.124236</td>\n",
       "      <td>0.023441</td>\n",
       "      <td>0.100210</td>\n",
       "      <td>0.019553</td>\n",
       "      <td>0.156221</td>\n",
       "      <td>0.024520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convergence_rate_parallel (cm/yr)</th>\n",
       "      <td>0.118329</td>\n",
       "      <td>0.022326</td>\n",
       "      <td>0.132390</td>\n",
       "      <td>0.025832</td>\n",
       "      <td>0.156414</td>\n",
       "      <td>0.024551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnetic_anomaly_min (nT)</th>\n",
       "      <td>0.087244</td>\n",
       "      <td>0.016461</td>\n",
       "      <td>0.068471</td>\n",
       "      <td>0.013360</td>\n",
       "      <td>0.091089</td>\n",
       "      <td>0.014297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnetic_anomaly_mean (nT)</th>\n",
       "      <td>0.079591</td>\n",
       "      <td>0.015017</td>\n",
       "      <td>0.081949</td>\n",
       "      <td>0.015990</td>\n",
       "      <td>0.093304</td>\n",
       "      <td>0.014645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    All: importance (relative)  \\\n",
       "crustal_thickness_mean (m)                                            1.000000   \n",
       "distance_to_trench (km)                                               0.359673   \n",
       "convergence_rate_orthogonal (cm/yr)                                   0.351202   \n",
       "trench_velocity_orthogonal (cm/yr)                                    0.309414   \n",
       "seafloor_spreading_rate (km/Myr)                                      0.297252   \n",
       "subducted_carbonates_volume (m)                                       0.219912   \n",
       "seafloor_age (Ma)                                                     0.184110   \n",
       "subducting_plate_absolute_velocity_orthogonal (...                    0.176010   \n",
       "trench_velocity_obliquity (degrees)                                   0.170294   \n",
       "subducted_sediment_volume (m)                                         0.168269   \n",
       "carbonate_thickness (m)                                               0.164037   \n",
       "magnetic_anomaly_std (nT)                                             0.161473   \n",
       "convergence_obliquity (degrees)                                       0.159407   \n",
       "trench_velocity_parallel (cm/yr)                                      0.157328   \n",
       "slab_flux (m^2/yr)                                                    0.156898   \n",
       "co2_volume (m^3/m^2)                                                  0.154391   \n",
       "subducting_plate_absolute_obliquity (degrees)                         0.142740   \n",
       "crustal_thickness_std (m)                                             0.140992   \n",
       "subducted_plate_volume (m)                                            0.140501   \n",
       "distance_to_trench_edge (degrees)                                     0.138710   \n",
       "sediment_thickness (m)                                                0.137942   \n",
       "subducting_plate_absolute_velocity_parallel (cm...                    0.124236   \n",
       "convergence_rate_parallel (cm/yr)                                     0.118329   \n",
       "magnetic_anomaly_min (nT)                                             0.087244   \n",
       "magnetic_anomaly_mean (nT)                                            0.079591   \n",
       "\n",
       "                                                    All: importance (absolute)  \\\n",
       "crustal_thickness_mean (m)                                            0.188681   \n",
       "distance_to_trench (km)                                               0.067863   \n",
       "convergence_rate_orthogonal (cm/yr)                                   0.066265   \n",
       "trench_velocity_orthogonal (cm/yr)                                    0.058381   \n",
       "seafloor_spreading_rate (km/Myr)                                      0.056086   \n",
       "subducted_carbonates_volume (m)                                       0.041493   \n",
       "seafloor_age (Ma)                                                     0.034738   \n",
       "subducting_plate_absolute_velocity_orthogonal (...                    0.033210   \n",
       "trench_velocity_obliquity (degrees)                                   0.032131   \n",
       "subducted_sediment_volume (m)                                         0.031749   \n",
       "carbonate_thickness (m)                                               0.030951   \n",
       "magnetic_anomaly_std (nT)                                             0.030467   \n",
       "convergence_obliquity (degrees)                                       0.030077   \n",
       "trench_velocity_parallel (cm/yr)                                      0.029685   \n",
       "slab_flux (m^2/yr)                                                    0.029604   \n",
       "co2_volume (m^3/m^2)                                                  0.029131   \n",
       "subducting_plate_absolute_obliquity (degrees)                         0.026932   \n",
       "crustal_thickness_std (m)                                             0.026602   \n",
       "subducted_plate_volume (m)                                            0.026510   \n",
       "distance_to_trench_edge (degrees)                                     0.026172   \n",
       "sediment_thickness (m)                                                0.026027   \n",
       "subducting_plate_absolute_velocity_parallel (cm...                    0.023441   \n",
       "convergence_rate_parallel (cm/yr)                                     0.022326   \n",
       "magnetic_anomaly_min (nT)                                             0.016461   \n",
       "magnetic_anomaly_mean (nT)                                            0.015017   \n",
       "\n",
       "                                                    NA: importance (relative)  \\\n",
       "crustal_thickness_mean (m)                                           1.000000   \n",
       "distance_to_trench (km)                                              0.278981   \n",
       "convergence_rate_orthogonal (cm/yr)                                  0.266122   \n",
       "trench_velocity_orthogonal (cm/yr)                                   0.477813   \n",
       "seafloor_spreading_rate (km/Myr)                                     0.273516   \n",
       "subducted_carbonates_volume (m)                                      0.118902   \n",
       "seafloor_age (Ma)                                                    0.279399   \n",
       "subducting_plate_absolute_velocity_orthogonal (...                   0.178054   \n",
       "trench_velocity_obliquity (degrees)                                  0.157590   \n",
       "subducted_sediment_volume (m)                                        0.142447   \n",
       "carbonate_thickness (m)                                              0.134097   \n",
       "magnetic_anomaly_std (nT)                                            0.206152   \n",
       "convergence_obliquity (degrees)                                      0.173206   \n",
       "trench_velocity_parallel (cm/yr)                                     0.136085   \n",
       "slab_flux (m^2/yr)                                                   0.116042   \n",
       "co2_volume (m^3/m^2)                                                 0.172947   \n",
       "subducting_plate_absolute_obliquity (degrees)                        0.128964   \n",
       "crustal_thickness_std (m)                                            0.096003   \n",
       "subducted_plate_volume (m)                                           0.149017   \n",
       "distance_to_trench_edge (degrees)                                    0.126495   \n",
       "sediment_thickness (m)                                               0.130143   \n",
       "subducting_plate_absolute_velocity_parallel (cm...                   0.100210   \n",
       "convergence_rate_parallel (cm/yr)                                    0.132390   \n",
       "magnetic_anomaly_min (nT)                                            0.068471   \n",
       "magnetic_anomaly_mean (nT)                                           0.081949   \n",
       "\n",
       "                                                    NA: importance (absolute)  \\\n",
       "crustal_thickness_mean (m)                                           0.195122   \n",
       "distance_to_trench (km)                                              0.054435   \n",
       "convergence_rate_orthogonal (cm/yr)                                  0.051926   \n",
       "trench_velocity_orthogonal (cm/yr)                                   0.093232   \n",
       "seafloor_spreading_rate (km/Myr)                                     0.053369   \n",
       "subducted_carbonates_volume (m)                                      0.023200   \n",
       "seafloor_age (Ma)                                                    0.054517   \n",
       "subducting_plate_absolute_velocity_orthogonal (...                   0.034742   \n",
       "trench_velocity_obliquity (degrees)                                  0.030749   \n",
       "subducted_sediment_volume (m)                                        0.027795   \n",
       "carbonate_thickness (m)                                              0.026165   \n",
       "magnetic_anomaly_std (nT)                                            0.040225   \n",
       "convergence_obliquity (degrees)                                      0.033796   \n",
       "trench_velocity_parallel (cm/yr)                                     0.026553   \n",
       "slab_flux (m^2/yr)                                                   0.022642   \n",
       "co2_volume (m^3/m^2)                                                 0.033746   \n",
       "subducting_plate_absolute_obliquity (degrees)                        0.025164   \n",
       "crustal_thickness_std (m)                                            0.018732   \n",
       "subducted_plate_volume (m)                                           0.029077   \n",
       "distance_to_trench_edge (degrees)                                    0.024682   \n",
       "sediment_thickness (m)                                               0.025394   \n",
       "subducting_plate_absolute_velocity_parallel (cm...                   0.019553   \n",
       "convergence_rate_parallel (cm/yr)                                    0.025832   \n",
       "magnetic_anomaly_min (nT)                                            0.013360   \n",
       "magnetic_anomaly_mean (nT)                                           0.015990   \n",
       "\n",
       "                                                    SA: importance (relative)  \\\n",
       "crustal_thickness_mean (m)                                           1.000000   \n",
       "distance_to_trench (km)                                              0.662303   \n",
       "convergence_rate_orthogonal (cm/yr)                                  0.268803   \n",
       "trench_velocity_orthogonal (cm/yr)                                   0.155889   \n",
       "seafloor_spreading_rate (km/Myr)                                     0.214918   \n",
       "subducted_carbonates_volume (m)                                      0.399737   \n",
       "seafloor_age (Ma)                                                    0.163110   \n",
       "subducting_plate_absolute_velocity_orthogonal (...                   0.199097   \n",
       "trench_velocity_obliquity (degrees)                                  0.334611   \n",
       "subducted_sediment_volume (m)                                        0.102050   \n",
       "carbonate_thickness (m)                                              0.284102   \n",
       "magnetic_anomaly_std (nT)                                            0.109819   \n",
       "convergence_obliquity (degrees)                                      0.212094   \n",
       "trench_velocity_parallel (cm/yr)                                     0.336822   \n",
       "slab_flux (m^2/yr)                                                   0.453220   \n",
       "co2_volume (m^3/m^2)                                                 0.193867   \n",
       "subducting_plate_absolute_obliquity (degrees)                        0.168662   \n",
       "crustal_thickness_std (m)                                            0.254302   \n",
       "subducted_plate_volume (m)                                           0.104624   \n",
       "distance_to_trench_edge (degrees)                                    0.139731   \n",
       "sediment_thickness (m)                                               0.116259   \n",
       "subducting_plate_absolute_velocity_parallel (cm...                   0.156221   \n",
       "convergence_rate_parallel (cm/yr)                                    0.156414   \n",
       "magnetic_anomaly_min (nT)                                            0.091089   \n",
       "magnetic_anomaly_mean (nT)                                           0.093304   \n",
       "\n",
       "                                                    SA: importance (absolute)  \n",
       "crustal_thickness_mean (m)                                           0.156960  \n",
       "distance_to_trench (km)                                              0.103955  \n",
       "convergence_rate_orthogonal (cm/yr)                                  0.042191  \n",
       "trench_velocity_orthogonal (cm/yr)                                   0.024468  \n",
       "seafloor_spreading_rate (km/Myr)                                     0.033734  \n",
       "subducted_carbonates_volume (m)                                      0.062743  \n",
       "seafloor_age (Ma)                                                    0.025602  \n",
       "subducting_plate_absolute_velocity_orthogonal (...                   0.031250  \n",
       "trench_velocity_obliquity (degrees)                                  0.052521  \n",
       "subducted_sediment_volume (m)                                        0.016018  \n",
       "carbonate_thickness (m)                                              0.044593  \n",
       "magnetic_anomaly_std (nT)                                            0.017237  \n",
       "convergence_obliquity (degrees)                                      0.033290  \n",
       "trench_velocity_parallel (cm/yr)                                     0.052868  \n",
       "slab_flux (m^2/yr)                                                   0.071137  \n",
       "co2_volume (m^3/m^2)                                                 0.030429  \n",
       "subducting_plate_absolute_obliquity (degrees)                        0.026473  \n",
       "crustal_thickness_std (m)                                            0.039915  \n",
       "subducted_plate_volume (m)                                           0.016422  \n",
       "distance_to_trench_edge (degrees)                                    0.021932  \n",
       "sediment_thickness (m)                                               0.018248  \n",
       "subducting_plate_absolute_velocity_parallel (cm...                   0.024520  \n",
       "convergence_rate_parallel (cm/yr)                                    0.024551  \n",
       "magnetic_anomaly_min (nT)                                            0.014297  \n",
       "magnetic_anomaly_mean (nT)                                           0.014645  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = {\n",
    "    region: calculate_importances(model, feature_names)\n",
    "    for region, model in models.items()\n",
    "}\n",
    "\n",
    "pd.concat(\n",
    "    [\n",
    "        pd.DataFrame(\n",
    "            {\n",
    "                f\"{region}: importance (relative)\": values / values.max(),\n",
    "                f\"{region}: importance (absolute)\": values,\n",
    "            }\n",
    "        )\n",
    "        for region, values in importances.items()\n",
    "    ],\n",
    "    axis=\"columns\",\n",
    ").sort_values(by=\"All: importance (relative)\", ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot feature importances (bar chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalise = True  # show relative importance values\n",
    "num_to_keep = 6  # show 6 most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 798x1122 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(\n",
    "    3, 1,\n",
    "    figsize=(num_to_keep * 1.33, num_to_keep * 1.87),\n",
    "    gridspec_kw={\"hspace\": 0.17},\n",
    ")\n",
    "for ax, region in zip(axs, regions):\n",
    "    title = f\"Top {num_to_keep} features ({region})\"\n",
    "    plot_importances(\n",
    "        models[region],\n",
    "        names=feature_names,\n",
    "        normalise=normalise,\n",
    "        num_to_keep=num_to_keep,\n",
    "        ax=ax,\n",
    "        title=title,\n",
    "        fontsize=12,\n",
    "        titlesize=19.2,\n",
    "    )\n",
    "    if ax is not axs[-1]:\n",
    "        ax.set_xlabel(None)\n",
    "        ax.tick_params(labelbottom=False)\n",
    "\n",
    "for extension in (\".png\", \".pdf\"):\n",
    "    output_filename = importances_basename + extension\n",
    "    fig.savefig(\n",
    "        output_filename,\n",
    "        dpi=350,\n",
    "        bbox_inches=\"tight\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare feature importance rankings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = {\n",
    "    region: calculate_importances(model, feature_names)\n",
    "    for region, model in models.items()\n",
    "}\n",
    "ranks = {\n",
    "    region: get_ranks(values) for region, values in importances.items()\n",
    "}\n",
    "ranks = pd.concat(\n",
    "    [\n",
    "        pd.DataFrame(rank_values, columns=[f\"Rank ({region})\"])\n",
    "        for region, rank_values in ranks.items()\n",
    "    ],\n",
    "    axis=\"columns\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank (All)</th>\n",
       "      <th>Rank (NA)</th>\n",
       "      <th>Rank (SA)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crustal_thickness_mean (m)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_to_trench (km)</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convergence_rate_orthogonal (cm/yr)</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trench_velocity_orthogonal (cm/yr)</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seafloor_spreading_rate (km/Myr)</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducted_carbonates_volume (m)</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seafloor_age (Ma)</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducting_plate_absolute_velocity_orthogonal (cm/yr)</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trench_velocity_obliquity (degrees)</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducted_sediment_volume (m)</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbonate_thickness (m)</th>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnetic_anomaly_std (nT)</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convergence_obliquity (degrees)</th>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trench_velocity_parallel (cm/yr)</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slab_flux (m^2/yr)</th>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co2_volume (m^3/m^2)</th>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducting_plate_absolute_obliquity (degrees)</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crustal_thickness_std (m)</th>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducted_plate_volume (m)</th>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_to_trench_edge (degrees)</th>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sediment_thickness (m)</th>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducting_plate_absolute_velocity_parallel (cm/yr)</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convergence_rate_parallel (cm/yr)</th>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnetic_anomaly_min (nT)</th>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnetic_anomaly_mean (nT)</th>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Rank (All)  Rank (NA)  \\\n",
       "crustal_thickness_mean (m)                                   1          1   \n",
       "distance_to_trench (km)                                      2          4   \n",
       "convergence_rate_orthogonal (cm/yr)                          3          6   \n",
       "trench_velocity_orthogonal (cm/yr)                           4          2   \n",
       "seafloor_spreading_rate (km/Myr)                             5          5   \n",
       "subducted_carbonates_volume (m)                              6         20   \n",
       "seafloor_age (Ma)                                            7          3   \n",
       "subducting_plate_absolute_velocity_orthogonal (...           8          8   \n",
       "trench_velocity_obliquity (degrees)                          9         11   \n",
       "subducted_sediment_volume (m)                               10         13   \n",
       "carbonate_thickness (m)                                     11         15   \n",
       "magnetic_anomaly_std (nT)                                   12          7   \n",
       "convergence_obliquity (degrees)                             13          9   \n",
       "trench_velocity_parallel (cm/yr)                            14         14   \n",
       "slab_flux (m^2/yr)                                          15         21   \n",
       "co2_volume (m^3/m^2)                                        16         10   \n",
       "subducting_plate_absolute_obliquity (degrees)               17         18   \n",
       "crustal_thickness_std (m)                                   18         23   \n",
       "subducted_plate_volume (m)                                  19         12   \n",
       "distance_to_trench_edge (degrees)                           20         19   \n",
       "sediment_thickness (m)                                      21         17   \n",
       "subducting_plate_absolute_velocity_parallel (cm...          22         22   \n",
       "convergence_rate_parallel (cm/yr)                           23         16   \n",
       "magnetic_anomaly_min (nT)                                   24         25   \n",
       "magnetic_anomaly_mean (nT)                                  25         24   \n",
       "\n",
       "                                                    Rank (SA)  \n",
       "crustal_thickness_mean (m)                                  1  \n",
       "distance_to_trench (km)                                     2  \n",
       "convergence_rate_orthogonal (cm/yr)                         8  \n",
       "trench_velocity_orthogonal (cm/yr)                         18  \n",
       "seafloor_spreading_rate (km/Myr)                           10  \n",
       "subducted_carbonates_volume (m)                             4  \n",
       "seafloor_age (Ma)                                          15  \n",
       "subducting_plate_absolute_velocity_orthogonal (...         12  \n",
       "trench_velocity_obliquity (degrees)                         6  \n",
       "subducted_sediment_volume (m)                              23  \n",
       "carbonate_thickness (m)                                     7  \n",
       "magnetic_anomaly_std (nT)                                  21  \n",
       "convergence_obliquity (degrees)                            11  \n",
       "trench_velocity_parallel (cm/yr)                            5  \n",
       "slab_flux (m^2/yr)                                          3  \n",
       "co2_volume (m^3/m^2)                                       13  \n",
       "subducting_plate_absolute_obliquity (degrees)              14  \n",
       "crustal_thickness_std (m)                                   9  \n",
       "subducted_plate_volume (m)                                 22  \n",
       "distance_to_trench_edge (degrees)                          19  \n",
       "sediment_thickness (m)                                     20  \n",
       "subducting_plate_absolute_velocity_parallel (cm...         17  \n",
       "convergence_rate_parallel (cm/yr)                          16  \n",
       "magnetic_anomaly_min (nT)                                  25  \n",
       "magnetic_anomaly_mean (nT)                                 24  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_by = \"All\"\n",
    "\n",
    "ranks.sort_values(f\"Rank ({sort_by})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All/NA: tau = 0.64, p = 0.00\n",
      "All/SA: tau = 0.44, p = 0.00\n",
      "NA/SA: tau = 0.20, p = 0.08\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_correlations(\n",
    "    models,\n",
    "    names=feature_names,\n",
    "    verbose=True,\n",
    "    cmap=\"plasma\" if colour else \"Greys\",\n",
    "    text_kw={\"color\": \"black\" if colour else \"white\"}\n",
    ")\n",
    "for ext in (\".pdf\", \".png\"):\n",
    "    output_filename = correlation_filename + ext\n",
    "    fig.savefig(output_filename, dpi=350, bbox_inches=\"tight\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot important feature distributions (box plots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean values:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th colspan=\"3\" halign=\"left\">All</th>\n",
       "      <th colspan=\"3\" halign=\"left\">NAm</th>\n",
       "      <th colspan=\"3\" halign=\"left\">SAm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>unlabelled</th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>unlabelled</th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>unlabelled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crustal_thickness_mean (m)</th>\n",
       "      <td>45036.618112</td>\n",
       "      <td>46594.015383</td>\n",
       "      <td>38570.683283</td>\n",
       "      <td>35824.774054</td>\n",
       "      <td>47539.517326</td>\n",
       "      <td>39151.725505</td>\n",
       "      <td>45867.433161</td>\n",
       "      <td>45015.754447</td>\n",
       "      <td>37976.185745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_to_trench (km)</th>\n",
       "      <td>370.332834</td>\n",
       "      <td>415.948199</td>\n",
       "      <td>277.418006</td>\n",
       "      <td>219.424041</td>\n",
       "      <td>406.638555</td>\n",
       "      <td>274.153981</td>\n",
       "      <td>383.943279</td>\n",
       "      <td>431.488145</td>\n",
       "      <td>281.240567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convergence_rate_orthogonal (cm/yr)</th>\n",
       "      <td>7.978948</td>\n",
       "      <td>9.017506</td>\n",
       "      <td>5.623068</td>\n",
       "      <td>5.040007</td>\n",
       "      <td>9.027425</td>\n",
       "      <td>5.034337</td>\n",
       "      <td>8.244011</td>\n",
       "      <td>9.000948</td>\n",
       "      <td>6.312542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trench_velocity_orthogonal (cm/yr)</th>\n",
       "      <td>-0.568777</td>\n",
       "      <td>-2.203915</td>\n",
       "      <td>-0.914783</td>\n",
       "      <td>-1.108740</td>\n",
       "      <td>-2.766340</td>\n",
       "      <td>-1.103310</td>\n",
       "      <td>-0.520078</td>\n",
       "      <td>-1.265097</td>\n",
       "      <td>-0.693997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seafloor_spreading_rate (km/Myr)</th>\n",
       "      <td>100.533424</td>\n",
       "      <td>118.214227</td>\n",
       "      <td>87.182149</td>\n",
       "      <td>116.739547</td>\n",
       "      <td>109.388207</td>\n",
       "      <td>80.647479</td>\n",
       "      <td>99.071796</td>\n",
       "      <td>132.946891</td>\n",
       "      <td>94.835023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subducted_carbonates_volume (m)</th>\n",
       "      <td>1603.454966</td>\n",
       "      <td>339.846246</td>\n",
       "      <td>207.082500</td>\n",
       "      <td>247.304046</td>\n",
       "      <td>299.675457</td>\n",
       "      <td>200.441820</td>\n",
       "      <td>1725.766045</td>\n",
       "      <td>406.900564</td>\n",
       "      <td>214.859525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "region                                        All                              \\\n",
       "label                                    negative      positive    unlabelled   \n",
       "crustal_thickness_mean (m)           45036.618112  46594.015383  38570.683283   \n",
       "distance_to_trench (km)                370.332834    415.948199    277.418006   \n",
       "convergence_rate_orthogonal (cm/yr)      7.978948      9.017506      5.623068   \n",
       "trench_velocity_orthogonal (cm/yr)      -0.568777     -2.203915     -0.914783   \n",
       "seafloor_spreading_rate (km/Myr)       100.533424    118.214227     87.182149   \n",
       "subducted_carbonates_volume (m)       1603.454966    339.846246    207.082500   \n",
       "\n",
       "region                                        NAm                              \\\n",
       "label                                    negative      positive    unlabelled   \n",
       "crustal_thickness_mean (m)           35824.774054  47539.517326  39151.725505   \n",
       "distance_to_trench (km)                219.424041    406.638555    274.153981   \n",
       "convergence_rate_orthogonal (cm/yr)      5.040007      9.027425      5.034337   \n",
       "trench_velocity_orthogonal (cm/yr)      -1.108740     -2.766340     -1.103310   \n",
       "seafloor_spreading_rate (km/Myr)       116.739547    109.388207     80.647479   \n",
       "subducted_carbonates_volume (m)        247.304046    299.675457    200.441820   \n",
       "\n",
       "region                                        SAm                              \n",
       "label                                    negative      positive    unlabelled  \n",
       "crustal_thickness_mean (m)           45867.433161  45015.754447  37976.185745  \n",
       "distance_to_trench (km)                383.943279    431.488145    281.240567  \n",
       "convergence_rate_orthogonal (cm/yr)      8.244011      9.000948      6.312542  \n",
       "trench_velocity_orthogonal (cm/yr)      -0.520078     -1.265097     -0.693997  \n",
       "seafloor_spreading_rate (km/Myr)        99.071796    132.946891     94.835023  \n",
       "subducted_carbonates_volume (m)       1725.766045    406.900564    214.859525  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances_sorted = importances[\"All\"].sort_values(ascending=False)\n",
    "columns = importances_sorted.iloc[:num_to_keep].index\n",
    "\n",
    "df_boxplots = df_training.copy()\n",
    "df_boxplots = df_boxplots[df_boxplots[\"region\"].isin({\"NAm\", \"SAm\"})]\n",
    "\n",
    "mean_values = pd.concat(\n",
    "    [\n",
    "        df_boxplots,\n",
    "        df_boxplots.assign(region=\"All\"),\n",
    "    ]\n",
    ").groupby([\"region\", \"label\"])[columns].mean()\n",
    "print(\"Mean values:\")\n",
    "mean_values.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_filters = {\n",
    "    i: df_boxplots[\"label\"] == i\n",
    "    for i in (\"positive\", \"unlabelled\")\n",
    "}\n",
    "region_filters = {\n",
    "    \"All\": np.full(df_boxplots.shape[0], True),\n",
    "    **{i: df_boxplots[\"region\"] == i + \"m\" for i in (\"NA\", \"SA\")}\n",
    "}\n",
    "\n",
    "disp = 0.25\n",
    "fontsize = 13\n",
    "titlesize = fontsize * 1.15\n",
    "\n",
    "data = []\n",
    "keys = []\n",
    "positions = []\n",
    "for i, label in enumerate(label_filters.keys()):\n",
    "    for j, region in enumerate((\"NA\", \"All\", \"SA\")):\n",
    "        filt = (label_filters[label]) & (region_filters[region])\n",
    "        keys.append((label, region))\n",
    "        data.append(df_boxplots[filt])\n",
    "        positions.append(i + (j - 1) * disp)\n",
    "positions = np.array(positions)\n",
    "xlims = (positions.min() - disp, positions.max() + disp)\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    3, 2,\n",
    "    figsize=(12, 12),\n",
    "    gridspec_kw={\"hspace\": 0.18},\n",
    ")\n",
    "\n",
    "for i, (column, ax) in enumerate(\n",
    "    zip(\n",
    "        columns,\n",
    "        np.ravel(axs),\n",
    "    )\n",
    "):\n",
    "    panel_label = ascii_uppercase[i]\n",
    "    for d, key, position in zip(data, keys, positions):\n",
    "        label, region = key\n",
    "        ticklabel = region\n",
    "        if i >= num_to_keep - 2:\n",
    "            if region == \"All\":\n",
    "                if label == \"positive\":\n",
    "                    ticklabel += \"\\n(positive)\"\n",
    "                else:\n",
    "                    ticklabel += \"\\n(random)\"\n",
    "        else:\n",
    "            ticklabel = \"\"\n",
    "\n",
    "        ax.boxplot(\n",
    "            d[column].dropna(),\n",
    "            positions=[position],\n",
    "            patch_artist=True,\n",
    "            boxprops={**(boxprops[region]), \"alpha\": 0.25},\n",
    "            showfliers=False,\n",
    "            showmeans=True,\n",
    "            meanprops=dict(\n",
    "                marker=\"^\",\n",
    "                markerfacecolor=\"black\",\n",
    "                markeredgecolor=\"black\",\n",
    "                markersize=8,\n",
    "            ),\n",
    "            medianprops=dict(\n",
    "                color=\"red\",\n",
    "                linewidth=1.5,\n",
    "            ),\n",
    "            labels=[ticklabel],\n",
    "            widths=[disp * 0.8],\n",
    "        )\n",
    "\n",
    "    ax.text(\n",
    "        0.025, 0.95,\n",
    "        panel_label,\n",
    "        ha=\"left\",\n",
    "        va=\"top\",\n",
    "        fontsize=titlesize,\n",
    "        transform=ax.transAxes,\n",
    "        bbox=dict(\n",
    "            facecolor=\"white\",\n",
    "            edgecolor=\"lightgrey\",\n",
    "            linewidth=0.5,\n",
    "            boxstyle=\"Round, pad=0.15, rounding_size=0.15\",\n",
    "        )\n",
    "    )\n",
    "    ax.set_title(format_feature_name(column), fontsize=titlesize)\n",
    "    ax.set_xlim(*xlims)\n",
    "    ax.grid(linestyle=\"dashed\", color=\"grey\", alpha=0.5, axis=\"y\")\n",
    "    ax.tick_params(labelsize=fontsize)\n",
    "\n",
    "for ext in (\".png\", \".pdf\"):\n",
    "    output_filename = boxplots_filename + ext\n",
    "    fig.savefig(\n",
    "        output_filename,\n",
    "        dpi=350,\n",
    "        bbox_inches=\"tight\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pygplates",
   "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": "3.11.5"
  },
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