GRU_With_Attention.ipynb

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    "colab": {
      "name": "GRU_With_Attention.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZUzGcIueJPw",
        "colab_type": "code",
        "outputId": "a04011ef-6888-4747-b297-418bb06c94db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MllrZqg7eQ9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import re\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from matplotlib import pyplot as plt\n",
        "from bs4 import BeautifulSoup\n",
        "from nltk.corpus import stopwords"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpeBe0c4eYW2",
        "colab_type": "code",
        "outputId": "b54cf38a-6b84-4c60-dd75-868dd497f41a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Sr1HjBX8eaCe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# read data from the csv file (from the location it is stored)\n",
        "Data = pd.read_csv(r'/content/drive/My Drive/IDS_576_Project/wikihowAll.csv')\n",
        "Data = Data.astype(str)\n",
        "rows, columns = Data.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qYfQiKJebOo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=Data[Data['text'].isnull()==False]\n",
        "df=Data[Data['headline'].isnull()==False]\n",
        "df=df[df['text']!='nan']\n",
        "\n",
        "df.drop_duplicates(subset=['text'],inplace=True)  #dropping duplicates"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puYPQ9uHedsL",
        "colab_type": "code",
        "outputId": "4ed4f934-f59a-43df-87bf-2250a9b7b6fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "1       \\nCreate a sketch in the NeoPopRealist manner ...  ...   See the image for how this drawing develops s...\n",
              "2       \\nGet a bachelor’s degree.,\\nEnroll in a studi...  ...   It is possible to become a VFX artist without...\n",
              "3       \\nStart with some experience or interest in ar...  ...   The best art investors do their research on t...\n",
              "4       \\nKeep your reference materials, sketches, art...  ...   As you start planning for a project or work, ...\n",
              "...                                                   ...  ...                                                ...\n",
              "215360  \\nConsider changing the spelling of your name....  ...   If you have a name that you like, you might f...\n",
              "215361  \\nTry out your name.,\\nDon’t legally change yo...  ...   Your name might sound great to you when you s...\n",
              "215362  \\nUnderstand the process of relief printing.,\\...  ...   Relief printing is the oldest and most tradit...\n",
              "215363  \\nUnderstand the process of intaglio printing....  ...   Intaglio is Italian for \"incis­ing,\" and corr...\n",
              "215364  \\nUnderstand the different varieties of lithog...  ...   Lithography is a big term often used to refer...\n",
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      "metadata": {
        "id": "smqXBGEtee-3",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "stop_words = set(stopwords.words('english')) \n",
        "\n",
        "def text_cleaner(text,num):\n",
        "  str = text.lower()\n",
        "  str = BeautifulSoup(str, \"lxml\").text\n",
        "  str = re.sub(r'\\([^)]*\\)', '', str)\n",
        "  str = re.sub('\"','', str)\n",
        "  str = ' '.join([contraction_mapping[t] if t in contraction_mapping else t for t in str.split(\" \")])    \n",
        "  str = re.sub(r\"'s\\b\",\"\",str)\n",
        "  str = re.sub(\"[^a-zA-Z]\", \" \", str) \n",
        "  str = re.sub('[m]{2,}', 'mm', str)\n",
        "  if(num==0):\n",
        "    str = re.sub(r'\\.',' . ',str)\n",
        "  if(num==0):\n",
        "      tokens = [w for w in str.split() if not w in stop_words]\n",
        "      \n",
        "  else:\n",
        "      tokens=str.split()\n",
        "  long_words=[]\n",
        "  for i in tokens:\n",
        "      if len(i)>1:          #removing short words\n",
        "          long_words.append(i)\n",
        "  return (\" \".join(long_words)).strip()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WktFRr81eg4j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "contraction_mapping = {\"ain't\": \"is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\",\"didn't\": \"did not\", \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\",\"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\",\"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \"I've\": \"I have\", \"i'd\": \"i would\",\"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\",\"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\",\"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\",\"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\",\"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\",\"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\",\"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\",\"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\",\"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \"they'd've\": \"they would have\",\"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\",\"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\",\"we've\": \"we have\", \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",\"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\",\"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\",\"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\",\"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\",\"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\",\"you're\": \"you are\", \"you've\": \"you have\"}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8r4sAn9MeitH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#call the function\n",
        "clean_text = []\n",
        "for t in df['text']:\n",
        "    clean_text.append(text_cleaner(t,0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYypqoYGekJa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#call the function\n",
        "clean_summary = []\n",
        "for t in df['headline']:\n",
        "    clean_summary.append(text_cleaner(t,0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjbNXqKgelhG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['text']=clean_text\n",
        "df['headline']=clean_summary\n",
        "\n",
        "df.replace('', np.nan, inplace=True)\n",
        "df.dropna(axis=0,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sD6BTcGserQS",
        "colab_type": "text"
      },
      "source": [
        "Analyzing the sequence distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3ph13qgenFe",
        "colab_type": "code",
        "outputId": "fb7efe8e-0438-4a92-f229-b4f944a30165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "text_word_count = []\n",
        "headline_word_count = []\n",
        "\n",
        "# populate the lists with sentence lengths\n",
        "for i in df['text']:\n",
        "      temp=i.split()\n",
        "      text_word_count.append(len(temp))\n",
        "\n",
        "for j in df['headline']:\n",
        "  #print(j)\n",
        "  temp1=j.split()\n",
        "  headline_word_count.append(len(temp1))\n",
        "\n",
        "length_df = pd.DataFrame({'text':text_word_count, 'headline':headline_word_count})\n",
        "length_df.hist(bins = 30,range=[0,1000])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "becxtmAFewPn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# From the graph\n",
        "# We can fix maximum length of text = 150 since most of the reviews have a length of 150 and maximum headline length of 50, since maximum headlines are of size 50\n",
        "\n",
        "max_len_text= 150\n",
        "max_len_headline=50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fHaefdFezFu",
        "colab_type": "code",
        "outputId": "21a65d09-ac51-49c9-dac4-0305a0d8f5a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cnt=0\n",
        "for i in df['text']:\n",
        "    if(len(i.split())<=150):\n",
        "        cnt=cnt+1\n",
        "print(cnt/len(df['text']))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.4835318078351504\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKc3wuxGe3GN",
        "colab_type": "text"
      },
      "source": [
        "Selecting text and headlines below the maximum lengths "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVwgzEMIe0YY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text1 =np.array(df['text'])\n",
        "headline1=np.array(df['headline'])\n",
        "\n",
        "short_text=[]\n",
        "short_summary=[]\n",
        "\n",
        "for i in range(len(text1)):\n",
        "    if(len(headline1[i].split())<=50 and len(text1[i].split())<=150):\n",
        "        short_text.append(text1[i])\n",
        "        short_summary.append(headline1[i])\n",
        "        \n",
        "df=pd.DataFrame({'text':short_text,'summary':short_summary})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvcB2AFJe5nC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Validating the lengths\n",
        "\n",
        "text1 =np.array(df['text'])\n",
        "headline1=np.array(df['summary'])\n",
        "\n",
        "\n",
        "for i in range(len(text1)):\n",
        "    if(len(headline1[i].split())>=150):\n",
        "      print(i)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYcH03qLe6sp",
        "colab_type": "code",
        "outputId": "2c9224a0-998a-4943-886a-da00a7b571eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "print(df['text'][50],df['summary'][50],sep='\\n')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "guy like every facebook status update instagram picture post better yet frequently comment posts sign wants interact may indicate likes look see comments people posts well comments frequently may reveal avid social media user however rarely likes comments posts sign might feelings publicly comments one photos posts respond eager start conversation way may reveal likes least enjoys messaging example may say great photo could respond saying vancouver last week beautiful city guy met starts liking commenting old photos sig interested means spent time looking back old photos probably wants know better enjoys looking pictures guy likes want connect follow variety different social media platforms example may add facebook snapchat start following twitter instagram adding number social media sites likely sign wants look posts photos selfies attempt get know better\n",
            "see interacts posts reply comments notice comments old pictures posts check see added multiple social media platforms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9osSaQmJe-s9",
        "colab_type": "text"
      },
      "source": [
        "**Splitting data into train, test  --  70 - 30**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xb5TVd5He8dA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(df['text'],df['summary'],test_size=0.3,random_state=0,shuffle=True) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYFhEegofAOE",
        "colab_type": "code",
        "outputId": "26b3170d-6004-425d-e8d2-880bd5da6b11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(len(x_train))\n",
        "print(len(x_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "59677\n",
            "25576\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsnA_isBfCEH",
        "colab_type": "text"
      },
      "source": [
        "Language Translation Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3s-nOM9fBVy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import unicode_literals, print_function, division\n",
        "from io import open\n",
        "import unicodedata\n",
        "import string\n",
        "import re\n",
        "import random\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FUR67hXfILP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SOS_token = 0\n",
        "EOS_token = 1\n",
        "\n",
        "class Lang:\n",
        "    def __init__(self, name):\n",
        "        self.name = name\n",
        "        self.word2index = {}\n",
        "        self.word2count = {}\n",
        "        self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
        "        self.n_words = 2  # Count SOS and EOS\n",
        "\n",
        "    def addSentence(self, sentence):\n",
        "        for word in sentence.split(' '):\n",
        "            self.addWord(word)\n",
        "\n",
        "    def addWord(self, word):\n",
        "        if word not in self.word2index:\n",
        "            self.word2index[word] = self.n_words\n",
        "            self.word2count[word] = 1\n",
        "            self.index2word[self.n_words] = word\n",
        "            self.n_words += 1\n",
        "        else:\n",
        "            self.word2count[word] += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXc__27yfKZk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def readLangs(text, summary, reverse=False):\n",
        "    print(\"Reading lines...\")\n",
        "    \n",
        "    # Split every line into pairs and normalize\n",
        "    text=np.array(text)\n",
        "    summary=np.array(summary)\n",
        "    pairs = [[text[i],summary[i]] for i in range(len(text))]\n",
        "\n",
        "    # Reverse pairs, make Lang instances\n",
        "    if reverse:\n",
        "        pairs = [list(reversed(p)) for p in pairs]\n",
        "        input_lang = Lang(summary)\n",
        "        output_lang = Lang(text)\n",
        "    else:\n",
        "        input_lang = Lang(text)\n",
        "        output_lang = Lang(summary)\n",
        "\n",
        "    return input_lang, output_lang, pairs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNkG1MnOfMY6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepareData(lang1, lang2, reverse=False):\n",
        "    input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)\n",
        "    print(\"Read %s sentence pairs\" % len(pairs))\n",
        "    print(\"Counting words...\")\n",
        "    for pair in pairs:\n",
        "        input_lang.addSentence(pair[0])\n",
        "        output_lang.addSentence(pair[1])\n",
        "    print(\"Counted words:\")\n",
        "    print(input_lang.name, input_lang.n_words)\n",
        "    print(output_lang.name, output_lang.n_words)\n",
        "    return input_lang, output_lang, pairs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AI95WhJdfN-D",
        "colab_type": "code",
        "outputId": "1560a233-c704-40c4-83b3-28e829c7a8cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "input_lang, output_lang, pairs = prepareData(x_train, y_train , False)\n",
        "print(random.choice(pairs))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading lines...\n",
            "Read 59677 sentence pairs\n",
            "Counting words...\n",
            "Counted words:\n",
            "['save hardware later use remove styrofoam lining necessary proper fit grind sand ensure perfect fit'\n",
            " 'use mug microwave safe suitable drinking handle good shape mug must large enough make brownie make sure mug also hard sturdy glass mug mix wet ingredients together metal spoon break egg yolk pushing breaking yolk gently slowly add dry ingredient mixture stir well combined beating egg yolk adding wet ingredients mixture make brownie light fluffy place mug microwave operate minute seconds bakes puff rise fully cooked remove microwave oven mitts place safe place gets burned mistakenly cool minutes avoid spills brownie cooking place small dish bowl beneath mug helps catch brownie mix leaking microwave floor test brownie ready poke toothpick middle comes dry ready scoop vanilla ice cream sprinkle chocolate sprinkles add chopped walnuts spread marshmallow fluff creative many topping decisions choose go choosing serve enjoy eating brownie either eat brownie mug remove enjoy sweet delicious brownie'\n",
            " 'consolidator make sure travel agent use member professional organization like universal federation travel agents association national association career travel agents also look reviews travel agents consumer affairs website better business bureau whether booking business flight group travel others best meet travel agent first time person gives chance meet agent discuss specific travel needs longer conversation phone email allow make sure ask travel agents plenty questions clarify details trip know fees top price ticket help hotline call travel plans disrupted also ask travel agent best way communicate prefer phone email particularly important need contact travel met travel agent create schedule due dates phases trip includes knowing need traveler information agent need copies passports ids payments final payments due'\n",
            " ...\n",
            " 'app gray gear usually located home screen top half page near top page phone ringer sound adjusting able hear volume level setting want change alarm volume future slide change buttons setting position setting underneath volume slider enable adjust alarm volume using iphone volume buttons phone unlocked'\n",
            " 'make sure latest version downloaded google play store options bottom screen based availability vehicles area uberx cars seat passengers least expensive uber service get picked car like prius honda accord similar uberxl cars seat passengers cost uberx rides get picked either suv minivan uberselect entry level luxury car service uberselect cars seat get picked bmw mercedes audi something similar leather interior uberpool allows share ride split cost trip another rider headed direction uberblack uber luxury service expensive expect picked professional driver typically driving black suv luxury sedan box magnifying glass left side pickup location automatically set based gps need change tap pickup location box green dot left side type pickup address tap pickup address update pickup location request sent ride approved information driver appear receive message driver minute away'\n",
            " 'matter kind long yummy repeat necessary confectioner sugar'] 59885\n",
            "['remove factory installed rear bumper trim panel line new body kit rear bumper make sure lines correctly use factory bumper template drill new holes new body kit rear bumper install onto car factory hardware'\n",
            " 'find good mug cooking mix ingredients heat microwave minute seconds add desired toppings serve'\n",
            " 'check credentials meet travel agent person ask questions create schedule'\n",
            " ... 'open settings tap sounds slide ringer alerts volume desired level'\n",
            " 'tap uber app tap service type review economy options drag ride button right larger vehicles drag ride button right premium options drag ride button right special accommodation vehicles tap set pickup location tap destination location type destination tap destination address tap request uber wait driver'\n",
            " 'make toast gather ingredients slather nutella toast place banana slices top cinnamon sugar done put last piece bread put little butter top piece bread make sure melted time put chocolate sauce like'] 34996\n",
            "['find warren buffett twitter account https twitter com warrenbuffett note twitter page updated frequently may take time sees tweet even see expect reply method contact best used intend send quick comment require response', 'tweet']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7m31nE3fWqz",
        "colab_type": "text"
      },
      "source": [
        "Deep Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RI8g3CTJfPKl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SOS_token = 0\n",
        "EOS_token = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5DMPIAVfY-r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def indexesFromSentence(lang, sentence):\n",
        "    return [lang.word2index[word] for word in sentence.split(' ')]\n",
        "\n",
        "def tensorFromSentence(lang, sentence):\n",
        "    indexes = indexesFromSentence(lang, sentence)\n",
        "    indexes.append(EOS_token)\n",
        "    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n",
        "\n",
        "def tensorsFromPair(pair):\n",
        "    input_tensor = tensorFromSentence(input_lang, pair[0])\n",
        "    target_tensor = tensorFromSentence(output_lang, pair[1])\n",
        "    return (input_tensor, target_tensor)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oC_7am4mfaAL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import unicode_literals, print_function, division\n",
        "from io import open\n",
        "import unicodedata\n",
        "import string\n",
        "import re\n",
        "import random\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVWgewjGfblN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class EncoderRNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size):\n",
        "        super(EncoderRNN, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "\n",
        "        self.embedding = nn.Embedding(input_size, hidden_size)\n",
        "        self.gru = nn.GRU(hidden_size, hidden_size)\n",
        "\n",
        "    def forward(self, input, hidden):\n",
        "        embedded = self.embedding(input).view(1, 1, -1)\n",
        "        output = embedded\n",
        "        output, hidden = self.gru(output, hidden)\n",
        "        return output, hidden\n",
        "\n",
        "    def initHidden(self):\n",
        "        return torch.zeros(1, 1, self.hidden_size, device=device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CD6w3XQfloq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DecoderRNN(nn.Module):\n",
        "    def __init__(self, hidden_size, output_size):\n",
        "        super(DecoderRNN, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "\n",
        "        self.embedding = nn.Embedding(output_size, hidden_size)\n",
        "        self.gru = nn.GRU(hidden_size, hidden_size)\n",
        "        self.out = nn.Linear(hidden_size, output_size)\n",
        "        self.softmax = nn.LogSoftmax(dim=1)\n",
        "\n",
        "    def forward(self, input, hidden):\n",
        "        output = self.embedding(input).view(1, 1, -1)\n",
        "        output = F.relu(output)\n",
        "        output, hidden = self.gru(output, hidden)\n",
        "        output = self.softmax(self.out(output[0]))\n",
        "        return output, hidden\n",
        "\n",
        "    def initHidden(self):\n",
        "        return torch.zeros(1, 1, self.hidden_size, device=device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jLJEQpPfnV4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MAX_LENGTH = 150"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BgSwYNNfpWH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AttnDecoderRNN(nn.Module):\n",
        "    def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n",
        "        super(AttnDecoderRNN, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.output_size = output_size\n",
        "        self.dropout_p = dropout_p\n",
        "        self.max_length = max_length\n",
        "\n",
        "        self.embedding = nn.Embedding(self.output_size, self.hidden_size)\n",
        "        self.attn = nn.Linear(self.hidden_size * 2, self.max_length)\n",
        "        self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)\n",
        "        self.dropout = nn.Dropout(self.dropout_p)\n",
        "        self.gru = nn.GRU(self.hidden_size, self.hidden_size)\n",
        "        self.out = nn.Linear(self.hidden_size, self.output_size)\n",
        "\n",
        "    def forward(self, input, hidden, encoder_outputs):\n",
        "        embedded = self.embedding(input).view(1, 1, -1)\n",
        "        embedded = self.dropout(embedded)\n",
        "\n",
        "        attn_weights = F.softmax(\n",
        "            self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n",
        "        attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n",
        "                                 encoder_outputs.unsqueeze(0))\n",
        "\n",
        "        output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
        "        output = self.attn_combine(output).unsqueeze(0)\n",
        "\n",
        "        output = F.relu(output)\n",
        "        output, hidden = self.gru(output, hidden)\n",
        "\n",
        "        output = F.log_softmax(self.out(output[0]), dim=1)\n",
        "        return output, hidden, attn_weights\n",
        "\n",
        "    def initHidden(self):\n",
        "        return torch.zeros(1, 1, self.hidden_size, device=device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNbZhLkwfrT3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def indexesFromSentence(lang, sentence):\n",
        "    return [lang.word2index[word] for word in sentence.split(' ')]\n",
        "\n",
        "\n",
        "def tensorFromSentence(lang, sentence):\n",
        "    indexes = indexesFromSentence(lang, sentence)\n",
        "    indexes.append(EOS_token)\n",
        "    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n",
        "\n",
        "\n",
        "def tensorsFromPair(pair):\n",
        "    input_tensor = tensorFromSentence(input_lang, pair[0])\n",
        "    target_tensor = tensorFromSentence(output_lang, pair[1])\n",
        "    return (input_tensor, target_tensor)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmVp28I4gx0L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "import math\n",
        "\n",
        "\n",
        "def asMinutes(s):\n",
        "    m = math.floor(s / 60)\n",
        "    s -= m * 60\n",
        "    return '%dm %ds' % (m, s)\n",
        "\n",
        "\n",
        "def timeSince(since, percent):\n",
        "    now = time.time()\n",
        "    s = now - since\n",
        "    es = s / (percent)\n",
        "    rs = es - s\n",
        "    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PowpwxXyKTeT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.switch_backend('agg')\n",
        "import matplotlib.ticker as ticker\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def showPlot(points):\n",
        "    plt.figure()\n",
        "    fig, ax = plt.subplots()\n",
        "    # this locator puts ticks at regular intervals\n",
        "    loc = ticker.MultipleLocator(base=0.2)\n",
        "    ax.yaxis.set_major_locator(loc)\n",
        "    plt.plot(points)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ne_OJj66ftIT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "teacher_forcing_ratio = 0.5\n",
        "def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):\n",
        "    encoder_hidden = encoder.initHidden()\n",
        "\n",
        "    encoder_optimizer.zero_grad()\n",
        "    decoder_optimizer.zero_grad()\n",
        "\n",
        "    input_length = input_tensor.size(0)\n",
        "    target_length = target_tensor.size(0)\n",
        "\n",
        "    encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
        "\n",
        "    loss = 0\n",
        "\n",
        "    for ei in range(input_length):\n",
        "        encoder_output, encoder_hidden = encoder(\n",
        "            input_tensor[ei], encoder_hidden)\n",
        "        encoder_outputs[ei] = encoder_output[0, 0]\n",
        "\n",
        "    decoder_input = torch.tensor([[SOS_token]], device=device)\n",
        "\n",
        "    decoder_hidden = encoder_hidden\n",
        "\n",
        "    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
        "\n",
        "    if use_teacher_forcing:\n",
        "        # Teacher forcing: Feed the target as the next input\n",
        "        for di in range(target_length):\n",
        "            decoder_output, decoder_hidden, decoder_attention = decoder(\n",
        "                decoder_input, decoder_hidden, encoder_outputs)\n",
        "            loss += criterion(decoder_output, target_tensor[di])\n",
        "            decoder_input = target_tensor[di]  # Teacher forcing\n",
        "\n",
        "    else:\n",
        "        # Without teacher forcing: use its own predictions as the next input\n",
        "        for di in range(target_length):\n",
        "            decoder_output, decoder_hidden, decoder_attention = decoder(\n",
        "                decoder_input, decoder_hidden, encoder_outputs)\n",
        "            topv, topi = decoder_output.topk(1)\n",
        "            decoder_input = topi.squeeze().detach()  # detach from history as input\n",
        "\n",
        "            loss += criterion(decoder_output, target_tensor[di])\n",
        "            if decoder_input.item() == EOS_token:\n",
        "                break\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    encoder_optimizer.step()\n",
        "    decoder_optimizer.step()\n",
        "\n",
        "    return loss.item() / target_length"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkUmPPWOfuw_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01):\n",
        "    print(\"Training....\")\n",
        "    start = time.time()\n",
        "    plot_losses = []\n",
        "    print_loss_total = 0  # Reset every print_every\n",
        "    plot_loss_total = 0  # Reset every plot_every\n",
        "\n",
        "    encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)\n",
        "    decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)\n",
        "    training_pairs = [tensorsFromPair(random.choice(pairs))\n",
        "                      for i in range(n_iters)]\n",
        "    criterion = nn.NLLLoss()\n",
        "\n",
        "    for iter in range(1, n_iters + 1):\n",
        "        if iter% 1000 == 0:\n",
        "            print(iter,\"/\",n_iters + 1)\n",
        "        training_pair = training_pairs[iter - 1]\n",
        "        input_tensor = training_pair[0]\n",
        "        target_tensor = training_pair[1]\n",
        "\n",
        "        input_length = input_tensor.size(0)\n",
        "        if(input_length > 150):\n",
        "          #print(input_length)\n",
        "          continue\n",
        "\n",
        "        loss = train(input_tensor, target_tensor, encoder,\n",
        "                     decoder, encoder_optimizer, decoder_optimizer, criterion)\n",
        "        print_loss_total += loss\n",
        "        plot_loss_total += loss\n",
        "\n",
        "        if iter % print_every == 0:\n",
        "            print_loss_avg = print_loss_total / print_every\n",
        "            print_loss_total = 0\n",
        "            print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),\n",
        "                                         iter, iter / n_iters * 100, print_loss_avg))\n",
        "\n",
        "        if iter % plot_every == 0:\n",
        "            plot_loss_avg = plot_loss_total / plot_every\n",
        "            plot_losses.append(plot_loss_avg)\n",
        "            plot_loss_total = 0\n",
        "\n",
        "    showPlot(plot_losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8AL_zkWfwRG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
        "    with torch.no_grad():\n",
        "        input_tensor = tensorFromSentence(input_lang, sentence)\n",
        "        input_length = input_tensor.size()[0]\n",
        "        encoder_hidden = encoder.initHidden()\n",
        "\n",
        "        encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
        "\n",
        "        for ei in range(input_length):\n",
        "            encoder_output, encoder_hidden = encoder(input_tensor[ei],\n",
        "                                                     encoder_hidden)\n",
        "            encoder_outputs[ei] += encoder_output[0, 0]\n",
        "\n",
        "        decoder_input = torch.tensor([[SOS_token]], device=device)  # SOS\n",
        "\n",
        "        decoder_hidden = encoder_hidden\n",
        "\n",
        "        decoded_words = []\n",
        "        decoder_attentions = torch.zeros(max_length, max_length)\n",
        "\n",
        "        for di in range(max_length):\n",
        "            decoder_output, decoder_hidden, decoder_attention = decoder(\n",
        "                decoder_input, decoder_hidden, encoder_outputs)\n",
        "            decoder_attentions[di] = decoder_attention.data\n",
        "            topv, topi = decoder_output.data.topk(1)\n",
        "            if topi.item() == EOS_token:\n",
        "                decoded_words.append('<EOS>')\n",
        "                break\n",
        "            else:\n",
        "                decoded_words.append(output_lang.index2word[topi.item()])\n",
        "\n",
        "            decoder_input = topi.squeeze().detach()\n",
        "\n",
        "        return decoded_words, decoder_attentions[:di + 1]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_E9u7A6_fx08",
        "colab_type": "code",
        "outputId": "42c3990a-88d6-4035-e431-03afdc38249d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "hidden_size = 300\n",
        "encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)\n",
        "attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=0.1).to(device)\n",
        "\n",
        "trainIters(encoder1, attn_decoder1, 150000, print_every=1000)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training....\n",
            "1000 / 150001\n",
            "2m 15s (- 336m 49s) (1000 0%) 5.9627\n",
            "2000 / 150001\n",
            "4m 11s (- 309m 36s) (2000 1%) 5.7089\n",
            "3000 / 150001\n",
            "6m 10s (- 302m 49s) (3000 2%) 6.0220\n",
            "4000 / 150001\n",
            "8m 10s (- 298m 25s) (4000 2%) 5.7237\n",
            "5000 / 150001\n",
            "10m 8s (- 294m 9s) (5000 3%) 5.7627\n",
            "6000 / 150001\n",
            "12m 9s (- 291m 56s) (6000 4%) 5.8217\n",
            "7000 / 150001\n",
            "14m 9s (- 289m 14s) (7000 4%) 5.9252\n",
            "8000 / 150001\n",
            "16m 12s (- 287m 45s) (8000 5%) 5.8616\n",
            "9000 / 150001\n",
            "18m 13s (- 285m 24s) (9000 6%) 5.7532\n",
            "10000 / 150001\n",
            "20m 13s (- 283m 2s) (10000 6%) 5.7008\n",
            "11000 / 150001\n",
            "22m 12s (- 280m 41s) (11000 7%) 5.6502\n",
            "12000 / 150001\n",
            "24m 11s (- 278m 10s) (12000 8%) 5.5483\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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": {
        "id": "TbAEKnoS-dw7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUvPetkBf0a9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluateRandomly(encoder, decoder, n=5):\n",
        "    text=list()\n",
        "    headline=list()\n",
        "    pred_headline=list()    \n",
        "    for i in range(n):\n",
        "        pair = random.choice(pairs)\n",
        "        \n",
        "        if(len(pair[0].split())>=150):\n",
        "          continue\n",
        "        else:\n",
        "          if(i%1000==0):\n",
        "            print(i*100/n,\"% complete\")\n",
        "          \n",
        "          #print('>', pair[0])\n",
        "          text.append(pair[0])\n",
        "          #print('=', pair[1])\n",
        "          headline.append(pair[1])\n",
        "          output_words, attentions = evaluate(encoder, decoder, pair[0])\n",
        "          output_sentence = ' '.join(output_words)\n",
        "          pred_headline.append(output_sentence)\n",
        "          #print('<', output_sentence)\n",
        "          #print('')\n",
        "    return(text,headline,pred_headline)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUHCkrSfhOB4",
        "colab_type": "code",
        "outputId": "cffb58c5-36dc-4aae-ad4e-5dfe0908de33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "text,headline,pred_headline=evaluateRandomly(encoder1, attn_decoder1,15000)\n",
        "\n",
        "pred_df_GRU=pd.DataFrame()\n",
        "\n",
        "pred_df_GRU['text']=text\n",
        "pred_df_GRU['headline']=headline\n",
        "pred_df_GRU['pred_headline']=pred_headline"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0 % complete\n",
            "6.666666666666667 % complete\n",
            "13.333333333333334 % complete\n",
            "20.0 % complete\n",
            "26.666666666666668 % complete\n",
            "33.333333333333336 % complete\n",
            "40.0 % complete\n",
            "46.666666666666664 % complete\n",
            "53.333333333333336 % complete\n",
            "60.0 % complete\n",
            "66.66666666666667 % complete\n",
            "73.33333333333333 % complete\n",
            "80.0 % complete\n",
            "86.66666666666667 % complete\n",
            "93.33333333333333 % complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQ4MaZEOhTWj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.chdir ('/content/drive/My Drive/IDS_576_Project')\n",
        "pred_df_GRU.to_csv('Attention_GRU_Pred.csv',sep='\\t',index=False,header=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9D_OaDUNWjd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def calculate_rogue(src_trg, pred_trg):\n",
        "      \n",
        "  #cut off <eos> token\n",
        "  pred_trg = pred_trg[:-6]\n",
        "\n",
        "  \n",
        "  if (len(pred_trg) == 0):\n",
        "    rogue_score = 0.0\n",
        "  else:\n",
        "    s = rouge.get_scores(pred_trg, src_trg, avg= True)\n",
        "    rogue_score = s['rouge-1']['f']\n",
        "    \n",
        "  return rogue_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5mk0oXQNfuu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "attn_plot_threshold = 0.45\n",
        "\n",
        "def evaluateRandomlyprint_1(encoder, decoder, n=5):\n",
        "    text=list()\n",
        "    headline=list()\n",
        "    pred_headline=list()    \n",
        "\n",
        "    for i in range(n):\n",
        "        pair = random.choice(pairs)\n",
        "        \n",
        "        if(len(pair[0].split())>=150):\n",
        "          continue\n",
        "        else:\n",
        "          #if(i%1000==0):\n",
        "           # print(i*100/n,\"% complete\")\n",
        "          \n",
        "          tokenized_input = nltk.word_tokenize(pair[0])\n",
        "          #print(len(tokenized_input))\n",
        "          output_words, attentions = evaluate(encoder, decoder, pair[0])\n",
        "\n",
        "          output_sentence = ' '.join(output_words)\n",
        "\n",
        "          #get rogue f-score -- need to pass tokenized version here\n",
        "          #print(pair[0])\n",
        "          #print(output_sentence)\n",
        "          score = calculate_rogue(pair[0], output_sentence)\n",
        "          #if(score>0.25):\n",
        "           # print(score)\n",
        "\n",
        "          if score > attn_plot_threshold:\n",
        "            plot_attention(tokenized_input, output_words, attentions, i)\n",
        "          \n",
        "          #print('<', output_sentence)\n",
        "          #print('')\n",
        "    #return(text,headline,pred_headline)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzkivB-DXLvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#sentence, translation, attention\n",
        "def plot_attention(sentence, predicted_sentence, attention, i):\n",
        "  plt.rcParams.update({'font.size': 18})\n",
        "\n",
        "  fig = plt.figure(figsize=(30,30))\n",
        "\n",
        "  ax = fig.add_subplot(1,1,1)\n",
        "  #print(len(attention))  \n",
        "  ax.matshow(attention, cmap='bone')\n",
        "  #fig.colorbar(cax)\n",
        "  #fontdict = {'fontsize': 25}\n",
        "\n",
        "  #ax.set_xticklabels([''] + sentence.split(' ') +['<EOS>'], rotation=90)\n",
        "  #ax.set_yticklabels([''] + predicted_sentence)\n",
        "\n",
        "  ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'], rotation=90)\n",
        "  ax.set_yticklabels(['']+predicted_sentence)\n",
        "\n",
        "  ax.xaxis.set_major_locator(ticker.MultipleLocator(3))\n",
        "  ax.yaxis.set_major_locator(ticker.MultipleLocator(3))\n",
        "  #xlim(right=3)\n",
        "\n",
        "  plt.show()\n",
        "  #plt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UideDTnDNqn2",
        "colab_type": "code",
        "outputId": "152c2eef-7fcf-4851-e240-935385dd4dbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install rouge\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from rouge import Rouge\n",
        "\n",
        "rouge = Rouge()\n",
        "\n",
        "\n",
        "evaluateRandomlyprint_1(encoder1, attn_decoder1,15000)\n",
        "#plt.close()"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: rouge in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from rouge) (1.12.0)\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x2160 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}