whole_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9d8e92b-5db0-4cee-adf0-f79a47ab4536",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(1, './imports')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from exploratory_analysis import *\n",
    "from preprocessing import *\n",
    "from model_selection import *\n",
    "from model_end_to_end import *\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa8ddb49-129a-4cba-a4ce-59efcd6b5729",
   "metadata": {},
   "outputs": [],
   "source": [
    "fires = pd.read_csv(\"fires.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0581121-39d9-447d-ab34-5f24b4a85df9",
   "metadata": {},
   "source": [
    "# Estimation of the average absolute loss in hA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b84249-cee8-4e20-bd47-cc828cbc4df8",
   "metadata": {},
   "source": [
    "## Model with preclassification and split of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a519d8ae-3ca2-4812-8899-4089a918cacc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average absolute loss per instance, in hA:  2.70763448575178\n"
     ]
    }
   ],
   "source": [
    "msinho_acc = MontesinhoCompleteModel_evaluator(SVC(kernel = 'rbf', gamma=0.002652859881404326, C= 100, degree=5),\n",
    "                                           SVC(C=8.41850316809234, gamma=0.04349773501643578, kernel='linear'),\n",
    "                                           SVC(C=0.26869573356106563, degree=2, gamma=0.9600363819999834, kernel='poly'),\n",
    "                                           PolynomialFeatures(degree=3),\n",
    "                                           PolynomialFeatures(degree=1),\n",
    "                                           PolynomialFeatures(degree=3),\n",
    "                                           GradientBoostingRegressor(learning_rate= 0.1875, n_estimators= 20),\n",
    "                                           GradientBoostingRegressor(learning_rate= 0.46, n_estimators= 20),\n",
    "                                           GradientBoostingRegressor(learning_rate= 0.6, n_estimators= 20))\n",
    "msinho_acc.fit(fires)\n",
    "print(\"average absolute loss per instance, in hA: \", np.exp(msinho_acc.average_loss()) - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2e27dd-9c37-4587-9e83-cd331d7d0260",
   "metadata": {},
   "source": [
    "## Random Forest Regressor with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e2c0a76-2c0c-4509-9852-099b19921f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average absolute loss per instance, in hA:  2.256564260339103\n"
     ]
    }
   ],
   "source": [
    "montesinho_rfr = MontesinhoCompleteModel_evaluator_simple(RandomForestRegressor(criterion = \"mse\", n_estimators= 100), 1)\n",
    "montesinho_rfr.fit(fires)\n",
    "print(\"average absolute loss per instance, in hA: \", np.exp(montesinho_rfr.evaluate()) - 1)\n",
    "montesinho_rfr.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cccf90e-3371-4051-b602-19f13d3c4f5a",
   "metadata": {},
   "source": [
    "## Gradient Boosting Regressor with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c7573be-676f-4f29-81cc-76652e5c2c8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average absolute loss per instance, in hA:  2.216605048066975\n"
     ]
    }
   ],
   "source": [
    "montesinho_gbr = MontesinhoCompleteModel_evaluator_simple(GradientBoostingRegressor(learning_rate= 0.05, n_estimators= 100), 1)\n",
    "montesinho_gbr.fit(fires)\n",
    "print(\"average absolute loss per instance, in hA: \", np.exp(montesinho_gbr.evaluate()) - 1)\n",
    "montesinho_gbr.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ace9afb-bead-4a03-905a-eed5a6a10006",
   "metadata": {},
   "source": [
    "## Gradient Boosting Regressor with feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "562b38a5-cad2-4f1e-a025-eb62445bd773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average absolute loss per instance, in hA:  2.029817016475741\n"
     ]
    }
   ],
   "source": [
    "features_removed = ['apr_bool', '66', '73', '65', '74', '64', '75', '95', '63', '99', '84', '55', '13', 'aug_bool', '76', '43', '44', '85', 'rain', '94', 'oct_bool', '45', '33', 'nov_bool', 'feb_bool', '23',\n",
    " 'wed', 'mar_bool', 'fri', 'jun_bool', '14', 'tue', '24', '88', '46', 'sat']\n",
    "features_added = [[\"22\",\"sat\"], [\"wed\",\"pc5\"], [\"tue\",\"pc2\"], [\"94\", \"pc6\"], ['sep_bool','pc4']]\n",
    "montesinho_gbr_fe = MontesinhoCompleteModel_evaluator_fe(GradientBoostingRegressor(learning_rate= 0.05, n_estimators= 100), 1, features_added, features_removed)\n",
    "montesinho_gbr_fe.fit(fires)\n",
    "print(\"average absolute loss per instance, in hA: \", np.exp(montesinho_gbr_fe.evaluate()) - 1)\n",
    "montesinho_gbr_fe.train()\n",
    "#montesinho_gbr_fe.save_model(\"model_gbr_re.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a226ec6-e886-4e7a-ba77-706c95e6bd46",
   "metadata": {},
   "source": [
    "# Model piecewise behaviour for the three simple models\n",
    "Now, even if it's goinf to depend highly on chance, let's try to observe how the model works depending on the value of the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d4bf618-739f-4311-9f24-b98c72987e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "indexes_0 = fires[fires[\"area\"] == 0].sample(25, random_state=3558).index\n",
    "indexes_0_10 = fires[(fires[\"area\"] != 0) & (fires[\"area\"] <= 10)].sample(20, random_state=3558).index\n",
    "indexes_10_40 = fires[(fires[\"area\"] <= 40) & (fires[\"area\"] > 10)].sample(20, random_state=3558).index\n",
    "indexes_40_plus = fires[(fires[\"area\"] > 40)].sample(10, random_state=3558).index\n",
    "evaluation_indexes = [*indexes_0, *indexes_0_10, *indexes_10_40, *indexes_40_plus]\n",
    "train_indexes = np.array([i for i in np.arange(0, fires.shape[0]-1) if i not in evaluation_indexes])\n",
    "data_train = fires.iloc[train_indexes]\n",
    "data_evaluation = fires.iloc[evaluation_indexes]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "636fb5d0-f98e-4999-aa7a-91a4de6f5bfe",
   "metadata": {},
   "source": [
    "## Random Forest Regressor with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06f906b2-0247-4d91-9535-651af1962a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "montesinho_rfr.fit(data_train)\n",
    "montesinho_rfr.train()\n",
    "df_0_rfr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_0_10_rfr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_10_40_rfr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_40_plus_rfr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "for i in indexes_0:\n",
    "    df_0_rfr = df_0_rfr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_rfr.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_0_10:\n",
    "    df_0_10_rfr = df_0_10_rfr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_rfr.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_10_40:\n",
    "    df_10_40_rfr = df_10_40_rfr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_rfr.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_40_plus:\n",
    "    df_40_plus_rfr = df_40_plus_rfr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_rfr.predict_instance(fires.iloc[i])}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e21cb0c9-8747-4b22-9adf-b185b4233d7b",
   "metadata": {},
   "source": [
    "## Gradient Boosting Regressor with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "913e6461-fb94-4489-99e6-388060cc83c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "montesinho_gbr.fit(fires)\n",
    "montesinho_gbr.train()\n",
    "df_0_gbr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_0_10_gbr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_10_40_gbr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_40_plus_gbr = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "for i in indexes_0:\n",
    "    df_0_gbr = df_0_gbr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_0_10:\n",
    "    df_0_10_gbr = df_0_10_gbr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_10_40:\n",
    "    df_10_40_gbr = df_10_40_gbr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_40_plus:\n",
    "    df_40_plus_gbr = df_40_plus_gbr.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr.predict_instance(fires.iloc[i])}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5975abfe-d5b7-49aa-b0d6-73e22a2d0790",
   "metadata": {},
   "source": [
    "## Gradient Boosting Regressor with feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8337e665-6df4-4497-b3f8-fd45342749ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "montesinho_gbr_fe.fit(fires)\n",
    "montesinho_gbr_fe.train()\n",
    "df_0_gbr_fe = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_0_10_gbr_fe = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_10_40_gbr_fe = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "df_40_plus_gbr_fe = pd.DataFrame(columns =[\"obs\", \"pred\"])\n",
    "for i in indexes_0:\n",
    "    df_0_gbr_fe = df_0_gbr_fe.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr_fe.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_0_10:\n",
    "    df_0_10_gbr_fe = df_0_10_gbr_fe.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr_fe.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_10_40:\n",
    "    df_10_40_gbr_fe = df_10_40_gbr_fe.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr_fe.predict_instance(fires.iloc[i])}, ignore_index=True)\n",
    "for i in indexes_40_plus:\n",
    "    df_40_plus_gbr_fe = df_40_plus_gbr_fe.append({\"obs\":fires[\"area\"].iloc[i], \"pred\":montesinho_gbr_fe.predict_instance(fires.iloc[i])}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa010701-dbe2-4705-8676-1f4dc85f0849",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3080d5db-19b4-412c-b024-04c6104c20f5",
   "metadata": {},
   "source": [
    "### Visual comparaison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6a2a756-8cc7-4ae6-a988-1a3ca993cfe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 10))\n",
    "fig.suptitle(\"Different preddictions of the models when the real area was 0\", fontsize=20)\n",
    "g1 = sns.boxplot(y = \"pred\", data=df_0_rfr, ax=ax1)\n",
    "g1.set_ylim([0, 8])\n",
    "g1.set_ylabel(\"Predicted area (hA)\")\n",
    "g1.set_title(\"Random Forest Regressor\", fontsize=16)\n",
    "g2 = sns.boxplot(y = \"pred\", data=df_0_gbr, ax=ax2)\n",
    "g2.set_ylim([0, 8])\n",
    "g2.set_ylabel(\"\")\n",
    "g3 = sns.boxplot(y = \"pred\", data=df_0_gbr_fe, ax=ax3)\n",
    "g3.set_ylim([0, 8])\n",
    "g2.set_title(\"Gradient Boosting Regressor\", fontsize=16)\n",
    "g3.set_ylabel(\"\")\n",
    "g3.set_title(\"GBR + Feature modifications\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87accbf1-6a39-4bb1-9a29-60495faca67c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2448x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(34, 10))\n",
    "fig.suptitle(\"Different preddictions of the models when the real area $\\in$ (0, 10]\", fontsize=20)\n",
    "g1 = sns.regplot(y = \"pred\", x=\"obs\", data=df_0_10_rfr, ax=ax1)\n",
    "g1.set_ylim([1, 8])\n",
    "ax1.text(1, 7, \"Pearson R:{}\".format(pearsonr(df_0_10_rfr[\"obs\"], df_0_10_rfr[\"pred\"])[0]), fontsize=12)\n",
    "g1.set_ylabel(\"Predicted area (hA)\")\n",
    "g1.set_title(\"Random Forest Regressor\", fontsize=16)\n",
    "g2 = sns.regplot(y = \"pred\", x=\"obs\", data=df_0_10_gbr, ax=ax2)\n",
    "g2.set_ylim([1, 8])\n",
    "ax2.text(1, 7, \"Pearson R:{}\".format(pearsonr(df_0_10_gbr[\"obs\"], df_0_10_gbr[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_ylabel(\"\")\n",
    "g3 = sns.regplot(y = \"pred\", x=\"obs\", data=df_0_10_gbr_fe, ax=ax3)\n",
    "g3.set_ylim([1, 8])\n",
    "ax3.text(1, 7, \"Pearson R:{}\".format(pearsonr(df_0_10_gbr_fe[\"obs\"], df_0_10_gbr_fe[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_title(\"Gradient Boosting Regressor\", fontsize=16)\n",
    "g3.set_ylabel(\"\")\n",
    "g3.set_title(\"GBR + Feature modifications\", fontsize=16)\n",
    "g1.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g2.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g3.set_xlabel(\"Actual area burnt (hA)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b51b9431-fc43-4d6c-8980-9473444762a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2448x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(34, 10))\n",
    "fig.suptitle(\"Different preddictions of the models when the real area $\\in$ (10, 40]\", fontsize=20)\n",
    "g1 = sns.regplot(y = \"pred\", x=\"obs\", data=df_10_40_rfr, ax=ax1)\n",
    "g1.set_ylim([2, 14])\n",
    "ax1.text(15, 13, \"Pearson R:{}\".format(pearsonr(df_10_40_rfr[\"obs\"], df_10_40_rfr[\"pred\"])[0]), fontsize=12)\n",
    "g1.set_ylabel(\"Predicted area (hA)\")\n",
    "g1.set_title(\"Random Forest Regressor\", fontsize=16)\n",
    "g2 = sns.regplot(y = \"pred\", x=\"obs\", data=df_10_40_gbr, ax=ax2)\n",
    "g2.set_ylim([2, 14])\n",
    "ax2.text(15, 13, \"Pearson R:{}\".format(pearsonr(df_10_40_gbr[\"obs\"], df_10_40_gbr[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_ylabel(\"\")\n",
    "g3 = sns.regplot(y = \"pred\", x=\"obs\", data=df_10_40_gbr_fe, ax=ax3)\n",
    "g3.set_ylim([2, 14])\n",
    "ax3.text(15, 13, \"Pearson R:{}\".format(pearsonr(df_10_40_gbr_fe[\"obs\"], df_10_40_gbr_fe[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_title(\"Gradient Boosting Regressor\", fontsize=16)\n",
    "g3.set_ylabel(\"\")\n",
    "g3.set_title(\"GBR + Feature modifications\", fontsize=16)\n",
    "g1.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g2.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g3.set_xlabel(\"Actual area burnt (hA)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "be6a01a5-fb1e-4c6c-a353-0d68f27c68da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2448x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(34, 10))\n",
    "fig.suptitle(\"Different preddictions of the models when the real area $\\in$ (40, $\\infty$)\", fontsize=20)\n",
    "g1 = sns.regplot(y = \"pred\", x=\"obs\", data=df_40_plus_rfr, ax=ax1)\n",
    "g1.set_ylim([0, 55])\n",
    "ax1.text(60, 45, \"Pearson R:{}\".format(pearsonr(df_40_plus_rfr[\"obs\"], df_40_plus_rfr[\"pred\"])[0]), fontsize=12)\n",
    "g1.set_ylabel(\"Predicted area (hA)\")\n",
    "g1.set_title(\"Random Forest Regressor\", fontsize=16)\n",
    "g2 = sns.regplot(y = \"pred\", x=\"obs\", data=df_40_plus_gbr, ax=ax2)\n",
    "g2.set_ylim([0, 55])\n",
    "ax2.text(60, 45, \"Pearson R:{}\".format(pearsonr(df_40_plus_gbr[\"obs\"], df_40_plus_gbr[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_ylabel(\"\")\n",
    "g3 = sns.regplot(y = \"pred\", x=\"obs\", data=df_40_plus_gbr_fe, ax=ax3)\n",
    "g3.set_ylim([0, 55])\n",
    "ax3.text(60, 45, \"Pearson R:{}\".format(pearsonr(df_40_plus_gbr_fe[\"obs\"], df_40_plus_gbr_fe[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_title(\"Gradient Boosting Regressor\", fontsize=16)\n",
    "g3.set_ylabel(\"\")\n",
    "g3.set_title(\"GBR + Feature modifications\", fontsize=16)\n",
    "g1.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g2.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g3.set_xlabel(\"Actual area burnt (hA)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50f1ac9c-1c29-423b-ae8c-201ac3bbd626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2448x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(34, 10))\n",
    "fig.suptitle(\"Different preddictions of the models when the real area $\\in$ (0, $\\infty$)\", fontsize=20)\n",
    "g1 = sns.regplot(y = \"pred\", x=\"obs\", data=pd.concat([df_0_10_rfr, df_10_40_rfr, df_40_plus_rfr], axis=0), ax=ax1)\n",
    "g1.set_ylim([0, 55])\n",
    "ax1.text(60, 45, \"Pearson R:{}\".format(pearsonr(df_40_plus_rfr[\"obs\"], df_40_plus_rfr[\"pred\"])[0]), fontsize=12)\n",
    "g1.set_ylabel(\"Predicted area (hA)\")\n",
    "g1.set_title(\"Random Forest Regressor\", fontsize=16)\n",
    "g2 = sns.regplot(y = \"pred\", x=\"obs\", data=pd.concat([df_0_10_gbr, df_10_40_gbr, df_40_plus_gbr], axis=0), ax=ax2)\n",
    "g2.set_ylim([0, 55])\n",
    "ax2.text(60, 45, \"Pearson R:{}\".format(pearsonr(df_40_plus_gbr[\"obs\"], df_40_plus_gbr[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_ylabel(\"\")\n",
    "g3 = sns.regplot(y = \"pred\", x=\"obs\", data=pd.concat([df_0_10_gbr_fe, df_10_40_gbr_fe, df_40_plus_gbr_fe], axis=0), ax=ax3)\n",
    "g3.set_ylim([0, 55])\n",
    "ax3.text(60, 45, \"Pearson R:{}\".format(pearsonr(df_40_plus_gbr_fe[\"obs\"], df_40_plus_gbr_fe[\"pred\"])[0]), fontsize=12)\n",
    "g2.set_title(\"Gradient Boosting Regressor\", fontsize=16)\n",
    "g3.set_ylabel(\"\")\n",
    "g3.set_title(\"GBR + Feature modifications\", fontsize=16)\n",
    "g1.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g2.set_xlabel(\"Actual area burnt (hA)\")\n",
    "g3.set_xlabel(\"Actual area burnt (hA)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "In conclussion:\n",
    "\n",
    "- Even when the difference in average loss was not huge, a huge difference in the models can be observed. This is probably because fires with lower areas burnt are a major fraction of the observations.\n",
    "- With respect to the mean in the intervals, the models work better where the burnt area is small\n",
    "- All models are overestimating the fires where the area burnt was 0. This is to expected, due to the use of log-scaling for training the algorithms\n",
    "- Models 2 and 3 perform (apparently) significantly better in the instances where the area was 0\n",
    "- For $y \\in (0, 10]$ the fine-tuned model performs (apparently) better than the other two models. The RF regressor behaves here very close to random, and the GB regressor without feature engineering is between both.\n",
    "- There seems to be some instances where the third model yields accurate predictions and others where it behaves randomly. This supports the idea of one or more latent variables that are not captured in the data.\n",
    "- For $y \\in (10, 40]$ both three models have a similar behaviour: Few instances are correctly predicted, the other have even a negative correlation\n",
    "- $y \\in (40, \\infty]$ the models 2 and 3 behave similarly, again there are some instances where the predictions are closer to the real value, and others where they are close to the average on the interval. Model 1 doesn't have apparently any predictive power on this interval\n",
    "- On the interval $y \\in (0, \\infty]$, models 2 and 3 behave similarly, model 2 apparently has a higher correlation coefficient between the real and predicted values. The random forest regressor seems almost like a blob with no correlation.\n",
    "- Model 3 and 2 perform better than 1. Model 3 performs better predicting small fires, which correspond to the majority of the training instances. That's the probably the reason why it has a slighly lower average absolute loss."
   ]
  }
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