Copy_of_Bankruptcy_Prediction.ipynb

{
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  "metadata": {
    "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": "3.6.3"
    },
    "nav_menu": {
      "height": "252px",
      "width": "333px"
    },
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
      "sideBar": true,
      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
      "toc_window_display": false
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BillionShields/AiCopilotSearchPrePrints/blob/main/Copy_of_Bankruptcy_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1pDk7LbCtux"
      },
      "source": [
        "# Week 12: Python Business Analytics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ci-SJQQLCweW"
      },
      "source": [
        "See the Repository for Future Work: https://github.com/firmai/python-business-analytics or\n",
        "\n",
        "\n",
        "Sign up to the mailing list:\n",
        "https://mailchi.mp/ec4942d52cc5/firmai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQbJboNoCiT-"
      },
      "source": [
        "# Bankuptcy Prediction Ensemble Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEiHxOZECiUA"
      },
      "source": [
        "# To support both python 2 and python 3\n",
        "from __future__ import division, print_function, unicode_literals\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# to make this notebook's output stable across runs\n",
        "np.random.seed(42)\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# Where to save the figures\n",
        "PROJECT_ROOT_DIR = \"../..\"\n",
        "CHAPTER_ID = \"ensembles\"\n",
        "\n",
        "def image_path(fig_id):\n",
        "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True):\n",
        "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
        "    print(\"Saving figure\", fig_id)\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    try:\n",
        "        plt.savefig(path, format='png', dpi=300)\n",
        "    except:\n",
        "        plt.savefig(fig_id + \".png\", format='png', dpi=300)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_POrE0zCiUM"
      },
      "source": [
        "# Voting classifiers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16KFyF3kCiUO"
      },
      "source": [
        "heads_proba = 0.51\n",
        "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
        "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_MM11xcCiUV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "48bd49df-159d-4d4a-9f43-3e27143c49bc"
      },
      "source": [
        "plt.figure(figsize=(8,3.5))\n",
        "plt.plot(cumulative_heads_ratio)\n",
        "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
        "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
        "plt.xlabel(\"Number of coin tosses\")\n",
        "plt.ylabel(\"Heads ratio\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.axis([0, 10000, 0.42, 0.58])\n",
        "save_fig(\"law_of_large_numbers_plot\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving figure law_of_large_numbers_plot\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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wjSQXCVT7NpRSejqnbBVCvABMA1wCRwiRAKwGXvUUPh6+OgD7hRCPA/cSQOA0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\nuSPt9K06yJ7I7nzUPYlLTnxF3dIHeC1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I6cMGtiv78OlTW3+6xse0O88WDtbfaWBqH1n5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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x252 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BKe_bt9CCiUy"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import make_moons\n",
        "\n",
        "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ABrYv8GCiU9"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
        "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
        "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
        "\n",
        "voting_clf = VotingClassifier(\n",
        "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
        "    voting='hard')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gwh5mVzpCiVG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "e84510c1-16ec-4ee5-816b-914e2d292cb9"
      },
      "source": [
        "voting_clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('lr',\n",
              "                              LogisticRegression(C=1.0, class_weight=None,\n",
              "                                                 dual=False, fit_intercept=True,\n",
              "                                                 intercept_scaling=1,\n",
              "                                                 l1_ratio=None, max_iter=100,\n",
              "                                                 multi_class='warn',\n",
              "                                                 n_jobs=None, penalty='l2',\n",
              "                                                 random_state=42,\n",
              "                                                 solver='liblinear', tol=0.0001,\n",
              "                                                 verbose=0, warm_start=False)),\n",
              "                             ('rf',\n",
              "                              RandomForestClassifier(bootstrap=True,\n",
              "                                                     class_weight=None,\n",
              "                                                     criterion='gin...\n",
              "                                                     n_estimators=10,\n",
              "                                                     n_jobs=None,\n",
              "                                                     oob_score=False,\n",
              "                                                     random_state=42, verbose=0,\n",
              "                                                     warm_start=False)),\n",
              "                             ('svc',\n",
              "                              SVC(C=1.0, cache_size=200, class_weight=None,\n",
              "                                  coef0=0.0, decision_function_shape='ovr',\n",
              "                                  degree=3, gamma='auto', kernel='rbf',\n",
              "                                  max_iter=-1, probability=False,\n",
              "                                  random_state=42, shrinking=True, tol=0.001,\n",
              "                                  verbose=False))],\n",
              "                 flatten_transform=True, n_jobs=None, voting='hard',\n",
              "                 weights=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUst3fhKCiVR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "ff299ec1-9cbb-4ec3-9e5d-ab16c59100e8"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogisticRegression 0.864\n",
            "RandomForestClassifier 0.872\n",
            "SVC 0.888\n",
            "VotingClassifier 0.896\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9OJfU84CiVh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "d0428642-3f99-4782-eaba-79fcb5d6817e"
      },
      "source": [
        "log_clf = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
        "rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)\n",
        "svm_clf = SVC(gamma=\"auto\", probability=True, random_state=42)\n",
        "\n",
        "voting_clf = VotingClassifier(\n",
        "    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
        "    voting='soft')\n",
        "voting_clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('lr',\n",
              "                              LogisticRegression(C=1.0, class_weight=None,\n",
              "                                                 dual=False, fit_intercept=True,\n",
              "                                                 intercept_scaling=1,\n",
              "                                                 l1_ratio=None, max_iter=100,\n",
              "                                                 multi_class='warn',\n",
              "                                                 n_jobs=None, penalty='l2',\n",
              "                                                 random_state=42,\n",
              "                                                 solver='liblinear', tol=0.0001,\n",
              "                                                 verbose=0, warm_start=False)),\n",
              "                             ('rf',\n",
              "                              RandomForestClassifier(bootstrap=True,\n",
              "                                                     class_weight=None,\n",
              "                                                     criterion='gin...\n",
              "                                                     n_estimators=10,\n",
              "                                                     n_jobs=None,\n",
              "                                                     oob_score=False,\n",
              "                                                     random_state=42, verbose=0,\n",
              "                                                     warm_start=False)),\n",
              "                             ('svc',\n",
              "                              SVC(C=1.0, cache_size=200, class_weight=None,\n",
              "                                  coef0=0.0, decision_function_shape='ovr',\n",
              "                                  degree=3, gamma='auto', kernel='rbf',\n",
              "                                  max_iter=-1, probability=True,\n",
              "                                  random_state=42, shrinking=True, tol=0.001,\n",
              "                                  verbose=False))],\n",
              "                 flatten_transform=True, n_jobs=None, voting='soft',\n",
              "                 weights=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERSkG-ttCiV0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "4c010223-e2ed-4888-b03d-20e572256b32"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogisticRegression 0.864\n",
            "RandomForestClassifier 0.872\n",
            "SVC 0.888\n",
            "VotingClassifier 0.912\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJkiyPPvCiWF"
      },
      "source": [
        "# Bagging ensembles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OouZmQXCiWI"
      },
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "bag_clf = BaggingClassifier(\n",
        "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
        "    max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)\n",
        "bag_clf.fit(X_train, y_train)\n",
        "y_pred = bag_clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pImCzp0OCiWc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1696bb5b-afde-4792-fc44-2460c7902afe"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.904\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKvReSfZCiWp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "50fd941e-7805-40b6-b7b1-eedd73a026bd"
      },
      "source": [
        "tree_clf = DecisionTreeClassifier(random_state=42)\n",
        "tree_clf.fit(X_train, y_train)\n",
        "y_pred_tree = tree_clf.predict(X_test)\n",
        "print(accuracy_score(y_test, y_pred_tree))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.856\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1urKSwoCiXK"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "\n",
        "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
        "    x1s = np.linspace(axes[0], axes[1], 100)\n",
        "    x2s = np.linspace(axes[2], axes[3], 100)\n",
        "    x1, x2 = np.meshgrid(x1s, x2s)\n",
        "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
        "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
        "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
        "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
        "    if contour:\n",
        "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
        "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
        "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
        "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
        "    plt.axis(axes)\n",
        "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
        "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygeGJkNNCiXd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "e4b42fdf-a3ea-48bc-edb9-f270fd087304"
      },
      "source": [
        "plt.figure(figsize=(11,4))\n",
        "plt.subplot(121)\n",
        "plot_decision_boundary(tree_clf, X, y)\n",
        "plt.title(\"Decision Tree\", fontsize=14)\n",
        "plt.subplot(122)\n",
        "plot_decision_boundary(bag_clf, X, y)\n",
        "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
        "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving figure decision_tree_without_and_with_bagging_plot\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7/WuZVnJG2yWTmcQ0oxXfNfLyLEYi8TimGcUwAoBgGAFMM0oi8XgXWuui82M1mt6jtWHl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\nF6jlGAR/rtNJ2cCz5Qi3j3e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cW7fOt9y+UOhC4vHHALdz4ji5rr7INq5S1Z3OYyKxG9OM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zGVmeQLX/kiv3HDjdzyb4oX07OTagrAbepLFw3Urib4vxRrRRvcMLCIAvmtNE3xFDhRrE9Xxr+j\nSimV70urtX4AeADgmmuucszPth4P7PUQJLuLptXKaiNyO0br1hv5L79sdMzmknb+8fsGUg3LGrSa\nW1Gts5KrXT9uWbkqx73A/H+xIj9PPhFk9Kw3ZzYI4E1vr29aWesA9vyol2BIA2pJUKqlGf3Ca0S0\noULs1oaVdKGtbR3d3R1EzsZZWAihI/Ok02kuTFyo2v7VaIQuQKE2WO896zkaPUO8Wpap2aln2bnu\nIHOLitm5KCF/gldf+TTPPv8qJid6iEYSbN7cR0dHZ8FnV7t28t/XWjM6OsXQ0LM89riHdCYEGIX3\nQpYA8YXFIDrjQWc0QxP26MhqfeFqBeCKacO2HWl+7Q3xptMGN+qCGwYWMSA/31gHUCwiKX/fDiDm\n5gC9ejyw10OQ7C6aVitO3RArnTf/AbUknjSZZIB0OsW//Ms/Fa36XAs+3z+h1DywwEMP3cT58z1A\nCq0zaD1NJBLlla/s4BOfmCv6+dWuH7esXFXiXlDsgWP0rJfe/nRVGb6qpW8glU15aQhFIgGgCIRq\n76LWUvBfFtGGCrFbG0rpwnPPjfHDH36VF0+sJ9k3gQoliEQivOvtv82Vl11Ztf2r4UZdcBNt+imu\nuOw1PHvsBP/09+9janwzHk+GTMbD+dkuSPr55/89y+HDf87VV1+JUssPtD5fDKUOAtZ4vnm0DnPy\n5CMF709OTjAxEWMq0c5s3zBKlbjVPBq/z89v3vRmbrr+Jlu+p2jDMm7UBTco0jHAp5TaqbV+Kbvt\nKiA/OI/stquAA6vs5xrq8cBeiSCVE8iXv09//z2udHuphmqWRKtl4/qN+Lw+0t3jXJhuZ+6FLo4e\nfYmDB28mFltXsH80Osnu3Y9VfJ4bbjjJ3FwUmOfYsQ66u08BmmAwweBgiunpFOfPb2bPnja6ugoH\nQ6tdP25auSqXYsJfbKaqEXZYO/rHH2lbmqGamWqOwFYXIdpQIfXWBnOf//t/v8PERBuJ0CLecIJL\nd1zC3b99N8FgsGrbG0n+w6ZTrk52zzanF4cJh3v5tas38Z0HL6dv6wlA41NxMhNeFhYXmJ7cyC+P\nezn20os5xQzXretk167jJBJBEokggUCcQCDO4cO/yuTk0YL3lT9OoHOWlyd3EIoE2dq7GWuadZOO\n9g7e/sZb6C6iRY1AtKHxOD6w0FrPKaUeBj6hlPoARuaPW4Hri+z+VWCPUuoHGAkIPgr81WrniMfP\n8eKL/9GxdH7m+cwO2vRlr9aOcgWpHJeZat1qfvjDWzhzppuFjEJFFhh/6iK+vb7b1uU3Ozrd1ZZE\nK2G1Zfj+3n7+4+/ey5f+/iHOq/MsdsRY1IrJpwOEN58q+NzkdA8zfWcrtmPCowmuGyeeCpDxJ0gH\n43g9aRYyXuLtM8TTPhZH03zuc/t429vexjXX5N5Kpa6fv/iLaxkc9JJMXkY8PkQ6PY/XG+biizdk\nYzxycXuqTKG5aYQ2LC6e4fTpfY5du82qDUYTK5RSKI/i6iuuJhgMNsQtw25dgPprQynsnm22Zo4K\nt4Xp6+1DZ+IoT5Btl7yGU2dO8cwz09A3ymwm92F2BoiNbeEVm87S3jnF7GKYl89tYbwtwQv/7wYW\nZjbw/f+ToKNtHp83TSrtRbfFuP33zvBbb3sHbaFljTArhJuxIBHfOcCZgYXQeBwfWGT5feBLwBgw\nDnxIa31YKfU64DGtdTS7398AO4Dns6//NrttRbROO5rhph5ZnMpJ71lOIF+1wX5TU910dEygUgrV\nGaOnbyNbNq+zdfnN7k631kwKq4mWIXjdaL2PsbERzk9cQKOJjW9lQ3+sYP90IsiG3so72/H05VzW\n/RzxtAev30swqPB5FOfnegiEfCQTSdLeNOfOhfjpT/+Z3buvxefL/d7Frp/BQS/bt6cxXNiX/XiN\nbFTTOfu2at0Tu2nvzDAz5WFs1MPCvIeHv2a4EYQjmj132TsQb1Hqqg1K+R29dptVG0rRCLeMepyj\nMdpQePxDB/22zpxbM0ehNToTR2fiBKNXoJTiooGLyCym2XnpTiYnJ4scoZvjczsg60WrorAhCplk\nPxv6x43D4iOJD5/PS3fw13jfO5I5RyhWIXxq6AG6ttxjWxXyVqCVtcEVAwut9QRGDvL87T/DCMoz\nX2vgP2X/ykYpL0p5HMtw41SNiHIC+dZCmlkTO27SlWbLrIK39aKNgJF14+GvhXn9db9e8JmhUz7+\n24f/uCo7zBmh76/rYEvfDgLhnewIbGDk3AjPHX0uW13Vg9bJkjnLa6FV657YgfUhZWbaw3xMsTDv\noS2siUSN36K9M+NIAb9mo97aAMrRa7dZtSGT6cKaha3ZaaQ2WDF87u3Dmjkqk1lAeYIEo1fgzVbm\nBggGgnz4vf+houNOPV18RcfovyZythWrEG5uX+sDC1MbDh30M59NeW/VBmt642bWhua1vEqceGh2\n6uG9nEC+tZBmdiUqXVa3Y7as2vzVubbeANzA888HOD+R5vob4zz5RJCxc5uZmA6Smo/wxBPv5Ze/\nzDA52cEnP7lQtn3lYMc1vVrb1zqLuNSJP+Nnfm7Z9zccre+MUDH3O6s/LVTnU+v23OXNjFOTKc2m\nDQsL5zl2bIQTJ7sYjWvUxnN4lIdoJEqr0WhtyK+zZGpDOf1UOdpQ7bErpViFcI+3nfTicNnHaHVt\nsLre1Rpr4UZdWHMDCycemp16eC8nkK9eaWbdmAKtGE5kVMjPmV7uOYvZOnjSu5RrffSsF0/2EKHI\nFO3tE6xbl+bMGfu/ix3X9GptX+t1slIqWut5moVa2qP4/Xjx9poMaiGcmkxpHm2IMjR0lOHhl/jp\nL65jJDSLWrdIIBjk7W96G7t2Fi0ZUhTRhuLk11kyz13O+crRBnO11JoNqR7fpViF8Ex6Fm+ob4VP\n5SLaUD61tkU9tKF5Wq8GtE6jdcaRDDex2AvE42NMTv6UQKCbcHgXXm+oIXaUE8hXrzSzbkyBthLW\nGR0wZnXedPUm0HDF1cs+pAf2B3KK2ZRDOKLrloHk+hvjS7nW99zVjbfNcIXKzEYolqHDLioZkEYi\nk1x77SDt/WdIeDqJx464bkm8WR52qqX4/RhPOGKMq9CkUtOOZD5rJm2YnDzG6dMjPPXU9YwohScy\nx8C2Af79Oz9ARzQ/I/DKNLs2nB308tqdvYQjOkcbqomXsGqD3VkL87WhUVmQilUIz6Rm6Oi9I2e/\n/ADv6PqbRRccoB7a4M472WaU8pJIDDe8NoM1MK+r63XMzR1mevpndHW9vmGBguUE8pWzTz5dXRPL\nWaF0G+eHIxD32br81sglvtzZIljyH1bk3HSHD/orLmZzxdXJpQJ84Eyqu3IYGEhnA7WXSaWS9PbO\nMT5+Pm/vHqLRO/izPwsxPNyGUm34/ZuXZl77+xN8/ONjxGJH2LLlCKOjG5hdbKOjI87U0AM89NCn\nObA/wOGD/pyjtndmGLio8ZXPm+1hR7AHrZP4/Z0Nr9nTbNqg1AkGB/+G8fHNqN5hQuEgd9763oJB\nRSP67Ea7fuRrw9lBL5u3pJmZyl1xriZewqoNbtUFqKzNg9HLeOihTzN4fJxMeh6PN4wv2I/X37X0\nQL5SgPdf/cV1rtEG0YXqWBOtEwxu4tJLP9fw81oD83y+ToLBTaRS0/j9nU0f4PqWt/wjJ054GI57\n8G49xx23vJPXXP0aW8/RDDMC1niJcFQviYt1NqtZfODvv385c1UqleInP3mcn/3s5yQSsG9f8c/s\n3/+7dHaOY9QsG1vafujQeny+L/GKVzxNKtXOojcFHkjpNjy+DgaPjxOJbs0bzJk+po0fWNQbMwOI\nyVxMMXTK3oG4UDmh0Fa2bftYw8/bbNrg8/nxeDxACp2Cxfk4Dz/2Xe78d+8lEo4s7deIPruZdAHI\n0Yb8VY5muf8rbfNz53q5+IoNeVuXBycrBXgPD95AJKrXpDaYugDNc20UY00MLOrFann811LGpbVC\ne2dmyW8VWPJd7e3PDZKbiy27IQ0P+ti3t6MpBBFgcPAE//APf8ehF2DK50f7kiX3nU5Cssii6XwS\njs7CVu8cMe2HYIJgMMgrd74yuzw+X8dvUDmHnvEXzJAB1JpQy5zpM2ba0jnbS10Pa2H5vdVpNW3Y\nvHkL119/E3NzT3DsdDdJX5IXjh3hvs/9Ke986+1cc+U1KFU/18tmwOrWlB/TYGrD6NncFeFm0wa7\nsCPAu1Hku8KBMQCo5XezrgBZtaFVdEEGFlVSTv7xtZ5xqRkwb3CrfysYA4hiLk9Wv1UoXL4+/qIf\nBSQS5DyoHnrGv3TzV7uUv9rn+gZSPP9smLmpHvR8iAwKpTK8+tXlz3xMTk7wzW8+yJEj7UxHZ1Dr\npvF6l9vh8BPvZGF6eSZqYuxiZqb68Yfm6dm+XOjYk0ji65llTvkJhZNs6tnBpTsuxev1kk5N4/GG\nC2bxweiwV2uHSjvYctp7fk6xeUvhbNjIkLdgWyVU0+HL8ntz04raoJTi13/9jezatZuHHvqfHDm6\nnvG5ReLrJ/nmP36LzvZOLtlxidNm2kopbfCX8Hhaya3pySeCHH3e0AOrLlhjKWpx8SpHG+rpPpbf\nJ5uuTNb0qVZWC/B2kzYUukkDeIqep1xaXRfcZ1GTUE7+8XplXGoG3JgCrRgrZYd4/JHCatOrkUxA\ne4dRldbaGVkfUKstptQ3kMqJ1cjnY/fP8Mvnn+Zr3/066eEeNioPr3hFknvv/WOgcDa+GHNzs6RS\nKVKpICqYJhQO8q5b7qC3x5hduv/oRfRdu7xE8ZPHOmjvjDA7vZHXvrprafvwmQB/9KE/JL34Ekx/\ni2DbBjweRTo1TSY1gy/YX1Rwhk75Vm2fSjvYSjvxM6d8JBaN2df5+fqmZmwUxe/HoL1J9AWgdbVh\nbGyUv//7r3HsJQ+T3iSqewrlUWzfvp2tfVvLPk6za0M1ujA77SGQvdsK006nc863ErVoQz3J75MP\nH/TT0VU4ODBZLcDbjdpg1YVEYjltbzPrAtRHG2RgUSXlLGXXK+NSM9BsN1qxm0tn/5O/vd4C6KaZ\nCQX0bexjU88m9u3t4NBTYU4eXfYPGhv2Mh/TdHZlcnLZh0M+Nm/cDGwmHuvJyf7R0XsHXn8X4J4H\niXBUL4lgbEYtPQS0hXVdUzM2imL34z989fipxlvS+rSiNkxNTfKVr3yeI0ciTIUXUd1TtLWFedet\nv8Xuy3ZX5AbV7NqgMSaK8rP9NWJg5CZtsJLvSnp20Mv5US+lPEmthfysuuC2rFB9A6ls3KQnRxei\nHUZBu1YocloPbWjuFnGQcpeyq8m4JDSeZhM7Jxge9BUE1U1PGR2uz6dKimwwelmBYLht1vKK3Um2\nbE/x5BNBYHl1KZkwZiidylYlNB+tqA0zM5Mkk0kSiRBq/SShcJAPv+9DFa1UNCuiDauT70o6PeUh\nsahYmK9MF8x93KINH7t/Zmkw97/+enniLLGoODvo5fFH2mqOw2tFZGBRJc24lC3YT34nmEop4oug\nvHFGxs5hTtnMzHbwN19/oKxjvnji33JuZqpg+9SFLv7m699d8bPjU+NkMhqtNRpNW9s4H/3oCwwN\nhfB6wwSDW/D7DZelgYF0Tiaoati6PcXMlIddu5MrLsXn41axNl0WgiFTLZRlSX9ZOJspkE5oLK2p\nDeaKhAatSSXTvHz6ZbZs3rLmg7aLka8LczFFImHMdLsBs4bE5//iBsbODSylgzWptR/bml1VGRny\nVqQL4F5tWHZzNjG0wermLLpgIAOLKqnXUvZq2UQEd2HtLPbt7cDr1czE4mTSaSYnjCBnjzdFKDLF\n4SNHyjrm1NRNJLhQsH1+Sq16jBf+z2+zMN2DmovQ3TXDqVOLHD26m97eWa677gUymZN0dLyaQKCn\noGZFK1FtEJ/5AGA+SAVCmjOnfMRm1FJhLIALY17awhk29mZyAhTtWBZ304ydUDn10AandWHz5n76\n+7cxNjbKmfEuksERHvnhozx95CC/e/v7Wde5rmG2NAP5ugAB/AHDzfLIc4bLkD9guNM06r42+8R0\ncorEvB+lbufgLy+lZ9M411z7c0Idr8IbMDSr2d17SlHNg7/ZH5uThiaLi3D8qJ/5ecWbdm9ifk7l\n6AzT/QIAACAASURBVAKwpA1rTRda8+ppEHYvZZeTTURw76zAiZdSRNe/QCThw6M0mZSPRCJIfDHM\nlk3HCA4OlHUc71wEnyrM4eqdi6x6jMTINro7L9Dzinl27EgSCCjOnEkxO9uGxxMCYH7+JQKBnsq/\nINVn66iF/N/70DN+Iyd8VHPF7uI54asN4ttzV/dS4KHJ8aN+AgEjYM9c7p+PechdzbCPtTSz1arY\nqQ1u0AW/P8Cdd36Qyy//Bd///mO8eKqfePc4p0+d5lNf/Az3vv8P6O/tb4gtpXCrLgwP+ujtTzMz\nuUAmNYvWSZTys7DYxW3vbpxtZp+4MPU8OhNHeYIcO5pgLtaO8gRJzL9EWyC/9kR5WGPU8rfXi2K/\n96Fn/Bw66M/RBVjWhmpiVMzf58D+3hx3r+NH/QRDmkQClDK0waoLgK3a0Ey6IAMLF1FONhGh8QFs\n5QrW/Nw5rr76f5OK+yEZIBKOEwx6GRrazd13/wLoKDhGMRZmFxgf31Sw/eKLpnnLv1n5GEOnAlx8\ncR8DAzuYmPgRSrXnvK9UkFSq+g6q2mwdtZD/e1uDqctdZjdzkVtXHWC5kKH1t8wfPCWyY7xSaSYF\noZ64RRc8Hg+vfvUNXHbZlXzhC5/ipZfXMbEYJt47weDwoOMDC7fqAsB1N5xlceaXKE8QpQJoneDs\nmQ4+/IdJoPyAZTtmrTOpWTzeaM42pQJkUrNlHyMfM0Ytn3qufBT7vc1g6nroQv7gyVzZFl0oRAYW\nLqLZiiatFcoVLE/mPOmMh3TKj18B+Onu3kQi4eN97/tg2ed73/tKvdMLXLriZ594opPt241ZFZ+v\ng0xmMed9reP4fKsPcJ599o3MZIJ4Qxnue3Y7baG2VWeD3IyZi/zsoDdn1mlmylOQ2SN/8FRNeklB\nsAu36UI02k4gECQUTEN87T5VVTKQScy/ZAwqPEHAmOBRyk9s/NGKMiHZMYHj8bUbKxYquLRN6wQe\nX/sKn1qmmpUCt1KJLuQPnh5/pK0uK9atgAwsXESzFU1qFhq2RK4XSWdy4xY8niBph6pMh8M7mZra\nTzodQ2vNwsJJvN4wXV2vXPWz8/NdhDedwR/O0Lc1QSTir3g2qNGYv/Ohg34GT/hIJSGTUXg8mkxG\n4fdrkkkjH/nWIg8EUHxGcC6m6O1PFy2YKAj1RnShPjTSdar4KoHfkUrTgfBOFqZ+TiY9Tzq1DaU8\nZFLTtHXdUNbn7VgpaCTW3/lHjxruwIsLRgydqQ/JpDJcXkO6bG0wCyeWKqa7lpGBhYtozWwizmPt\nCM2lTzCqg5odjlVMKq0iuoQK4fWkSVmK0WUycbzeZb/VvXujDA4WBk3bkaGpGFprotFZxsY2Mjy8\nGY8nyPR0CL/fy8BAa6VPNX/nwwf9hMOauZgiGNSkUuD1Gr6+Hg9LRY6KUeyBwiyQZV25CIQ0sRnF\nzJSHudhySkW3z9AJzYfbdCEejy8V0cTbvNd7w3SBUqsEyaVK08WOb1KPgY6ZIjUajXH+/CaGz24i\nOB3C62+9fix/INTeoUkmDA3wejU+HySTRibAeAXaYC2caGqDVReAJW1opfYsBxlYuIhmK5rUjJhL\nnwaeosXPKq0iarK+L8ipY32kEz48GR+hUIp0uoNLL10eWAwOepdclazYlaFpYCC9dKxY7AKZzMVc\ndJHmhhuO8pGP/JRUahq//5/Ztu1jtpzPrQRCmplpQyTSlub2VtHM5kyV1ssV1H0+Td9Ahl27k44H\nhwqtjZt04ciR5/judx/m6MtBYu2zqI5ZfL4AfRv7Vv+wi6mnLvQNpDhz/JUk5o+hlB+l/GidZFPv\nCNH1N5c8voldcQpmPxaPjaMzO1CeAAM7NNe+9ggf/PC38Ph/yPpte2w5l1vxB4zBQzpPgqvJmGxd\nwTC1waoL5j5rURtkYOEymqloklPUO+3aoYO5VUSPv7h8LnNmYmzUSzJhzFqYTEzM0tk9zN133o9X\ne0gmu1m//g6UOs2Pf2zsMzLyWrQuXJkYHY3y4x/vr9n2179++d9KPYhS3ZipU0+dAtBofYTjxx/L\n+dxDD13N2FiEVKqdiYkPMDa2He9sD8HoPFxds1k1zcZV83tv3Z4isehfdRaqHNaiMAjuwmldSKVS\nPPzw1zlw4CVOjkXQW4ZRvgwbN27kd3/r/fRtcn5g0Yh0nNaVDVMXMhnF2KiXjb3G0+rYiDdHF9LJ\nKVLxs2zaNMwHPvhXeLydBNsvJ7r+5oZWmjb7sbEXP4s30ItSywMirdtLumXVslKzGo3WhY29GTq6\nMhw/uqzvwZDm/LnKXZlEF0ojA4s8nM4XLqxOvW/o+VhuFVFzljtpiUtLJozZD3OGKZ24QCT4NC8c\n2cKJyR6CaQ8RpTlw4CWmpoxid08/fTPHj3fh9+cGAwcCi0Sj43zjGwd5+umbicUKc8JHo5P86q8+\nVrB9Ja68MkkwOEgisbz8HgjEiceDPPfcwZx9//VfL6e9fcT4bhmFt20Of3iWVLyDQKC6AE2raBzY\nHyCSTT1Yad0HO35vc4naH9AszHuWVh7CEW3rUrVbU14KtbOWtWFkZIiXX36B0dFNsGkEX1Bx0w2/\nwVtveisejzv8yxtxf1lXNpZXPzVJS3bwZJIcXTCzQY2cfQXByOVkUjM5gwqzzzAf2E3yH9zt6lu8\noT4yyWm8lpidTHo2xy3LSrUrNSth/c4RS0raSuo+2KUL5sTT7IwilVKMDHlt1wVYW9ogAwsLbsgX\nLriPSFQTDGlmZxRvvm0BKMwUlJh/iWCwne5162lrC7OwuACeJD2XPcuJ47sAmEiH8LTN4QnN5Xx2\nYTFCoG2BiY0jTKRDhHsHC2yYmO5hYuNIzrYjP3snCzOFecfbOi5w2eu+zfOxTl7Vc4xM0k885Sfo\nS6L9SZ4/eUnBsRJtCyxEsispSuOfikF6HZHgBs6dXe5cK+lorYJkrQ3hRBYNs0L4m29bqGuQYaNT\nXgqNYa1rg9bGvauUBzyKQMjPdVdf55pBhROYuhBfVPT0ppe04eGvhZf2yckGpdTSw3xs/LGlgYU1\nNsxaPye/nyy3b1ntATa6/mamhh4AwONtJ5OeJZOaoaP3jrK+t5mO2xpbZh6/XMr9zvXGDNQWbbCX\n1vtGNeCWfOGCvViXTM1MDmB0kMWYmvBkC90YzEwrvLFCv0wrZtaPgD/Ar1/7Oo6fPs7ZsbNsiM6x\nrsdI4xcI+vH6PPj83rzPeggE/azraScQ9BMKF64QpBb9S8cxScc30725sEJ3bGIz63raydDOS1Nh\ntq07zfroHHOJKC9NbiPTtp51eRlUref1eDz8xm966NsYZeh0ypWZPoqxFAuB4e+aSMDUpPE7Pv90\nAJ9P8/DXwoSjmn17O1pulkioH6INrUelugCGC+z5UaP/tuqCpnjwVrFsUB5vabcjO1jtATYYvYyu\nLfcQG3+M9OIw3lAfHb13lO2WZV1tbgZtyImFwNCGmWlFJmPoArCkDZv6WiuhiVPIwMKC2/KFC/Zg\nfYDMn82xK5uPx9fOgSe3c35sPXv/6D0A6EwC5Qlw0eWX8rH7Z9jzXDeDYW9Baro5FG+9uZOPfeQ+\n9jzXXVIUPvGR+3K2VbJvPvntkJwMkEhqW/xmy8H0VZ6LqRx/5FqWhVfK6JRPK84SCfVDtKH1qFQX\n+gZSLMwrwuHcatKBIDmuUFZMXZiLtTM/H+BP/vBdFl3IndzIL8xZz4xCwehlRQcSxVY7DuwPMHjS\n2xBdAEMbRs/mxqlA9dogutB4pBUtSL7w1qecjqmrO5MTY3H8qH/JFaoUgfBOYrMe2toW6OsfR+sE\nOhMn1PEqfvRoW1n+pHvu6l7Vz9Yu6uE3WwnLvsqeHDvc1rGvJb9YoTSiDa1NOffyx+6f4ZFvhJe0\nwdQFoKQ2mLrQ3jELRNncN7KkC8ODPvbt7Sjo88Ho93ftTvOXX55g396OhmlDsdWOwwf9Da3TMDvt\nIRLVBXa4SRtEF1bGPb+UC3BbvnDBGcJRnfOArVkeVFjTy6GsnV0vi3Ho6Rkhk47h8bUTjF6BN7CB\n+Zha1Z/UaZ9TO/xmi2GuTIyNejmbrd+RSil8vuVAbjezlvxihdKINgiQqw1WXYDlPmFTX7pAF9Ts\nFJHoNMoTXNIFMPqXSFTn9Plg9vvppX2c1obRs96CPs8ObbDqAixrw6+8MrnCJ51HdGFlpBUsuClf\nuOAcV+xOluw0VvIpNZZXe4A3FX1/paVuc/ajkcvhVurhN9s3kFpapTFTMcKyUJnBjq1EI1JeCo1H\ntEGA6rTB0IUoEAX6C97P7/PB6Pfz+wwntcHueApTG6y6AMva0Ci3q0aylrRBBhZ5OJ0vXHAX1rzl\n1niAapY88ztLa2dtHte6z5NPBJmLGbM5w4O+gnPXq6Oya5n3Y/fPlJzZsWZOaSVqXQaXJXb3Itog\nWLFLG4o9RA+d8hUco1xtqOcDrGhD9awlbZCBhSBkMW/cQ8/4ObDfyBZxYcxLWzjDxt4Mvf3pohVZ\n60UpX9MfPtJW1w7GDcu8lXaipfY/faJ4tpZ6zRLV2vm7oe0FQVjGek9btWFqwkP/gDHj7gZtePKJ\nIAf2B7j2tbmR5HY+eLqhf6qkj3WLLqxkSytqg/ssEgSHMG9c681r1qsox22n1ExROKKL7F09ZsxG\nPpV0MCvNahXr/IpRS0cZjuoVZ9Uq7USL7f/kE0HODXvZtiN3ub2eMzzN1PkLgrA61nvaem8//LVw\nTbpQSV9bDnYEPdu12uEWbSi176Fn/JgxLI1iLWlD630jQXCIUh3mvr0dq3bWxTr0uZiit7+2zm+l\nDn4ln+ByqKWjvGJ3su450N0ktIIgrE1WepB2Shuq0YVKcLs2zM/VNjknurAyMrAQhDpjCkt+Z276\nxpaaxSmVa7sSnJ4lOX3Cm+M6kEoZWVTawpma4lUahVvtEgSh+XFKG5zWBWhubXCjTW7C8YGFUqob\neBAjlc4F4D9rrb9RYt/7gP8KWKOdrtRan6i3nYJQK27ozBvNth1pbrjJuF0ff6RtKV3izJSnoT7J\nQnMhuiCsJUQbRBtaCTf8al8AEsAmYDfwfaXUs1rrwyX2/5bW+r0Ns05Y0+Tn8D500M98TBGOaNsq\nRpei2pgN6+yXtahSuQWV3LLMa826YjIXU+zb28EvfmLET5hcGPPi82mi7Zp33jXXUDvtxC1t7wJE\nFwRXE47oAl0wt9d7xr0a96hadaHUec3tjeLQM/6CgoJg1Jbat7eDH32vjfk547cwdcEf0Fx8aaqp\n09i6oe3LxdGBhVIqAtwOXKG1jgH7lVKPAncCH3fSNmHtUezGHbgozWteH18Sh1JL0PWYWbEKUs5S\nuTICB6cmjIfurm5jpscUtEPP+HnLvzWCCq1Flcwc6E8+EWT0rLcglsIUQTcs81prYFjp7U8zPOjj\n3LA3pzp6KqVILCqmJj1Lv4UdMSrV2F1L5++Gtnca0QXBTZS6p99064IjugDFtSEc1Yye9RZog6kL\nB/YH6O1Pc/2N8ap0If+8jlG8yDkoY+VHKZa0wdSFRELlTBCGo/YmVCmHtaQNTq9YXAKktNbHLNue\nBV6/wmduUUpNACPA57XWXyy2k1LqHuAegIGBwqI0gpCPm2/cYplJSmWsMv1WS2FHUDPUdwalVJ5z\nM6XihTEv87Hl1YxASHPxryQZGfIuBf6Zgmt3xdjV7BZqpm66AKINQmW4/Z4uN5vh4YP+ghVgK3bp\nAtRXG4oVKTQHRQf2B5ia8HB+1FjNNnVhZsrDLktQeDlB83bj9uvITpweWESB/NaeBtpL7P9t4AHg\nHHAd8B2l1JTW+u/yd9RaP5Ddl2uuuarxw1OhaVkpY0aznddardWs1GrXTH61HeWZUz5iM2pJ/Mzi\nUiu5DZhuUWcHDcFIxBU6A14fdHZliC8WTmOtpY68xaibLoBog1A9pfroQwf9NSfaqOa81WpDPXUB\nqut7z5zyZVcXyNGGfXs7Vjzek08EOfr8smvU4oIimTC0wU77hPKp68BCKfUTSs8y/Rz4MNCRt70D\nmC32Aa31C5aXTyql/gfwDqCogAhCNdQrkG61WZx6nNfqU2pW+rYj21S5WL+z1nDkOT9zsx68Pr00\nSPAHNIMnixcsMpmd9tDRlVmaiVpc0Ph8kHKfe6mwCqILQrNSqo9ebZV4NRqtDU7rAuR+57ERL1MT\nHpRH4/GwpA3Rdr1qrY/ZaQ8BS/MnE4g2OExdBxZa6zes9H7Wl9anlNqptX4pu/kqoFSAXsEpKO1x\nJ6xBnCx7v9q51+IsSf533nNXd45/r4kxe9bYeAjBGUQXhEbjZl2AtTmDbv3OK+uC0Gw46gqltZ5T\nSj0MfEIp9QGM7B+3AtcX218pdSvwU2AKeDVwL/BfGmSu0ATYNbNTKiPRpr7iD79mFdVKz21Hpo58\nVqtc6nbMNjl00L80E3hhzEtbWJNMQLRDE1hUxOOQyShmZxSplGJkyFvy9xGaB9EFwW7s0oVSGYnm\nZj0l+9xqz223NuRnOLTa2Czs29uRowtgaEMqCRs2GYOSudiyNszPw8iQl3BUN9X3bHacjrEA+H3g\nS8AYMA58yEwpqJR6HfCY1jqa3feO7L5BYAj4tNb6K403eWVisRcYH/8Bi4tDhEJbWL/+N4lGL3fa\nLKECTNebXDxs25GuuWK1yb69HTzyjfBS5qOpCQ/zMSPgLJ9iS+U6+5/87W96+8KKM2CNSltXbKbu\nR4+GWFxQdHTmfkeNZlf23ysFIwI5v8vMlIc337awtJwvtAwtpwsg2tDszM+pnGx0JnMxZZsuQH20\nIT/DYTnHMbfbTb42/OjREPNzCr+fnEyAVl0AQxvekpes5PFH2jg76GVrVi+2bje25wdsC43D8YGF\n1noCuK3Eez/DCOQzX7+rUXZVSyz2AkNDX8Tn6yIQ6COZnGZo6Its2fIhERAhh+FBH5GoXnpQPj/q\nJRjSdQlEtssVoJLjlJqpAwjmCeTsTHmeK9agQ1gOPJTZqNai1XQBRBuE8mmUNjihC1BcG/zZhSCr\nNpSrC/6ALnCbmosp0QWHcHxg0WqMj/8An68Ln68TYOn/4+M/EPFoEswaCpDbUbV35q9g2EsgKxyJ\nxPIDs2lPKcrt0O1yBbDjOF4vBQKZSq0sAuYy/q7daayxGI3wkRYEOxBtaH7C0cIHWHN7PalUG5pS\nF3yQiOdqw2q6AIY2zMUUu3Ync7aLNjiHDCxsZnFxiECgL2eb19vO4uKQQxYJlVKqhgLUr+ARsLSU\nW8kSrhN21oI/AKDp6c11J9B65Zm362+Mi7uT0NSINjQ/xWooQP3720q1oRl1QWFkhLJqw2q6AKIN\nbsSdV1kTEwptIZmcXpqNAkinZwmFtjho1drBybL3Tp67XuTPfJlBhNUGEG7MikZ+UT+3Cp4g2IVo\ng3OILtiPncHlG3vTdHRllmLmTEQXmhP51Wxm/frfZGjIKPrq9baTTs+SSk3R29sUbsBNi93pBKsR\ng2rOkx8zAO7yDc2f+TJTAtYzDWCrCrGwthFtcAY7taFRugDNpQ3WVLGiDYIMLGwmGr2cLVs+lJP5\no7f3XeJDW2fsXvpthG/mcmeYLthezvn37e3ImSkyae/MMHBR/dKu5lfOhuIVUotmK8lWGFgp5aH4\nxQqtiGiDM9ipDY3qm2rVhlJpcXUdQ0EqqZydrw1aL6eFtW7PHzCINjQHMrCoA9Ho5SIWQt0L5uVn\nDjEpVmyumpmeYgOXsVEPs9MeQm25CtXbny74ruV+P7Od8tMyui34zskiW0JrINogQP21oVRa3JEh\nb8H5qlkByNeGsVEP05Me/H4jQ5NJMV2AyrShWLpet/W5og25yMBCEOpEIwLoyl0ur6ZzKzZw6ejK\ncOQ5P/0DadviJJol0LBZ7BQEwd3Uuy8pN3tVtQ+9+drQ0ZXh+FFjkNHTm7YtTqJZ+txmsbNRrM1v\nLQgtQrEguaFTPlvqXhzYH2BqwsP50eVZrmIFmgRBEAT3UM/sVaW0YS6mcorbCWsXGVgIglCAOSOV\nSmkSlrzisRlFKqVo78zw5BNBZqdzi9Xtuau7pZZ/7cx8IgiC0OyU0oZkEhIJCrTB1AVoHdcgu7Ml\nthoysBBaAskWUR+25s16zUx5mIsprr8xzuOPtC0thZsB3YcP+jmwP7DU6Ta7kDiR+UQQBPsQbagP\n+dpgusjma8PYqG9pQqZVtMGJbInNhAwshJbAjg6q2QKwnBLMcMTI3DEXU5jVyWMzimiH6XPrWep0\n16qPqSAI7sAOt9Bm0gVwRhv8geWq4FZtAGWJ0xNtWAvILysIWewOwKp3515vUSsVGH7bu+f52P0z\n7Lmre6m9rDNUldIsM4rW9jAFFNxnpyAI9lGPwNxW1IZINFNSG6ql2bTBqgvgPjsbhQwsBKFO1Ktz\nLzaDdugZPygjaM9KLbNq9QoMz8ets375WNtj6JSPv/zyhIPWCILQrIg2lEezaYPogoEMLAShQpxc\nGt+3t4NHvhEuyL5x/pyXjb3pgpk1t662VEszuiUIgtD6ON03iTaINrgFGVgIQoU4mbO6VFG8s4Pe\nEp+ojnI6YqvAWH1q2ztzbbOzw2902+eL6KGDfuZjinBE5xRuEvEShLWN07UMmk0bDh3021r8rpHt\nL7qwMjKwEIQWopEpYK3Hyx88WOMP8jt800ZrhhBzX7d1wvn2WH2HrUggoiAIbsZt2oBGdKFFWZvf\nWhCK4NYl3kqYnfbkzVgZWTjq3cGt1PHnz0ot2+jJ6YzXaicsCIJ7aQVdAPdpg+hC6yK/mCBkcdus\niCAIguAsoguCUBkysBAEl7FSTAIUT/UHoIH5nPzhhfEOgiAIQvNRTqyaaIPgBmRgIQgVUu+l8ZWC\n0JbPkc55741vXyjIH14JzZJRo1XcEgRBaC2c1IXc84g25G8XGosMLAShQpzsTPPPbXb6w4M+9tzV\nzYH9AQ4f9NPemSmaa7wU9cyokd/hm1lCqpkxc1rIRLwEQSiG031TqYBpqzYMnvRWpAvm5+uhDXbq\nAjjb/qILucjAQhCamPxOf/Ckl9lpD6Nnva6pAFpqMAS4xsZycfrhQRAEYTWKDQYGT3oLdAGc63dF\nF1oXGVgIQgvRDBVApRMWBEFoLNffGBddEBqCDCwEQagrzeKjKwiCIDQO0YbWRAYWguASzE720DN+\nDuwPLG0PRzVX7E42xZJwMZyuSCsIgtCstKougGhDqyK/niC4BLOTze9oG7F8LcFngiAI7sNJXQDR\nBqFyZGAhCE2MXZ2+LDsLgiC0BnYOBkQbhEqRgYUg2EijfUal0xcEQXA3ogvCWkIGFoJgI3b4jD75\nRJDZ6eUKqXMxxZ67uiWgTRAEoQkRXRDWEjKwEASXMTvtoaPLWiTIw5btxZe2mwHx0RUEQaiNVtMF\nEG1oVZr3ihSEFsPsZM0KpCbVViJ1CzKbJgiCUB2tqgsg2tCqyMBCEFyC2cnuuau76LK5IAiCsLYQ\nXRCaDc/qu9QPpdQfKKWeUkrFlVIPlbH/R5RSo0qpGaXUl5RSwQaYKQiCIDQQ0QZBEITmxOkVi2Hg\nk8CbgbaVdlRKvRn4OHBT9nPfBe7PbhMEVyA+o4JgC6INQssguiCsJRwdWGitHwZQSl0DbFll998B\nHtRaH85+5k+BryPi4Qidnd0Eg7P4EiEC/jYibRGnTXIFdviMNlqEGp0KURBWQ7TBXfj9QSKRCG1t\nHpKqjWgEfF6n5yWbh2bUBRBtEKpDaa2dtgGl1CeBLVrr96+wz7PAn2mtv5V9vQE4D2zQWo8X2f8e\n4J7syyuAQ3bbbTMbgAtOG7EKbrfR7faBK228eDvEE8uvdRjUPAQDcPyUU1atggvbsQC323ip1rrd\naSNWQrTB9dcQiI124UIbm04bXNiGBTSDjTVpQzNNOUSBactr89/tQIF4aK0fAB4AUEo9pbW+pu4W\n1oDYWDtutw/ERrsQG2tHKfWU0zbYRMtqg9vtA7HRLsTG2nG7fdA8Ntby+boFbyulfqKU0iX+9ldx\nyBjQYXlt/nu2dmsFQRCERiDaIAiC0LrUbcVCa/0Gmw95GLgK+Hb29VXAuWJL3YIgCII7EW0QBEFo\nXZxON+tTSoUAL+BVSoWUUqUGO18F7lZKXa6U6gL+GHiozFM9ULu1dUdsrB232wdio12IjbXjWvtE\nG5Zwu30gNtqF2Fg7brcP1oCNjgZvK6XuA/bmbb5fa32fUmoAeAG4XGs9mN1/D/BHGOkHvwN8UGsd\nb6DJgiAIQp0RbRAEQWhOXJEVShAEQRAEQRCE5sZRVyhBEARBEARBEFoDGVgIgiAIgiAIglAzLTmw\nUEr9gVLqKaVUXCn10Cr7vl8plVZKxSx/b3CTjdn9P6KUGlVKzSilvqSUCjbAxm6l1HeVUnNKqdNK\nqXevsO99SqlkXjvucMomZfBppdR49u/TSilltz012tiQNity3kruj4Zfd5XY6OD9G1RKPZj9fWeV\nUgeVUjevsL8T92/ZNjrVjo1EdME2G0UX6mujI7qQPbertcHtupA995rXhpYcWADDwCeBL5W5/y+0\n1lHL30/qZ9oSZduolHoz8HHgN4BtwA7g/rpaZ/AFIAFsAt4DfFEptWuF/b+V144nHLTpHuA2jNST\nVwK3AL9XB3tqsREa02b5lHXtOXjdQWX3sBP3rw84A7we6MTIRPRtpdT2/B0dbMeybcziRDs2EtEF\nexBdqK+N4IwugPu1we26AKINrTmw0Fo/rLV+hCJVV91ChTb+DvCg1vqw1noS+FPg/fW0TykVAW4H\n/kRrHdNa7wceBe6s53lttOl3gM9qrYe01meBz1LnNqvCRkeo4Npr+HVn4vZ7WGs9p7W+T2t9Smud\n0Vr/E3ASeFWR3R1pxwptbHncfk2B6EIDbBJdWAG3a0OT3MNrXhtacmBRBVcrpS4opY4ppf5Elc6X\n7hS7gGctr58FNiml1tfxnJcAKa31sbzzrjQzdYtSakIpdVgp9SGHbSrWZivZbheVtlu926wW0Tb3\nvgAABbVJREFUnLjuqsHx+1cptQnjtz9c5G1XtOMqNoIL2tFluL09RBcqt0l0wR5c0aetgivu37Wo\nDW7rKJ3gp8AVwGmMH/lbQAr4cyeNyiMKTFtem/9up34j9ygwk7dtOnvOYnwbo6jKOeA64DtKqSmt\n9d85ZFOxNosqpZSub47lSmxsRJvVghPXXaU4fv8qpfzA14GvaK2PFtnF8XYsw0bH29FlNEN7iC5U\nbpPogj043qetgivu37WqDU23YqGU+olSSpf421/p8bTWJ7TWJ7PLQc8DnwDe4SYbgRjQYXlt/nu2\njjbmn9M8b9Fzaq1f0FoPa63TWusngf9Bje1YhEpsKtZmsTqLR7HzmucusLFBbVYLtl93dlOP+7cS\nlFIe4H9h+E7/QYndHG3Hcmx0uh1rRXQBEF0oxybRBXtwtTa4oT9by9rQdAMLrfUbtNaqxN9r7TgF\nUFOWiDrYeBgj2MzkKuCc1rrqEW0ZNh4DfEqpnXnnLbVUVnAKamzHIlRiU7E2K9f2Wqil3erRZrVg\n+3XXABrWhkopBTyIEYx5u9Y6WWJXx9qxAhvzcdu1uCKiC4DoguhC42g2bWhoG651bWi6gUU5KKV8\nSqkQ4AW8SqlQKZ8wpdTNWf8ylFK/AvwJ8D032Qh8FbhbKXW5UqoLI4L/oXrap7WeAx4GPqGUiiil\nbgBuxRjdFqCUulUptU4ZXAvci83tWKFNXwX2KKX6lVJ9wEepc5tVamMj2qwYFVx7Db/uKrXRqfs3\nyxeBy4BbtNYLK+znWDtSpo0Ot2NDEF2oHdGF+tvolC5kz+1qbWgSXYC1rg1a65b7A+7DGFVZ/+7L\nvjeAsfw0kH29D8OXcQ44gbHM43eTjdlte7J2zgBfBoINsLEbeCTbNoPAuy3vvQ5jCdl8/XcYPoEx\n4ChwbyNtKmKPAj4DTGT/PgOoBl1/5drYkDYr99pzy3VXiY0O3r/bsjYtZu0x/97jlnasxEan2rGR\nf6Wuqex7rmiPSmx08LoSXaivjY7owkrXn4uuvbLsc7I/Q7TBuKEEQRAEQRAEQRBqoSVdoQRBEARB\nEARBaCwysBAEQRAEQRAEoWZkYCEIgiAIgiAIQs3IwEIQBEEQBEEQhJqRgYUgCIIgCIIgCDUjAwtB\nEARBEARBEGpGBhaCIAiCIAiCINSMDCwEQRAEQRAEQagZGVgIgiAIgiAIglAzMrAQBBtRSrUppYaU\nUoNKqWDee3+rlEorpe5wyj5BEASh8Yg2CGsFGVgIgo1orReAvcBW4PfN7UqpPwfuBj6stf6mQ+YJ\ngiAIDiDaIKwVlNbaaRsEoaVQSnmBZ4GNwA7gA8B/B/ZqrT/hpG2CIAiCM4g2CGsBGVgIQh1QSr0N\n+Efgx8CNwOe11vc6a5UgCILgJKINQqsjAwtBqBNKqaeBq4FvAu/WeTebUuqdwL3AbuCC1np7w40U\nBEEQGopog9DKSIyFINQBpdRvA1dlX87mC0eWSeDzwH9tmGGCIAiCY4g2CK2OrFgIgs0opd6EsdT9\nj0AS+C3glVrrIyX2vw34nMxKCYIgtC6iDcJaQFYsBMFGlFLXAQ8DPwfeA/wxkAH+3Em7BEEQBOcQ\nbRDWCjKwEASbUEpdDvwAOAbcprWOa62PAw8CtyqlbnDUQEEQBKHhiDYIawkZWAiCDSilBoDHMXxj\nb9Zaz1je/lNgAfiME7YJgiAIziDaIKw1fE4bIAitgNZ6EKPwUbH3hoFwYy0SBEEQnEa0QVhryMBC\nEBwiWyzJn/1TSqkQoLXWcWctEwRBEJxCtEFoZmRgIQjOcSfwZcvrBeA0sN0RawRBEAQ3INogNC2S\nblYQBEEQBEEQhJqR4G1BEARBEARBEGpGBhaCIAiCIAiCINSMDCwEQRAEQRAEQagZGVgIgiAIgiAI\nglAzMrAQBEEQBEEQBKFmZGAhCIIgCIIgCELNyMBCEARBEARBEISa+f9ULiq5AplTMQAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 792x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y84O9csnCiXt"
      },
      "source": [
        "# Random Forests"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZuuhxcUCiX_"
      },
      "source": [
        "bag_clf = BaggingClassifier(\n",
        "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
        "    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BneKOnsCiYO"
      },
      "source": [
        "bag_clf.fit(X_train, y_train)\n",
        "y_pred = bag_clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUV1RunOCiYm"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
        "rnd_clf.fit(X_train, y_train)\n",
        "\n",
        "y_pred_rf = rnd_clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-ZOWZOaCiY6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "55b2d09b-6a53-4561-d96e-551d8c7792ec"
      },
      "source": [
        "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.976"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mudM1tu3CiZL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "0597f3be-09b8-4045-b82c-0256963c1fb6"
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)\n",
        "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
        "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n",
        "    print(name, score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sepal length (cm) 0.11249225099876375\n",
            "sepal width (cm) 0.02311928828251033\n",
            "petal length (cm) 0.4410304643639577\n",
            "petal width (cm) 0.4233579963547682\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eO6hYj4CiZU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e1895a46-3413-4550-a0a4-01076498b4cd"
      },
      "source": [
        "rnd_clf.feature_importances_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LmjZvTRCiZd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "d87ebc4a-91d8-4ce9-978b-46aac117c6dc"
      },
      "source": [
        "features = iris['feature_names']\n",
        "importances = rnd_clf.feature_importances_\n",
        "indices = np.argsort(importances)\n",
        "\n",
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
        "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "plt.xlabel('Relative Importance')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+ERHTcp/cnc/d4YcR8UZEjANeA66IiBciYhpwB7BVZd+OtlepPXFEnB8RoyNiNKzewEsy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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpLnpBSBCiZw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "c7434059-0aea-4bd9-a87f-8c0389f4687c"
      },
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "\n",
        "for i in range(15):\n",
        "    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
        "    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
        "    tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])\n",
        "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7TltWhifZT3H2TVLXBbX8p3jcwzDvkBxPUy5EyDsxRCrHWEiysWMQMvdQYzVy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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEf2FUrXCiZ4"
      },
      "source": [
        "### Applying a Random Forest and XGBoost model to Bankruptcy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlfqmnywCiZ6"
      },
      "source": [
        "The following datasets have been set up so that we can predict a bankruptcy one (df_1) and two (df_2) years in advance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vOfzU9xCiZ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "3e229ee4-1f5c-4a39-bd3f-6e62a6cba8da"
      },
      "source": [
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "github_p = \"https://raw.githubusercontent.com/Finance-781/FinML/master/Lecture%206%20-%20Ensembles/Inclass/\"\n",
        "\n",
        "my_file = Path(\"data/one_year.csv\") # Defines path\n",
        "if my_file.is_file():              # See if file exists\n",
        "    print(\"Local file found\")\n",
        "    df_1 = pd.read_csv(\"data/one_year.csv\")\n",
        "    df_2 = pd.read_csv(\"data/two_year.csv\")\n",
        "else:\n",
        "    print(\"Be patient: loading from github (2 minutes)\")\n",
        "    df_1 = pd.read_csv(github_p+'data/one_year.csv')\n",
        "    df_2 = pd.read_csv(github_p+'data/two_year.csv')\n",
        "    print(\"Done\")\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Be patient: loading from github (2 minutes)\n",
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTF0jlPLCiaE"
      },
      "source": [
        "## Lets put the files in a dictionary\n",
        "df = {}\n",
        "df[0] = df_1\n",
        "df[1] = df_2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7r5vPUXuCiaY"
      },
      "source": [
        "### Dealing With Imbalanced Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKX633UACiaa"
      },
      "source": [
        "Data Imbalance is a condition where the samples belonging to one or more 'majority' class labels of a labelled dataset heavily outnumber the sample belonging to the other 'minority' classes. It critically affects the modeling as the models won't have sufficient data belonging to minority classes to train on and this leads to biased models, ultimately leading to poor performance on test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "px57XmNVCiad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "5f9160e7-7155-4d0c-a3bd-3f1a36e4afe9"
      },
      "source": [
        "\n",
        "def check_data_imbalance(dfs):\n",
        "    for i in range(len(dfs)):\n",
        "        print('Dataset: '+str(i+1)+'year')\n",
        "        print(dfs[i].groupby('Y').size())\n",
        "        minority_percent = (dfs[i]['Y'].tolist().count(1) / len(dfs[i]['Y'].tolist()))*100\n",
        "        print('Minority (label 1) percentage: '+  str(minority_percent) + '%')\n",
        "        print('-'*64)\n",
        "\n",
        "check_data_imbalance(df)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset: 1year\n",
            "Y\n",
            "0.0    6756\n",
            "1.0     271\n",
            "dtype: int64\n",
            "Minority (label 1) percentage: 3.856553294435748%\n",
            "----------------------------------------------------------------\n",
            "Dataset: 2year\n",
            "Y\n",
            "0.0    9773\n",
            "1.0     400\n",
            "dtype: int64\n",
            "Minority (label 1) percentage: 3.931976801336872%\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLD4euNBCia1"
      },
      "source": [
        "#### Oversampling with SMOTE (Synthetic Minority Over Sampling Technique)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "In3bBl3HCia4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "bcad45ed-2558-47fb-cf79-1b54d9a25efc"
      },
      "source": [
        "from collections import OrderedDict\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "def set_new_headers(dataframes):\n",
        "    cols = ['X' + str(i+1) for i in range(len(dataframes[0].columns)-1)]\n",
        "    cols.append('Y')\n",
        "    for df in dataframes:\n",
        "        df.columns = cols\n",
        "\n",
        "# Split the features and labels into separate dataframes for all the original dataframes\n",
        "def split_dataframes_features_labels(dfs):\n",
        "    feature_dfs = [dfs[i].iloc[:,0:64] for i in range(len(dfs))]\n",
        "    label_dfs = [dfs[i].iloc[:,64] for i in range(len(dfs))]\n",
        "    return feature_dfs, label_dfs\n",
        "\n",
        "# Performs the SMOTE oversampling fro given dataframes.\n",
        "def oversample_data_SMOTE(dfs, verbose=False):\n",
        "    smote = SMOTE(ratio='auto' , random_state=42, k_neighbors=10)\n",
        "    #Split the features and labels for each dataframe\n",
        "    feature_dfs, label_dfs = split_dataframes_features_labels(dfs)\n",
        "    resampled_feature_arrays = []\n",
        "    resampled_label_arrays = []\n",
        "    for i in range(len(dfs)):\n",
        "        if verbose: print('Dataset: ' + str(i+1) + 'year:')\n",
        "        if verbose: print('Original dataset shape {}'.format(Counter(label_dfs[i])))\n",
        "        dfi_features_res, dfi_label_res = smote.fit_sample(feature_dfs[i], label_dfs[i])\n",
        "        if verbose: print('Resampled dataset shape {}\\n'.format(Counter(dfi_label_res)))\n",
        "        # Append the resampled feature and label arrays of ith dataframe to their respective list of arrays\n",
        "        resampled_feature_arrays.append(dfi_features_res)\n",
        "        resampled_label_arrays.append(dfi_label_res)\n",
        "    return resampled_feature_arrays, resampled_label_arrays\n",
        "\n",
        "# Utility Function to convert the arrays of features and labels to pandas dataframes, and then join them.\n",
        "# Also re-assign the columns headers.\n",
        "def restructure_arrays_to_dataframes(feature_arrays, label_arrays):\n",
        "    resampled_dfs = []\n",
        "    for i in range(len(feature_arrays)):\n",
        "        feature_df = pd.DataFrame(data=feature_arrays[i])\n",
        "        label_df = pd.DataFrame(data=label_arrays[i])\n",
        "        # Must set the column header for label_df, otherwise it wont join with feature_df, as columns overlap (with col names '0')\n",
        "        label_df.columns=['Y']\n",
        "        resampled_dfs.append(feature_df.join(label_df))\n",
        "    # re-assign the column headers for features and labels\n",
        "    set_new_headers(resampled_dfs)\n",
        "    return resampled_dfs\n",
        "\n",
        "# Perform SMOTE oversampling on all the imputed dataframes, and return them in a dictionary.\n",
        "def perform_oversampling(dfs):\n",
        "\n",
        "    smote_feature_arrays, smote_label_arrays = oversample_data_SMOTE(dfs, verbose=True)\n",
        "    oversampled_dataframes = restructure_arrays_to_dataframes(smote_feature_arrays, smote_label_arrays)\n",
        "    print('-'*100)\n",
        "    return oversampled_dataframes\n",
        "\n",
        "oversampled_df = perform_oversampling(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Dataset: 1year:\n",
            "Original dataset shape Counter({0.0: 6756, 1.0: 271})\n",
            "Resampled dataset shape Counter({0.0: 6756, 1.0: 6756})\n",
            "\n",
            "Dataset: 2year:\n",
            "Original dataset shape Counter({0.0: 9773, 1.0: 400})\n",
            "Resampled dataset shape Counter({0.0: 9773, 1.0: 9773})\n",
            "\n",
            "----------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2AhvJxvCibD"
      },
      "source": [
        "### Building Classification Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MoCpTos5CibF"
      },
      "source": [
        "#### Cross-Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pr6vmcsBCibG"
      },
      "source": [
        "def prepare_kfold_cv_data(k, X, y, verbose=False):\n",
        "    X = X.values\n",
        "    y = y.values\n",
        "    kf = KFold(n_splits=k, shuffle=False, random_state=42)\n",
        "    X_train = []\n",
        "    y_train = []\n",
        "    X_test = []\n",
        "    y_test = []\n",
        "\n",
        "    for train_index, test_index in kf.split(X):\n",
        "        X_train.append(X[train_index])\n",
        "        y_train.append(y[train_index])\n",
        "        X_test.append(X[test_index])\n",
        "        y_test.append(y[test_index])\n",
        "    return X_train, y_train, X_test, y_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYFtklx6CibP"
      },
      "source": [
        "#### Random Forest Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56paVfDiCibR"
      },
      "source": [
        "# Random Forest Classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators = 5, criterion = 'entropy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BAfDevhCibf"
      },
      "source": [
        "#### XGBoost Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Yr5XPirCibv"
      },
      "source": [
        "# eXtreme Gradient Boosting Classifier (XGBClassifier)\n",
        "xgb_classifier = XGBClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hyV0vklCib1"
      },
      "source": [
        "# creating a dictionary of models\n",
        "models_dictionary = OrderedDict()\n",
        "\n",
        "models_dictionary['Random Forest'] = rf_classifier\n",
        "models_dictionary['Extreme Gradient Boosting'] = xgb_classifier\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8jZ91IECib_"
      },
      "source": [
        "#### Perform Data Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHY2ZjKHCicB"
      },
      "source": [
        "import numpy\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qe3-OMC-CicS"
      },
      "source": [
        "feature_dfs, label_dfs = split_dataframes_features_labels(oversampled_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDCMYYfJCicZ"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "def perform_data_modeling(_models_, _imputers_, verbose=False, k_folds=5):\n",
        "\n",
        "    # 2 Models\n",
        "    # 2 datasets (year_1, year_2)\n",
        "    # 7 metrics, averaged over all the K-Folds\n",
        "    model_results = OrderedDict()\n",
        "\n",
        "    # Iterate over the models\n",
        "    for model_name, clf in _models_.items():\n",
        "        if verbose: print(\"-\"*120, \"\\n\", \"Model: \" + '\\033[1m' + model_name + '\\033[0m' + \" Classifier\")\n",
        "\n",
        "        # call the split_dataframes_features_labels function to get a list of features and labels for all the dataframes\n",
        "        feature_dfs, label_dfs = split_dataframes_features_labels(oversampled_df)\n",
        "\n",
        "        year_results = OrderedDict()\n",
        "\n",
        "        # Iterate over dataframe_list individually\n",
        "        for df_index in range(len(oversampled_df)):\n",
        "            if verbose: print('\\t\\tDataset: ' + '\\033[1m' + str(df_index+1) + 'year' + '\\033[0m')\n",
        "\n",
        "            # Calling the 'prepare_kfold_cv_data' returns lists of features and labels\n",
        "            # for train and test sets respectively.\n",
        "            # The number of items in the list is equal to k_folds\n",
        "            X_train_list, y_train_list, X_test_list, y_test_list = prepare_kfold_cv_data(k_folds, feature_dfs[df_index], label_dfs[df_index], verbose)\n",
        "\n",
        "            metrics_results = OrderedDict()\n",
        "            accuracy_list = np.zeros([k_folds])\n",
        "            precision_list = np.zeros([k_folds,2])\n",
        "            recall_list = np.zeros([k_folds,2])\n",
        "            roc_au_list = np.zeros([k_folds,2])\n",
        "            TN_list = np.zeros([k_folds])\n",
        "            FP_list = np.zeros([k_folds])\n",
        "            FN_list = np.zeros([k_folds])\n",
        "            TP_list = np.zeros([k_folds])\n",
        "\n",
        "            # Iterate over all the k-folds\n",
        "            for k_index in range(k_folds):\n",
        "                X_train = X_train_list[k_index]\n",
        "                y_train = y_train_list[k_index]\n",
        "                X_test = X_test_list[k_index]\n",
        "                y_test = y_test_list[k_index]\n",
        "\n",
        "\n",
        "                # Fit the model and\n",
        "                clf = clf.fit(X_train, y_train)\n",
        "                y_test_predicted = clf.predict(X_test)\n",
        "\n",
        "                y_test_predicted_prob = clf.predict_proba(X_test)\n",
        "\n",
        "                #code for calculating accuracy\n",
        "                _accuracy_ = accuracy_score(y_test, y_test_predicted, normalize=True)\n",
        "                accuracy_list[k_index] = _accuracy_\n",
        "\n",
        "                #code for calculating recall\n",
        "                _recalls_ = recall_score(y_test, y_test_predicted, average=None)\n",
        "                recall_list[k_index] = _recalls_\n",
        "\n",
        "                _roc_au_ = roc_auc_score(y_test, y_test_predicted)\n",
        "                roc_au_list[k_index] = _roc_au_\n",
        "\n",
        "                #code for calculating precision\n",
        "                _precisions_ = precision_score(y_test, y_test_predicted, average=None)\n",
        "                precision_list[k_index] = _precisions_\n",
        "\n",
        "                #code for calculating confusion matrix\n",
        "                _confusion_matrix_ = confusion_matrix(y_test, y_test_predicted)\n",
        "                TN_list[k_index] = _confusion_matrix_[0][0]\n",
        "                FP_list[k_index] = _confusion_matrix_[0][1]\n",
        "                FN_list[k_index] = _confusion_matrix_[1][0]\n",
        "                TP_list[k_index] = _confusion_matrix_[1][1]\n",
        "\n",
        "            # creating a metrics dictionary\n",
        "            metrics_results['Accuracy'] = np.mean(accuracy_list)\n",
        "            metrics_results['Precisions'] = np.mean(precision_list, axis=0)\n",
        "            metrics_results['Recalls'] = np.mean(recall_list, axis=0)\n",
        "            metrics_results['ROC AUC'] = np.mean(roc_au_list, axis=0)\n",
        "            metrics_results['TN'] = np.mean(TN_list)\n",
        "            metrics_results['FP'] = np.mean(FP_list)\n",
        "            metrics_results['FN'] = np.mean(FN_list)\n",
        "            metrics_results['TP'] = np.mean(TP_list)\n",
        "\n",
        "            if verbose:\n",
        "                print('\\t\\t\\tAccuracy:', metrics_results['Accuracy'])\n",
        "                print('\\t\\t\\tPrecision:', metrics_results['Precisions'])\n",
        "                print('\\t\\t\\tRecall:', metrics_results['Recalls'])\n",
        "                print('\\t\\t\\tRoc AUC:', metrics_results['ROC AUC'][0])\n",
        "\n",
        "            year_results[str(df_index+1)+'year'] = metrics_results\n",
        "\n",
        "\n",
        "        model_results[model_name] = year_results\n",
        "\n",
        "    return model_results"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSogbF8nCich"
      },
      "source": [
        "### Randomising Order\n",
        "oversampled_df[0] = oversampled_df[0].sample(len(oversampled_df[0]))\n",
        "oversampled_df[1] = oversampled_df[1].sample(len(oversampled_df[1]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FV8Zl0RKCicr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "015a4d79-f2bb-4879-9ec4-ec16d2d377da"
      },
      "source": [
        "### We would expect XGBoost to perform better, so some hyperperamater tuning might be necessary.\n",
        "results = perform_data_modeling(models_dictionary, oversampled_df, verbose=True, k_folds=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------------------------------------------------------ \n",
            " Model: \u001b[1mRandom Forest\u001b[0m Classifier\n",
            "\t\tDataset: \u001b[1m1year\u001b[0m\n",
            "\t\t\tAccuracy: 0.9694341046615147\n",
            "\t\t\tPrecision: [0.97554328 0.96343702]\n",
            "\t\t\tRecall: [0.96304489 0.97588902]\n",
            "\t\t\tRoc AUC: 0.9694669590932747\n",
            "\t\tDataset: \u001b[1m2year\u001b[0m\n",
            "\t\t\tAccuracy: 0.9618337772561059\n",
            "\t\t\tPrecision: [0.96835929 0.95548048]\n",
            "\t\t\tRecall: [0.9548793  0.96877466]\n",
            "\t\t\tRoc AUC: 0.9618269791427349\n",
            "------------------------------------------------------------------------------------------------------------------------ \n",
            " Model: \u001b[1mExtreme Gradient Boosting\u001b[0m Classifier\n",
            "\t\tDataset: \u001b[1m1year\u001b[0m\n",
            "\t\t\tAccuracy: 0.9602569231818252\n",
            "\t\t\tPrecision: [0.95507422 0.96557487]\n",
            "\t\t\tRecall: [0.9660027  0.95458992]\n",
            "\t\t\tRoc AUC: 0.960296310199578\n",
            "\t\tDataset: \u001b[1m2year\u001b[0m\n",
            "\t\t\tAccuracy: 0.9477133560888735\n",
            "\t\t\tPrecision: [0.94128745 0.95431598]\n",
            "\t\t\tRecall: [0.95496429 0.94045467]\n",
            "\t\t\tRoc AUC: 0.9477094830044901\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTSSDaebDCKK"
      },
      "source": [
        "Credit: [Derek Snow](https://linkedin.com/company/firmai)"
      ]
    }
  ]
}