Back_order_combining_algo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Back_order_combining_algo.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "x093MFk6TlmK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import pandas as pd "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3y8wlMUJTlmR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x=pd.read_csv(\"train1.csv\")\n",
        "y=pd.read_csv(\"train2.csv\")\n",
        "test=pd.read_csv(\"test_k.csv\")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IiL9LbbCTlmU",
        "colab_type": "code",
        "colab": {},
        "outputId": "79baafdb-6db8-4d4d-f3e7-d22d6d219e5c"
      },
      "cell_type": "code",
      "source": [
        "train=pd.concat([x,y])\n",
        "train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1587292, 18)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "kS2iRl-9Tlmd",
        "colab_type": "code",
        "colab": {},
        "outputId": "978277b8-96cf-450d-fa57-e9a220b1a89f"
      },
      "cell_type": "code",
      "source": [
        "train.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "_ipMGp1RTlmi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 8\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A1vdsAEbTlml",
        "colab_type": "code",
        "colab": {},
        "outputId": "fc09f3ee-6333-4d58-ca82-20c80f032555"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "sns.countplot(x='went_on_backorder', data=train)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x24803417b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2bdum7OxsPf744woJCVFycrIWLVpkjPHoo4/q0qVLeuGFF+RyuWS1WpWfn28E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\nWJhpv1u3bpWnp6emTp0qwzA0Z84cvf3229q2bZv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            "text/plain": [
              "<matplotlib.figure.Figure at 0x24801ff55f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-uLbHtUbTlmq",
        "colab_type": "code",
        "colab": {},
        "outputId": "ac3c9295-bd53-4ea1-e952-1d098c6c046e"
      },
      "cell_type": "code",
      "source": [
        "counts_df = pd.DataFrame(train.groupby('went_on_backorder').size().rename('counts'))\n",
        "print(counts_df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                    counts\n",
            "went_on_backorder         \n",
            "No                 1575999\n",
            "Yes                  11293\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "9JlCDaM4Tlmv",
        "colab_type": "code",
        "colab": {},
        "outputId": "0c28469b-9be5-4887-a4a5-6d927d7aa19c"
      },
      "cell_type": "code",
      "source": [
        "test.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>national_inv</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>in_transit_qty</th>\n",
              "      <th>forecast_3_month</th>\n",
              "      <th>sales_1_month</th>\n",
              "      <th>sales_9_month</th>\n",
              "      <th>min_bank</th>\n",
              "      <th>potential_issue</th>\n",
              "      <th>pieces_past_due</th>\n",
              "      <th>perf_6_month_avg</th>\n",
              "      <th>perf_12_month_avg</th>\n",
              "      <th>local_bo_qty</th>\n",
              "      <th>deck_risk</th>\n",
              "      <th>oe_constraint</th>\n",
              "      <th>ppap_risk</th>\n",
              "      <th>stop_auto_buy</th>\n",
              "      <th>rev_stop</th>\n",
              "      <th>went_on_backorder</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.015845</td>\n",
              "      <td>0.010931</td>\n",
              "      <td>-0.049796</td>\n",
              "      <td>-0.033019</td>\n",
              "      <td>-0.036963</td>\n",
              "      <td>-0.037467</td>\n",
              "      <td>-0.046246</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.010528</td>\n",
              "      <td>0.151589</td>\n",
              "      <td>0.136445</td>\n",
              "      <td>-0.018855</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.016110</td>\n",
              "      <td>-0.841331</td>\n",
              "      <td>-0.049796</td>\n",
              "      <td>-0.033019</td>\n",
              "      <td>-0.036963</td>\n",
              "      <td>-0.037326</td>\n",
              "      <td>-0.046246</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.010528</td>\n",
              "      <td>0.141472</td>\n",
              "      <td>0.119773</td>\n",
              "      <td>-0.018855</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.016343</td>\n",
              "      <td>0.010931</td>\n",
              "      <td>-0.049796</td>\n",
              "      <td>-0.033019</td>\n",
              "      <td>-0.036963</td>\n",
              "      <td>-0.037467</td>\n",
              "      <td>-0.046246</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.010528</td>\n",
              "      <td>0.124129</td>\n",
              "      <td>0.116438</td>\n",
              "      <td>-0.018855</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.015911</td>\n",
              "      <td>-0.841331</td>\n",
              "      <td>-0.049796</td>\n",
              "      <td>-0.033019</td>\n",
              "      <td>-0.036963</td>\n",
              "      <td>-0.037326</td>\n",
              "      <td>-0.046246</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.010528</td>\n",
              "      <td>0.111844</td>\n",
              "      <td>0.113937</td>\n",
              "      <td>-0.018855</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.009508</td>\n",
              "      <td>1.147279</td>\n",
              "      <td>-0.049796</td>\n",
              "      <td>-0.033019</td>\n",
              "      <td>-0.036963</td>\n",
              "      <td>-0.037467</td>\n",
              "      <td>-0.046246</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.010528</td>\n",
              "      <td>0.132800</td>\n",
              "      <td>0.110603</td>\n",
              "      <td>-0.018855</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   national_inv  lead_time  in_transit_qty  forecast_3_month  sales_1_month  \\\n",
              "0     -0.015845   0.010931       -0.049796         -0.033019      -0.036963   \n",
              "1     -0.016110  -0.841331       -0.049796         -0.033019      -0.036963   \n",
              "2     -0.016343   0.010931       -0.049796         -0.033019      -0.036963   \n",
              "3     -0.015911  -0.841331       -0.049796         -0.033019      -0.036963   \n",
              "4     -0.009508   1.147279       -0.049796         -0.033019      -0.036963   \n",
              "\n",
              "   sales_9_month  min_bank  potential_issue  pieces_past_due  \\\n",
              "0      -0.037467 -0.046246                0        -0.010528   \n",
              "1      -0.037326 -0.046246                0        -0.010528   \n",
              "2      -0.037467 -0.046246                0        -0.010528   \n",
              "3      -0.037326 -0.046246                0        -0.010528   \n",
              "4      -0.037467 -0.046246                0        -0.010528   \n",
              "\n",
              "   perf_6_month_avg  perf_12_month_avg  local_bo_qty  deck_risk  \\\n",
              "0          0.151589           0.136445     -0.018855          0   \n",
              "1          0.141472           0.119773     -0.018855          0   \n",
              "2          0.124129           0.116438     -0.018855          0   \n",
              "3          0.111844           0.113937     -0.018855          0   \n",
              "4          0.132800           0.110603     -0.018855          0   \n",
              "\n",
              "   oe_constraint  ppap_risk  stop_auto_buy  rev_stop went_on_backorder  \n",
              "0              0          0              1         0                No  \n",
              "1              0          1              1         0                No  \n",
              "2              0          0              1         0                No  \n",
              "3              0          0              1         0                No  \n",
              "4              0          0              1         0                No  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "xhl_pf3CTlm0",
        "colab_type": "code",
        "colab": {},
        "outputId": "37f4f6ab-d475-4e6d-a612-f11206df012e"
      },
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(51293, 18)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "IwHkiWHaTlm4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "target_names = ['No','Yes']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9xRutZdKTlm8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Tree based feature selection:"
      ]
    },
    {
      "metadata": {
        "id": "RF_LJlWQTlm_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.feature_selection import SelectFromModel\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_mvwnGv2TlnD",
        "colab_type": "code",
        "colab": {},
        "outputId": "e68802c0-056b-40c9-dbd5-18203a3580e1"
      },
      "cell_type": "code",
      "source": [
        "features = train.iloc[:,0:17]\n",
        "label = train['went_on_backorder']\n",
        "clf = ExtraTreesClassifier()\n",
        "clf = clf.fit(features, label)\n",
        "model = SelectFromModel(clf, prefit=True)\n",
        "New_features = model.transform(features)\n",
        "print(New_features.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1587292, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dkY8PpV_TlnJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "L1-based feature selection:"
      ]
    },
    {
      "metadata": {
        "id": "2bFyEzSfTlnK",
        "colab_type": "code",
        "colab": {},
        "outputId": "d5cacb83-1863-4c5a-9db1-cf13b27e249e"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(features, label)\n",
        "model_2 = SelectFromModel(lsvc, prefit=True)\n",
        "New_features_2 = model_2.transform(features)\n",
        "print(New_features_2.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1587292, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "id": "r4iIqnOxTlnP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Loading the classifiers."
      ]
    },
    {
      "metadata": {
        "id": "NfsJ8hsGTlnS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
        "Classifiers = [LogisticRegression(C=10),DecisionTreeClassifier(),GaussianNB(),GradientBoostingClassifier(n_estimators=200)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SvZC0LwDTlnY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Without Feature Selection:"
      ]
    },
    {
      "metadata": {
        "id": "94fHcLPhTlnZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "c3e8dd8d-f29f-4d16-ab5c-930e0b5ca1f1"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "import timeit\n",
        "test_features= test.iloc[:,0:17]\n",
        "Time_1=[]\n",
        "Model_1=[]\n",
        "Out_Accuracy_1=[]\n",
        "for clf in Classifiers:\n",
        "    start_time = timeit.default_timer()\n",
        "    fit=clf.fit(features,label)\n",
        "    pred=fit.predict(test_features)\n",
        "    print(clf.__class__.__name__)\n",
        "    print(classification_report(test['went_on_backorder'],pred,target_names=target_names))\n",
        "    print(confusion_matrix(test['went_on_backorder'], pred))\n",
        "    print(\"\\n\")\n",
        "    elapsed = timeit.default_timer() - start_time\n",
        "    Time_1.append(elapsed)\n",
        "    Model_1.append(clf.__class__.__name__)\n",
        "    Out_Accuracy_1.append(accuracy_score(test['went_on_backorder'],pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogisticRegression\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      1.00      0.99    224748\n",
            "        Yes       0.00      0.00      0.00      2688\n",
            "\n",
            "avg / total       0.98      0.99      0.98    227436\n",
            "\n",
            "[[224746      2]\n",
            " [  2688      0]]\n",
            "\n",
            "\n",
            "DecisionTreeClassifier\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      0.94      0.96    224748\n",
            "        Yes       0.03      0.16      0.05      2688\n",
            "\n",
            "avg / total       0.98      0.93      0.95    227436\n",
            "\n",
            "[[211608  13140]\n",
            " [  2254    434]]\n",
            "\n",
            "\n",
            "GaussianNB\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       1.00      0.06      0.11    224748\n",
            "        Yes       0.01      0.98      0.02      2688\n",
            "\n",
            "avg / total       0.98      0.07      0.11    227436\n",
            "\n",
            "[[ 13505 211243]\n",
            " [    50   2638]]\n",
            "\n",
            "\n",
            "GradientBoostingClassifier\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      1.00      0.99    224748\n",
            "        Yes       0.47      0.03      0.05      2688\n",
            "\n",
            "avg / total       0.98      0.99      0.98    227436\n",
            "\n",
            "[[224665     83]\n",
            " [  2614     74]]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bTrwe1FQTlng",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "L1-based feature selection:"
      ]
    },
    {
      "metadata": {
        "id": "bOIJqIfVTlnh",
        "colab_type": "code",
        "colab": {},
        "outputId": "6b6a50e0-37c1-4987-bf88-cc1db12b4b5e"
      },
      "cell_type": "code",
      "source": [
        "test_features= model_2.transform(test.iloc[:,0:17])\n",
        "Time_3=[]\n",
        "Model_3=[]\n",
        "Out_Accuracy_3=[]\n",
        "for clf in Classifiers:\n",
        "    start_time = timeit.default_timer()\n",
        "    fit=clf.fit(New_features_2,label)\n",
        "    pred=fit.predict(test_features)\n",
        "    print(clf.__class__.__name__)\n",
        "    print(classification_report(test['went_on_backorder'],pred,target_names=target_names))\n",
        "    print(confusion_matrix(test['went_on_backorder'], pred))\n",
        "    print(\"\\n\")\n",
        "    elapsed = timeit.default_timer() - start_time\n",
        "    Time_3.append(elapsed)\n",
        "    Model_3.append(clf.__class__.__name__)\n",
        "    Out_Accuracy_3.append(accuracy_score(test['went_on_backorder'],pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogisticRegression\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      1.00      0.99    224748\n",
            "        Yes       0.00      0.00      0.00      2688\n",
            "\n",
            "avg / total       0.98      0.99      0.98    227436\n",
            "\n",
            "[[224745      3]\n",
            " [  2688      0]]\n",
            "\n",
            "\n",
            "DecisionTreeClassifier\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      0.97      0.98    224748\n",
            "        Yes       0.05      0.14      0.08      2688\n",
            "\n",
            "avg / total       0.98      0.96      0.97    227436\n",
            "\n",
            "[[217925   6823]\n",
            " [  2311    377]]\n",
            "\n",
            "\n",
            "GaussianNB\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       1.00      0.08      0.14    224748\n",
            "        Yes       0.01      0.98      0.02      2688\n",
            "\n",
            "avg / total       0.98      0.09      0.14    227436\n",
            "\n",
            "[[ 17088 207660]\n",
            " [    60   2628]]\n",
            "\n",
            "\n",
            "GradientBoostingClassifier\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      1.00      0.99    224748\n",
            "        Yes       0.18      0.04      0.07      2688\n",
            "\n",
            "avg / total       0.98      0.99      0.98    227436\n",
            "\n",
            "[[224214    534]\n",
            " [  2573    115]]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uanxurTPTlno",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Tree-Based feature selection:"
      ]
    },
    {
      "metadata": {
        "id": "pkpS6kWDTlnp",
        "colab_type": "code",
        "colab": {},
        "outputId": "6ac8eb8f-a79d-4d13-8c88-566e8452753d"
      },
      "cell_type": "code",
      "source": [
        "test_features= model.transform(test.iloc[:,0:17])\n",
        "Time_2=[]\n",
        "Model_2=[]\n",
        "Out_Accuracy_2=[]\n",
        "for clf in Classifiers:\n",
        "    start_time = timeit.default_timer()\n",
        "    fit=clf.fit(New_features,label)\n",
        "    pred=fit.predict(test_features)\n",
        "    print(clf.__class__.__name__)\n",
        "    print(classification_report(test['went_on_backorder'],pred,target_names=target_names))\n",
        "    print(confusion_matrix(test['went_on_backorder'], pred))\n",
        "    print(\"\\n\")\n",
        "    elapsed = timeit.default_timer() - start_time\n",
        "    Time_2.append(elapsed)\n",
        "    Model_2.append(clf.__class__.__name__)\n",
        "    Out_Accuracy_2.append(accuracy_score(test['went_on_backorder'],pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogisticRegression\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      1.00      0.99    224748\n",
            "        Yes       0.00      0.00      0.00      2688\n",
            "\n",
            "avg / total       0.98      0.99      0.98    227436\n",
            "\n",
            "[[224744      4]\n",
            " [  2688      0]]\n",
            "\n",
            "\n",
            "DecisionTreeClassifier\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      0.63      0.77    224748\n",
            "        Yes       0.02      0.52      0.03      2688\n",
            "\n",
            "avg / total       0.98      0.63      0.76    227436\n",
            "\n",
            "[[142026  82722]\n",
            " [  1279   1409]]\n",
            "\n",
            "\n",
            "GaussianNB\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      0.04      0.08    224748\n",
            "        Yes       0.01      0.98      0.02      2688\n",
            "\n",
            "avg / total       0.98      0.05      0.08    227436\n",
            "\n",
            "[[  9031 215717]\n",
            " [    47   2641]]\n",
            "\n",
            "\n",
            "GradientBoostingClassifier\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "         No       0.99      1.00      0.99    224748\n",
            "        Yes       0.20      0.00      0.00      2688\n",
            "\n",
            "avg / total       0.98      0.99      0.98    227436\n",
            "\n",
            "[[224736     12]\n",
            " [  2685      3]]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WPVfwx_LTlnu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Accuracy of the models:"
      ]
    },
    {
      "metadata": {
        "id": "-AzgXmN2Tlnw",
        "colab_type": "code",
        "colab": {},
        "outputId": "6fc0dcbb-7f30-4179-a7f8-1752b9253cc8"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "ind =  np.arange(4)   # the x locations for the groups\n",
        "width = 0.1     # the width of the bars\n",
        "fig, ax = plt.subplots()\n",
        "rects1 = ax.bar(ind, Out_Accuracy_1, width, color='r')\n",
        "rects2 = ax.bar(ind + width, Out_Accuracy_2, width, color='y')\n",
        "rects3 = ax.bar(ind + width + width ,Out_Accuracy_3, width, color='b')\n",
        "ax.set_ylabel('Accuracy')\n",
        "ax.set_title('Accuracy by Models and Selection Process')\n",
        "ax.set_xticks(ind + width)\n",
        "ax.set_xticklabels(Model_3,rotation=60)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rexSRcTpv4YQ0vvUxtX++m0VbagkVEhIuL3Vj6pu/gnixdyRSHu8nliq5jGj67ke8MYWB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\n/wd6ltCNcD0L8wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x248033f05c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HZkDGMHHTln1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Runtime of the models:"
      ]
    },
    {
      "metadata": {
        "id": "Dyrclv00Tln3",
        "colab_type": "code",
        "colab": {},
        "outputId": "1ef489e2-ad2b-4eac-b8ac-2f2d16087dbf"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "ind =  np.arange(4)   # the x locations for the groups\n",
        "width = 0.1       # the width of the bars\n",
        "fig, ax = plt.subplots()\n",
        "rects1 = ax.bar(ind, Time_1, width, color='r')\n",
        "rects2 = ax.bar(ind + width, Time_2, width, color='y')\n",
        "rects3 = ax.bar(ind + width + width ,Time_3, width, color='b')\n",
        "ax.set_ylabel('Running Time')\n",
        "ax.set_title('Time by Models and Selection Process')\n",
        "ax.set_xticks(ind + width)\n",
        "ax.set_xticklabels(Model_3,rotation=60)\n",
        "ax.legend((rects1[0], rects2[0],rects3[0]), ('No Selection', 'Tree_Based','L1_Based'))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mm27KCB7VqlVj7969PPLII/Tu3Tvobc6D+frrr2nUqBEAhw8fJiUlhWrVqtGl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\ndax5VMigNJKUNN1bhNT7csK65gZJuxDzkvqOqP+o0aCWAal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            "text/plain": [
              "<matplotlib.figure.Figure at 0x248705ac0f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vAli6PnaTln8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}