p7_02_modelisation_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nQlloe8DIh2T"
   },
   "source": [
    "# Implémentez un modèle de scoring\n",
    "\n",
    "\n",
    "Etude data - Projet 7 - Mars 2021\n",
    "\n",
    "*Data source* : [Jeu de données d’articles](https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/Parcours_data_scientist/Projet+-+Impl%C3%A9menter+un+mod%C3%A8le+de+scoring/Projet+Mise+en+prod+-+home-credit-default-risk.zip)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bcuzL_13JFnv"
   },
   "source": [
    "## Rappel de la mission\n",
    "Construire un modèle de scoring qui donnera une prédiction sur la probabilité de faillite d'un client de façon automatique.\n",
    "\n",
    "## Code de la modélisation\n",
    "Dans le Notebook Jupyter nous avons vu l'exploration, ainsi que le prétraitement des données. Ici nous utiliserons un GPU pour effectuer les modélisations Catboost, LightGBM & XGBoost. Ici nous sommes en Google Colab PRO, donc l'accès au GPU est illimité… _L'offre free ne permet pas de mener à bien ce projet, les limites ont rapidement étaient atteintes pour effectuer les différents tests._\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "2owEhWS2H4gK",
    "outputId": "564f9c83-947f-4a79-a39b-4fa1cac52d69"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'/device:GPU:0'"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test de l'activation du GPU\n",
    "#Si le GPU est activé, il donnera la sortie suivante - '/ device: GPU: 0 '\n",
    "import tensorflow as tf\n",
    "tf.test.gpu_device_name ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wirT4xPNH58E",
    "outputId": "24ef9475-260f-46e8-8752-0d9eec95a43f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[name: \"/device:CPU:0\"\n",
       " device_type: \"CPU\"\n",
       " memory_limit: 268435456\n",
       " locality {\n",
       " }\n",
       " incarnation: 16360478728543910270, name: \"/device:GPU:0\"\n",
       " device_type: \"GPU\"\n",
       " memory_limit: 15505193728\n",
       " locality {\n",
       "   bus_id: 1\n",
       "   links {\n",
       "   }\n",
       " }\n",
       " incarnation: 678723616778043399\n",
       " physical_device_desc: \"device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:04.0, compute capability: 7.0\"]"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Appareils utilisés lors de l'exécution du notebook dans le cloud\n",
    "from tensorflow.python.client import device_lib \n",
    "device_lib.list_local_devices ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oifzNyHbH5-h",
    "outputId": "070ea375-3e97-402c-923b-5a382c7fd492"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MemTotal:       26751704 kB\n",
      "MemFree:        18582216 kB\n",
      "MemAvailable:   24089964 kB\n",
      "Buffers:          117536 kB\n",
      "Cached:          2523264 kB\n",
      "SwapCached:            0 kB\n",
      "Active:          2782092 kB\n",
      "Inactive:        4980908 kB\n",
      "Active(anon):    2009992 kB\n",
      "Inactive(anon):      408 kB\n",
      "Active(file):     772100 kB\n",
      "Inactive(file):  4980500 kB\n",
      "Unevictable:           0 kB\n",
      "Mlocked:               0 kB\n",
      "SwapTotal:             0 kB\n",
      "SwapFree:              0 kB\n",
      "Dirty:               580 kB\n",
      "Writeback:             0 kB\n",
      "AnonPages:       5122172 kB\n",
      "Mapped:           268032 kB\n",
      "Shmem:              1012 kB\n",
      "Slab:             220652 kB\n",
      "SReclaimable:     155804 kB\n",
      "SUnreclaim:        64848 kB\n",
      "KernelStack:        5632 kB\n",
      "PageTables:        16380 kB\n",
      "NFS_Unstable:          0 kB\n",
      "Bounce:                0 kB\n",
      "WritebackTmp:          0 kB\n",
      "CommitLimit:    13375852 kB\n",
      "Committed_AS:    8723632 kB\n",
      "VmallocTotal:   34359738367 kB\n",
      "VmallocUsed:           0 kB\n",
      "VmallocChunk:          0 kB\n",
      "Percpu:             2096 kB\n",
      "AnonHugePages:   3262464 kB\n",
      "ShmemHugePages:        0 kB\n",
      "ShmemPmdMapped:        0 kB\n",
      "HugePages_Total:       0\n",
      "HugePages_Free:        0\n",
      "HugePages_Rsvd:        0\n",
      "HugePages_Surp:        0\n",
      "Hugepagesize:       2048 kB\n",
      "Hugetlb:               0 kB\n",
      "DirectMap4k:      152780 kB\n",
      "DirectMap2M:     6137856 kB\n",
      "DirectMap1G:    23068672 kB\n"
     ]
    }
   ],
   "source": [
    "#Vérification de la RAM\n",
    "!cat /proc/meminfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6tPO7jI7a4ez"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aRLf3WV8YPOM",
    "outputId": "a300566d-75f6-48b7-93ce-bba101df60b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting catboost\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/47/80/8e9c57ec32dfed6ba2922bc5c96462cbf8596ce1a6f5de532ad1e43e53fe/catboost-0.25.1-cp37-none-manylinux1_x86_64.whl (67.3MB)\n",
      "\u001b[K     |████████████████████████████████| 67.3MB 86kB/s \n",
      "\u001b[?25hRequirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
      "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
      "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
      "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
      "Installing collected packages: catboost\n",
      "Successfully installed catboost-0.25.1\n"
     ]
    }
   ],
   "source": [
    "pip install catboost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "und3OWteYPR9",
    "outputId": "c5a48cbe-952a-4c9e-ba01-0cf3103e09fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'LightGBM'...\n",
      "remote: Enumerating objects: 21795, done.\u001b[K\n",
      "remote: Counting objects: 100% (287/287), done.\u001b[K\n",
      "remote: Compressing objects: 100% (181/181), done.\u001b[K\n",
      "remote: Total 21795 (delta 156), reused 189 (delta 101), pack-reused 21508\u001b[K\n",
      "Receiving objects: 100% (21795/21795), 17.19 MiB | 23.43 MiB/s, done.\n",
      "Resolving deltas: 100% (15908/15908), done.\n",
      "Submodule 'include/boost/compute' (https://github.com/boostorg/compute) registered for path 'external_libs/compute'\n",
      "Submodule 'eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'external_libs/eigen'\n",
      "Submodule 'external_libs/fast_double_parser' (https://github.com/lemire/fast_double_parser.git) registered for path 'external_libs/fast_double_parser'\n",
      "Submodule 'external_libs/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'external_libs/fmt'\n",
      "Cloning into '/content/LightGBM/external_libs/compute'...\n",
      "remote: Enumerating objects: 21728, done.        \n",
      "remote: Total 21728 (delta 0), reused 0 (delta 0), pack-reused 21728        \n",
      "Receiving objects: 100% (21728/21728), 8.51 MiB | 28.66 MiB/s, done.\n",
      "Resolving deltas: 100% (17566/17566), done.\n",
      "Cloning into '/content/LightGBM/external_libs/eigen'...\n",
      "remote: Enumerating objects: 853, done.        \n",
      "remote: Counting objects: 100% (853/853), done.        \n",
      "remote: Compressing objects: 100% (298/298), done.        \n",
      "remote: Total 109487 (delta 660), reused 714 (delta 552), pack-reused 108634        \n",
      "Receiving objects: 100% (109487/109487), 101.71 MiB | 32.99 MiB/s, done.\n",
      "Resolving deltas: 100% (89927/89927), done.\n",
      "Cloning into '/content/LightGBM/external_libs/fast_double_parser'...\n",
      "remote: Enumerating objects: 672, done.        \n",
      "remote: Counting objects: 100% (172/172), done.        \n",
      "remote: Compressing objects: 100% (107/107), done.        \n",
      "remote: Total 672 (delta 84), reused 95 (delta 39), pack-reused 500        \n",
      "Receiving objects: 100% (672/672), 798.63 KiB | 18.15 MiB/s, done.\n",
      "Resolving deltas: 100% (338/338), done.\n",
      "Cloning into '/content/LightGBM/external_libs/fmt'...\n",
      "remote: Enumerating objects: 25043, done.        \n",
      "remote: Counting objects: 100% (215/215), done.        \n",
      "remote: Compressing objects: 100% (96/96), done.        \n",
      "remote: Total 25043 (delta 114), reused 167 (delta 94), pack-reused 24828        \n",
      "Receiving objects: 100% (25043/25043), 12.45 MiB | 31.02 MiB/s, done.\n",
      "Resolving deltas: 100% (16925/16925), done.\n",
      "Submodule path 'external_libs/compute': checked out '36c89134d4013b2e5e45bc55656a18bd6141995a'\n",
      "Submodule path 'external_libs/eigen': checked out '8ba1b0f41a7950dc3e1d4ed75859e36c73311235'\n",
      "Submodule path 'external_libs/fast_double_parser': checked out 'ace60646c02dc54c57f19d644e49a61e7e7758ec'\n",
      "Submodule 'benchmark/dependencies/abseil-cpp' (https://github.com/abseil/abseil-cpp.git) registered for path 'external_libs/fast_double_parser/benchmarks/dependencies/abseil-cpp'\n",
      "Submodule 'benchmark/dependencies/double-conversion' (https://github.com/google/double-conversion.git) registered for path 'external_libs/fast_double_parser/benchmarks/dependencies/double-conversion'\n",
      "Cloning into '/content/LightGBM/external_libs/fast_double_parser/benchmarks/dependencies/abseil-cpp'...\n",
      "remote: Enumerating objects: 14267, done.        \n",
      "remote: Counting objects: 100% (274/274), done.        \n",
      "remote: Compressing objects: 100% (229/229), done.        \n",
      "remote: Total 14267 (delta 124), reused 173 (delta 45), pack-reused 13993        \n",
      "Receiving objects: 100% (14267/14267), 9.50 MiB | 25.08 MiB/s, done.\n",
      "Resolving deltas: 100% (10802/10802), done.\n",
      "Cloning into '/content/LightGBM/external_libs/fast_double_parser/benchmarks/dependencies/double-conversion'...\n",
      "remote: Enumerating objects: 1220, done.        \n",
      "remote: Counting objects: 100% (64/64), done.        \n",
      "remote: Compressing objects: 100% (41/41), done.        \n",
      "remote: Total 1220 (delta 35), reused 40 (delta 23), pack-reused 1156        \n",
      "Receiving objects: 100% (1220/1220), 7.08 MiB | 27.14 MiB/s, done.\n",
      "Resolving deltas: 100% (807/807), done.\n",
      "Submodule path 'external_libs/fast_double_parser/benchmarks/dependencies/abseil-cpp': checked out 'd936052d32a5b7ca08b0199a6724724aea432309'\n",
      "Submodule path 'external_libs/fast_double_parser/benchmarks/dependencies/double-conversion': checked out 'f4cb2384efa55dee0e6652f8674b05763441ab09'\n",
      "Submodule path 'external_libs/fmt': checked out 'cc09f1a6798c085c325569ef466bcdcffdc266d4'\n"
     ]
    }
   ],
   "source": [
    "!git clone --recursive https://github.com/Microsoft/LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jpaoaDEoYPjg",
    "outputId": "c72714d8-eae9-4030-8e01-1100b87dcb9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- The C compiler identification is GNU 7.5.0\n",
      "-- The CXX compiler identification is GNU 7.5.0\n",
      "-- Check for working C compiler: /usr/bin/cc\n",
      "-- Check for working C compiler: /usr/bin/cc -- works\n",
      "-- Detecting C compiler ABI info\n",
      "-- Detecting C compiler ABI info - done\n",
      "-- Detecting C compile features\n",
      "-- Detecting C compile features - done\n",
      "-- Check for working CXX compiler: /usr/bin/c++\n",
      "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
      "-- Detecting CXX compiler ABI info\n",
      "-- Detecting CXX compiler ABI info - done\n",
      "-- Detecting CXX compile features\n",
      "-- Detecting CXX compile features - done\n",
      "-- Found OpenMP_C: -fopenmp (found version \"4.5\") \n",
      "-- Found OpenMP_CXX: -fopenmp (found version \"4.5\") \n",
      "-- Found OpenMP: TRUE (found version \"4.5\")  \n",
      "-- Looking for CL_VERSION_2_2\n",
      "-- Looking for CL_VERSION_2_2 - found\n",
      "-- Found OpenCL: /usr/lib/x86_64-linux-gnu/libOpenCL.so (found version \"2.2\") \n",
      "-- OpenCL include directory: /usr/include\n",
      "-- Boost version: 1.65.1\n",
      "-- Found the following Boost libraries:\n",
      "--   filesystem\n",
      "--   system\n",
      "-- Performing Test MM_PREFETCH\n",
      "-- Performing Test MM_PREFETCH - Success\n",
      "-- Using _mm_prefetch\n",
      "-- Performing Test MM_MALLOC\n",
      "-- Performing Test MM_MALLOC - Success\n",
      "-- Using _mm_malloc\n",
      "-- Configuring done\n",
      "-- Generating done\n",
      "-- Build files have been written to: /content/LightGBM/build\n",
      "\u001b[35m\u001b[1mScanning dependencies of target _lightgbm\u001b[0m\n",
      "\u001b[35m\u001b[1mScanning dependencies of target lightgbm\u001b[0m\n",
      "[  1%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/main.cpp.o\u001b[0m\n",
      "[  4%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/boosting/boosting.cpp.o\u001b[0m\n",
      "[  4%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/boosting/gbdt.cpp.o\u001b[0m\n",
      "[  5%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/application/application.cpp.o\u001b[0m\n",
      "[  7%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/boosting/gbdt_model_text.cpp.o\u001b[0m\n",
      "[  8%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/boosting/boosting.cpp.o\u001b[0m\n",
      "[ 10%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/boosting/gbdt_prediction.cpp.o\u001b[0m\n",
      "[ 11%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/boosting/prediction_early_stop.cpp.o\u001b[0m\n",
      "[ 13%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/bin.cpp.o\u001b[0m\n",
      "[ 14%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/config.cpp.o\u001b[0m\n",
      "[ 15%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/config_auto.cpp.o\u001b[0m\n",
      "[ 17%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/dataset.cpp.o\u001b[0m\n",
      "[ 18%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/boosting/gbdt.cpp.o\u001b[0m\n",
      "[ 20%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/dataset_loader.cpp.o\u001b[0m\n",
      "[ 21%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/boosting/gbdt_model_text.cpp.o\u001b[0m\n",
      "[ 23%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/boosting/gbdt_prediction.cpp.o\u001b[0m\n",
      "[ 24%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/file_io.cpp.o\u001b[0m\n",
      "[ 26%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/json11.cpp.o\u001b[0m\n",
      "[ 27%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/boosting/prediction_early_stop.cpp.o\u001b[0m\n",
      "[ 28%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/bin.cpp.o\u001b[0m\n",
      "[ 30%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/metadata.cpp.o\u001b[0m\n",
      "[ 31%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/config.cpp.o\u001b[0m\n",
      "[ 33%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/parser.cpp.o\u001b[0m\n",
      "[ 34%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/train_share_states.cpp.o\u001b[0m\n",
      "[ 36%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/config_auto.cpp.o\u001b[0m\n",
      "[ 37%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/io/tree.cpp.o\u001b[0m\n",
      "[ 39%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/metric/dcg_calculator.cpp.o\u001b[0m\n",
      "[ 40%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/dataset.cpp.o\u001b[0m\n",
      "[ 42%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/metric/metric.cpp.o\u001b[0m\n",
      "[ 43%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/network/ifaddrs_patch.cpp.o\u001b[0m\n",
      "[ 44%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/network/linker_topo.cpp.o\u001b[0m\n",
      "[ 46%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/network/linkers_mpi.cpp.o\u001b[0m\n",
      "[ 47%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/network/linkers_socket.cpp.o\u001b[0m\n",
      "[ 49%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/dataset_loader.cpp.o\u001b[0m\n",
      "[ 50%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/network/network.cpp.o\u001b[0m\n",
      "[ 52%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/file_io.cpp.o\u001b[0m\n",
      "[ 53%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/json11.cpp.o\u001b[0m\n",
      "[ 55%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/objective/objective_function.cpp.o\u001b[0m\n",
      "[ 56%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/cuda_tree_learner.cpp.o\u001b[0m\n",
      "[ 57%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/data_parallel_tree_learner.cpp.o\u001b[0m\n",
      "[ 59%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/metadata.cpp.o\u001b[0m\n",
      "[ 60%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/parser.cpp.o\u001b[0m\n",
      "[ 62%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/train_share_states.cpp.o\u001b[0m\n",
      "[ 63%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/io/tree.cpp.o\u001b[0m\n",
      "[ 65%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/metric/dcg_calculator.cpp.o\u001b[0m\n",
      "[ 66%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/feature_parallel_tree_learner.cpp.o\u001b[0m\n",
      "[ 68%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/metric/metric.cpp.o\u001b[0m\n",
      "[ 69%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/gpu_tree_learner.cpp.o\u001b[0m\n",
      "[ 71%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/network/ifaddrs_patch.cpp.o\u001b[0m\n",
      "[ 72%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/network/linker_topo.cpp.o\u001b[0m\n",
      "[ 73%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/linear_tree_learner.cpp.o\u001b[0m\n",
      "[ 75%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/network/linkers_mpi.cpp.o\u001b[0m\n",
      "[ 76%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/network/linkers_socket.cpp.o\u001b[0m\n",
      "[ 78%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/network/network.cpp.o\u001b[0m\n",
      "[ 79%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/objective/objective_function.cpp.o\u001b[0m\n",
      "[ 81%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/cuda_tree_learner.cpp.o\u001b[0m\n",
      "[ 82%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/data_parallel_tree_learner.cpp.o\u001b[0m\n",
      "[ 84%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/serial_tree_learner.cpp.o\u001b[0m\n",
      "[ 85%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/feature_parallel_tree_learner.cpp.o\u001b[0m\n",
      "[ 86%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/tree_learner.cpp.o\u001b[0m\n",
      "[ 88%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/gpu_tree_learner.cpp.o\u001b[0m\n",
      "[ 89%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/linear_tree_learner.cpp.o\u001b[0m\n",
      "[ 91%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/treelearner/voting_parallel_tree_learner.cpp.o\u001b[0m\n",
      "[ 92%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/serial_tree_learner.cpp.o\u001b[0m\n",
      "[ 94%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/tree_learner.cpp.o\u001b[0m\n",
      "[ 95%] \u001b[32mBuilding CXX object CMakeFiles/_lightgbm.dir/src/c_api.cpp.o\u001b[0m\n",
      "[ 97%] \u001b[32mBuilding CXX object CMakeFiles/lightgbm.dir/src/treelearner/voting_parallel_tree_learner.cpp.o\u001b[0m\n",
      "[ 98%] \u001b[32m\u001b[1mLinking CXX shared library ../lib_lightgbm.so\u001b[0m\n",
      "[ 98%] Built target _lightgbm\n",
      "[100%] \u001b[32m\u001b[1mLinking CXX executable ../lightgbm\u001b[0m\n",
      "[100%] Built target lightgbm\n",
      "running install\n",
      "running build\n",
      "running build_py\n",
      "INFO:root:Generating grammar tables from /usr/lib/python3.7/lib2to3/Grammar.txt\n",
      "INFO:root:Generating grammar tables from /usr/lib/python3.7/lib2to3/PatternGrammar.txt\n",
      "creating build\n",
      "creating build/lib\n",
      "creating build/lib/lightgbm\n",
      "copying lightgbm/libpath.py -> build/lib/lightgbm\n",
      "copying lightgbm/sklearn.py -> build/lib/lightgbm\n",
      "copying lightgbm/engine.py -> build/lib/lightgbm\n",
      "copying lightgbm/callback.py -> build/lib/lightgbm\n",
      "copying lightgbm/compat.py -> build/lib/lightgbm\n",
      "copying lightgbm/__init__.py -> build/lib/lightgbm\n",
      "copying lightgbm/plotting.py -> build/lib/lightgbm\n",
      "copying lightgbm/basic.py -> build/lib/lightgbm\n",
      "copying lightgbm/dask.py -> build/lib/lightgbm\n",
      "running egg_info\n",
      "creating lightgbm.egg-info\n",
      "writing lightgbm.egg-info/PKG-INFO\n",
      "writing dependency_links to lightgbm.egg-info/dependency_links.txt\n",
      "writing requirements to lightgbm.egg-info/requires.txt\n",
      "writing top-level names to lightgbm.egg-info/top_level.txt\n",
      "writing manifest file 'lightgbm.egg-info/SOURCES.txt'\n",
      "reading manifest template 'MANIFEST.in'\n",
      "no previously-included directories found matching 'build'\n",
      "warning: no files found matching 'LICENSE'\n",
      "warning: no files found matching '*.txt'\n",
      "warning: no files found matching '*.so' under directory 'lightgbm'\n",
      "warning: no files found matching 'compile/CMakeLists.txt'\n",
      "warning: no files found matching 'compile/cmake/IntegratedOpenCL.cmake'\n",
      "warning: no files found matching '*.so' under directory 'compile'\n",
      "warning: no files found matching '*.dll' under directory 'compile/Release'\n",
      "warning: no files found matching 'compile/external_libs/compute/CMakeLists.txt'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/compute/cmake'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/compute/include'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/compute/meta'\n",
      "warning: no files found matching 'compile/external_libs/eigen/CMakeLists.txt'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/Cholesky'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/Core'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/Dense'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/Eigenvalues'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/Geometry'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/Householder'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/Jacobi'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/LU'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/QR'\n",
      "warning: no files found matching 'compile/external_libs/eigen/Eigen/SVD'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/Cholesky'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/Core'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/Eigenvalues'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/Geometry'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/Householder'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/Jacobi'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/LU'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/misc'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/plugins'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/QR'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/eigen/Eigen/src/SVD'\n",
      "warning: no files found matching 'compile/external_libs/fast_double_parser/CMakeLists.txt'\n",
      "warning: no files found matching 'compile/external_libs/fast_double_parser/LICENSE'\n",
      "warning: no files found matching 'compile/external_libs/fast_double_parser/LICENSE.BSL'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/fast_double_parser/include'\n",
      "warning: no files found matching 'compile/external_libs/fmt/CMakeLists.txt'\n",
      "warning: no files found matching 'compile/external_libs/fmt/LICENSE.rst'\n",
      "warning: no files found matching '*' under directory 'compile/external_libs/fmt/include'\n",
      "warning: no files found matching '*' under directory 'compile/include'\n",
      "warning: no files found matching '*' under directory 'compile/src'\n",
      "warning: no files found matching 'LightGBM.sln' under directory 'compile/windows'\n",
      "warning: no files found matching 'LightGBM.vcxproj' under directory 'compile/windows'\n",
      "warning: no files found matching '*.dll' under directory 'compile/windows/x64/DLL'\n",
      "warning: no previously-included files matching '*.py[co]' found anywhere in distribution\n",
      "warning: no previously-included files found matching 'compile/external_libs/compute/.git'\n",
      "writing manifest file 'lightgbm.egg-info/SOURCES.txt'\n",
      "copying lightgbm/VERSION.txt -> build/lib/lightgbm\n",
      "running install_lib\n",
      "copying build/lib/lightgbm/libpath.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/sklearn.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/engine.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/callback.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/compat.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/__init__.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/plotting.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/basic.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/dask.py -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "copying build/lib/lightgbm/VERSION.txt -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "INFO:LightGBM:Installing lib_lightgbm from: ['../lib_lightgbm.so']\n",
      "copying ../lib_lightgbm.so -> /usr/local/lib/python3.7/dist-packages/lightgbm\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/libpath.py to libpath.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/sklearn.py to sklearn.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/engine.py to engine.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/callback.py to callback.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/compat.py to compat.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/__init__.py to __init__.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/plotting.py to plotting.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/basic.py to basic.cpython-37.pyc\n",
      "byte-compiling /usr/local/lib/python3.7/dist-packages/lightgbm/dask.py to dask.cpython-37.pyc\n",
      "running install_egg_info\n",
      "Copying lightgbm.egg-info to /usr/local/lib/python3.7/dist-packages/lightgbm-3.2.1.99-py3.7.egg-info\n",
      "running install_scripts\n"
     ]
    }
   ],
   "source": [
    "!cd LightGBM && rm -rf build && mkdir build && cd build && cmake -DUSE_GPU=1 ../../LightGBM && make -j4 && cd ../python-package && python3 setup.py install --precompile --gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "FcCAt5bPd5Yd"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import time, pickle\n",
    "\n",
    "from zipfile import ZipFile\n",
    "\n",
    "#Preprocessing, Upsampling, Model Selection, Model Evaluation\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, precision_score, recall_score \n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve\n",
    "from imblearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.model_selection import cross_val_predict, cross_val_score, learning_curve, cross_validate\n",
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3ap0DyoGYC7v",
    "outputId": "f68fb99a-200d-4246-affc-0fb283517fdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "eRzq7BAV56Ep"
   },
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams.update(\n",
    "    {\n",
    "     'xtick.labelsize':15,\n",
    "     'ytick.labelsize':15,\n",
    "     'axes.labelsize': 15,\n",
    "     'legend.fontsize': 15,\n",
    "     'axes.titlesize':15,\n",
    "     'axes.titleweight':'bold',\n",
    "     'axes.titleweight':'bold'\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mAtfS7B0iIi1",
    "outputId": "62148908-8786-4e51-cc52-d1a108c5f0c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "#Download files from google drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive', force_remount=True)\n",
    "#Second solution >>> import files from your computer\n",
    "#from google.colab import files\n",
    "#uploaded = files.upload()\n",
    "#for fn in uploaded.keys():\n",
    "#  print('Vous avez importé le fichier \"{name}\" qui a une taille de {length} bytes'.format(name=fn, length=len(uploaded[fn])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vUCUuqDYH6BH",
    "outputId": "fd3041dc-acf8-4d50-d0c5-c5e531a7a60f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 309)"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = ZipFile(\"/content/gdrive/My Drive/Colab Notebooks/p7_00_data/data_train.zip\")\n",
    "data_train = pd.read_csv(z.open('data_train.csv'), index_col='SK_ID_CURR', encoding ='utf-8')\n",
    "#data_train.drop('SK_ID_CURR', axis=1, inplace=True)\n",
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4-GgW4AH2hKy",
    "outputId": "6a3e3021-7a1f-4c1c-849d-b88443c5c9da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGET = pd.read_csv('/content/gdrive/My Drive/Colab Notebooks/p7_00_data/TARGET.csv', index_col='SK_ID_CURR')\n",
    "TARGET.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "TqxlfMmQjTZg"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_train.values, TARGET.values, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gWmEdoiWH6Gg",
    "outputId": "8beab4ea-b589-4157-8b87-4f0afa74b291"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((215257, 309), (92254, 309), (215257, 1), (92254, 1))"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wFF4JmwlzMue"
   },
   "source": [
    "### **Baseline model - Logistic Regression**\n",
    "Le projet consiste à mieux comprendre le défaut de crédit rencontré chez certain client, l'anticiper, l'estimer, etc… La prédiction peut se résumer par une sortie binaire, une acceptation de crédit, ou un refus de crédit. Afin d'avoir une première idée des performances possibles, la modélisation par régression logistique permettra d'obtenir une baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "VRLim5W-4v5X"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "QuA43h6OFUlF"
   },
   "outputs": [],
   "source": [
    "def cf_matrix_roc_auc(model, y_true, y_pred, y_pred_proba, feature_importances):\n",
    "    '''This function will make a pretty plot of \n",
    "  an sklearn Confusion Matrix using a Seaborn heatmap visualization + ROC Curve.'''\n",
    "    fig = plt.figure(figsize=(20,15))\n",
    "  \n",
    "    plt.subplot(221)\n",
    "    cf_matrix = confusion_matrix(y_true, y_pred)\n",
    "    group_names = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n",
    "    group_counts = [\"{0:0.0f}\".format(value) for value in cf_matrix.flatten()]\n",
    "    group_percentages = [\"{0:.2%}\".format(value) for value in cf_matrix.flatten()/np.sum(cf_matrix)]\n",
    "  \n",
    "    labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in zip(group_names,group_counts,group_percentages)]\n",
    "    labels = np.asarray(labels).reshape(2,2)\n",
    "    sns.heatmap(cf_matrix, annot=labels, fmt=\"\", cmap='Blues')\n",
    "\n",
    "    plt.subplot(222)\n",
    "    fpr,tpr,_ = roc_curve(y_true, y_pred_proba)\n",
    "    plt.plot(fpr, tpr, color='orange', linewidth=5, label='AUC = %0.4f' %roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend()\n",
    "\n",
    "    if feature_importances : \n",
    "        plt.subplot(212)\n",
    "        indices = np.argsort(model.feature_importances_)[::-1]\n",
    "    \n",
    "    features = []\n",
    "    for i in range(20):\n",
    "        features.append(X_data.columns[indices[i]]) #After RFECV selection > X_data created\n",
    "\n",
    "    sns.barplot(x=features, y=model.feature_importances_[indices[range(20)]], color=(\"orange\"))\n",
    "    plt.xlabel('Features importance')\n",
    "    plt.xticks(rotation=90)\n",
    "\n",
    "    show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nZ2uB6NqzMDs",
    "outputId": "bda1acb3-2f18-4549-9bb1-ae8d22745db1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC : 0.6939\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      1.00      0.96     84841\n",
      "         1.0       0.00      0.00      0.00      7413\n",
      "\n",
      "    accuracy                           0.92     92254\n",
      "   macro avg       0.46      0.50      0.48     92254\n",
      "weighted avg       0.85      0.92      0.88     92254\n",
      "\n",
      "CPU times: user 7.72 s, sys: 2.47 s, total: 10.2 s\n",
      "Wall time: 2.72 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lr = LogisticRegression(C = 0.0001, max_iter=100)\n",
    "lr.fit(X_train, y_train)\n",
    "roc_auc = roc_auc_score(y_test, lr.predict_proba(X_test)[:,1])\n",
    "print('AUC : %0.4f' %roc_auc)\n",
    "print(classification_report(y_test, lr.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "09Kru11SAcwP",
    "outputId": "7088f0a7-ef59-454d-e86b-1e0c7feb6e2c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix_roc_auc(lr, y_test, lr.predict(X_test), lr.predict_proba(X_test)[:,1], feature_importances=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZlY_LNvY04kT"
   },
   "source": [
    "Notons un fort déséquilibre entre la précision trouvée pour la Target 0 (0.92) et la Target 1 (0). Pour rappel, l'échantillon de travail n'est pas équilibré, avec 92% des individus classés en modalité 0 et 8% en modalité 1, à savoir en défaut de paiement de crédit..\n",
    "\n",
    "Il est donc intéressant de travailler cet Oversampling (ou suréchantillonnage en français)  en ajustant la distribution de classe de manière à avoir une répartition plus égalitaire."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1-zbJZKWDz4F"
   },
   "source": [
    "**Oversampling Data Using SMOTE**\n",
    "\n",
    "SMOTE with Imbalance Data using imblearn module\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VhDUWWlneY-l",
    "outputId": "9d44888e-aa43-4167-aec7-83e32ca6fdad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 1, Before using SMOTE: 17412 \n",
      "Label 0, Before using SMOTE: 197845 \n"
     ]
    }
   ],
   "source": [
    "print(\"Label 1, Before using SMOTE: {} \".format(sum(y_train==1)))\n",
    "print(\"Label 0, Before using SMOTE: {} \".format(sum(y_train==0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2edxsKAUE7vW",
    "outputId": "08039066-3542-4fbc-dd6b-dfaec0d5ce7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 9s, sys: 578 ms, total: 3min 10s\n",
      "Wall time: 3min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sm = SMOTE(random_state=2)\n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-P-nq06IFIMx",
    "outputId": "1dd70224-ce88-420c-9c69-5368d7f0f1f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 1, After using SMOTE: 197845\n",
      "Label 0, After using SMOTE: 197845\n"
     ]
    }
   ],
   "source": [
    "print(\"Label 1, After using SMOTE: {}\".format(sum(y_train_res==1)))\n",
    "print(\"Label 0, After using SMOTE: {}\".format(sum(y_train_res==0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OQkJYYiM04_N",
    "outputId": "5499e5ca-1e54-40b8-ba65-d6e98f1386d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC : 0.7196\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.96      0.67      0.79     84841\n",
      "         1.0       0.15      0.65      0.24      7413\n",
      "\n",
      "    accuracy                           0.67     92254\n",
      "   macro avg       0.55      0.66      0.52     92254\n",
      "weighted avg       0.89      0.67      0.75     92254\n",
      "\n",
      "CPU times: user 31.3 s, sys: 8.46 s, total: 39.7 s\n",
      "Wall time: 10.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lr = LogisticRegression(C = 0.0001, max_iter=100)\n",
    "lr.fit(X_train_res, y_train_res)\n",
    "roc_auc = roc_auc_score(y_test, lr.predict_proba(X_test)[:,1])\n",
    "print('AUC : %0.4f' %roc_auc)\n",
    "print(classification_report(y_test, lr.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "AlhqrHPix_7L",
    "outputId": "7cdc8918-e7c7-4793-cf6b-698e3c406bd7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix_roc_auc(lr, y_test, lr.predict(X_test), lr.predict_proba(X_test)[:,1], feature_importances=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UOhAkUVq1amv"
   },
   "source": [
    "Amélioration de l'AUC score 0.72 que nous prendrons comme baseline de travail pour la suite des essais…"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QIS3w7YO5y8P"
   },
   "source": [
    "### **Gradient Boosting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "9rmnaXYP4spE"
   },
   "outputs": [],
   "source": [
    "#Predictive Models\n",
    "from catboost import CatBoostClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "models = [\n",
    "          CatBoostClassifier(task_type=\"GPU\", logging_level='Silent'),\n",
    "          LGBMClassifier(device='gpu'),\n",
    "          XGBClassifier(tree_method='gpu_hist', predictor='gpu_predictor')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zSfdzezV8X-e"
   },
   "source": [
    "La fonction suivante ``train_models`` peut calculer les métriques auc, accuracy, f1, precision et recall. On peut la modifier selon les besoins attendus, il suffit de se référer aux [Scoring de Classification de Scikit-learn](https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "Ld6cQvzVzJpw"
   },
   "outputs": [],
   "source": [
    "def train_models(model, X_train, X_test, y_train, y_test):\n",
    "    model.fit(X_train, y_train)\n",
    "    output = {\n",
    "      'AUC': roc_auc_score(y_test, model.predict_proba(X_test)[:,1]),\n",
    "      'Accuracy': accuracy_score(y_test, model.predict(X_test)),\n",
    "      'Precision': precision_score(y_test, model.predict(X_test)),\n",
    "      'Recall': recall_score(y_test, model.predict(X_test)),\n",
    "      'F1': f1_score(y_test, model.predict(X_test))\n",
    "      }\n",
    "          \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O4ZPumZ-4sGV",
    "outputId": "1c04872c-3db6-4d3b-c754-e3d4bf5fe5f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 48s, sys: 9.56 s, total: 2min 57s\n",
      "Wall time: 2min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "name = []\n",
    "auc = []\n",
    "accuracy = []\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "time_ = []\n",
    "\n",
    "for model in models:\n",
    "    start = time.time()\n",
    "    results = train_models(model, X_train, X_test, y_train, y_test)\n",
    "\n",
    "    name.append(type(model).__name__)\n",
    "    auc.append(results['AUC'])\n",
    "    accuracy.append(results['Accuracy'])\n",
    "    precision.append(results['Precision'])\n",
    "    recall.append(results['Recall'])\n",
    "    f1.append(results['F1'])\n",
    "    time_.append(time.time()-start)\n",
    "\n",
    "#Initialise data of lists\n",
    "base_models = pd.DataFrame(data=[name, auc, accuracy, precision, recall, f1, time_]).T\n",
    "base_models.columns = ['Model', 'AUC', 'Accuracy', 'Precision', 'Recall', 'F1', 'Time']\n",
    "base_models.sort_values('AUC', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "9Xmh1ypfmZxG",
    "outputId": "113291d4-b851-4462-87e5-5ff3fb3fd2b6",
    "scrolled": true
   },
   "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>Model</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.773782</td>\n",
       "      <td>0.920264</td>\n",
       "      <td>0.563758</td>\n",
       "      <td>0.0339943</td>\n",
       "      <td>0.0641221</td>\n",
       "      <td>10.7289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatBoostClassifier</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>0.920144</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.0279239</td>\n",
       "      <td>0.0532065</td>\n",
       "      <td>133.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.764208</td>\n",
       "      <td>0.919841</td>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.00768919</td>\n",
       "      <td>0.0151818</td>\n",
       "      <td>4.2202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model       AUC  Accuracy  ...      Recall         F1     Time\n",
       "1      LGBMClassifier  0.773782  0.920264  ...   0.0339943  0.0641221  10.7289\n",
       "0  CatBoostClassifier  0.773559  0.920144  ...   0.0279239  0.0532065  133.957\n",
       "2       XGBClassifier  0.764208  0.919841  ...  0.00768919  0.0151818   4.2202\n",
       "\n",
       "[3 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LZ_HdbsKwEDi"
   },
   "source": [
    "Nous allons comparer une nouvelle fois nos modèles, cette fois-ci l'entrainement se fera avec les données SMOTE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "27GCw8GmwbE9",
    "outputId": "2df5c145-9ad6-4a07-fac9-e7c01941ffc3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 6s, sys: 17.3 s, total: 2min 23s\n",
      "Wall time: 2min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "name = []\n",
    "auc = []\n",
    "accuracy = []\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "time_ = []\n",
    "\n",
    "for model in models:\n",
    "    start = time.time()\n",
    "    results = train_models(model, X_train_res, X_test, y_train_res, y_test)\n",
    "\n",
    "    name.append(type(model).__name__)\n",
    "    auc.append(results['AUC'])\n",
    "    accuracy.append(results['Accuracy'])\n",
    "    precision.append(results['Precision'])\n",
    "    recall.append(results['Recall'])\n",
    "    f1.append(results['F1'])\n",
    "    time_.append(time.time()-start)\n",
    "\n",
    "#Initialise data of lists\n",
    "models_res = pd.DataFrame(data=[name, auc, accuracy, precision, recall, f1, time_]).T        \n",
    "models_res.columns = ['Model', 'AUC', 'Accuracy', 'Precision', 'Recall', 'F1', 'Time']\n",
    "models_res.sort_values('AUC', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "29sWWyjwwgi4",
    "outputId": "0cf88d10-828b-4146-a826-8188ae170517"
   },
   "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>Model</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatBoostClassifier</td>\n",
       "      <td>0.760364</td>\n",
       "      <td>0.919592</td>\n",
       "      <td>0.493703</td>\n",
       "      <td>0.02644</td>\n",
       "      <td>0.0501921</td>\n",
       "      <td>94.3644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.750893</td>\n",
       "      <td>0.918789</td>\n",
       "      <td>0.389356</td>\n",
       "      <td>0.0187508</td>\n",
       "      <td>0.0357786</td>\n",
       "      <td>23.3849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.697457</td>\n",
       "      <td>0.912535</td>\n",
       "      <td>0.257396</td>\n",
       "      <td>0.0469446</td>\n",
       "      <td>0.0794067</td>\n",
       "      <td>5.1259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model       AUC  Accuracy  ...     Recall         F1     Time\n",
       "0  CatBoostClassifier  0.760364  0.919592  ...    0.02644  0.0501921  94.3644\n",
       "1      LGBMClassifier  0.750893  0.918789  ...  0.0187508  0.0357786  23.3849\n",
       "2       XGBClassifier  0.697457  0.912535  ...  0.0469446  0.0794067   5.1259\n",
       "\n",
       "[3 rows x 7 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PaBU-AUMwoer"
   },
   "source": [
    "LGBMClassifier reste le \"plus performant\" selon le couple métrique/temps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bFPMsrVvxtG-"
   },
   "source": [
    "### **Feature Selection - Recursive Feature Elimination**\n",
    "Maintenant que nous avons testé ces 3 modèles, on peut effectuer cette étape avec ``LGBMClassifier`` qui permet d'obtenir les meilleurs performances AUC score / Time. À ce stade, nos ensembles de données contiennent 309 features, dont beaucoup peuvent ne pas contenir d'informations utiles. RFECV avec Scikit-learn appliquera une validation croisée pour trouver l'ensemble des features optimal qui maximisera nos performances. Le but est donc d'optimiser la métrique AUC tout en éliminant les features les moins importantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PKTdQaeiwi6o",
    "outputId": "05080071-feab-49ab-8627-02bab2fc0954"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 309 features.\n",
      "Fitting estimator with 304 features.\n",
      "Fitting estimator with 299 features.\n",
      "Fitting estimator with 294 features.\n",
      "Fitting estimator with 289 features.\n",
      "Fitting estimator with 284 features.\n",
      "Fitting estimator with 279 features.\n",
      "Fitting estimator with 274 features.\n",
      "Fitting estimator with 269 features.\n",
      "Fitting estimator with 264 features.\n",
      "Fitting estimator with 259 features.\n",
      "Fitting estimator with 254 features.\n",
      "Fitting estimator with 249 features.\n",
      "Fitting estimator with 244 features.\n",
      "Fitting estimator with 239 features.\n",
      "Fitting estimator with 234 features.\n",
      "Fitting estimator with 229 features.\n",
      "Fitting estimator with 224 features.\n",
      "Fitting estimator with 219 features.\n",
      "Fitting estimator with 214 features.\n",
      "Fitting estimator with 209 features.\n",
      "Fitting estimator with 204 features.\n",
      "Fitting estimator with 199 features.\n",
      "Fitting estimator with 194 features.\n",
      "Fitting estimator with 189 features.\n",
      "Fitting estimator with 184 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 174 features.\n",
      "Fitting estimator with 169 features.\n",
      "Fitting estimator with 164 features.\n",
      "Fitting estimator with 159 features.\n",
      "Fitting estimator with 154 features.\n",
      "Time Elapsed: 2200.2918679714203\n",
      "CPU times: user 8min 44s, sys: 2.69 s, total: 8min 47s\n",
      "Wall time: 36min 40s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start = time.time()\n",
    "rfecv = RFECV(estimator=LGBMClassifier(objective='binary'), \n",
    "              step=5, \n",
    "              cv=StratifiedKFold(5), \n",
    "              scoring='roc_auc', \n",
    "              verbose=2,\n",
    "              n_jobs=-1)\n",
    "rfecv.fit(X_train, y_train)\n",
    "\n",
    "print('Time Elapsed: {}'.format(time.time()-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mSoFy-29xvlT",
    "outputId": "e248d353-8cb1-4b33-d00f-fad41376b1cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 149\n",
      "Selected Features: [ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True False False  True False False False]\n",
      "Feature Ranking : [ 1  1  1  1  1  1  1  1  1  1  1  1  1  1 14  3  1 12  3  4]\n"
     ]
    }
   ],
   "source": [
    "print(\"Optimal number of features : %d\" % rfecv.n_features_)\n",
    "print(\"Selected Features: %s\" % rfecv.support_[:20])\n",
    "print(\"Feature Ranking : %s\" % rfecv.ranking_[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-pF0FqEA6pNH"
   },
   "source": [
    "RFECV conserve les features avec un Rank 1 > True. Ci-dessous une vérification de la sélection de Rank 1…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b7Th2Tyd6itu",
    "outputId": "2f53eb14-c974-415e-db44-6929b27c6183"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149\n",
      "['NAME_CONTRACT_TYPE', 'PREV_APPL_MEAN_INSTALL_MEAN_NUM_INSTALMENT_NUMBER', 'PREV_APPL_MEAN_INSTALL_MEAN_NUM_INSTALMENT_VERSION', 'PREV_APPL_MEAN_CARD_MEAN_SK_DPD_DEF', 'PREV_APPL_MEAN_CARD_MEAN_SK_DPD', 'PREV_APPL_MEAN_CARD_MEAN_CNT_INSTALMENT_MATURE_CUM', 'PREV_APPL_MEAN_CARD_MEAN_CNT_DRAWINGS_POS_CURRENT', 'AMT_REQ_CREDIT_BUREAU_DAY', 'PREV_APPL_MEAN_CARD_MEAN_CNT_DRAWINGS_CURRENT', 'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'PREV_APPL_MEAN_CARD_MEAN_CNT_DRAWINGS_ATM_CURRENT', 'PREVIOUS_LOANS_COUNT', 'PREV_BUR_MEAN_DAYS_CREDIT', 'PREV_APPL_MEAN_CARD_MEAN_AMT_RECIVABLE', 'PREV_APPL_MEAN_INSTALL_MEAN_DAYS_INSTALMENT', 'PREV_BUR_MEAN_DAYS_CREDIT_ENDDATE', 'PREV_APPL_MEAN_INSTALL_MEAN_DAYS_ENTRY_PAYMENT', 'PREV_APPL_MEAN_INSTALL_MEAN_AMT_PAYMENT', 'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE', 'NAME_EDUCATION_TYPE_Secondary / secondary special', 'FLAG_DOCUMENT_3', 'NAME_EDUCATION_TYPE_Higher education', 'NAME_INCOME_TYPE_Working', 'NAME_INCOME_TYPE_State servant', 'CODE_GENDER_M', 'CODE_GENDER_F', 'DAYS_EMPLOYED_PERCENT', 'CREDIT_TERM', 'ANNUITY_INCOME_PERCENT', 'CREDIT_INCOME_PERCENT', 'PREV_APPL_MEAN_INSTALL_MEAN_AMT_INSTALMENT', 'OBS_30_CNT_SOCIAL_CIRCLE', 'PREV_BUR_MEAN_DAYS_ENDDATE_FACT', 'PREV_BUR_MEAN_CNT_CREDIT_PROLONG', 'PREV_APPL_MEAN_SELLERPLACE_AREA', 'PREV_APPL_MEAN_CNT_PAYMENT', 'PREV_APPL_MEAN_DAYS_FIRST_DRAWING', 'PREV_APPL_MEAN_DAYS_FIRST_DUE', 'PREV_APPL_MEAN_DAYS_LAST_DUE_1ST_VERSION', 'PREV_APPL_MEAN_DAYS_LAST_DUE', 'PREV_APPL_MEAN_DAYS_TERMINATION', 'PREV_APPL_MEAN_NFLAG_INSURED_ON_APPROVAL', 'PREV_APPL_MEAN_CARD_MEAN_MONTHS_BALANCE', 'PREV_APPL_MEAN_CARD_MEAN_AMT_BALANCE', 'PREV_APPL_MEAN_CARD_MEAN_AMT_CREDIT_LIMIT_ACTUAL', 'PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_ATM_CURRENT', 'PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_CURRENT', 'PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_OTHER_CURRENT', 'PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_POS_CURRENT', 'PREV_APPL_MEAN_DAYS_DECISION', 'PREV_BUR_MEAN_AMT_CREDIT_MAX_OVERDUE', 'PREV_APPL_MEAN_CARD_MEAN_AMT_PAYMENT_TOTAL_CURRENT', 'PREV_APPL_MEAN_RATE_DOWN_PAYMENT', 'PREV_BUR_MEAN_AMT_CREDIT_SUM', 'PREV_BUR_MEAN_AMT_CREDIT_SUM_DEBT', 'PREV_BUR_MEAN_AMT_CREDIT_SUM_LIMIT', 'PREV_BUR_MEAN_AMT_CREDIT_SUM_OVERDUE', 'PREV_BUR_MEAN_DAYS_CREDIT_UPDATE', 'PREV_BUR_MEAN_AMT_ANNUITY', 'PREV_BUR_MEAN_MONTHS_BALANCE_MEAN', 'PREVIOUS_APPLICATION_COUNT', 'PREV_APPL_MEAN_AMT_ANNUITY', 'PREV_APPL_MEAN_AMT_APPLICATION', 'PREV_APPL_MEAN_AMT_CREDIT', 'PREV_APPL_MEAN_AMT_DOWN_PAYMENT', 'PREV_APPL_MEAN_AMT_GOODS_PRICE', 'PREV_APPL_MEAN_HOUR_APPR_PROCESS_START', 'PREV_APPL_MEAN_NFLAG_LAST_APPL_IN_DAY', 'PREV_APPL_MEAN_CARD_MEAN_AMT_RECEIVABLE_PRINCIPAL', 'PREV_APPL_MEAN_CARD_MEAN_AMT_INST_MIN_REGULARITY', 'TOTALAREA_MODE', 'NAME_FAMILY_STATUS_Married', 'ORGANIZATION_TYPE_Services', 'REGION_RATING_CLIENT_W_CITY', 'HOUR_APPR_PROCESS_START', 'ORGANIZATION_TYPE_Self-employed', 'ORGANIZATION_TYPE_Security Ministries', 'ORGANIZATION_TYPE_School', 'CNT_FAM_MEMBERS', 'REG_CITY_NOT_LIVE_CITY', 'ORGANIZATION_TYPE_Realtor', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'ORGANIZATION_TYPE_Restaurant', 'ORGANIZATION_TYPE_Transport: type 3', 'ORGANIZATION_TYPE_Transport: type 4', 'FLAG_WORK_PHONE', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OWN_CAR_AGE', 'WALLSMATERIAL_MODE_Panel', 'WALLSMATERIAL_MODE_Others', 'YEARS_BEGINEXPLUATATION_AVG', 'NONLIVINGAREA_MEDI', 'YEARS_BUILD_AVG', 'ORGANIZATION_TYPE_Police', 'LIVINGAREA_MODE', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI', 'OCCUPATION_TYPE_Laborers', 'YEARS_BEGINEXPLUATATION_MEDI', 'OCCUPATION_TYPE_High skill tech staff', 'COMMONAREA_MEDI', 'OCCUPATION_TYPE_Drivers', 'ENTRANCES_MEDI', 'OCCUPATION_TYPE_Core staff', 'OCCUPATION_TYPE_Accountants', 'LANDAREA_MEDI', 'NAME_FAMILY_STATUS_Single / not married', 'LIVINGAREA_MEDI', 'LIVINGAPARTMENTS_MODE', 'LANDAREA_MODE', 'WEEKDAY_APPR_PROCESS_START_SATURDAY', 'NONLIVINGAREA_AVG', 'ENTRANCES_AVG', 'FLOORSMAX_AVG', 'ORGANIZATION_TYPE_Military', 'LANDAREA_AVG', 'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'COMMONAREA_AVG', 'PREV_APPL_MEAN_CARD_MEAN_AMT_PAYMENT_CURRENT', 'ORGANIZATION_TYPE_Construction', 'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE', 'ORGANIZATION_TYPE_Business Entity Type 3', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'APARTMENTS_MODE']\n"
     ]
    }
   ],
   "source": [
    "ranking = pd.DataFrame({'Features': data_train.columns})\n",
    "ranking['RANK'] = np.asarray(rfecv.ranking_)\n",
    "ranking.sort_values('RANK', inplace=True)\n",
    "\n",
    "features_selection = ranking[ranking.RANK == 1]['Features'].to_list()\n",
    "print(len(features_selection))\n",
    "print(features_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "kWY7zhIs6rT8",
    "outputId": "edea64a9-213c-4ba0-f0d4-92bd2dbdf256"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot number of features vs CV scores\n",
    "step=5\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
    "plt.xlabel('Number of features tested (x%d)' % step)\n",
    "plt.ylabel('Cross-validation score (roc_auc)')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_dSRvT8R7DSj"
   },
   "source": [
    "RFECV explique l'importance des features sélectionnées en fonction de l'évolution du score AUC, comme l'atteste également le plot ci-dessus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TrqtDY8162ZK",
    "outputId": "0ee730a3-b330-4a84-c914-4741e29776b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 149)"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_data is very important to implement the feature importance plot...\n",
    "X_data = data_train.loc[:, data_train.columns[rfecv.get_support()]]\n",
    "X_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jSpIITqt7gaM"
   },
   "source": [
    "Il est nécessaire de sauvegarder le dataset ``X_data`` de manière à pouvoir le réutiliser facilement…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "dyOUD4hD_H1d"
   },
   "outputs": [],
   "source": [
    "#Merge operation with TARGET before save this reduced sample\n",
    "X_data = X_data.merge(TARGET, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F5zESMa5_69_",
    "outputId": "45443362-e2fd-4208-de64-c5df383da39b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 150)"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z6uALn3p7US0",
    "outputId": "2e80af93-b044-4fed-a0f3-0f86cdba50ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 14s, sys: 769 ms, total: 1min 15s\n",
      "Wall time: 1min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "compression_opts = dict(method='zip', archive_name='X_data.csv')\n",
    "X_data.to_csv('/content/gdrive/My Drive/Colab Notebooks/p7_00_data/X_data.zip', index=True, compression=compression_opts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "naeYMChz7_9B"
   },
   "source": [
    "Nous allons sauvegarder deux jeux de données, le premier ci-dessus stocke la totalité des individus en rapport avec les 149 best features identifiées. Le second sera un échantillon permettant, si besoin, d'effectuer des tests plus rapidement, par exemple si l'utilité est nécessaire pour la conception de l'application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DAewFRR8-Enl",
    "outputId": "49fe3abb-2590-44af-8edb-4f38a3f6ce4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.73 s, sys: 49.5 ms, total: 7.78 s\n",
      "Wall time: 7.79 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "compression_opts = dict(method='zip', archive_name='X_sample.csv')\n",
    "X_data.sample(30000).to_csv('/content/gdrive/My Drive/Colab Notebooks/p7_00_data/X_sample.zip', index=True, compression=compression_opts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IzEE6DSk8lMA",
    "outputId": "33a595f2-4523-4409-8702-376246030777"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.66 s, sys: 32.3 ms, total: 1.7 s\n",
      "Wall time: 1.75 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "X_data.sample(10000).to_csv('/content/gdrive/My Drive/Colab Notebooks/p7_00_data/X_sample.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "a5zpNOaeAbFN"
   },
   "outputs": [],
   "source": [
    "#Transform data with rfecv \n",
    "X_train_selected = rfecv.transform(X_train)\n",
    "X_test_selected = rfecv.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "Y9Hx7zSt-mvV"
   },
   "outputs": [],
   "source": [
    "def train_models(model, X_train_selected, X_test_selected, y_train, y_test):\n",
    "    models = [\n",
    "          #Ensemble methods\n",
    "          CatBoostClassifier(task_type=\"GPU\", logging_level='Silent'),\n",
    "          LGBMClassifier(device='gpu'),\n",
    "          XGBClassifier(tree_method='gpu_hist', predictor='gpu_predictor')\n",
    "          ]\n",
    "\n",
    "    model.fit(X_train_selected, y_train)\n",
    "    output = {\n",
    "      'AUC': roc_auc_score(y_test, model.predict_proba(X_test_selected)[:,1]),\n",
    "      'Accuracy': accuracy_score(y_test, model.predict(X_test_selected)),\n",
    "      'Precision': precision_score(y_test, model.predict(X_test_selected)),\n",
    "      'Recall': recall_score(y_test, model.predict(X_test_selected)),\n",
    "      'F1': f1_score(y_test, model.predict(X_test_selected))\n",
    "      }\n",
    "          \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1dcdwCmUA-MR",
    "outputId": "ec25a64b-06e6-46b8-8145-0354073ff6ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27.8 s, sys: 8.38 s, total: 36.2 s\n",
      "Wall time: 24.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "name = []\n",
    "auc = []\n",
    "accuracy = []\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "time_ = []\n",
    "\n",
    "for model in models:\n",
    "    start = time.time()\n",
    "    results = train_models(model, X_train_selected, X_test_selected, y_train, y_test)\n",
    "\n",
    "    name.append(type(model).__name__)\n",
    "    auc.append(results['AUC'])\n",
    "    accuracy.append(results['Accuracy'])\n",
    "    precision.append(results['Precision'])\n",
    "    recall.append(results['Recall'])\n",
    "    f1.append(results['F1'])\n",
    "    time_.append(time.time()-start)\n",
    "\n",
    "#Initialise data of lists\n",
    "models_rfe = pd.DataFrame(data=[name, auc, accuracy, precision, recall, f1, time_]).T\n",
    "models_rfe.columns = ['Model', 'AUC', 'Accuracy', 'Precision', 'Recall', 'F1', 'Time']\n",
    "models_rfe.sort_values('AUC', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "N4nFnPQUA_Co",
    "outputId": "53443135-646e-4a9a-8c3c-7e833ccdc03e"
   },
   "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>Model</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.774652</td>\n",
       "      <td>0.920047</td>\n",
       "      <td>0.540481</td>\n",
       "      <td>0.0333198</td>\n",
       "      <td>0.06277</td>\n",
       "      <td>12.0603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatBoostClassifier</td>\n",
       "      <td>0.774078</td>\n",
       "      <td>0.920025</td>\n",
       "      <td>0.544987</td>\n",
       "      <td>0.0285984</td>\n",
       "      <td>0.054345</td>\n",
       "      <td>9.96622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.764208</td>\n",
       "      <td>0.919841</td>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.00768919</td>\n",
       "      <td>0.0151818</td>\n",
       "      <td>2.67147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model       AUC  Accuracy  ...      Recall         F1     Time\n",
       "1      LGBMClassifier  0.774652  0.920047  ...   0.0333198    0.06277  12.0603\n",
       "0  CatBoostClassifier  0.774078  0.920025  ...   0.0285984   0.054345  9.96622\n",
       "2       XGBClassifier  0.764208  0.919841  ...  0.00768919  0.0151818  2.67147\n",
       "\n",
       "[3 rows x 7 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Models performance with features selection\n",
    "models_rfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "IP-e0fKPBCEk",
    "outputId": "59d0c1d8-88bd-47d7-b415-13cdcd7836b3"
   },
   "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>Model</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.773782</td>\n",
       "      <td>0.920264</td>\n",
       "      <td>0.563758</td>\n",
       "      <td>0.0339943</td>\n",
       "      <td>0.0641221</td>\n",
       "      <td>10.7289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatBoostClassifier</td>\n",
       "      <td>0.773559</td>\n",
       "      <td>0.920144</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.0279239</td>\n",
       "      <td>0.0532065</td>\n",
       "      <td>133.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.764208</td>\n",
       "      <td>0.919841</td>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.00768919</td>\n",
       "      <td>0.0151818</td>\n",
       "      <td>4.2202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model       AUC  Accuracy  ...      Recall         F1     Time\n",
       "1      LGBMClassifier  0.773782  0.920264  ...   0.0339943  0.0641221  10.7289\n",
       "0  CatBoostClassifier  0.773559  0.920144  ...   0.0279239  0.0532065  133.957\n",
       "2       XGBClassifier  0.764208  0.919841  ...  0.00768919  0.0151818   4.2202\n",
       "\n",
       "[3 rows x 7 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Reminder before… features selection\n",
    "base_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fC9KqQOpCUfI"
   },
   "source": [
    "**LGBMClassifier reste le modèle le plus performant selon le couple AUC score/Time.** CatBoostClassifier nécessite beaucoup plus de temps de calcul, il ne peut pas être retenu dans le contexte métier actuel. XGBClassifier est très rapide, il est en effet plus rapide que le LGBMClassifier mais avec une baisse de l'AUC score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1a15egabDMat"
   },
   "source": [
    "----\n",
    "Dans la dernière partie de l'étude on développera les 3 volets suivants : **_La fonction coût, l'algorithme d'optimisation et la métrique d'évaluation._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nki8ieYuEKfC"
   },
   "source": [
    "### **La fonction coût sera déterminée par l'analyse des erreurs de prédiction**\n",
    "\n",
    "**FP (False Positive) :** les cas où la prédiction est positive, mais où la valeur réelle est négative. \n",
    "\n",
    "_Perte d'opportunité si le crédit client est refusé à tort, alors qu'il aurait été en mesure d'être remboursé._\n",
    "\n",
    "\n",
    "**FN (False Negative) :** les cas où la prédiction est négative, mais où la valeur réelle est positive. \n",
    "\n",
    "_Perte réelle si le crédit client accepté se transforme en défaut de paiement._\n",
    "\n",
    "\n",
    "**Ainsi, les pertes d'un crédit en raison d'une mauvaise classification dépendront des probabilités Faux Positifs et Faux Négatifs.**\n",
    "\n",
    "\n",
    "L'idée est d'éviter les clients avec un fort risque de défaut. Il est donc nécessaire de pénaliser les FP et FN cités précédemment. Pour réduire ce rique de perte financière, il faut maximiser deux critères Recall et Precision.\n",
    "\n",
    "\\begin{aligned}{\\text{Precision}}&={\\frac {tp}{tp+fp}}\\\\{\\text{Recall}}&={\\frac {tp}{tp+fn}}\\,\\end{aligned}\n",
    "\n",
    "\n",
    "Fonction qui optimise Precision et Recall avec une importance plus forte pour le critère Precision:\n",
    "\\begin{aligned}{\\text{Fscore}}&={\\frac {precision.recall}{Beta^2.precision + recall}}\\,\\end{aligned}\n",
    "\n",
    "\n",
    "\n",
    "Avec Beta le coefficient d'importance relative au critère Precision par rapport au recall : \n",
    "\\begin{aligned}{\\text{Fscore}}&={\\frac {precision.recall}{Beta^2.precision + recall}}\\,&=(1+Beta^2) . {\\frac {tp}{(1+Beta^2).tp + Beta^2.fn + fp}}\\,\\end{aligned}\n",
    "\n",
    "\n",
    "\n",
    "_Explication sur la F-measure [Wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall)_\n",
    "\n",
    "\n",
    "**L'application de cette métrique métier passe par la quantification de l'importance relative entre recall et précision, à savoir Beta (β).**\n",
    "Cela revient à estimer le coût moyen d'un défaut, et le coût d'opportunité d'un client refusé par erreur. Cette connaissance métier n'est pas évoquée à ce stade du projet, nous allons donc l'estimer. Cette hypothèse pourra bien entendu être modifiée avec un interlocuteur métier.\n",
    "\n",
    "- Défaut de paiement 30% du montant du crédit en pertes et autres frais de recouvrement.\n",
    "- 10% de chance d'obtenir un crédit pour un client lambda qui souhaite emprunter.\n",
    "\n",
    "\\begin{aligned}{\\text{Beta}}&={\\frac {coef Recall}{coef Precision}}\\end{aligned}\\\n",
    "On peut se fixer l'hypothèse d'un **Beta = 3**. Vérifions-le avec une étape de tests fonctionnels.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "duYX7-YmEK4l",
    "outputId": "181f7602-91b6-4b4d-92ce-4bdab5d2ecaf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############################\n",
      "Test 0 : [500, 300, 10, 30]\n",
      "Score : 0.04761904761904767\n",
      "############################\n",
      "Test 1 : [500, 300, 30, 10]\n",
      "Score : 0.029126213592232997\n",
      "############################\n",
      "Test 2 : [400, 300, 70, 50]\n",
      "Score : 0.1208791208791209\n",
      "############################\n",
      "Test 3 : [400, 300, 50, 70]\n",
      "Score : 0.13978494623655913\n",
      "############################\n",
      "Test 4 : [350, 250, 80, 120]\n",
      "Score : 0.23913043478260865\n",
      "############################\n",
      "Test 5 : [350, 250, 180, 90]\n",
      "Score : 0.2432432432432432\n"
     ]
    }
   ],
   "source": [
    "#Tests fonctionels avec des listes contenant 4 int: tp, tn, fp, fn\n",
    "test_0 = [500, 300, 10, 30]\n",
    "test_1 = [500, 300, 30, 10]\n",
    "test_2 = [400, 300, 70, 50]\n",
    "test_3 = [400, 300, 50, 70]\n",
    "test_4 = [350, 250, 80, 120]\n",
    "test_5 = [350, 250, 180, 90]\n",
    "\n",
    "tests = [test_0, test_1, test_2, test_3, test_4, test_5]\n",
    "\n",
    "\n",
    "def my_score(predictions_success: list) -> int:\n",
    "    '''scoring fonction'''\n",
    "    tp, tn, fp, fn = predictions_success\n",
    "  \n",
    "    beta = 3\n",
    "    recall = tp / (tp + fn)\n",
    "    precision = tp / (tp + fp) \n",
    "    fscore = (1+beta)*(tp / ((1+3)*tp + beta*fn + fp))\n",
    "    \n",
    "    return 1-fscore\n",
    "\n",
    "for i, t in enumerate(tests):\n",
    "    print(\"############################\")\n",
    "    print(\"Test\", i, \":\", t)\n",
    "    print(\"Score :\", my_score(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CHBVvsmqEVRm"
   },
   "source": [
    "Les résultats sont satisfaisants, les risques liés aux erreurs de faux négatifs et positifs font ressortir un score plus importants, à l'inverse un dossier présentant moins de risque aura un score proche de zéro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "8I9gPbP_ERF-"
   },
   "outputs": [],
   "source": [
    "def custom_score(y_true, y_pred, beta=3) :\n",
    "    '''function penalize fp and fn…'''\n",
    "    tp, tn, fp, fn = confusion_matrix(y_true, y_pred).ravel()\n",
    "\n",
    "    recall = tp / (tp + fn)\n",
    "    precision = tp / (tp + fp)\n",
    "    fscore = (1+beta)*((tp / ((1+beta)*tp + beta*fn + fp)))\n",
    "\n",
    "    return 1-fscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "naPlYLmpEYv4"
   },
   "outputs": [],
   "source": [
    "def cost_false(model, y_true, X_true):\n",
    "    '''Cost function analyzes prediction errors False Pos. and False Neg.'''\n",
    "    cm = confusion_matrix(y_true, model.predict(X_true))\n",
    "    FP = cm[0][1]/np.sum(cm)\n",
    "    FN = cm[1][0]/np.sum(cm)\n",
    "  \n",
    "    print(\"False Pos: {0:.2%}\".format(FP))\n",
    "    print(\"False Neg: {0:.2%}\".format(FN))\n",
    "    return FP, FN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R9sUXHM9FNjh"
   },
   "source": [
    "### **Hyperparameters tunning - Hyperopt et AUC score**\n",
    "Choisir les hyperparamètres appropriés est nécessaire pour affiner et booster les performances d'un algorithme d’apprentissage automatique. La métrique utilisée dans le contexte de notre classification binaire sera l'AUC score.\n",
    "\n",
    "Grid Search ou Random Search étaient également une alternative possible, à la différence d'Hyperopt une \"méthode basique\" ne permet pas de traiter un large espace de paramètres, ici très largement privilégié.\n",
    "\n",
    "\n",
    "Avec Hyperopt, on peut facilement analyser notre modèle de Boosting tout en variant les hyperparamètres dans l'espace que nous allons définir ci-dessous. Hyperopt fonctionne avec les algorithmes de ML distribué, comme Apache Spark MLlib et Horovod, ainsi qu’avec les modèles ML mono-machine, comme scikit-learn et TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "Wlx5hD0pEb2w"
   },
   "outputs": [],
   "source": [
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, space_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "IizRyrDCFQXr"
   },
   "outputs": [],
   "source": [
    "#Parameter space\n",
    "space = {\n",
    "    'n_estimators': hp.quniform('n_estimators', 100, 600, 100),\n",
    "    'learning_rate': hp.uniform('learning_rate', 0.001, 0.03),\n",
    "    'max_depth': hp.quniform('max_depth', 3, 7, 1),\n",
    "    'subsample': hp.uniform('subsample', 0.60, 0.95),\n",
    "    'colsample_bytree': hp.uniform('colsample_bytree', 0.60, 0.95),\n",
    "    'reg_lambda': hp.uniform('reg_lambda', 1, 20)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "Gtwa5OJ3FlSo"
   },
   "outputs": [],
   "source": [
    "def objective(params, eval_metric='custom_score'):\n",
    "    #objective function to be minimized. \n",
    "    #Hyperopt will seek to minimize the loss returned by this function.\n",
    "    \n",
    "    params = {\n",
    "        'n_estimators': int(params['n_estimators']),\n",
    "        'learning_rate': params['learning_rate'],\n",
    "        'max_depth': int(params['max_depth']),\n",
    "        'subsample': params['subsample'],\n",
    "        'colsample_bytree': params['colsample_bytree'],\n",
    "        'reg_lambda': params['reg_lambda'],\n",
    "        'device':'gpu'\n",
    "        }\n",
    "  \n",
    "    \n",
    "    model= LGBMClassifier(**params)\n",
    "    cv = StratifiedKFold(5)\n",
    "    if eval_metric == 'roc_auc': \n",
    "        score = cross_val_score(model, X_train_selected, y_train, scoring='roc_auc', cv=cv).mean()\n",
    "\n",
    "    elif eval_metric == 'custom_score': \n",
    "        y_pred = cross_val_predict(model, X_train_selected, y_train, method='predict', cv=cv)\n",
    "        score = custom_score(y_train, y_pred)\n",
    "  \n",
    "    loss = 1 - score    \n",
    "    return {'loss': loss, 'params': params, 'status': STATUS_OK}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OcmyhKxoGERW",
    "outputId": "7466c4e3-7305-455c-bd2e-a1154b9b5b00"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [36:08<00:00, 72.28s/it, best loss: 0.9771273042187306]\n",
      "CPU times: user 36min 4s, sys: 42.8 s, total: 36min 46s\n",
      "Wall time: 36min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "best = fmin(fn=objective, space=space, max_evals=30, rstate=np.random.RandomState(1), algo=tpe.suggest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E-34bqfWIvK1",
    "outputId": "26d7471c-926d-4265-da2d-647037b6c161"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.8 s, sys: 398 ms, total: 22.2 s\n",
      "Wall time: 22.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgbm = LGBMClassifier( #Fit a new model based on the best parameters\n",
    "    n_estimators=int(best['n_estimators']), \n",
    "    colsample_bytree= best['colsample_bytree'],\n",
    "    learning_rate= best['learning_rate'],\n",
    "    max_depth= int(best['max_depth']),\n",
    "    subsample= best['subsample'],\n",
    "    reg_lambda= best['reg_lambda'],\n",
    "    device='gpu')\n",
    "\n",
    "lgbm.fit(X_train_selected, y_train)\n",
    "pickle.dump(lgbm, open(\"/content/gdrive/My Drive/Colab Notebooks/p7_00_models/LGBMClassifier.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B0NzpiedOjWr",
    "outputId": "e9424d24-200b-41b6-ce23-5ff9e11d60ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC : 0.778292\n"
     ]
    }
   ],
   "source": [
    "roc_auc = roc_auc_score(y_test, lgbm.predict_proba(X_test_selected)[:,1])\n",
    "print('AUC : %0.6f' %roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "YbwGr2cdOsTo",
    "outputId": "a5629e11-eafd-458b-d39a-ab760a285d3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix_roc_auc(lgbm, y_test, lgbm.predict(X_test_selected), lgbm.predict_proba(X_test_selected)[:,1], feature_importances=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_sHiG6fBOtJl",
    "outputId": "b7a86ab0-c78a-4d1f-f18e-57b6d78ee1ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Pos: 0.19%\n",
      "False Neg: 7.78%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0019402952717497343, 0.0778394432761723)"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FP and FN\n",
    "cost_false(lgbm, y_test, X_test_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kx7qB-AtO4tm",
    "outputId": "944ac717-457f-47e1-db02-a6c240abed15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CREDIT_TERM', 762]\n",
      "['EXT_SOURCE_1', 679]\n",
      "['EXT_SOURCE_3', 548]\n",
      "['EXT_SOURCE_2', 531]\n",
      "['DAYS_BIRTH', 522]\n",
      "['PREV_APPL_MEAN_CNT_PAYMENT', 401]\n",
      "['AMT_ANNUITY', 368]\n",
      "['PREV_APPL_MEAN_INSTALL_MEAN_AMT_PAYMENT', 336]\n",
      "['AMT_GOODS_PRICE', 296]\n",
      "['PREV_BUR_MEAN_AMT_CREDIT_SUM', 294]\n",
      "['DAYS_ID_PUBLISH', 287]\n",
      "['PREV_BUR_MEAN_AMT_CREDIT_SUM_DEBT', 275]\n",
      "['AMT_CREDIT', 257]\n",
      "['ANNUITY_INCOME_PERCENT', 244]\n",
      "['PREV_BUR_MEAN_DAYS_CREDIT', 237]\n",
      "['DAYS_EMPLOYED_PERCENT', 229]\n",
      "['PREV_APPL_MEAN_SELLERPLACE_AREA', 227]\n",
      "['PREV_BUR_MEAN_AMT_CREDIT_MAX_OVERDUE', 219]\n",
      "['DAYS_EMPLOYED', 217]\n",
      "['PREV_APPL_MEAN_AMT_ANNUITY', 206]\n",
      "['PREV_APPL_MEAN_INSTALL_MEAN_AMT_INSTALMENT', 191]\n",
      "['PREV_APPL_MEAN_INSTALL_MEAN_NUM_INSTALMENT_NUMBER', 190]\n",
      "['DAYS_REGISTRATION', 188]\n",
      "['PREV_APPL_MEAN_AMT_DOWN_PAYMENT', 187]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_CNT_DRAWINGS_ATM_CURRENT', 185]\n",
      "['PREV_APPL_MEAN_DAYS_LAST_DUE_1ST_VERSION', 179]\n",
      "['PREV_BUR_MEAN_DAYS_CREDIT_ENDDATE', 176]\n",
      "['DAYS_LAST_PHONE_CHANGE', 174]\n",
      "['PREVIOUS_LOANS_COUNT', 164]\n",
      "['CREDIT_INCOME_PERCENT', 156]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_CNT_DRAWINGS_CURRENT', 153]\n",
      "['PREV_APPL_MEAN_HOUR_APPR_PROCESS_START', 149]\n",
      "['PREV_APPL_MEAN_AMT_GOODS_PRICE', 142]\n",
      "['PREV_BUR_MEAN_AMT_CREDIT_SUM_LIMIT', 129]\n",
      "['PREV_APPL_MEAN_INSTALL_MEAN_NUM_INSTALMENT_VERSION', 127]\n",
      "['PREV_APPL_MEAN_DAYS_DECISION', 123]\n",
      "['PREV_BUR_MEAN_DAYS_CREDIT_UPDATE', 123]\n",
      "['REGION_POPULATION_RELATIVE', 121]\n",
      "['PREV_BUR_MEAN_DAYS_ENDDATE_FACT', 121]\n",
      "['PREV_APPL_MEAN_NFLAG_INSURED_ON_APPROVAL', 120]\n",
      "['PREV_APPL_MEAN_RATE_DOWN_PAYMENT', 118]\n",
      "['AMT_INCOME_TOTAL', 114]\n",
      "['PREV_APPL_MEAN_DAYS_FIRST_DUE', 111]\n",
      "['PREV_APPL_MEAN_AMT_APPLICATION', 109]\n",
      "['PREV_APPL_MEAN_DAYS_LAST_DUE', 105]\n",
      "['PREV_BUR_MEAN_MONTHS_BALANCE_MEAN', 105]\n",
      "['PREV_APPL_MEAN_DAYS_TERMINATION', 105]\n",
      "['CODE_GENDER_M', 104]\n",
      "['FLAG_OWN_CAR', 98]\n",
      "['OWN_CAR_AGE', 94]\n",
      "['DEF_30_CNT_SOCIAL_CIRCLE', 94]\n",
      "['CODE_GENDER_F', 91]\n",
      "['NAME_FAMILY_STATUS_Married', 89]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_BALANCE', 89]\n",
      "['PREV_APPL_MEAN_INSTALL_MEAN_DAYS_ENTRY_PAYMENT', 88]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_CREDIT_LIMIT_ACTUAL', 87]\n",
      "['PREV_APPL_MEAN_AMT_CREDIT', 82]\n",
      "['NAME_CONTRACT_TYPE', 82]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_ATM_CURRENT', 80]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_RECIVABLE', 79]\n",
      "['REGION_RATING_CLIENT_W_CITY', 77]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_MONTHS_BALANCE', 75]\n",
      "['NAME_EDUCATION_TYPE_Higher education', 74]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_RECEIVABLE_PRINCIPAL', 73]\n",
      "['PREVIOUS_APPLICATION_COUNT', 73]\n",
      "['PREV_BUR_MEAN_AMT_ANNUITY', 71]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_PAYMENT_CURRENT', 70]\n",
      "['PREV_APPL_MEAN_INSTALL_MEAN_DAYS_INSTALMENT', 70]\n",
      "['AMT_REQ_CREDIT_BUREAU_YEAR', 67]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_PAYMENT_TOTAL_CURRENT', 62]\n",
      "['PREV_BUR_MEAN_AMT_CREDIT_SUM_OVERDUE', 62]\n",
      "['PREV_APPL_MEAN_DAYS_FIRST_DRAWING', 62]\n",
      "['YEARS_BEGINEXPLUATATION_MODE', 60]\n",
      "['FLAG_DOCUMENT_3', 59]\n",
      "['TOTALAREA_MODE', 59]\n",
      "['OBS_60_CNT_SOCIAL_CIRCLE', 53]\n",
      "['REG_CITY_NOT_LIVE_CITY', 52]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_SK_DPD_DEF', 51]\n",
      "['AMT_REQ_CREDIT_BUREAU_QRT', 51]\n",
      "['HOUR_APPR_PROCESS_START', 49]\n",
      "['DEF_60_CNT_SOCIAL_CIRCLE', 49]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_CNT_INSTALMENT_MATURE_CUM', 47]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_CNT_DRAWINGS_POS_CURRENT', 46]\n",
      "['ORGANIZATION_TYPE_Self-employed', 45]\n",
      "['FLAG_WORK_PHONE', 44]\n",
      "['LIVINGAREA_MODE', 43]\n",
      "['NAME_EDUCATION_TYPE_Secondary / secondary special', 43]\n",
      "['LIVINGAREA_MEDI', 42]\n",
      "['BASEMENTAREA_AVG', 42]\n",
      "['YEARS_BEGINEXPLUATATION_AVG', 42]\n",
      "['LANDAREA_MEDI', 38]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_CURRENT', 37]\n",
      "['LANDAREA_MODE', 36]\n",
      "['APARTMENTS_MEDI', 36]\n",
      "['NONLIVINGAREA_MODE', 35]\n",
      "['ORGANIZATION_TYPE_Construction', 34]\n",
      "['LIVINGAPARTMENTS_AVG', 34]\n",
      "['COMMONAREA_MEDI', 33]\n",
      "['LIVINGAREA_AVG', 33]\n",
      "['APARTMENTS_MODE', 32]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_INST_MIN_REGULARITY', 32]\n",
      "['OCCUPATION_TYPE_Core staff', 32]\n",
      "['LANDAREA_AVG', 31]\n",
      "['COMMONAREA_AVG', 31]\n",
      "['OBS_30_CNT_SOCIAL_CIRCLE', 30]\n",
      "['NONLIVINGAREA_AVG', 30]\n",
      "['ORGANIZATION_TYPE_Business Entity Type 3', 28]\n",
      "['ORGANIZATION_TYPE_Military', 27]\n",
      "['OCCUPATION_TYPE_Drivers', 27]\n",
      "['FLOORSMAX_AVG', 26]\n",
      "['YEARS_BEGINEXPLUATATION_MEDI', 26]\n",
      "['NAME_INCOME_TYPE_State servant', 24]\n",
      "['OCCUPATION_TYPE_Laborers', 23]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_POS_CURRENT', 22]\n",
      "['YEARS_BUILD_MODE', 21]\n",
      "['WALLSMATERIAL_MODE_Panel', 20]\n",
      "['NAME_FAMILY_STATUS_Single / not married', 20]\n",
      "['ORGANIZATION_TYPE_Transport: type 3', 20]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_AMT_DRAWINGS_OTHER_CURRENT', 20]\n",
      "['APARTMENTS_AVG', 19]\n",
      "['NAME_INCOME_TYPE_Working', 19]\n",
      "['LIVINGAPARTMENTS_MODE', 19]\n",
      "['YEARS_BUILD_AVG', 18]\n",
      "['NONLIVINGAREA_MEDI', 17]\n",
      "['ORGANIZATION_TYPE_School', 16]\n",
      "['OCCUPATION_TYPE_Accountants', 16]\n",
      "['ENTRANCES_MODE', 15]\n",
      "['OCCUPATION_TYPE_High skill tech staff', 14]\n",
      "['CNT_CHILDREN', 14]\n",
      "['ORGANIZATION_TYPE_Transport: type 4', 13]\n",
      "['ENTRANCES_AVG', 11]\n",
      "['COMMONAREA_MODE', 11]\n",
      "['PREV_APPL_MEAN_NFLAG_LAST_APPL_IN_DAY', 11]\n",
      "['ENTRANCES_MEDI', 10]\n",
      "['ORGANIZATION_TYPE_Security Ministries', 9]\n",
      "['CNT_FAM_MEMBERS', 8]\n",
      "['NONLIVINGAPARTMENTS_MODE', 8]\n",
      "['ORGANIZATION_TYPE_Realtor', 8]\n",
      "['AMT_REQ_CREDIT_BUREAU_MON', 7]\n",
      "['FLAG_OWN_REALTY', 7]\n",
      "['PREV_APPL_MEAN_CARD_MEAN_SK_DPD', 6]\n",
      "['FLOORSMAX_MODE', 6]\n",
      "['ORGANIZATION_TYPE_Police', 5]\n",
      "['ORGANIZATION_TYPE_Restaurant', 3]\n",
      "['WEEKDAY_APPR_PROCESS_START_SATURDAY', 2]\n",
      "['WALLSMATERIAL_MODE_Others', 0]\n",
      "['PREV_BUR_MEAN_CNT_CREDIT_PROLONG', 0]\n",
      "['ORGANIZATION_TYPE_Services', 0]\n",
      "['AMT_REQ_CREDIT_BUREAU_DAY', 0]\n"
     ]
    }
   ],
   "source": [
    "#Check list features importance \n",
    "sorted_idx = np.argsort(lgbm.feature_importances_)[::-1]\n",
    "for index in sorted_idx:\n",
    "    print([X_data.columns[index], lgbm.feature_importances_[index]]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "k1ZcWG1UP3F_"
   },
   "outputs": [],
   "source": [
    "def objective(params, eval_metric='roc_auc'):\n",
    "  #objective function to be minimized. \n",
    "  #Hyperopt will seek to minimize the loss returned by this function.\n",
    "\n",
    "    params = {\n",
    "        'n_estimators': int(params['n_estimators']),\n",
    "        'learning_rate': params['learning_rate'],\n",
    "        'max_depth': int(params['max_depth']),\n",
    "        'subsample': params['subsample'],\n",
    "        'colsample_bytree': params['colsample_bytree'],\n",
    "        'reg_lambda': params['reg_lambda'],\n",
    "        'device':'gpu'}\n",
    "  \n",
    "    \n",
    "    model= LGBMClassifier(**params)\n",
    "    cv = StratifiedKFold(5)\n",
    "    if eval_metric == 'roc_auc': \n",
    "        score = cross_val_score(model, X_train_selected, y_train, scoring='roc_auc', cv=cv).mean()\n",
    "\n",
    "    elif eval_metric == 'custom_score': \n",
    "        y_pred = cross_val_predict(model, X_train_selected, y_train, method='predict', cv=cv)\n",
    "        score = custom_score(y_train, y_pred)\n",
    "  \n",
    "    loss = 1 - score    \n",
    "    return {'loss': loss, 'params': params, 'status': STATUS_OK}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IRTsx3ZTP-J6",
    "outputId": "7de3d04b-ff6c-465e-c56e-2d02188c71fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [36:06<00:00, 72.21s/it, best loss: 0.22355147070039993]\n",
      "CPU times: user 36min 1s, sys: 43.2 s, total: 36min 45s\n",
      "Wall time: 36min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "best = fmin(fn=objective, space=space, max_evals=30, rstate=np.random.RandomState(1), algo=tpe.suggest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4JIaoa1QQ6gz",
    "outputId": "e338aba1-172d-4ae7-c0bb-0f9492dab1aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23.2 s, sys: 405 ms, total: 23.7 s\n",
      "Wall time: 23.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgbm = LGBMClassifier( #Fit a new model based on the best parameters\n",
    "    n_estimators=int(best['n_estimators']), \n",
    "    colsample_bytree= best['colsample_bytree'],\n",
    "    learning_rate= best['learning_rate'],\n",
    "    max_depth= int(best['max_depth']),\n",
    "    subsample= best['subsample'],\n",
    "    reg_lambda= best['reg_lambda'],\n",
    "    device='gpu')\n",
    "\n",
    "lgbm.fit(X_train_selected, y_train)\n",
    "#pickle.dump(lgbm, open(\"/content/gdrive/My Drive/Colab Notebooks/p7_00_models/LGBMClassifier.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CROeo-j7Q5yr",
    "outputId": "f00f3586-796f-442c-a2c1-4273e8e7afa9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC : 0.779233\n"
     ]
    }
   ],
   "source": [
    "roc_auc = roc_auc_score(y_test, lgbm.predict_proba(X_test_selected)[:,1])\n",
    "print('AUC : %0.6f' %roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "AWDlqEfzRHnc",
    "outputId": "965a2060-b38d-4501-ccc6-540a4b6ce0af"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix_roc_auc(lgbm, y_test, lgbm.predict(X_test_selected), lgbm.predict_proba(X_test_selected)[:,1], feature_importances=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7rX52JlyRKRr",
    "outputId": "6dac730e-d52b-4ee9-cf9a-b1c95bb5c73a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Pos: 0.21%\n",
      "False Neg: 7.77%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.002070370932425694, 0.07768768833871702)"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FP and FN\n",
    "cost_false(lgbm, y_test, X_test_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_SbUuHjURNYW"
   },
   "outputs": [],
   "source": [
    "#Check list features importance \n",
    "sorted_idx = np.argsort(lgbm.feature_importances_)[::-1]\n",
    "for index in sorted_idx:\n",
    "    print([X_data.columns[index], lgbm.feature_importances_[index]]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bgrOWMI6RN1s"
   },
   "source": [
    "**Conclusion**\\\n",
    "Après une baseline faite avec un algorithme simple de régression logistique, l'AUC score avait été estimé ≈ 0.72 avec rééquilibrage (SMOTE) des données. La suite de l'étude a été déroulée vers 3 algorithmes plus complexes de gradient boosting implémentés par LightGbm vs CatBoost vs XGBoost. Nous avons pu démontrer les performances de ces algorithmes par une sélection de features, à l'origine > 300, après RFECV 149. LightGbm ressort comme étant le plus rapide, le plus performant sur la métrique classique de l'AUC, il a donc été choisi pour l'optimisation des Hyperparamètres (Hyperopt).\n",
    "\n",
    "La fonction coût permet de pénaliser les erreurs de prédiction qui peuvent coûter cher à l'entreprise. En effet, avec l'implémentation de la métrique métier les FP sont estimés à 0.19% (contre 0.21%), les FN sont à 0.78% (contre 0.77%). Au final la métrique métier permet de pénaliser légèrement mieux les erreurs du modèle."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "p7_modelisation_notebook.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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}