notebooks/CatBoost_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "coated-individual",
   "metadata": {
    "id": "coated-individual"
   },
   "source": [
    "# Training the ML Model for Forecasting Fuel Load in Mid-Latitudes\n",
    "This notebooks demonstrates training a CatBoost model with hyperparameter optimization, followed by feature importance visualization using SHAP. CatBoost is a machine learning algorithm that uses gradient boosting on decision trees. This notebook utilizes the `deepfuel-ML/src/models/catboost_module.py` script for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "continent-budget",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:22:59.893209Z",
     "start_time": "2021-03-19T08:22:56.680598Z"
    },
    "id": "continent-budget"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from joblib import dump, load\n",
    "import shap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foster-radiation",
   "metadata": {
    "id": "foster-radiation"
   },
   "source": [
    "### Data directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "skilled-halloween",
   "metadata": {
    "id": "skilled-halloween",
    "outputId": "d866a56c-92d3-46b5-a7f8-915b2e7d71fb",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/midlats\r\n",
      "├── midlats_test.csv\r\n",
      "├── midlats_train.csv\r\n",
      "├── midlats_val.csv\r\n",
      "└── results\r\n",
      "    ├── midlats_output_Aug.csv\r\n",
      "    ├── midlats_output_Dec.csv\r\n",
      "    ├── midlats_output_July.csv\r\n",
      "    ├── midlats_output_Nov.csv\r\n",
      "    ├── midlats_output_Oct.csv\r\n",
      "    └── midlats_output_Sept.csv\r\n",
      "\r\n",
      "1 directory, 9 files\r\n"
     ]
    }
   ],
   "source": [
    "# The training, validation and test set required for model training are placed in data/midlats/\n",
    "! tree ../data/midlats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ongoing-knowing",
   "metadata": {
    "id": "ongoing-knowing"
   },
   "source": [
    "### Input Features\n",
    "\n",
    "- Latitude\n",
    "- Longitude\n",
    "- Leaf Area Index\n",
    "- Fire Weather Index: fwinx\n",
    "- Drought Code: drtcode\n",
    "- Fire Danegr Severity Rating: fdsrte\n",
    "- Fraction of Burnable Area: fraction_of_burnable_area\n",
    "- d2m\n",
    "- Evaporation Rate: erate\n",
    "- fg10\n",
    "- si10\n",
    "- Volumetric Soil Water Level 1: swvl1\n",
    "- 2m Temperature: t2m\n",
    "- tprate\n",
    "- Climatic Region: climatic_region\n",
    "- Slope: slor\n",
    "- Month: month\n",
    "- Fuel Load: actual_load (target variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "expanded-forest",
   "metadata": {
    "id": "expanded-forest",
    "outputId": "d9d44c5a-43e4-477f-d50b-cc2702cba864"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "latitude,longitude,fwinx,drtcode,fdsrte,LAI,fraction_of_burnable_area,d2m,erate,fg10,si10,swvl1,t2m,tprate,climatic_region,slor,actual_load,month\r\n"
     ]
    }
   ],
   "source": [
    "# Check header of training set matches input features\n",
    "! head -n 1 ../data/midlats/midlats_train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broadband-trading",
   "metadata": {
    "id": "broadband-trading"
   },
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "intensive-cross",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:23:15.494504Z",
     "start_time": "2021-03-19T08:23:00.947482Z"
    },
    "id": "intensive-cross",
    "outputId": "721113d0-a035-4870-c133-8572366e34b1",
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Link for the created Neptune experiment--------\n",
      "https://ui.neptune.ai/shared/step-by-step-monitoring-experiments-live/e/STEP-499\n",
      "---------------------------------------\n",
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      "115:\tlearn: 0.5332300\ttest: 0.6277751\tbest: 0.6271986 (103)\ttotal: 14.3s\tremaining: 1m 49s\n",
      "116:\tlearn: 0.5325927\ttest: 0.6278736\tbest: 0.6271986 (103)\ttotal: 14.4s\tremaining: 1m 48s\n",
      "117:\tlearn: 0.5322730\ttest: 0.6276430\tbest: 0.6271986 (103)\ttotal: 14.7s\tremaining: 1m 49s\n",
      "118:\tlearn: 0.5319223\ttest: 0.6275982\tbest: 0.6271986 (103)\ttotal: 14.9s\tremaining: 1m 50s\n",
      "119:\tlearn: 0.5312797\ttest: 0.6281403\tbest: 0.6271986 (103)\ttotal: 15s\tremaining: 1m 50s\n",
      "120:\tlearn: 0.5307687\ttest: 0.6280090\tbest: 0.6271986 (103)\ttotal: 15.2s\tremaining: 1m 50s\n",
      "121:\tlearn: 0.5303204\ttest: 0.6278163\tbest: 0.6271986 (103)\ttotal: 15.3s\tremaining: 1m 49s\n",
      "122:\tlearn: 0.5297220\ttest: 0.6284622\tbest: 0.6271986 (103)\ttotal: 15.5s\tremaining: 1m 50s\n",
      "123:\tlearn: 0.5291532\ttest: 0.6285785\tbest: 0.6271986 (103)\ttotal: 15.6s\tremaining: 1m 50s\n",
      "Stopped by overfitting detector  (20 iterations wait)\n",
      "\n",
      "bestTest = 0.6271986476\n",
      "bestIteration = 103\n",
      "\n",
      "Shrink model to first 104 iterations.\n",
      "RMSE  : 0.6271986472276524\n",
      "-----------------------------------------------------------------\n",
      "Training results \n",
      "\n",
      "Training error:  2232749676.1163282\n",
      "Validation error:  2844489377.299632\n",
      "Test error:  2219991173.032113\n",
      "Note : The error printed above is calculated after the inverse transform of box-cox\n",
      "Model file save at ['/home/smose_roshni/roshni/deepfuel-ML/src/pre-trained_models/CatBoost.joblib']\n"
     ]
    }
   ],
   "source": [
    "!python '../src/train.py'  --model_name 'CatBoost' --data_path '../data/midlats/' --exp_name 'CatBoost_exp'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retained-philosophy",
   "metadata": {
    "id": "retained-philosophy"
   },
   "source": [
    "The training logs can be viewed live online at the following link: https://ui.neptune.ai/shared/step-by-step-monitoring-experiments-live/e/STEP-158"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "junior-disaster",
   "metadata": {
    "id": "junior-disaster"
   },
   "source": [
    "### Loading the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dutch-lawsuit",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:23:15.552035Z",
     "start_time": "2021-03-19T08:23:15.527949Z"
    },
    "id": "dutch-lawsuit"
   },
   "outputs": [],
   "source": [
    "model = load('../src/pre-trained_models/CatBoost.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compatible-composite",
   "metadata": {
    "id": "compatible-composite"
   },
   "source": [
    "### Feature importance using SHAP\n",
    "[SHAP](https://github.com/slundberg/shap) (SHapley Additive exPlanations) is used to explain the output of the trained machine learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "civic-player",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:23:27.666675Z",
     "start_time": "2021-03-19T08:23:27.567227Z"
    },
    "id": "civic-player"
   },
   "outputs": [],
   "source": [
    "midlat_train = pd.read_csv('../data/midlats/midlats_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "photographic-motorcycle",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-19T08:23:34.675586Z",
     "start_time": "2021-03-19T08:23:28.991602Z"
    },
    "id": "photographic-motorcycle",
    "outputId": "d1746435-f6d2-4008-cee5-bd4b339dd772"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x597.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values = shap.TreeExplainer(model).shap_values(midlat_train.drop([ 'actual_load'], axis=1))\n",
    "shap.summary_plot(shap_values, midlat_train.drop(['actual_load'], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assumed-prize",
   "metadata": {
    "id": "assumed-prize"
   },
   "source": [
    "The y-axis indicates the variable name, in order of importance from top to bottom.\n",
    "On the x-axis (Impact on model output), the horizontal location shows whether the effect of that value is associated with a higher or lower prediction. Gradient colour indicates feature value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "122f7254",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "CatBoost_training.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}