SVM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hb08Kmv68Til"
      },
      "source": [
        "**Upload spamham.csv Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "8_l8HNyIuxTN",
        "outputId": "932123b7-94eb-4f48-caee-2fb8ed4bd0ff"
      },
      "source": [
        "import pandas as pd\r\n",
        "from sklearn.feature_extraction.text import CountVectorizer\r\n",
        "from sklearn import svm\r\n",
        "from google.colab import files\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "uploaded=files.upload()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-dcff60fc-bdbc-44ee-b415-a89a0ec8d3ff\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-dcff60fc-bdbc-44ee-b415-a89a0ec8d3ff\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving spamham.csv to spamham.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "bJJ2rYCPu27K",
        "outputId": "45a5f3c7-a467-473c-f199-a99e72fa65d7"
      },
      "source": [
        "import numpy as np\r\n",
        "from numpy import random\r\n",
        "from random import randrange\r\n",
        "##Loading Dataset\r\n",
        "data = pd.read_csv(\"spamham.csv\")\r\n",
        "print(data.describe())\r\n",
        "data"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              spam\n",
            "count  5728.000000\n",
            "mean      0.238827\n",
            "std       0.426404\n",
            "min       0.000000\n",
            "25%       0.000000\n",
            "50%       0.000000\n",
            "75%       0.000000\n",
            "max       1.000000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>spam</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Subject: naturally irresistible your corporate...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Subject: the stock trading gunslinger  fanny i...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Subject: unbelievable new homes made easy  im ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Subject: 4 color printing special  request add...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Subject: do not have money , get software cds ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5723</th>\n",
              "      <td>Subject: re : research and development charges...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5724</th>\n",
              "      <td>Subject: re : receipts from visit  jim ,  than...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5725</th>\n",
              "      <td>Subject: re : enron case study update  wow ! a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5726</th>\n",
              "      <td>Subject: re : interest  david ,  please , call...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5727</th>\n",
              "      <td>Subject: news : aurora 5 . 2 update  aurora ve...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5728 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   text  spam\n",
              "0     Subject: naturally irresistible your corporate...     1\n",
              "1     Subject: the stock trading gunslinger  fanny i...     1\n",
              "2     Subject: unbelievable new homes made easy  im ...     1\n",
              "3     Subject: 4 color printing special  request add...     1\n",
              "4     Subject: do not have money , get software cds ...     1\n",
              "...                                                 ...   ...\n",
              "5723  Subject: re : research and development charges...     0\n",
              "5724  Subject: re : receipts from visit  jim ,  than...     0\n",
              "5725  Subject: re : enron case study update  wow ! a...     0\n",
              "5726  Subject: re : interest  david ,  please , call...     0\n",
              "5727  Subject: news : aurora 5 . 2 update  aurora ve...     0\n",
              "\n",
              "[5728 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ozTpbBWvznh",
        "outputId": "750b8240-c8dc-459a-cbd2-9a59575edefe"
      },
      "source": [
        "data['spam'].value_counts()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4360\n",
              "1    1368\n",
              "Name: spam, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GH0suFTsvlPt"
      },
      "source": [
        "def split(dataset, split=0.80):\r\n",
        "\ttrain = list()\r\n",
        "\ttest_size = split * len(dataset)\r\n",
        "\ttest = list(dataset)\r\n",
        "\twhile len(train) < test_size:\r\n",
        "\t\tindex = randrange(len(test))\r\n",
        "\t\ttrain.append(test.pop(index))\r\n",
        "\treturn train, test"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYUHuzq4wgBn"
      },
      "source": [
        "df=data.values.tolist()\r\n",
        "train,test=split(df,0.8)\r\n",
        "train,cv=split(train,0.8)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tF6qbF8wreUo",
        "outputId": "44250089-d9b4-447a-879a-c028acdfb61c"
      },
      "source": [
        "print (np.shape(df))\r\n",
        "print (np.shape(train))\r\n",
        "print (np.shape(test))\r\n",
        "print (np.shape(cv))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5728, 2)\n",
            "(3667, 2)\n",
            "(1145, 2)\n",
            "(916, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VlZoSRxwrlV",
        "outputId": "b2bca422-4636-45cc-ccf4-73cfd7bbe1db"
      },
      "source": [
        "TrainSet=pd.DataFrame(train,columns=['text','spam'])\r\n",
        "x_train = TrainSet[\"text\"]\r\n",
        "y_train = TrainSet[\"spam\"]\r\n",
        "y_train.value_counts()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2773\n",
              "1     894\n",
              "Name: spam, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dptiDSmDsUBg",
        "outputId": "d25b1294-40f8-4892-ed11-d6aa34129344"
      },
      "source": [
        "CVSet=pd.DataFrame(cv,columns=['text','spam'])\r\n",
        "x_cv = CVSet[\"text\"]\r\n",
        "y_cv = CVSet[\"spam\"]\r\n",
        "y_cv.value_counts()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    707\n",
              "1    209\n",
              "Name: spam, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkSqHR0_w8I9",
        "outputId": "78df18c3-5838-4e3c-cf3c-f0b2d6955ea6"
      },
      "source": [
        "TestSet=pd.DataFrame(test,columns=['text','spam'])\r\n",
        "x_test = TestSet[\"text\"]\r\n",
        "y_test = TestSet[\"spam\"]\r\n",
        "y_test.value_counts()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    880\n",
              "1    265\n",
              "Name: spam, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WILYW1RDxTNA"
      },
      "source": [
        "#Extracting Features\r\n",
        "cv = CountVectorizer()\r\n",
        "features = cv.fit_transform(x_train)"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gXBmZ-nndsu"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqZbDix0m3Nq",
        "outputId": "f7122788-f9a7-45f1-ceee-575128f8094c"
      },
      "source": [
        "#Hyper parameter Tuning (C)\r\n",
        "cv_accuracy=[]\r\n",
        "for c in range (1,111,10):\r\n",
        "  model = svm.SVC(C=c)\r\n",
        "  model.fit(features,y_train)\r\n",
        "  features_test = cv.transform(x_cv)\r\n",
        "  cv_accuracy.append(model.score(features_test,y_cv))\r\n",
        "print(len(cv_accuracy))\r\n",
        "print (cv_accuracy)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11\n",
            "[0.9377729257641921, 0.9847161572052402, 0.9858078602620087, 0.9858078602620087, 0.9847161572052402, 0.9847161572052402, 0.9847161572052402, 0.9836244541484717, 0.9836244541484717, 0.9836244541484717, 0.9836244541484717]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "7eZNY8g7pprG",
        "outputId": "7b3e2952-55af-42bf-ad10-b88ce45fbcdb"
      },
      "source": [
        "X_val=np.arange(1,111,10);\r\n",
        "plt.title('Cross Validation Accuracy')\r\n",
        "plt.xlabel('Hyper parameter C')\r\n",
        "plt.ylabel('Cross Validation Accuracy')\r\n",
        "plt.plot(X_val,cv_accuracy)\r\n",
        "plt.show()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asQnrsQnuJ1d",
        "outputId": "9e69f582-e117-4aba-c0ee-6d0a3cdcdab5"
      },
      "source": [
        "id=np.argsort(cv_accuracy)[::-1]\r\n",
        "id[0]\r\n",
        "C_max=X_val[id[0]]\r\n",
        "C_max"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZSo_5r1xWqA",
        "outputId": "699601e5-c4e3-4fdf-907f-562ff1adc55a"
      },
      "source": [
        "#Building model using support vector machine\r\n",
        "model = svm.SVC(C=C_max)\r\n",
        "model.fit(features,y_train)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=31, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHruIwVCxbh0",
        "outputId": "8ae49813-e032-4a27-8ff0-9c7956019551"
      },
      "source": [
        "#Calculating Accuracy\r\n",
        "features_test = cv.transform(x_test)\r\n",
        "y_predict=model.predict(features_test)\r\n",
        "print(\"Accuracy is : \",model.score(features_test,y_test))"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy is :  0.982532751091703\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzQxttvFxg8B",
        "outputId": "cde82d0b-0825-4a65-9955-a5bcd090ab31"
      },
      "source": [
        "print (len(y_predict),len(y_test))"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1145 1145\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jukh4V6T0tvz",
        "outputId": "0753ba9f-81ec-4da9-df4a-91d718aee73e"
      },
      "source": [
        "False_positive=0\r\n",
        "True_positive=0\r\n",
        "False_negative=0\r\n",
        "True_negative=0\r\n",
        "for i in range(0,len(y_test)):\r\n",
        "  if (y_test[i]==0 and y_predict[i]==0):\r\n",
        "   True_negative+=1\r\n",
        "  elif (y_test[i]==0 and y_predict[i]==1):\r\n",
        "   False_positive+=1\r\n",
        "  elif (y_test[i]==1 and y_predict[i]==0):\r\n",
        "   False_negative+=1\r\n",
        "  elif (y_test[i]==1 and y_predict[i]==1):\r\n",
        "   True_positive+=1\r\n",
        "print(True_positive,True_negative,False_positive,False_negative)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "251 874 6 14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCqr4thJ1R4l",
        "outputId": "5e96a3d8-8baf-4921-dcbf-8d6bc6696bb4"
      },
      "source": [
        "#Performance of Spam Classifier\r\n",
        "Precision = True_positive / (True_positive + False_positive)\r\n",
        "Recall = True_positive / (True_positive + False_negative)\r\n",
        "Fscore = 2 * Precision * Recall / (Precision + Recall)\r\n",
        "Accuracy = (True_positive + True_negative) / (True_positive + True_negative + False_positive + False_negative)\r\n",
        "print (\"Precision :\",Precision)\r\n",
        "print (\"Recall :\",Recall)\r\n",
        "print (\"Fscore :\",Fscore)\r\n",
        "print (\"Accuracy :\",Accuracy)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision : 0.9766536964980544\n",
            "Recall : 0.9471698113207547\n",
            "Fscore : 0.9616858237547892\n",
            "Accuracy : 0.982532751091703\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1rB5CUac8O3z"
      },
      "source": [
        "**Upload test.zip, The zip file of the folder containing test emails**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "8wPWXeKJ1VWk",
        "outputId": "2b937016-d0bb-4c67-d0d0-240d4c541799"
      },
      "source": [
        "#To upload the test email Zip folder to current directory\r\n",
        "uploaded=files.upload()"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bd1ce974-31d9-44d1-a988-2419320f2aae\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-bd1ce974-31d9-44d1-a988-2419320f2aae\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving test.zip to test.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ttdO9SV12t7",
        "outputId": "c541a1d9-1ffc-428d-cc21-878ad54a2aa5"
      },
      "source": [
        "#UnZip testmails into folder 'test' in current directory\r\n",
        "!unzip test.zip"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  test.zip\n",
            "replace test/.DS_Store? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: test/.DS_Store          \n",
            "  inflating: test/email1.txt         \n",
            "  inflating: test/email2.txt         \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIUodc-o15PC"
      },
      "source": [
        "def predict(s):\r\n",
        "  file = open('/content/test/'+s, \"r\")\r\n",
        "  lines = file.readlines()\r\n",
        "  string = '\\t'.join([line.strip() for line in lines])\r\n",
        "  f = open(\"temp.txt\", \"w\")\r\n",
        "  f.write(string)\r\n",
        "  f.close()\r\n",
        "  ft = open('/content/temp.txt', \"r\")\r\n",
        "  features_test = cv.transform(ft)\r\n",
        "  y_predict=model.predict(features_test)\r\n",
        "  return y_predict"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Em40aF7719Gz"
      },
      "source": [
        "import os\r\n",
        "def readmail():\r\n",
        "  directory = '/content/test'\r\n",
        "  for filename in os.listdir(directory):\r\n",
        "      if filename.endswith(\".txt\"):\r\n",
        "        class_label=predict(filename)\r\n",
        "        print (filename,\"-\",class_label)\r\n",
        "        f = open(\"result.txt\", \"a\")\r\n",
        "        f.write(filename+\"-\"+str(class_label)+'\\n')\r\n",
        "        f.close()\r\n",
        "      else:\r\n",
        "          continue"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1RfwPr81-E4",
        "outputId": "33c3381a-f2e3-49ac-9e49-c347059a6696"
      },
      "source": [
        "readmail()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "email2.txt - [1]\n",
            "email1.txt - [0]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}