Table_summarizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "-JzteLqDVJk4",
    "outputId": "6f882872-6529-449d-a558-609d2e098956"
   },
   "outputs": [
    {
     "name": "stdout",
     "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/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "Cz-4Jk50x_Gw",
    "outputId": "994b3837-d03a-459b-faa9-25033b148b09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-06-11 06:31:14--  https://raw.githubusercontent.com/niravnb/Text-Summarizer/master/WeatherGov.zip\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2779488 (2.7M) [application/zip]\n",
      "Saving to: ‘WeatherGov.zip’\n",
      "\n",
      "\r",
      "WeatherGov.zip        0%[                    ]       0  --.-KB/s               \r",
      "WeatherGov.zip      100%[===================>]   2.65M  --.-KB/s    in 0.1s    \n",
      "\n",
      "2020-06-11 06:31:15 (18.1 MB/s) - ‘WeatherGov.zip’ saved [2779488/2779488]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/niravnb/Text-Summarizer/master/WeatherGov.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 765
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "O8H34ZXLyUbp",
    "outputId": "4eb8f362-80d6-419b-9dab-46744e229c85"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  WeatherGov.zip\n",
      "   creating: WeatherGov/\n",
      "  inflating: WeatherGov/.DS_Store    \n",
      "   creating: __MACOSX/\n",
      "   creating: __MACOSX/WeatherGov/\n",
      "  inflating: __MACOSX/WeatherGov/._.DS_Store  \n",
      "   creating: WeatherGov/test/\n",
      "  inflating: WeatherGov/test/test.len  \n",
      "   creating: __MACOSX/WeatherGov/test/\n",
      "  inflating: __MACOSX/WeatherGov/test/._test.len  \n",
      "  inflating: WeatherGov/test/test.combined  \n",
      "  inflating: __MACOSX/WeatherGov/test/._test.combined  \n",
      "  inflating: WeatherGov/test/test.proc  \n",
      "  inflating: __MACOSX/WeatherGov/test/._test.proc  \n",
      "  inflating: WeatherGov/test/test.field  \n",
      "  inflating: __MACOSX/WeatherGov/test/._test.field  \n",
      "  inflating: __MACOSX/WeatherGov/._test  \n",
      "   creating: WeatherGov/train/\n",
      "  inflating: WeatherGov/train/train.proc  \n",
      "   creating: __MACOSX/WeatherGov/train/\n",
      "  inflating: __MACOSX/WeatherGov/train/._train.proc  \n",
      "  inflating: WeatherGov/train/train.combined  \n",
      "  inflating: __MACOSX/WeatherGov/train/._train.combined  \n",
      "  inflating: WeatherGov/train/train.field  \n",
      "  inflating: __MACOSX/WeatherGov/train/._train.field  \n",
      "  inflating: WeatherGov/train/train.len  \n",
      "  inflating: __MACOSX/WeatherGov/train/._train.len  \n",
      "  inflating: WeatherGov/train/summaries.txt  \n",
      "  inflating: __MACOSX/WeatherGov/train/._summaries.txt  \n",
      "  inflating: __MACOSX/WeatherGov/._train  \n",
      "   creating: WeatherGov/dev/\n",
      "  inflating: WeatherGov/dev/dev.field  \n",
      "   creating: __MACOSX/WeatherGov/dev/\n",
      "  inflating: __MACOSX/WeatherGov/dev/._dev.field  \n",
      "  inflating: WeatherGov/dev/dev.combined  \n",
      "  inflating: __MACOSX/WeatherGov/dev/._dev.combined  \n",
      "  inflating: WeatherGov/dev/dev.len  \n",
      "  inflating: __MACOSX/WeatherGov/dev/._dev.len  \n",
      "  inflating: WeatherGov/dev/summaries.txt  \n",
      "  inflating: __MACOSX/WeatherGov/dev/._summaries.txt  \n",
      "  inflating: WeatherGov/dev/dev.proc  \n",
      "  inflating: __MACOSX/WeatherGov/dev/._dev.proc  \n",
      "  inflating: __MACOSX/WeatherGov/._dev  \n",
      "  inflating: __MACOSX/._WeatherGov   \n"
     ]
    }
   ],
   "source": [
    "!unzip WeatherGov.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-_ORTH5hyubf"
   },
   "outputs": [],
   "source": [
    "sentences_to_read = 25000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "colab_type": "code",
    "id": "3cf4Ly9Xy1m8",
    "outputId": "4b7386d4-e9fa-447b-a1f0-dfb9bd92beae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample translations (25000)\n",
      "( 0 ) Table:  temperature time 6-21 min 26 mean 43 max 53 windChill time 6-21 min 0 mean 22 max 46 windSpeed time 6-21 min 6 mean 8 max 10 mode-bucket-0-20-2 0-10 windDir time 6-21 mode SE gust time 6-21 min 0 mean 0 max 0 skyCover time 6-21 mode-bucket-0-100-4 50-75 skyCover time 6-9 mode-bucket-0-100-4 0-25 skyCover time 6-13 mode-bucket-0-100-4 25-50 skyCover time 9-21 mode-bucket-0-100-4 50-75 skyCover time 13-21 mode-bucket-0-100-4 50-75 precipPotential time 6-21 min 0 mean 2 max 12\n",
      "\n",
      "( 0 ) Summary:  Mostly sunny , with a high near 53 . Southeast wind between 7 and 9 mph . \n",
      "\n",
      "( 10000 ) Table:  temperature time 6-21 min 29 mean 31 max 35 windChill time 6-21 min 16 mean 21 max 25 windSpeed time 6-21 min 8 mean 15 max 21 mode-bucket-0-20-2 10-20 windDir time 6-21 mode WNW gust time 6-21 min 0 mean 12 max 29 skyCover time 6-21 mode-bucket-0-100-4 50-75 skyCover time 6-9 mode-bucket-0-100-4 50-75 skyCover time 6-13 mode-bucket-0-100-4 50-75 skyCover time 9-21 mode-bucket-0-100-4 50-75 skyCover time 13-21 mode-bucket-0-100-4 75-100 precipPotential time 6-21 min 15 mean 17 max 18 snowChance time 6-21 mode SChc snowChance time 6-9 mode SChc snowChance time 6-13 mode SChc snowChance time 9-21 mode SChc snowChance time 13-21 mode SChc\n",
      "\n",
      "( 10000 ) Summary:  A 20 percent chance of snow . Mostly cloudy , with a high near 35 . Breezy , with a west northwest wind 8 to 11 mph increasing to between 17 and 20 mph . Winds could gust as high as 28 mph . \n",
      "\n",
      "( 20000 ) Table:  temperature time 17-30 min 44 mean 48 max 57 windChill time 17-30 min 0 mean 29 max 49 windSpeed time 17-30 min 2 mean 2 max 3 mode-bucket-0-20-2 0-10 windDir time 17-30 mode SE gust time 17-30 min 0 mean 0 max 0 skyCover time 17-30 mode-bucket-0-100-4 50-75 skyCover time 17-21 mode-bucket-0-100-4 50-75 skyCover time 17-26 mode-bucket-0-100-4 50-75 skyCover time 21-30 mode-bucket-0-100-4 50-75 skyCover time 26-30 mode-bucket-0-100-4 50-75 precipPotential time 17-30 min 6 mean 11 max 16 rainChance time 26-30 mode SChc\n",
      "\n",
      "( 20000 ) Summary:  A slight chance of showers after 4am . Mostly cloudy , with a low around 43 . Light southeast wind . Chance of precipitation is 20 % . \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Loading train.combined as source_sent and summaries.txt as target_sent\n",
    "source_sent = []\n",
    "target_sent = []\n",
    "\n",
    "test_source_sent = []\n",
    "test_target_sent = []\n",
    "\n",
    "\n",
    "with open('WeatherGov/train/train.combined', 'r') as f:\n",
    "    for l_i, line in enumerate(f):\n",
    "        source_sent.append(line)\n",
    "        if len(source_sent)>=sentences_to_read:\n",
    "            break\n",
    "        \n",
    "            \n",
    "with open('WeatherGov/train/summaries.txt', 'r') as f:\n",
    "    for l_i, line in enumerate(f):\n",
    "        target_sent.append(line)\n",
    "        if len(target_sent)>=sentences_to_read:\n",
    "            break\n",
    "        \n",
    "            \n",
    "assert len(source_sent)==len(target_sent),'Source: %d, Target: %d'%(len(source_sent),len(target_sent))\n",
    "\n",
    "print('Sample translations (%d)'%len(source_sent))\n",
    "for i in range(0,sentences_to_read,10000):\n",
    "    print('(',i,') Table: ', source_sent[i])\n",
    "    print('(',i,') Summary: ', target_sent[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "MT9PP0y6y1qp",
    "outputId": "1c3a2516-b0ec-4094-cece-7feb4d06d735"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (3.2.5)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "2z8jykjt0NKP",
    "outputId": "c36ad0ab-9caa-49b0-f71a-f5b14bbccaff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "IliVUlcF0O2V",
    "outputId": "f271d84a-6dd2-4bfe-9041-2534f73d5a09"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "from keras.preprocessing.text import Tokenizer \n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from nltk.corpus import stopwords\n",
    "from tensorflow.keras.layers import Input, LSTM, Embedding, Dense, Concatenate, TimeDistributed, Bidirectional\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import warnings\n",
    "pd.set_option(\"display.max_colwidth\", 200)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_OnzIme70l9z"
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame()\n",
    "data['cleaned_text']=source_sent\n",
    "data['cleaned_summary']=target_sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "j0P_z5N80QIg",
    "outputId": "d1e25565-36c6-4282-cc01-63754b04ef27"
   },
   "outputs": [
    {
     "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>cleaned_text</th>\n",
       "      <th>cleaned_summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>temperature time 6-21 min 26 mean 43 max 53 windChill time 6-21 min 0 mean 22 max 46 windSpeed time 6-21 min 6 mean 8 max 10 mode-bucket-0-20-2 0-10 windDir time 6-21 mode SE gust time 6-21 min 0 ...</td>\n",
       "      <td>Mostly sunny , with a high near 53 . Southeast wind between 7 and 9 mph . \\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>temperature time 17-30 min 47 mean 50 max 58 windChill time 17-30 min 0 mean 30 max 49 windSpeed time 17-30 min 3 mean 5 max 9 mode-bucket-0-20-2 0-10 windDir time 17-30 mode E gust time 17-30 min...</td>\n",
       "      <td>A 50 percent chance of showers . Cloudy , with a low around 47 . East wind around 5 mph . \\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>temperature time 17-30 min 51 mean 55 max 62 windChill time 17-30 min 0 mean 0 max 0 windSpeed time 17-30 min 2 mean 3 max 6 mode-bucket-0-20-2 0-10 windDir time 17-30 mode SSE gust time 17-30 min...</td>\n",
       "      <td>Cloudy , with a low around 49 . Calm wind becoming south southeast around 5 mph . \\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>temperature time 17-30 min 26 mean 29 max 38 windChill time 17-30 min 26 mean 28 max 36 windSpeed time 17-30 min 0 mean 0 max 5 mode-bucket-0-20-2 0-10 windDir time 17-30 mode ESE gust time 17-30 ...</td>\n",
       "      <td>Partly cloudy , with a low around 23 . Calm wind . \\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>temperature time 17-30 min 55 mean 60 max 69 windChill time 17-30 min 0 mean 0 max 0 windSpeed time 17-30 min 8 mean 9 max 14 mode-bucket-0-20-2 0-10 windDir time 17-30 mode SSW gust time 17-30 mi...</td>\n",
       "      <td>Partly cloudy , with a low around 53 . South wind around 9 mph . \\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                              cleaned_text                                                                               cleaned_summary\n",
       "0  temperature time 6-21 min 26 mean 43 max 53 windChill time 6-21 min 0 mean 22 max 46 windSpeed time 6-21 min 6 mean 8 max 10 mode-bucket-0-20-2 0-10 windDir time 6-21 mode SE gust time 6-21 min 0 ...                  Mostly sunny , with a high near 53 . Southeast wind between 7 and 9 mph . \\n\n",
       "1  temperature time 17-30 min 47 mean 50 max 58 windChill time 17-30 min 0 mean 30 max 49 windSpeed time 17-30 min 3 mean 5 max 9 mode-bucket-0-20-2 0-10 windDir time 17-30 mode E gust time 17-30 min...  A 50 percent chance of showers . Cloudy , with a low around 47 . East wind around 5 mph . \\n\n",
       "2  temperature time 17-30 min 51 mean 55 max 62 windChill time 17-30 min 0 mean 0 max 0 windSpeed time 17-30 min 2 mean 3 max 6 mode-bucket-0-20-2 0-10 windDir time 17-30 mode SSE gust time 17-30 min...          Cloudy , with a low around 49 . Calm wind becoming south southeast around 5 mph . \\n\n",
       "3  temperature time 17-30 min 26 mean 29 max 38 windChill time 17-30 min 26 mean 28 max 36 windSpeed time 17-30 min 0 mean 0 max 5 mode-bucket-0-20-2 0-10 windDir time 17-30 mode ESE gust time 17-30 ...                                         Partly cloudy , with a low around 23 . Calm wind . \\n\n",
       "4  temperature time 17-30 min 55 mean 60 max 69 windChill time 17-30 min 0 mean 0 max 0 windSpeed time 17-30 min 8 mean 9 max 14 mode-bucket-0-20-2 0-10 windDir time 17-30 mode SSW gust time 17-30 mi...                           Partly cloudy , with a low around 53 . South wind around 9 mph . \\n"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2bsQ9iHS0YIQ"
   },
   "outputs": [],
   "source": [
    "data.replace('', np.nan, inplace=True)\n",
    "data.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "jNuM7ve_0iLi",
    "outputId": "aaa36693-a748-472d-e223-66ad9cf4b181"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "text_word_count = []\n",
    "summary_word_count = []\n",
    "\n",
    "# populate the lists with sentence lengths\n",
    "for i in data['cleaned_text']:\n",
    "      text_word_count.append(len(i.split()))\n",
    "\n",
    "for i in data['cleaned_summary']:\n",
    "      summary_word_count.append(len(i.split()))\n",
    "\n",
    "length_df = pd.DataFrame({'text':text_word_count, 'summary':summary_word_count})\n",
    "\n",
    "length_df.hist(bins = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Hh2VGlAi0qlv",
    "outputId": "70864763-0f03-4a93-8e54-53b46476e7aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96256\n"
     ]
    }
   ],
   "source": [
    "cnt=0\n",
    "for i in data['cleaned_summary']:\n",
    "    if(len(i.split())<=60):\n",
    "        cnt=cnt+1\n",
    "print(cnt/len(data['cleaned_summary']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4i4apTs_2Ywu",
    "outputId": "4a7ca3dc-8bd6-488f-828e-b10e622ca6eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.94176\n"
     ]
    }
   ],
   "source": [
    "cnt=0\n",
    "for i in data['cleaned_text']:\n",
    "    if(len(i.split())<=120):\n",
    "        cnt=cnt+1\n",
    "print(cnt/len(data['cleaned_text']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3UHiN1No1Nq_"
   },
   "outputs": [],
   "source": [
    "max_text_len=120\n",
    "max_summary_len=60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8ppJe-qQ1PxG"
   },
   "outputs": [],
   "source": [
    "cleaned_text =np.array(data['cleaned_text'])\n",
    "cleaned_summary=np.array(data['cleaned_summary'])\n",
    "\n",
    "short_text=[]\n",
    "short_summary=[]\n",
    "\n",
    "for i in range(len(cleaned_text)):\n",
    "    if(len(cleaned_summary[i].split())<=max_summary_len and len(cleaned_text[i].split())<=max_text_len):\n",
    "        short_text.append(cleaned_text[i])\n",
    "        short_summary.append(cleaned_summary[i])\n",
    "        \n",
    "df=pd.DataFrame({'text':short_text,'summary':short_summary})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CMgKY08_2jd7"
   },
   "outputs": [],
   "source": [
    "df['summary'] = df['summary'].apply(lambda x : 'sostok '+ x + ' eostok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wi2TLL0Z2oBq"
   },
   "outputs": [],
   "source": [
    "df.to_csv('cleaned_weather_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_rw9-f6gTOu4"
   },
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = max_text_len\n",
    "MAX_NUM_WORDS = 140\n",
    "EMBEDDING_DIM = 100\n",
    "VALIDATION_SPLIT = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zgap6TER2tag"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_tr,x_val,y_tr,y_val=train_test_split(np.array(df['text']),np.array(df['summary']),test_size=0.1,random_state=0,shuffle=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_BozpjTo228U"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.text import Tokenizer \n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "#prepare a tokenizer for reviews on training data\n",
    "x_tokenizer = Tokenizer() \n",
    "x_tokenizer.fit_on_texts(list(x_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "IK48_FPD4lo0",
    "outputId": "d08ae93d-effa-429e-b977-e99e10cdaf9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of rare words in vocabulary: 0.0\n",
      "Total Coverage of rare words: 0.0\n"
     ]
    }
   ],
   "source": [
    "thresh=4\n",
    "\n",
    "cnt=0\n",
    "tot_cnt=0\n",
    "freq=0\n",
    "tot_freq=0\n",
    "\n",
    "for key,value in x_tokenizer.word_counts.items():\n",
    "    tot_cnt=tot_cnt+1\n",
    "    tot_freq=tot_freq+value\n",
    "    if(value<thresh):\n",
    "        cnt=cnt+1\n",
    "        freq=freq+value\n",
    "    \n",
    "print(\"% of rare words in vocabulary:\",(cnt/tot_cnt)*100)\n",
    "print(\"Total Coverage of rare words:\",(freq/tot_freq)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EZTfZJTO26on"
   },
   "outputs": [],
   "source": [
    "#prepare a tokenizer for reviews on training data\n",
    "x_tokenizer = Tokenizer(num_words=tot_cnt-cnt) \n",
    "x_tokenizer.fit_on_texts(list(x_tr))\n",
    "\n",
    "#convert text sequences into integer sequences\n",
    "x_tr_seq    =   x_tokenizer.texts_to_sequences(x_tr) \n",
    "x_val_seq   =   x_tokenizer.texts_to_sequences(x_val)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6x3oNAgJ2_pZ"
   },
   "outputs": [],
   "source": [
    "#padding zero upto maximum length\n",
    "x_tr    =   pad_sequences(x_tr_seq,  maxlen=max_text_len, padding='post')\n",
    "x_val   =   pad_sequences(x_val_seq, maxlen=max_text_len, padding='post')\n",
    "\n",
    "#size of vocabulary ( +1 for padding token)\n",
    "x_voc   =  x_tokenizer.num_words + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kjSQRw9Z3PBX",
    "outputId": "a38c7b6c-ecac-4766-eabc-b4dbabed7ebd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_voc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "VOLV4HYn3frR",
    "outputId": "cf7aaffe-8ea7-4cd6-8cf8-b9dd30d00ebc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 25,   1,   9,   8,  11,  72,  12,  87,  13, 103,  26,   1,   9,\n",
       "         8,  11,   2,  12,  23,  13,  16,  27,   1,   9,   8,  11,  40,\n",
       "        12,  33,  13,  23,   3,   6,   2,  19,  24,   2,  23,  28,   1,\n",
       "         9,   8,   3, 100,  29,   1,   9,   8,  11,   2,  12,   2,  13,\n",
       "         2,  14,   1,   9,   8,   3,   6,   2,   4,  10,   2,  17,  14,\n",
       "         1,   9,   5,   3,   6,   2,   4,  10,   2,  17,  14,   1,   9,\n",
       "        22,   3,   6,   2,   4,  10,   2,  17,  14,   1,   5,   8,   3,\n",
       "         6,   2,   4,  10,   2,  17,  14,   1,  22,   8,   3,   6,   2,\n",
       "         4,  10,   2,  17,  30,   1,   9,   8,  11,   2,  12,   2,  13,\n",
       "         2,   0,   0], dtype=int32)"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_val[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9iZzN_RY3hOW"
   },
   "outputs": [],
   "source": [
    "#prepare a tokenizer for reviews on training data\n",
    "y_tokenizer = Tokenizer()   \n",
    "y_tokenizer.fit_on_texts(list(y_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "-zeUnYhs45sI",
    "outputId": "57e9dff1-bb77-4121-88bd-ff2907c0dd26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of rare words in vocabulary: 9.090909090909092\n",
      "Total Coverage of rare words: 0.0070722033736321495\n"
     ]
    }
   ],
   "source": [
    "thresh=3\n",
    "\n",
    "cnt=0\n",
    "tot_cnt=0\n",
    "freq=0\n",
    "tot_freq=0\n",
    "\n",
    "for key,value in y_tokenizer.word_counts.items():\n",
    "    tot_cnt=tot_cnt+1\n",
    "    tot_freq=tot_freq+value\n",
    "    if(value<thresh):\n",
    "        cnt=cnt+1\n",
    "        freq=freq+value\n",
    "    \n",
    "print(\"% of rare words in vocabulary:\",(cnt/tot_cnt)*100)\n",
    "print(\"Total Coverage of rare words:\",(freq/tot_freq)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FItKe8tS3lhU"
   },
   "outputs": [],
   "source": [
    "#prepare a tokenizer for reviews on training data\n",
    "y_tokenizer = Tokenizer(num_words=tot_cnt-cnt) \n",
    "y_tokenizer.fit_on_texts(list(y_tr))\n",
    "\n",
    "#convert text sequences into integer sequences\n",
    "y_tr_seq    =   y_tokenizer.texts_to_sequences(y_tr) \n",
    "y_val_seq   =   y_tokenizer.texts_to_sequences(y_val) \n",
    "\n",
    "#padding zero upto maximum length\n",
    "y_tr    =   pad_sequences(y_tr_seq, maxlen=max_summary_len, padding='post')\n",
    "y_val   =   pad_sequences(y_val_seq, maxlen=max_summary_len, padding='post')\n",
    "\n",
    "#size of vocabulary\n",
    "y_voc  =   y_tokenizer.num_words +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "jH1tsX3F3ogc",
    "outputId": "cd470677-fa4f-4517-979c-96aad1116a8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20584, 20584)"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_tokenizer.word_counts['sostok'],len(y_tr)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LUly4xcs3_qz"
   },
   "outputs": [],
   "source": [
    "# !wget http://nlp.stanford.edu/data/glove.6B.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "biUF8sRdlDoY",
    "outputId": "70c9f78c-6d91-4144-f8ce-b268d2f4cb9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-06-11 06:31:34--  https://www.floydhub.com/api/v1/resources/Av2ThePYtAHXMAuSXEBV8X/glove.6B.100d.txt?content=true\n",
      "Resolving www.floydhub.com (www.floydhub.com)... 172.67.72.144, 104.26.1.30, 104.26.0.30, ...\n",
      "Connecting to www.floydhub.com (www.floydhub.com)|172.67.72.144|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [application/octet-stream]\n",
      "Saving to: ‘glove.6B.100d.txt?content=true’\n",
      "\n",
      "glove.6B.100d.txt?c     [            <=>     ] 331.04M  34.1MB/s    in 9.4s    \n",
      "\n",
      "2020-06-11 06:31:44 (35.2 MB/s) - ‘glove.6B.100d.txt?content=true’ saved [347116733]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# !wget https://media.githubusercontent.com/media/allenai/spv2/master/model/glove.6B.100d.txt.gz\n",
    "!wget https://www.floydhub.com/api/v1/resources/Av2ThePYtAHXMAuSXEBV8X/glove.6B.100d.txt?content=true&rename=glove6b100dtxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MrPUnBJiRzAw"
   },
   "outputs": [],
   "source": [
    "# !unzip glove.6B.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "okBG5clTTBlj"
   },
   "outputs": [],
   "source": [
    "EMBEDDING_DIM = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HoYz7YbuSQz-",
    "outputId": "91df92da-3a99-4426-a632-bcbec6bed729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "embeddings_index = {}\n",
    "f = open( 'glove.6B.100d.txt?content=true')\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l0GFEmmSUZso"
   },
   "outputs": [],
   "source": [
    "reverse_target_word_index=y_tokenizer.index_word\n",
    "reverse_source_word_index=x_tokenizer.index_word\n",
    "target_word_index=y_tokenizer.word_index\n",
    "word_index=x_tokenizer.word_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3GcDgXU2ShON"
   },
   "outputs": [],
   "source": [
    "embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))\n",
    "for word, i in word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L7xhJN9OYrlB"
   },
   "outputs": [],
   "source": [
    "embedding_matrix_1 = np.zeros((len(target_word_index) + 1, EMBEDDING_DIM))\n",
    "for word, i in target_word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix_1[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "FAb5miza5HWI",
    "outputId": "c77d7c48-0d42-4887-8368-0fccca71b171"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-06-11 06:31:56--  https://raw.githubusercontent.com/thushv89/attention_keras/master/layers/attention.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5230 (5.1K) [text/plain]\n",
      "Saving to: ‘attention.py’\n",
      "\n",
      "\r",
      "attention.py          0%[                    ]       0  --.-KB/s               \r",
      "attention.py        100%[===================>]   5.11K  --.-KB/s    in 0s      \n",
      "\n",
      "2020-06-11 06:31:56 (96.3 MB/s) - ‘attention.py’ saved [5230/5230]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/thushv89/attention_keras/master/layers/attention.py\n",
    "from attention import AttentionLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "imulutl-XDQo"
   },
   "outputs": [],
   "source": [
    "# from math import log\n",
    "# from numpy import array\n",
    "# from numpy import argmax\n",
    " \n",
    "# # beam search\n",
    "# def beam_search_decoder(data, k):\n",
    "# \tsequences = [[list(), 0.0]]\n",
    "# \t# walk over each step in sequence\n",
    "# \tfor row in data:\n",
    "# \t\tall_candidates = list()\n",
    "# \t\t# expand each current candidate\n",
    "# \t\tfor i in range(len(sequences)):\n",
    "# \t\t\tseq, score = sequences[i]\n",
    "# \t\t\tfor j in range(len(row)):\n",
    "# \t\t\t\tcandidate = [seq + [j], score - log(row[j])]\n",
    "# \t\t\t\tall_candidates.append(candidate)\n",
    "# \t\t# order all candidates by score\n",
    "# \t\tordered = sorted(all_candidates, key=lambda tup:tup[1])\n",
    "# \t\t# select k best\n",
    "# \t\tsequences = ordered[:k]\n",
    "# \treturn sequences\n",
    " \n",
    "# # define a sequence of 10 words over a vocab of 5 words\n",
    "# data = [[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "# \t\t[0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "# \t\t[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "# \t\t[0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "# \t\t[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "# \t\t[0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "# \t\t[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "# \t\t[0.5, 0.4, 0.3, 0.2, 0.1],\n",
    "# \t\t[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "# \t\t[0.5, 0.4, 0.3, 0.2, 0.1]]\n",
    "# data = array(data)\n",
    "# # decode sequence\n",
    "# result = beam_search_decoder(data, 3)\n",
    "# # print result\n",
    "# for seq in result:\n",
    "# \tprint(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 697
    },
    "colab_type": "code",
    "id": "Tkb0C6U85Hdl",
    "outputId": "f06421d2-c18e-43a5-8117-95bfb1390d7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 120)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 120, 100)     14000       input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional (Bidirectional)   [(None, 120, 256), ( 234496      embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, None, 100)    30900       input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 256)          0           bidirectional[0][1]              \n",
      "                                                                 bidirectional[0][3]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 256)          0           bidirectional[0][2]              \n",
      "                                                                 bidirectional[0][4]              \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   [(None, None, 256),  365568      embedding_1[0][0]                \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "attention_layer (AttentionLayer ((None, None, 256),  131328      bidirectional[0][0]              \n",
      "                                                                 lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concat_layer (Concatenate)      (None, None, 512)    0           lstm_1[0][0]                     \n",
      "                                                                 attention_layer[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed (TimeDistribut (None, None, 281)    144153      concat_layer[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 920,445\n",
      "Trainable params: 875,545\n",
      "Non-trainable params: 44,900\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Bi-directional model\n",
    "from keras import backend as K \n",
    "K.clear_session()\n",
    "\n",
    "latent_dim = 128\n",
    "embedding_dim=EMBEDDING_DIM\n",
    "\n",
    "# Encoder\n",
    "encoder_inputs = Input(shape=(max_text_len,))\n",
    "\n",
    "#embedding layer\n",
    "enc_emb = Embedding(len(word_index) + 1,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=MAX_SEQUENCE_LENGTH,\n",
    "                            trainable=False)(encoder_inputs)\n",
    "\n",
    "#encoder lstm 1\n",
    "encoder_lstm = LSTM(latent_dim,return_sequences=True,return_state=True,dropout=0.3,recurrent_dropout=0.2)\n",
    "encoder_lstm1 = Bidirectional(encoder_lstm)\n",
    "encoder_outputs, forward_h, forward_c, backward_h, backward_c = encoder_lstm1(enc_emb)\n",
    "\n",
    "state_h = Concatenate()([forward_h, backward_h])\n",
    "state_c = Concatenate()([forward_c, backward_c])\n",
    "\n",
    "\n",
    "# Set up the decoder, using `encoder_states` as initial state.\n",
    "decoder_inputs = Input(shape=(None,))\n",
    "\n",
    "#embedding layer\n",
    "dec_emb_layer = Embedding(len(target_word_index) + 1,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix_1],\n",
    "                            input_length=max_summary_len,\n",
    "                            trainable=False)\n",
    "\n",
    "dec_emb = dec_emb_layer(decoder_inputs)\n",
    "\n",
    "\n",
    "decoder_lstm = LSTM(latent_dim* 2, return_sequences=True, return_state=True,dropout=0.3,recurrent_dropout=0.2)\n",
    "decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c])\n",
    "\n",
    "# Attention layer\n",
    "attn_layer = AttentionLayer(name='attention_layer')\n",
    "attn_out, attn_states = attn_layer([encoder_outputs, decoder_outputs])\n",
    "\n",
    "# Concat attention input and decoder LSTM output\n",
    "decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])\n",
    "\n",
    "#dense layer\n",
    "decoder_dense =  TimeDistributed(Dense(y_voc, activation='softmax'))\n",
    "decoder_outputs = decoder_dense(decoder_concat_input)\n",
    "\n",
    "# Define the model \n",
    "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
    "\n",
    "model.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QiCiMRV2vEuH"
   },
   "outputs": [],
   "source": [
    "# from tensorflow import keras\n",
    "# opt = keras.optimizers.Adam(learning_rate=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ky67YJi55HaB"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "biq7Lbhk5led"
   },
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,patience=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "YorSwH0E5mqH",
    "outputId": "26eca400-7fa0-4486-8b9f-14e1e72407c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "322/322 [==============================] - 322s 1s/step - loss: 0.8584 - accuracy: 0.8055 - val_loss: 0.3903 - val_accuracy: 0.8847\n",
      "Epoch 2/10\n",
      "322/322 [==============================] - 325s 1s/step - loss: 0.3505 - accuracy: 0.8942 - val_loss: 0.3009 - val_accuracy: 0.9063\n",
      "Epoch 3/10\n",
      "322/322 [==============================] - 322s 1s/step - loss: 0.2916 - accuracy: 0.9091 - val_loss: 0.2637 - val_accuracy: 0.9158\n",
      "Epoch 4/10\n",
      "322/322 [==============================] - 324s 1s/step - loss: 0.2546 - accuracy: 0.9191 - val_loss: 0.2286 - val_accuracy: 0.9261\n",
      "Epoch 5/10\n",
      "322/322 [==============================] - 322s 1000ms/step - loss: 0.2274 - accuracy: 0.9266 - val_loss: 0.2056 - val_accuracy: 0.9324\n",
      "Epoch 6/10\n",
      "322/322 [==============================] - 320s 993ms/step - loss: 0.2081 - accuracy: 0.9319 - val_loss: 0.1924 - val_accuracy: 0.9363\n",
      "Epoch 7/10\n",
      "322/322 [==============================] - 321s 996ms/step - loss: 0.1931 - accuracy: 0.9360 - val_loss: 0.1789 - val_accuracy: 0.9400\n",
      "Epoch 8/10\n",
      "322/322 [==============================] - 321s 998ms/step - loss: 0.1819 - accuracy: 0.9390 - val_loss: 0.1693 - val_accuracy: 0.9430\n",
      "Epoch 9/10\n",
      "322/322 [==============================] - 319s 991ms/step - loss: 0.1725 - accuracy: 0.9419 - val_loss: 0.1616 - val_accuracy: 0.9454\n",
      "Epoch 10/10\n",
      "322/322 [==============================] - 322s 1s/step - loss: 0.1650 - accuracy: 0.9439 - val_loss: 0.1612 - val_accuracy: 0.9449\n"
     ]
    }
   ],
   "source": [
    "history=model.fit([x_tr,y_tr[:,:-1]], y_tr.reshape(y_tr.shape[0],y_tr.shape[1], 1)[:,1:] ,epochs=10,callbacks=[es],batch_size=64, validation_data=([x_val,y_val[:,:-1]], y_val.reshape(y_val.shape[0],y_val.shape[1], 1)[:,1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "OLtxX4uhavmi",
    "outputId": "c4c718f4-f6f4-4292-9cae-cb1e70e5a26a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "model_save_name = 'weather_summ_bidir.h5'\n",
    "path = F\"/content/gdrive/My Drive/Resume DL Projects/{model_save_name}\" \n",
    "# torch.save(model.state_dict(), path)\n",
    "model.save(path)\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vc4YJuXZbwio"
   },
   "outputs": [],
   "source": [
    "# # save model and architecture to single file\n",
    "# model.save(\"model.h5\")\n",
    "# print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "28awpsmOb0kL"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    " \n",
    "# # load model\n",
    "# model = load_model('model.h5')\n",
    "# # summarize model.\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FzTfjASjWzHW"
   },
   "outputs": [],
   "source": [
    "# model_save_name = 'unidir_model.h5'\n",
    "# path = F\"/content/gdrive/My Drive/Resume DL Projects/{model_save_name}\" \n",
    "# # model_save_name = 'weather_summ_unidir.h5'\n",
    "# # path = F\"/content/gdrive/My Drive/Resume DL Projects/{model_save_name}\" \n",
    "# model = load_model(path, custom_objects={'AttentionLayer': AttentionLayer})\n",
    "# # # # summarize model.\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "-40wYLAH5vRZ",
    "outputId": "cd99c3ef-4b73-4479-b9e5-502ea5b8da03"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NHuno8p75vKE"
   },
   "outputs": [],
   "source": [
    "# Encode the input sequence to get the feature vector\n",
    "encoder_model = Model(inputs=encoder_inputs,outputs=[encoder_outputs, state_h, state_c])\n",
    "\n",
    "# Decoder setup\n",
    "# Below tensors will hold the states of the previous time step\n",
    "decoder_state_input_h = Input(shape=(latent_dim*2,))\n",
    "decoder_state_input_c = Input(shape=(latent_dim*2,))\n",
    "decoder_hidden_state_input = Input(shape=(max_text_len,latent_dim*2))\n",
    "\n",
    "# Get the embeddings of the decoder sequence\n",
    "dec_emb2= dec_emb_layer(decoder_inputs) \n",
    "# To predict the next word in the sequence, set the initial states to the states from the previous time step\n",
    "decoder_outputs2, state_h2, state_c2 = decoder_lstm(dec_emb2, initial_state=[decoder_state_input_h, decoder_state_input_c])\n",
    "\n",
    "#attention inference\n",
    "attn_out_inf, attn_states_inf = attn_layer([decoder_hidden_state_input, decoder_outputs2])\n",
    "decoder_inf_concat = Concatenate(axis=-1, name='concat')([decoder_outputs2, attn_out_inf])\n",
    "\n",
    "# A dense softmax layer to generate prob dist. over the target vocabulary\n",
    "decoder_outputs2 = decoder_dense(decoder_inf_concat) \n",
    "\n",
    "# Final decoder model\n",
    "decoder_model = Model(\n",
    "    [decoder_inputs] + [decoder_hidden_state_input,decoder_state_input_h, decoder_state_input_c],\n",
    "    [decoder_outputs2] + [state_h2, state_c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F3M-PqdJ5vHC"
   },
   "outputs": [],
   "source": [
    "def decode_sequence(input_seq):\n",
    "    # Encode the input as state vectors.\n",
    "    e_out, e_h, e_c = encoder_model.predict(input_seq)\n",
    "    \n",
    "    # Generate empty target sequence of length 1.\n",
    "    target_seq = np.zeros((1,1))\n",
    "    # e_h = e_h*0\n",
    "    # e_c = e_c*0\n",
    "\n",
    "    # Populate the first word of target sequence with the start word.\n",
    "    target_seq[0, 0] = target_word_index['sostok']\n",
    "\n",
    "    stop_condition = False\n",
    "    decoded_sentence = ''\n",
    "    while not stop_condition:\n",
    "      \n",
    "        output_tokens, h, c = decoder_model.predict([target_seq] + [e_out, e_h, e_c])\n",
    "\n",
    "        # Sample a token\n",
    "        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
    "        sampled_token = reverse_target_word_index[sampled_token_index]\n",
    "        # print(sampled_token)\n",
    "        if(sampled_token!='eostok'):\n",
    "            decoded_sentence += ' '+sampled_token\n",
    "\n",
    "        # Exit condition: either hit max length or find stop word.\n",
    "        if (sampled_token == 'eostok'  or len(decoded_sentence.split()) >= (max_summary_len-1)):\n",
    "            stop_condition = True\n",
    "\n",
    "        # Update the target sequence (of length 1).\n",
    "        target_seq = np.zeros((1,1))\n",
    "        target_seq[0, 0] = sampled_token_index\n",
    "\n",
    "        # Update internal states\n",
    "        e_h, e_c = h, c\n",
    "\n",
    "    return decoded_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XnLCbbxE5vDp"
   },
   "outputs": [],
   "source": [
    "def seq2summary(input_seq):\n",
    "    newString=''\n",
    "    for i in input_seq:\n",
    "        if((i!=0 and i!=target_word_index['sostok']) and i!=target_word_index['eostok']):\n",
    "            newString=newString+reverse_target_word_index[i]+' '\n",
    "    return newString\n",
    "\n",
    "def seq2text(input_seq):\n",
    "    newString=''\n",
    "    for i in input_seq:\n",
    "        if(i!=0):\n",
    "            newString=newString+reverse_source_word_index[i]+' '\n",
    "    return newString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "colab_type": "code",
    "id": "neS0-iA_56NQ",
    "outputId": "a8704338-d4b9-44e7-eef5-a90bbce388a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tabular data: temperature time 17 30 min 67 mean 70 max 77 windchill time 17 30 min 0 mean 0 max 0 windspeed time 17 30 min 11 mean 12 max 16 mode bucket 0 20 2 10 20 winddir time 17 30 mode ese gust time 17 30 min 0 mean 3 max 21 skycover time 17 30 mode bucket 0 100 4 25 50 skycover time 17 21 mode bucket 0 100 4 25 50 skycover time 17 26 mode bucket 0 100 4 25 50 skycover time 21 30 mode bucket 0 100 4 25 50 skycover time 26 30 mode bucket 0 100 4 25 50 precippotential time 17 30 min 2 mean 4 max 5 \n",
      "Original summary: partly cloudy with a low around 67 east wind between 11 and 13 mph \n",
      "Predicted summary:  partly cloudy with a low around 66 east wind between 11 and 13 mph\n",
      "\n",
      "\n",
      "Tabular data: max 9 mode bucket 0 20 2 0 10 winddir time 6 21 mode nw gust time 6 21 min 0 mean 0 max 0 skycover time 6 21 mode bucket 0 100 4 75 100 skycover time 6 9 mode bucket 0 100 4 75 100 skycover time 6 13 mode bucket 0 100 4 75 100 skycover time 9 21 mode bucket 0 100 4 75 100 skycover time 13 21 mode bucket 0 100 4 50 75 precippotential time 6 21 min 17 mean 36 max 60 snowchance time 6 21 mode chc snowchance time 6 9 mode lkly snowchance time 6 13 mode lkly snowchance time 9 21 mode chc snowchance time 13 21 mode schc \n",
      "Original summary: snow likely mainly before 11am mostly cloudy with a high near 37 east wind 6 to 9 mph becoming northwest chance of precipitation is 60 new snow accumulation of less than one inch possible \n",
      "Predicted summary:  snow likely mainly before 11am mostly cloudy with a high near 37 south southwest wind around 7 mph chance of precipitation is 60 new snow accumulation of less than one inch possible\n",
      "\n",
      "\n",
      "Tabular data: windchill time 17 30 min 35 mean 36 max 39 windspeed time 17 30 min 10 mean 14 max 16 mode bucket 0 20 2 10 20 winddir time 17 30 mode sse gust time 17 30 min 0 mean 0 max 0 skycover time 17 30 mode bucket 0 100 4 50 75 skycover time 17 21 mode bucket 0 100 4 50 75 skycover time 17 26 mode bucket 0 100 4 50 75 skycover time 21 30 mode bucket 0 100 4 50 75 skycover time 26 30 mode bucket 0 100 4 75 100 precippotential time 17 30 min 5 mean 27 max 77 rainchance time 21 30 mode schc rainchance time 26 30 mode def \n",
      "Original summary: occasional rain mainly after 3am low around 42 south wind between 11 and 16 mph chance of precipitation is 80 new rainfall amounts between a quarter and half of an inch possible \n",
      "Predicted summary:  occasional rain mainly after 3am low around 43 south wind between 10 and 15 mph chance of precipitation is 80 new rainfall amounts between a tenth and quarter of an inch possible\n",
      "\n",
      "\n",
      "Tabular data: min 43 mean 44 max 46 windchill time 17 30 min 36 mean 38 max 41 windspeed time 17 30 min 10 mean 10 max 13 mode bucket 0 20 2 10 20 winddir time 17 30 mode se gust time 17 30 min 0 mean 0 max 0 skycover time 17 30 mode bucket 0 100 4 50 75 skycover time 17 21 mode bucket 0 100 4 50 75 skycover time 17 26 mode bucket 0 100 4 50 75 skycover time 21 30 mode bucket 0 100 4 50 75 skycover time 26 30 mode bucket 0 100 4 75 100 precippotential time 17 30 min 5 mean 26 max 75 rainchance time 26 30 mode def \n",
      "Original summary: occasional rain after 3am low around 43 south wind between 10 and 13 mph chance of precipitation is 80 new rainfall amounts between a quarter and half of an inch possible \n",
      "Predicted summary:  occasional rain mainly after 3am low around 43 south wind between 10 and 15 mph chance of precipitation is 80 new rainfall amounts between a quarter and half of an inch possible\n",
      "\n",
      "\n",
      "Tabular data: windchill time 6 21 min 0 mean 38 max 48 windspeed time 6 21 min 3 mean 5 max 7 mode bucket 0 20 2 0 10 winddir time 6 21 mode n gust time 6 21 min 0 mean 0 max 0 skycover time 6 21 mode bucket 0 100 4 25 50 skycover time 6 9 mode bucket 0 100 4 75 100 skycover time 6 13 mode bucket 0 100 4 75 100 skycover time 9 21 mode bucket 0 100 4 25 50 skycover time 13 21 mode bucket 0 100 4 25 50 precippotential time 6 21 min 2 mean 17 max 53 rainchance time 6 9 mode chc rainchance time 6 13 mode chc \n",
      "Original summary: scattered showers mainly before 10am cloudy then gradually becoming mostly sunny with a high near 52 north wind around 7 mph chance of precipitation is 50 new rainfall amounts of less than a tenth of an inch possible \n",
      "Predicted summary:  a chance of drizzle before 1pm mostly cloudy with a high near 54 east wind between 3 and 8 mph\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,5):\n",
    "    print(\"Tabular data:\",seq2text(x_tr[i]))\n",
    "    print(\"Original summary:\",seq2summary(y_tr[i]))\n",
    "    print(\"Predicted summary:\",decode_sequence(x_tr[i].reshape(1,max_text_len)))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_num = 200\n",
    "reference = []\n",
    "for i in range(test_num):\n",
    "  reference.append(seq2summary(y_val[i]))\n",
    "\n",
    "prediction = []\n",
    "for i in range(test_num):\n",
    "  prediction.append(decode_sequence(x_val[i].reshape(1,max_text_len)))\n",
    "\n",
    "\n",
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "score = 0.\n",
    "\n",
    "for i in range(len(reference)):\n",
    "    score += sentence_bleu([reference[i].strip().split()], prediction[i].strip().split())\n",
    "\n",
    "score /= len(reference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "S-FYmmhThYIc",
    "outputId": "3b5c44a9-c24e-4543-bf17-3ada693a7e05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The bleu score is: 0.7513361605009568\n"
     ]
    }
   ],
   "source": [
    "print(\"The bleu score is: \"+str(score))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
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   "collapsed_sections": [],
   "name": "Table summarizer.ipynb",
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   "display_name": "Python 3",
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