Spam_Classifier.ipynb

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      "name": "Spam Classifier.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNxmbMy30/RQDURWqxA7ThF",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Pogayo/ADH-EN_MT_Dataset/blob/master/Spam_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeCmUexUlRLI"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from google.colab import drive"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mD2I7CE-l7RK",
        "outputId": "d23ac9db-dca7-4cff-eb71-df3fca9282b4"
      },
      "source": [
        "drive.mount('./drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at ./drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Zcfnlaym6SW"
      },
      "source": [
        "!cp \"/content/drive/MyDrive/Year 4/Machine Learning/spam_or_not_spam.csv.zip\" spam_or_not_spam.csv.zip\n",
        "!unzip -q spam_or_not_spam.csv.zip"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AvciHOHoH9d"
      },
      "source": [
        "df=pd.read_csv(\"spam_or_not_spam.csv\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "B-T_eKgAoQwf",
        "outputId": "3fd47da5-87bd-4146-f1a9-f9afe46c0cc0"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>email</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>date wed NUMBER aug NUMBER NUMBER NUMBER NUMB...</td>\n",
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              "      <th>3</th>\n",
              "      <td>klez the virus that won t die already the most...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>in adding cream to spaghetti carbonara which ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                                               email  label\n",
              "0   date wed NUMBER aug NUMBER NUMBER NUMBER NUMB...      0\n",
              "1  martin a posted tassos papadopoulos the greek ...      0\n",
              "2  man threatens explosion in moscow thursday aug...      0\n",
              "3  klez the virus that won t die already the most...      0\n",
              "4   in adding cream to spaghetti carbonara which ...      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
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      "cell_type": "code",
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          "height": 286
        },
        "id": "6Yi1QGBUwMcl",
        "outputId": "1581e23c-2752-403f-a5fd-3541edfc5e36"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.372740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
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            ],
            "text/plain": [
              "             label\n",
              "count  3000.000000\n",
              "mean      0.166667\n",
              "std       0.372740\n",
              "min       0.000000\n",
              "25%       0.000000\n",
              "50%       0.000000\n",
              "75%       0.000000\n",
              "max       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
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        "id": "PnySdI_731HL",
        "outputId": "77104247-1254-43ea-ea3a-143f6fe9f211"
      },
      "source": [
        "df[df.label==1]"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>email</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2500</th>\n",
              "      <td>save up to NUMBER on life insurance why spend...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2501</th>\n",
              "      <td>NUMBER fight the risk of cancer URL NUMBER sli...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2502</th>\n",
              "      <td>NUMBER fight the risk of cancer URL NUMBER sli...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2503</th>\n",
              "      <td>adult club offers free membership instant acc...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2504</th>\n",
              "      <td>i thought you might like these NUMBER slim dow...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>abc s good morning america ranks it the NUMBE...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>hyperlink hyperlink hyperlink let mortgage le...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>thank you for shopping with us gifts for all ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>the famous ebay marketing e course learn to s...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>hello this is chinese traditional 子 件 NUMBER世...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>500 rows × 2 columns</p>\n",
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            "text/plain": [
              "                                                  email  label\n",
              "2500   save up to NUMBER on life insurance why spend...      1\n",
              "2501  NUMBER fight the risk of cancer URL NUMBER sli...      1\n",
              "2502  NUMBER fight the risk of cancer URL NUMBER sli...      1\n",
              "2503   adult club offers free membership instant acc...      1\n",
              "2504  i thought you might like these NUMBER slim dow...      1\n",
              "...                                                 ...    ...\n",
              "2995   abc s good morning america ranks it the NUMBE...      1\n",
              "2996   hyperlink hyperlink hyperlink let mortgage le...      1\n",
              "2997   thank you for shopping with us gifts for all ...      1\n",
              "2998   the famous ebay marketing e course learn to s...      1\n",
              "2999   hello this is chinese traditional 子 件 NUMBER世...      1\n",
              "\n",
              "[500 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "jWZewJTZjdXP",
        "outputId": "efd2ce03-b360-4bdf-c6ca-910ac3155b7a"
      },
      "source": [
        "df.iloc[0,0]"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' date wed NUMBER aug NUMBER NUMBER NUMBER NUMBER NUMBER from chris garrigues cwg dated NUMBER NUMBERfaNUMBERd deepeddy com message id NUMBER NUMBER tmda deepeddy vircio com i can t reproduce this error for me it is very repeatable like every time without fail this is the debug log of the pick happening NUMBER NUMBER NUMBER pick_it exec pick inbox list lbrace lbrace subject ftp rbrace rbrace NUMBER NUMBER sequence mercury NUMBER NUMBER NUMBER exec pick inbox list lbrace lbrace subject ftp rbrace rbrace NUMBER NUMBER sequence mercury NUMBER NUMBER NUMBER ftoc_pickmsgs NUMBER hit NUMBER NUMBER NUMBER marking NUMBER hits NUMBER NUMBER NUMBER tkerror syntax error in expression int note if i run the pick command by hand delta pick inbox list lbrace lbrace subject ftp rbrace rbrace NUMBER NUMBER sequence mercury NUMBER hit that s where the NUMBER hit comes from obviously the version of nmh i m using is delta pick version pick nmh NUMBER NUMBER NUMBER compiled on URL at sun mar NUMBER NUMBER NUMBER NUMBER ict NUMBER and the relevant part of my mh_profile delta mhparam pick seq sel list since the pick command works the sequence actually both of them the one that s explicit on the command line from the search popup and the one that comes from mh_profile do get created kre ps this is still using the version of the code form a day ago i haven t been able to reach the cvs repository today local routing issue i think _______________________________________________ exmh workers mailing list exmh workers URL URL '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LdwfUgOPl99"
      },
      "source": [
        "**If an email is spam - 1 if no, 0**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5ZBoNtGrbS-"
      },
      "source": [
        "##Splitting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhtX9Kk9Fmvp"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "5gxsVIEiEtzL",
        "outputId": "ec7d226f-4a00-4be0-a43d-1f5b692744aa"
      },
      "source": [
        "df.groupby('label').describe().T"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">email</th>\n",
              "      <th>count</th>\n",
              "      <td>2500</td>\n",
              "      <td>499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2445</td>\n",
              "      <td>427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>url URL date not supplied URL</td>\n",
              "      <td>lowest rates available for term life insurance...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "label                                      0                                                  1\n",
              "email count                             2500                                                499\n",
              "      unique                            2445                                                427\n",
              "      top     url URL date not supplied URL   lowest rates available for term life insurance...\n",
              "      freq                                10                                                  5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFNcPH3UGYy_",
        "outputId": "cf9838ad-f7c5-4028-f6d7-126a3c5bfc5a"
      },
      "source": [
        "ham_size=df.groupby('label').describe().T.iloc[0,0]\n",
        "spam_size=df.groupby('label').describe().T.iloc[0,1]\n",
        "print(ham_size, spam_size)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2500 499\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "p2rdfl3HFk6B",
        "outputId": "81df469f-e329-498b-9397-890cabf73b3b"
      },
      "source": [
        "# Visualizing imbalance data here \n",
        "plt.figure(figsize=(8,6))\n",
        "sns.countplot(df.label)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f169ca767f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVZeRhqrF9ck",
        "outputId": "fe384baf-72f4-4442-f4be-72cb83db5c56"
      },
      "source": [
        "(spam_size/len(df))*100 # # Percentage of spam messages"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.633333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbs5vnNgE3S1"
      },
      "source": [
        "This is an imbalanced dataset, so we should use a split strategy that takes this into account."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQhrV7Enoc9o"
      },
      "source": [
        "from keras.utils import to_categorical\n",
        "X=df[\"email\"]\n",
        "y_encoded=to_categorical(df[\"label\"]) #encoding the data\n",
        "y=df[\"label\"]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2MJTVCkGAs6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pl4co0bPJwP4"
      },
      "source": [
        "X=X.apply(str) #making sure we are only dealing with strings"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBwU6U3rqvru"
      },
      "source": [
        "X_train, X_test, y_train, y_test=train_test_split(X, y,test_size=0.2, random_state=23, stratify=y) #using stratify to make the split proportional to the different labels"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0vQ-RCdugwa",
        "outputId": "4a913beb-5369-4228-d539-3a1d0e1d1f5a"
      },
      "source": [
        "type(X)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTGUGUhRruZf"
      },
      "source": [
        "##Creating and saving the vocabulary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0NQqAKprP_u",
        "outputId": "04275ef8-62f8-4582-babe-1a5f3a25e415"
      },
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# create the tokenizer\n",
        "t = Tokenizer()\n",
        "# fit the tokenizer on the documents\n",
        "t.fit_on_texts(X.values)\n",
        "\n",
        "print(t.word_counts)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OrderedDict([('date', 874), ('wed', 144), ('number', 33888), ('aug', 213), ('from', 3454), ('chris', 153), ('garrigues', 57), ('cwg', 22), ('dated', 29), ('numberfanumberd', 2), ('deepeddy', 30), ('com', 429), ('message', 854), ('id', 89), ('tmda', 51), ('vircio', 44), ('i', 9009), ('can', 2417), ('t', 3309), ('reproduce', 12), ('this', 5324), ('error', 278), ('for', 6760), ('me', 1107), ('it', 7059), ('is', 8007), ('very', 530), ('repeatable', 1), ('like', 1248), ('every', 412), ('time', 1294), ('without', 379), ('fail', 37), ('the', 28722), ('debug', 35), ('log', 216), ('of', 14264), ('pick', 97), ('happening', 16), ('exec', 15), ('inbox', 51), ('list', 2177), ('lbrace', 8), ('subject', 487), ('ftp', 25), ('rbrace', 8), ('sequence', 84), ('mercury', 7), ('ftoc', 59), ('pickmsgs', 1), ('hit', 142), ('marking', 6), ('hits', 32), ('tkerror', 1), ('syntax', 20), ('in', 9490), ('expression', 24), ('int', 52), ('note', 125), ('if', 2625), ('run', 414), ('command', 94), ('by', 2672), ('hand', 127), ('delta', 19), ('that', 7515), ('s', 4915), ('where', 599), ('comes', 102), ('obviously', 44), ('version', 287), ('nmh', 31), ('m', 997), ('using', 576), ('compiled', 43), ('on', 5033), ('url', 8686), ('at', 2686), ('sun', 116), ('mar', 1), ('ict', 1), ('and', 14390), ('relevant', 46), ('part', 284), ('my', 2045), ('mh', 69), ('profile', 18), ('mhparam', 1), ('seq', 31), ('sel', 27), ('since', 404), ('works', 214), ('actually', 296), ('both', 289), ('them', 926), ('one', 1675), ('explicit', 9), ('line', 526), ('search', 154), ('popup', 8), ('do', 1655), ('get', 1598), ('created', 68), ('kre', 33), ('ps', 32), ('still', 473), ('code', 325), ('form', 260), ('a', 12982), ('day', 421), ('ago', 174), ('haven', 94), ('been', 1003), ('able', 186), ('to', 18409), ('reach', 83), ('cvs', 122), ('repository', 46), ('today', 391), ('local', 204), ('routing', 18), ('issue', 175), ('think', 699), ('exmh', 620), ('workers', 140), ('mailing', 871), ('martin', 30), ('posted', 121), ('tassos', 1), ('papadopoulos', 1), ('greek', 2), ('sculptor', 3), ('behind', 72), ('plan', 129), ('judged', 5), ('limestone', 5), ('mount', 10), ('kerdylio', 1), ('miles', 42), ('east', 41), ('salonika', 1), ('not', 3403), ('far', 212), ('athos', 1), ('monastic', 1), ('community', 105), ('was', 2357), ('ideal', 12), ('patriotic', 1), ('sculpture', 3), ('as', 3352), ('well', 576), ('alexander', 2), ('granite', 2), ('features', 75), ('ft', 12), ('high', 296), ('wide', 44), ('museum', 10), ('restored', 9), ('amphitheatre', 1), ('car', 89), ('park', 43), ('admiring', 1), ('crowds', 2), ('are', 3435), ('planned', 17), ('so', 1776), ('mountain', 26), ('or', 3153), ('ll', 559), ('weather', 32), ('pretty', 139), ('fast', 128), ('yahoo', 276), ('groups', 292), ('sponsor', 103), ('dvds', 83), ('free', 1180), ('p', 230), ('join', 127), ('now', 1152), ('unsubscribe', 378), ('group', 472), ('send', 541), ('an', 2219), ('email', 1175), ('forteana', 152), ('your', 3062), ('use', 1258), ('man', 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('率比', 1), ('普通横幅广告', 1), ('普通信件', 1), ('一', 1), ('件出售', 1), ('i全球', 1), ('件地址', 3), ('每number', 4), ('numberhk', 11), ('i全部number', 1), ('i香港', 1), ('i香港全部number', 1), ('赤', 1), ('橙', 1), ('部number', 1), ('l送', 2), ('件代理', 1), ('f起', 1), ('蚀', 1), ('率number', 1), ('以上', 1), ('包周群', 1), ('一周number', 2), ('保', 2), ('w站日', 2), ('量提升number以上', 2), ('包月群', 1), ('一月number', 1), ('n套群', 1), ('搜索', 2), ('w路', 5), ('衍', 1), ('你自己', 1), ('碛幸惶淄晟频碾', 1), ('件群', 1), ('严到y', 1), ('有意者请来信联系', 1), ('yhzxnumber', 3), ('二', 1), ('搜索引擎', 1), ('缘顷', 1), ('超值服', 1), ('眨', 1), ('特惠', 1), ('y河之星', 3), ('湍', 2), ('登', 1), ('全球number', 1), ('英文搜索引擎', 1), ('中文搜索引擎', 1), ('你的', 1), ('w站享', 1), ('u全球', 1), ('mail地址在各地', 1), ('的分', 1), ('盗', 1), ('香港', 1), ('场', 1), ('全球', 1), ('韧馍', 1), ('有效的email位址', 1), ('y料', 1), ('欤', 1), ('可以根', 1), ('客', 1), ('舻男枰', 1), ('在指定的地方', 1), ('域', 1), ('行', 1), ('e等', 1), ('傩酝斗烹', 1), ('如', 1), ('你想', 1), ('某地number', 1), ('f人一天之', 1), ('都知道你的', 1), ('a品', 1), ('那麽', 1), ('件投放就是你最好的解', 1), ('q方案', 1), ('它的', 1), ('r格是所有', 1), ('髅', 1), ('r格中最低', 1), ('但又是最有效的', 1), ('c遇每', 1), ('r每刻都等待著你', 1), ('也', 1), ('s你正是用了', 1), ('拥', 1), ('髅菏侄危', 1), ('而打垮了所有的', 1), ('κ帧ｚs快行', 1), ('影桑', 1), ('你的企', 1), ('公司立即掌握商', 1), ('髅降拿', 1), ('v告e', 1), ('是普通', 1), ('麂n', 1), ('i所不能比', 1), ('m的', 1), ('m及付款方式', 1), ('本公司', 1), ('氖戮w路', 1), ('v告多年', 1), ('宣', 1), ('髁χ任痪又', 1), ('第四大', 1), ('v告商之一', 1), ('我', 2), ('有', 1), ('ｉt的技', 1), ('g人', 1), ('t和高速', 1), ('i的服', 1), ('障到y', 1), ('氖麓隧', 1), ('工作', 1), ('必', 1), ('您的企', 1), ('i提供最迅捷', 1), ('最有效', 1), ('最', 1), ('m意的服', 1), ('铡', 2), ('需求客', 1), ('粽', 1), ('暨', 1), ('箱', 1), ('硇耪', 1), ('注明您需要的服', 1), ('张c', 1), ('m方式', 1), ('快回', 1), ('港', 1), ('澳', 1), ('地', 3), ('付款方式', 1), ('任何', 1), ('家均可到', 1), ('y行', 4), ('number天到', 2), ('ぃ', 2), ('需到', 1), ('k理', 1), ('benificiary', 2), ('wangjingjing', 1), ('mianyang', 1), ('中文', 1), ('φ眨', 1), ('收', 1), ('款', 1), ('人', 1), ('王晶晶', 1), ('收款', 1), ('中', 2), ('分行', 1), ('收款人', 1), ('款到後我', 1), ('⒘⒓', 1), ('您提供最', 1), ('m意服', 1), ('g迎', 2), ('硇', 2), ('黼', 2), ('有限公司', 1), ('公司地址', 1), ('大', 1), ('公司服', 1), ('number福壕w路推', 1), ('w站建', 1), ('名', 1), ('寄存', 1), ('功能', 1), ('得', 1), ('q空', 1), ('l展等', 1), ('x低廉', 1), ('服', 1)])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vY1B6c6LAtW3"
      },
      "source": [
        "Saving and loading the tokenizer for future inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fD-VMt2LAkcZ"
      },
      "source": [
        "import pickle\n",
        "\n",
        "# saving\n",
        "with open('tokenizer.pickle', 'wb') as handle:\n",
        "    pickle.dump(t, handle, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aW9OtfOax4gZ",
        "outputId": "eb76e37c-7313-4911-b4a9-2caaa3a182e9"
      },
      "source": [
        "vocab=list(t.word_index.keys())\n",
        "vocab_size=len(t.word_index)+1\n",
        "max_length=max([len(s.split(\" \")) for s in X])\n",
        "print(\"Maximum length: \", max_length)\n",
        "print(\"Vocabulary size:\", vocab_size)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum length:  13305\n",
            "Vocabulary size: 33674\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hOivZ5m4XUm",
        "outputId": "eeda36a0-f000-4ea2-d61b-75a75663cbf7"
      },
      "source": [
        "vocab[:20]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['number',\n",
              " 'the',\n",
              " 'to',\n",
              " 'and',\n",
              " 'of',\n",
              " 'a',\n",
              " 'in',\n",
              " 'i',\n",
              " 'url',\n",
              " 'is',\n",
              " 'that',\n",
              " 'you',\n",
              " 'it',\n",
              " 'for',\n",
              " 'this',\n",
              " 'on',\n",
              " 's',\n",
              " 'with',\n",
              " 'be',\n",
              " 'have']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4L1EkrbyYm5"
      },
      "source": [
        "def save_vocab(words, filename):\n",
        "  data = '\\n'.join(words)\n",
        "  file = open(filename, 'w')\n",
        "  file.write(data)\n",
        "  file.close()\n",
        "\n",
        "save_vocab(vocab, 'vocab.txt')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dk5TF5y114pc"
      },
      "source": [
        "def load_vocab(filename): # function to load the vocabulary from a file\n",
        "  file = open(filename, 'r')\n",
        "  text = file.read()# read all text\n",
        "  vocab = text.split()\n",
        "  vocab = list(set(vocab))\n",
        "  file.close() # close the file\n",
        "  return vocab\n",
        "\n",
        "\n",
        "# load vocabulary\n",
        "vocab_filename = 'vocab.txt'\n",
        "vocab = load_vocab(vocab_filename)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0VpgGKB46oi"
      },
      "source": [
        "## Generate word embeddings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7_MHHcP44x-"
      },
      "source": [
        "from numpy import asarray\n",
        "from numpy import zeros\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.layers import Embedding"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VR1kMEQI7fSI",
        "outputId": "f898b533-00fb-4bfb-f695-2bbd46475081"
      },
      "source": [
        "#loading glove embeddings from Kaggle as I am running this on Google Colab - change according to your source\n",
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = \"pogayo\" # username from the json file\n",
        "os.environ['KAGGLE_KEY'] = \"08102192841cd4c94b1dc524e620689b\" # key from the json file\n",
        "!kaggle datasets download -d danielwillgeorge/glove6b100dtxt # api copied from kaggle"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading glove6b100dtxt.zip to /content\n",
            " 98% 129M/131M [00:10<00:00, 15.3MB/s]\n",
            "100% 131M/131M [00:10<00:00, 12.7MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DFrtT0BBmYt"
      },
      "source": [
        "!unzip -q glove6b100dtxt.zip"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBaG7bG37zcP"
      },
      "source": [
        "def create_embeddings_index(filename):\n",
        "  embeddings_index = dict()\n",
        "  f = open( filename )\n",
        "  for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = asarray(values[1:], dtype= 'float32' )\n",
        "    embeddings_index[word] = coefs\n",
        "  f.close()\n",
        "  return embeddings_index"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bwNJ7jP-C4v"
      },
      "source": [
        "def create_embedding_matrix(tokenizer, embeddings_index):\n",
        "  vocab_size=len(tokenizer.word_index)+1\n",
        "  embedding_matrix = zeros((vocab_size, 100))\n",
        "  for word, i in tokenizer.word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "  return embedding_matrix\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0PM7gYn7IuL",
        "outputId": "1c82ed67-6e44-418c-8e1a-951aa86b1280"
      },
      "source": [
        "embeddings_index = create_embeddings_index('glove.6B.100d.txt')\n",
        "print( ' Loaded %s word vectors. ' % len(embeddings_index))"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Loaded 400000 word vectors. \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lj8Ey56m7hHE"
      },
      "source": [
        "embedding_matrix=create_embedding_matrix(t, embeddings_index)"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qybd3G23AX6t"
      },
      "source": [
        "t.word_index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8b1wmH58DbPQ"
      },
      "source": [
        "# Sequencing and padding on train and test sets \n",
        "max_len=50 #change accordingly\n",
        "training_sequences = t.texts_to_sequences(X_train)\n",
        "training_padded = pad_sequences (training_sequences, maxlen = max_len, padding = 'post' )\n",
        "\n",
        "testing_sequences = t.texts_to_sequences(X_test)\n",
        "testing_padded = pad_sequences(testing_sequences, maxlen = max_len,\n",
        "padding = 'post')"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lUprA-f6myF"
      },
      "source": [
        "####Save a shorter embeddings file with only the words in our training vocabulary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jaZXfLFTfUlA"
      },
      "source": [
        "trimmed_embeddings=[]\n",
        "f=open(\"embeddings.txt\", 'w+')\n",
        "for word, i in t.word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    line=word\n",
        "    if embedding_vector is not None:\n",
        "      embedding_string=[str(n) for n in embedding_vector]\n",
        "      line=line+' '+' '.join(embedding_string)+\"\\n\"\n",
        "    f.write(line)\n",
        "f.close()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_KwiYVc6yDN"
      },
      "source": [
        "######final function that puts everything together and outputs tokens ready for training/testing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLpNMPiYkAeg"
      },
      "source": [
        "def preprocess(data, tokenizer_filename, emb_filename, vocab_size= 33674,):\n",
        "\n",
        "  \"\"\"\n",
        "  data is a pandas series object\n",
        "  \"\"\"\n",
        "  # if (type(data)!='pandas.core.series.Series'):\n",
        "  #   print(\"Please enter a series object\")\n",
        "  \n",
        "  from keras.preprocessing.text import Tokenizer\n",
        "  from numpy import asarray\n",
        "  from numpy import zeros\n",
        "  from keras.preprocessing.text import Tokenizer\n",
        "  from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "  # loading\n",
        "  with open(tokenizer_filename, 'rb') as handle:\n",
        "    tokenizer = pickle.load(handle)\n",
        "\n",
        " \n",
        "  # fit the tokenizer on the documents\n",
        "  data=data.apply(str)\n",
        "  \n",
        "  embedding_index=create_embeddings_index(emb_filename)\n",
        "  embedding_matrix=create_embedding_matrix(tokenizer, embeddings_index)\n",
        "  \n",
        "  max_len=50 #change accordingly\n",
        "  test_sequences = tokenizer.texts_to_sequences(data)\n",
        "  test_padded = pad_sequences(test_sequences, maxlen = max_len, padding = 'post' )\n",
        "  \n",
        "  return test_padded"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qHEj1s4D08t"
      },
      "source": [
        "test_tokens=preprocess(pd.Series(df.iloc[2999, 0]), \"tokenizer.pickle\", \"embeddings.txt\")"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLtcvW0SFcl9",
        "outputId": "2866ad73-42b8-44ec-bfe5-aef1394edbcd"
      },
      "source": [
        "test_tokens"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[33657, 33658, 33659, 18662, 18667, 18668, 18669,   500,    78,\n",
              "            1,     1,     1, 13501,  9443,  1411, 33660, 33661, 18666,\n",
              "        33662,   102,    68,    97, 13500,     9,   500,     1,     1,\n",
              "            1, 33663, 33664,   821, 33665,   408,  9443, 33666,   355,\n",
              "        33667, 33668, 33669, 33670,   497,   355, 33671,   254, 33672,\n",
              "        33673, 18667, 18668, 18669,    78]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lR_Jy869QNaW",
        "outputId": "68223697-250b-4387-84a1-b1a4afeede17"
      },
      "source": [
        "max_len"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnYxhdTR7Kru"
      },
      "source": [
        "####Finally, create the embedding layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLaFL1oCCN1W"
      },
      "source": [
        "#Create an embedding layer\n",
        "e = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=max_len, trainable=False)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIM3SWbJD-SD"
      },
      "source": [
        "##Defining the Deep Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6d_uypbfLNvG"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, Flatten, Embedding"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWgHs0bSKnqT"
      },
      "source": [
        "#LSTM hyperparameters\n",
        "n_lstm = 20\n",
        "drop_lstm =0.2"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdYueWMSENZu"
      },
      "source": [
        "#LSTM Spam detection architecture\n",
        "model= Sequential()\n",
        "model.add(e)\n",
        "model.add(LSTM(n_lstm, dropout=drop_lstm, return_sequences=True))\n",
        "model.add(LSTM(n_lstm, dropout=drop_lstm))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "josmQAlrOZOC"
      },
      "source": [
        "initial_weights = model.get_weights(); #Will be useful in fine-tuning as we can compare different hyperparameters"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aTfON7iLWEE"
      },
      "source": [
        "model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahFhjCWRLpLz"
      },
      "source": [
        "##Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nX5GwyZXL_GR"
      },
      "source": [
        "from keras.callbacks import EarlyStopping"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHxFRjsmPa1x",
        "outputId": "7fa56dff-2b4a-4c90-9a80-ffe6808b8935"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 50, 100)           3367400   \n",
            "_________________________________________________________________\n",
            "lstm_6 (LSTM)                (None, 50, 20)            9680      \n",
            "_________________________________________________________________\n",
            "lstm_7 (LSTM)                (None, 20)                3280      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 21        \n",
            "=================================================================\n",
            "Total params: 3,380,381\n",
            "Trainable params: 12,981\n",
            "Non-trainable params: 3,367,400\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEJR90HILoSx"
      },
      "source": [
        "num_epochs = 10\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=2)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NY1yK7XPK07",
        "outputId": "b31b0364-38c6-4ef9-df8e-1b29d1145678"
      },
      "source": [
        "model.fit(training_padded, y_train, epochs=num_epochs, validation_split=0.3,callbacks =[early_stop], verbose=2)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "53/53 - 3s - loss: 0.5035 - accuracy: 0.8226 - val_loss: 0.4184 - val_accuracy: 0.8500\n",
            "Epoch 2/10\n",
            "53/53 - 2s - loss: 0.4065 - accuracy: 0.8315 - val_loss: 0.2713 - val_accuracy: 0.8917\n",
            "Epoch 3/10\n",
            "53/53 - 2s - loss: 0.2500 - accuracy: 0.9060 - val_loss: 0.1881 - val_accuracy: 0.9264\n",
            "Epoch 4/10\n",
            "53/53 - 2s - loss: 0.1647 - accuracy: 0.9488 - val_loss: 0.1654 - val_accuracy: 0.9472\n",
            "Epoch 5/10\n",
            "53/53 - 2s - loss: 0.1585 - accuracy: 0.9440 - val_loss: 0.1348 - val_accuracy: 0.9444\n",
            "Epoch 6/10\n",
            "53/53 - 2s - loss: 0.1099 - accuracy: 0.9667 - val_loss: 0.1021 - val_accuracy: 0.9653\n",
            "Epoch 7/10\n",
            "53/53 - 2s - loss: 0.0865 - accuracy: 0.9726 - val_loss: 0.1109 - val_accuracy: 0.9653\n",
            "Epoch 8/10\n",
            "53/53 - 2s - loss: 0.0889 - accuracy: 0.9696 - val_loss: 0.1321 - val_accuracy: 0.9583\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1654e8af60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIXVBaAvHily",
        "outputId": "b278db5a-aaeb-47f2-a306-e14d669654b0"
      },
      "source": [
        "res=model.predict_classes(test_tokens)\n",
        "res"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V38qLgGc_iXn",
        "outputId": "a7a47c79-1009-4a81-821d-dececc7ecbc3"
      },
      "source": [
        "res.flatten()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3ZOXOUTj2HW",
        "outputId": "d494f539-e819-4ebf-86f0-c59d277fe492"
      },
      "source": [
        "res.flatten()[0]"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14b0oUb2_X--",
        "outputId": "dd3ddd10-77e5-4dbb-8b33-47196f194098"
      },
      "source": [
        "res[0][0]"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erk-URfQQ3tr"
      },
      "source": [
        "## Model evaluation & hyperparameter tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVg0xwhCFaR3"
      },
      "source": [
        "### First, we will test the default model that we created"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cP4SAjBhQ2hI",
        "outputId": "7777b1c2-7623-4081-fb7a-e8851001e66b"
      },
      "source": [
        "model.evaluate(testing_padded, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 0s 8ms/step - loss: 0.1961 - accuracy: 0.9321\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.1960604190826416, 0.9321333765983582]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ob_H8oRGGXYk"
      },
      "source": [
        "def gen_model(embedding,n_neurons, drop_lstm ):\n",
        "    model= Sequential()\n",
        "    model.add(embedding)\n",
        "    model.add(LSTM(n_neurons, dropout=drop_lstm, return_sequences=True))\n",
        "    model.add(LSTM(n_neurons, dropout=drop_lstm, return_sequences=True))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SdcRL5nFsOV"
      },
      "source": [
        "###Then we will test early stopping so that we do not stop training too early\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qUWs9GeX1ZQ"
      },
      "source": [
        "def fit_lstm_early(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop):\n",
        "  train_acc=[]\n",
        "  test_acc=[]\n",
        " \n",
        "  for i in early_stop:\n",
        "    # prepare model\n",
        "    model= gen_model(embedding, n_lstm, drop_lstm )\n",
        "    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])\n",
        "\n",
        "    print(\"Early stopping patience: \", i)\n",
        "    early_stop = EarlyStopping(monitor='val_loss', patience=i)\n",
        "    model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stop], verbose=0)\n",
        "\t\t# evaluate model on train data\n",
        "    train_acc.append(model.evaluate(X_train, y_train))\n",
        "\t\t# evaluate model on test data\n",
        "    test_acc.append(model.evaluate(testing_padded, y_test))\n",
        "    model.reset_states()\n",
        "  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )\n",
        "  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )\n",
        "  history=history.join(test_history)  \n",
        "  return history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "8ubyboi-os7U",
        "outputId": "2983bb82-fad2-474f-a2be-43e7c8aa196b"
      },
      "source": [
        "fit_lstm_early(training_padded, y_train, e,n_lstm, drop_lstm, 20,[2,3,4,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Early stopping patience:  2\n",
            "75/75 [==============================] - 1s 10ms/step - loss: 0.1478 - accuracy: 0.9463\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.1864 - accuracy: 0.9370\n",
            "Early stopping patience:  3\n",
            "75/75 [==============================] - 1s 9ms/step - loss: 0.1139 - accuracy: 0.9575\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.1842 - accuracy: 0.9322\n",
            "Early stopping patience:  4\n",
            "75/75 [==============================] - 1s 9ms/step - loss: 0.1174 - accuracy: 0.9586\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.1932 - accuracy: 0.9324\n",
            "Early stopping patience:  5\n",
            "75/75 [==============================] - 1s 9ms/step - loss: 0.1618 - accuracy: 0.9370\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.2551 - accuracy: 0.9113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>0.147820</td>\n",
              "      <td>0.946325</td>\n",
              "      <td>0.186350</td>\n",
              "      <td>0.936967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.113853</td>\n",
              "      <td>0.957492</td>\n",
              "      <td>0.184168</td>\n",
              "      <td>0.932200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.117359</td>\n",
              "      <td>0.958608</td>\n",
              "      <td>0.193169</td>\n",
              "      <td>0.932367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.161750</td>\n",
              "      <td>0.937034</td>\n",
              "      <td>0.255064</td>\n",
              "      <td>0.911267</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc\n",
              "0    0.147820   0.946325   0.186350  0.936967\n",
              "1    0.113853   0.957492   0.184168  0.932200\n",
              "2    0.117359   0.958608   0.193169  0.932367\n",
              "3    0.161750   0.937034   0.255064  0.911267"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6x1ks4bJGbt"
      },
      "source": [
        "\n",
        "Early stopping after 2 steps seems to have the highest test accuracy. So we will maintain it.\n",
        "###Second, we will test the number of neurons in the lstm layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZArGuNGP51oS"
      },
      "source": [
        "def fit_lstm_nodes(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop):\n",
        "  train_acc=[]\n",
        "  test_acc=[]\n",
        "  early_stopping = EarlyStopping(monitor='val_loss', patience=early_stop)\n",
        "\n",
        " \n",
        "  for i in n_lstm:\n",
        "    # prepare model\n",
        "\n",
        "    model= Sequential()\n",
        "    model.add(embedding)\n",
        "    model.add(LSTM(i, dropout=drop_lstm, return_sequences=True))\n",
        "    model.add(LSTM(i, dropout=drop_lstm, return_sequences=True))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])\n",
        "\n",
        "    print(\"Number of nodes: \", i)\n",
        "    model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stopping], verbose=0)\n",
        "\t\t# evaluate model on train data\n",
        "    train_acc.append(model.evaluate(X_train, y_train))\n",
        "\t\t# evaluate model on test data\n",
        "    test_acc.append(model.evaluate(testing_padded, y_test))\n",
        "    model.reset_states()\n",
        "  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )\n",
        "  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )\n",
        "  history=history.join(test_history)  \n",
        "\n",
        "  \n",
        "  return history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "O_UhWabT7ZpT",
        "outputId": "39273941-10b3-483f-9c5a-cd4736e43d85"
      },
      "source": [
        "fit_lstm_nodes(training_padded, y_train, e,[10, 20, 30, 40], drop_lstm, 20,2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of nodes:  10\n",
            "75/75 [==============================] - 1s 8ms/step - loss: 0.1706 - accuracy: 0.9426\n",
            "19/19 [==============================] - 0s 7ms/step - loss: 0.2084 - accuracy: 0.9288\n",
            "Number of nodes:  20\n",
            "75/75 [==============================] - 1s 9ms/step - loss: 0.1659 - accuracy: 0.9418\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.2090 - accuracy: 0.9288\n",
            "Number of nodes:  30\n",
            "75/75 [==============================] - 1s 10ms/step - loss: 0.1337 - accuracy: 0.9496\n",
            "19/19 [==============================] - 0s 10ms/step - loss: 0.1928 - accuracy: 0.9342\n",
            "Number of nodes:  40\n",
            "75/75 [==============================] - 1s 12ms/step - loss: 0.1203 - accuracy: 0.9568\n",
            "19/19 [==============================] - 0s 12ms/step - loss: 0.2018 - accuracy: 0.9342\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.170615</td>\n",
              "      <td>0.942575</td>\n",
              "      <td>0.208429</td>\n",
              "      <td>0.928833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.165922</td>\n",
              "      <td>0.941825</td>\n",
              "      <td>0.209035</td>\n",
              "      <td>0.928800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.133669</td>\n",
              "      <td>0.949600</td>\n",
              "      <td>0.192844</td>\n",
              "      <td>0.934233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.120308</td>\n",
              "      <td>0.956817</td>\n",
              "      <td>0.201767</td>\n",
              "      <td>0.934167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc\n",
              "0    0.170615   0.942575   0.208429  0.928833\n",
              "1    0.165922   0.941825   0.209035  0.928800\n",
              "2    0.133669   0.949600   0.192844  0.934233\n",
              "3    0.120308   0.956817   0.201767  0.934167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbuTWxMwKg0l"
      },
      "source": [
        "30 neurons seems to have the best test accuracy so we will use that for our final model\n",
        "\n",
        "###Next, we will try and see the best optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgXi0K68BRXP"
      },
      "source": [
        "def fit_lstm_optimizer(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop, optimizer):\n",
        "  train_acc=[]\n",
        "  test_acc=[]\n",
        "  early_stopping = EarlyStopping(monitor='val_loss', patience=early_stop)\n",
        "\n",
        "  model= Sequential()\n",
        "  model.add(embedding)\n",
        "  model.add(LSTM(n_lstm,dropout=drop_lstm, return_sequences=True))\n",
        "  model.add(LSTM(n_lstm, dropout=drop_lstm, return_sequences=True))\n",
        "  model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "  model.compile(loss = 'binary_crossentropy', optimizer = optimizer, metrics=['accuracy'])\n",
        "  model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stopping], verbose=0)\n",
        "  # evaluate model on train data\n",
        "  train_acc.append(model.evaluate(X_train, y_train))\n",
        "  # evaluate model on test data\n",
        "  test_acc.append(model.evaluate(testing_padded, y_test))\n",
        "  model.reset_states()\n",
        "  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )\n",
        "  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )\n",
        "  history=history.join(test_history)  \n",
        "  return history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "_RDDArATB7ZM",
        "outputId": "10ed03fe-74ee-466b-dce9-e7511c28f9f7"
      },
      "source": [
        "fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'SGD')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "75/75 [==============================] - 1s 10ms/step - loss: 0.4289 - accuracy: 0.8333\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.4317 - accuracy: 0.8333\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>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.42887</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.431734</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc\n",
              "0     0.42887   0.833333   0.431734  0.833333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "WiNJWvxeCocA",
        "outputId": "0e1d752a-bf82-47fa-ff76-6e8bd669ca7a"
      },
      "source": [
        "fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'RMSprop')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "75/75 [==============================] - 1s 9ms/step - loss: 0.2733 - accuracy: 0.9003\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.2909 - accuracy: 0.8971\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",
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              "\n",
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              "\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>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.273256</td>\n",
              "      <td>0.900292</td>\n",
              "      <td>0.290901</td>\n",
              "      <td>0.8971</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc\n",
              "0    0.273256   0.900292   0.290901    0.8971"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "n8tg9by8DBWf",
        "outputId": "3e2f2091-fc2a-4a84-950e-0c6830fc7752"
      },
      "source": [
        "fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'adam')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "75/75 [==============================] - 1s 9ms/step - loss: 0.1756 - accuracy: 0.9346\n",
            "19/19 [==============================] - 0s 9ms/step - loss: 0.2159 - accuracy: 0.9253\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>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.175622</td>\n",
              "      <td>0.934592</td>\n",
              "      <td>0.215877</td>\n",
              "      <td>0.925333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc\n",
              "0    0.175622   0.934592   0.215877  0.925333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "jbcoNw5JDTby",
        "outputId": "27fcabc8-d6b3-4f85-9732-42b857c80758"
      },
      "source": [
        "fit_lstm_optimizer(training_padded, y_train, e, 20, drop_lstm, 20,2,'Adagrad')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "75/75 [==============================] - 1s 9ms/step - loss: 0.4720 - accuracy: 0.8339\n",
            "19/19 [==============================] - 0s 8ms/step - loss: 0.4741 - accuracy: 0.8333\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>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.472019</td>\n",
              "      <td>0.833892</td>\n",
              "      <td>0.474065</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc\n",
              "0    0.472019   0.833892   0.474065  0.833333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3T6FmfEwPOPI"
      },
      "source": [
        "#Finally, we will try to"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIl51zZmLh23"
      },
      "source": [
        "Adam optimizer wins\n",
        "Build Our final optimized model\n",
        "\n",
        "#Finally, we will try add more layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY1_WRdmPe0A"
      },
      "source": [
        "import keras\n",
        "def gen_lstm_layers(model, number, n_neurons, activation, drop_lstm):\n",
        "  for i in range(2, number):\n",
        "    model.add(LSTM( n_neurons, dropout=drop_lstm, return_sequences=True))\n",
        "  return model\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQT8tr34UJt_"
      },
      "source": [
        "def fit_lstm_layers(X_train, y_train, embedding, n_lstm, drop_lstm, num_epochs, early_stop, n_layers):\n",
        "  train_acc=[]\n",
        "  test_acc=[]\n",
        "  early_stopping = EarlyStopping(monitor='val_loss', patience=early_stop)\n",
        "\n",
        " \n",
        "  for i in n_layers:\n",
        "    # prepare model\n",
        "\n",
        "    model= Sequential()\n",
        "    model.add(embedding)\n",
        "    model=gen_lstm_layers(mod, i,n_neurons, \"relu\", drop_lstm)\n",
        "    model.add(LSTM(i, dropout=drop_lstm, return_sequences=True))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics=['accuracy'])\n",
        "\n",
        "    print(\"Number of layers: \", i)\n",
        "    model.fit(X_train, y_train, epochs=num_epochs, validation_split=0.3, callbacks =[early_stopping], verbose=0)\n",
        "\t\t# evaluate model on train data\n",
        "    train_acc.append(model.evaluate(X_train, y_train))\n",
        "\t\t# evaluate model on test data\n",
        "    test_acc.append(model.evaluate(testing_padded, y_test))\n",
        "    model.reset_states()\n",
        "  history =pd.DataFrame(train_acc,columns=['train_loss','train_acc' ] )\n",
        "  test_history =pd.DataFrame(test_acc,columns=['test_loss','test_acc' ] )\n",
        "  history=history.join(test_history)  \n",
        "\n",
        "  \n",
        "  return history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "4Dl7Ajk2U3ko",
        "outputId": "d3b46122-b086-4bf3-b23e-0f95858dcd6c"
      },
      "source": [
        "fit_lstm_layers(training_padded, y_train, e, 20, drop_lstm, 20,2,[2, 3, 4, 5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of layers:  2\n",
            "75/75 [==============================] - 2s 26ms/step - loss: 0.4201 - accuracy: 0.8514\n",
            "19/19 [==============================] - 0s 26ms/step - loss: 0.4250 - accuracy: 0.8480\n",
            "Number of layers:  3\n",
            "75/75 [==============================] - 2s 33ms/step - loss: 0.4272 - accuracy: 0.8333\n",
            "19/19 [==============================] - 1s 34ms/step - loss: 0.4322 - accuracy: 0.8333\n",
            "Number of layers:  4\n",
            "75/75 [==============================] - 3s 46ms/step - loss: 0.4369 - accuracy: 0.8333\n",
            "19/19 [==============================] - 1s 46ms/step - loss: 0.4376 - accuracy: 0.8333\n",
            "Number of layers:  5\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 0.4513 - accuracy: 0.8333\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.4513 - accuracy: 0.8333\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>train_loss</th>\n",
              "      <th>train_acc</th>\n",
              "      <th>test_loss</th>\n",
              "      <th>test_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.420148</td>\n",
              "      <td>0.851392</td>\n",
              "      <td>0.424975</td>\n",
              "      <td>0.848000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.427218</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.432161</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.436947</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.437617</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.451313</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.451313</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   train_loss  train_acc  test_loss  test_acc\n",
              "0    0.420148   0.851392   0.424975  0.848000\n",
              "1    0.427218   0.833333   0.432161  0.833333\n",
              "2    0.436947   0.833333   0.437617  0.833333\n",
              "3    0.451313   0.833333   0.451313  0.833333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GiNnf2KedD2-"
      },
      "source": [
        "Two layers won\n",
        "##Build the final model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuwG-GaNMPJB"
      },
      "source": [
        "n_neurons=50\n",
        "optimizer=\"adam\"\n",
        "early_stop=2\n",
        "num_epochs=20"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aepaUBraLfbE"
      },
      "source": [
        "model= Sequential()\n",
        "model.add(e)\n",
        "model.add(LSTM(n_neurons, dropout=drop_lstm, return_sequences=True))\n",
        "model.add(LSTM(n_neurons, dropout=drop_lstm))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=early_stop)\n",
        "\n",
        "model.compile(loss = 'binary_crossentropy', optimizer = optimizer, metrics=['accuracy'])"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtapwxHsNIfA",
        "outputId": "860c3f59-e484-4981-8d05-eebf285259d6"
      },
      "source": [
        "model.fit(training_padded, y_train, epochs=num_epochs, validation_split=0.3,callbacks =[early_stop], verbose=2)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "53/53 - 4s - loss: 0.4820 - accuracy: 0.8232 - val_loss: 0.3834 - val_accuracy: 0.8500\n",
            "Epoch 2/20\n",
            "53/53 - 3s - loss: 0.3342 - accuracy: 0.8601 - val_loss: 0.2338 - val_accuracy: 0.9319\n",
            "Epoch 3/20\n",
            "53/53 - 3s - loss: 0.1867 - accuracy: 0.9226 - val_loss: 0.1516 - val_accuracy: 0.9431\n",
            "Epoch 4/20\n",
            "53/53 - 3s - loss: 0.1181 - accuracy: 0.9601 - val_loss: 0.0972 - val_accuracy: 0.9653\n",
            "Epoch 5/20\n",
            "53/53 - 3s - loss: 0.1091 - accuracy: 0.9619 - val_loss: 0.0995 - val_accuracy: 0.9653\n",
            "Epoch 6/20\n",
            "53/53 - 3s - loss: 0.0871 - accuracy: 0.9696 - val_loss: 0.1234 - val_accuracy: 0.9486\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f164d0a3cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egZcfx4ROMCz",
        "outputId": "59436783-f7fd-4a00-b17e-c01f66941ae0"
      },
      "source": [
        "model.evaluate(testing_padded, y_test)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 0s 14ms/step - loss: 0.1110 - accuracy: 0.9583\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11100617796182632, 0.9583333134651184]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3YBKZeNadKAN"
      },
      "source": [
        "model.save('spam2.h5')"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBnUBDkcHoNa"
      },
      "source": [
        "http://localhost:5000/predict?"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4c2cg9Itbk-c"
      },
      "source": [
        "!cp embeddings.txt  \"/content/drive/MyDrive/Year 4/Machine Learning/\""
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpyS5Afvb6IW"
      },
      "source": [
        "!cp spam_or_not_spam.csv.zip \"/content/drive/MyDrive/Year 4/Machine Learning/spam_or_not_spam.csv.zip\""
      ],
      "execution_count": 103,
      "outputs": []
    }
  ]
}