Negative_Sampling_on_GPU.ipynb

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  "metadata": {
    "colab": {
      "name": "Negative_Sampling_on_GPU.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFaP8nGGHJj_"
      },
      "source": [
        "del text\n",
        "del classified_text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQqLXlEgFI4u"
      },
      "source": [
        "text = [\n",
        "        [0,0.5],\n",
        "        [1,0.5],\n",
        "        [5,0.375],\n",
        "        [6,0.41667],\n",
        "        [7,0.54167],\n",
        "        [11,0.59722],\n",
        "        [23,0.31944],\n",
        "        [24,0.73611],\n",
        "        [235,0.80556],\n",
        "        [264,0.375],\n",
        "        [265,0.66667]\n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdSb0PwVKVp0",
        "outputId": "47248bcd-03ec-49e2-87c0-3b2b6aa77f9e"
      },
      "source": [
        "print(text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0, 0.5], [1, 0.5], [5, 0.375], [6, 0.41667], [7, 0.54167], [11, 0.59722], [23, 0.31944], [24, 0.73611], [235, 0.80556], [264, 0.375], [265, 0.66667]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lA9zdRfFHsak"
      },
      "source": [
        "classified_text = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DSBFZb_TF0KP"
      },
      "source": [
        "for num,i in enumerate(text):\n",
        "  classified_text.append(i)\n",
        "  if i[1] < 0.2:\n",
        "    classified_text[num][1] = 0\n",
        "  elif i[1] < 0.4:\n",
        "    classified_text[num][1] = 1\n",
        "  elif i[1] < 0.6:\n",
        "    classified_text[num][1] = 2\n",
        "  elif i[1] < 0.8:\n",
        "    classified_text[num][1] = 3\n",
        "  else:\n",
        "    classified_text[num][1] = 4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptHKPZqmIS6V",
        "outputId": "8154c984-42cb-425b-a3c7-3b52e8ce8e11"
      },
      "source": [
        "print(text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0, 2], [1, 2], [5, 1], [6, 2], [7, 2], [11, 2], [23, 1], [24, 3], [235, 4], [264, 1], [265, 3]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_D8seUVF0G0",
        "outputId": "f69edae5-5d0a-49fd-b8cb-764921e6e2dd"
      },
      "source": [
        "print(\"The original label was : \", text[3][1], \"and the modified sentiment class is :\", classified_text[3][])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The original label was :  2 and the modified sentiment class is : 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyWQqftZCuwV"
      },
      "source": [
        "## Negative Sampling to increase performance\n",
        "\n",
        "Firstly, for each training sample, only the weights corresponding to the target word might get a significant update. While training a neural network model, in each back-propagation pass we try to update all the weights in the hidden layer. The weight corresponding to non-target words would receive a marginal or no change at all, i.e. in each pass we only make very sparse updates.\n",
        "Secondly, for every training sample, the calculation of the final probabilities using the softmax is quite an expensive operation as it involves a summation of scores over all the words in our vocabulary for normalizing.\n",
        "\n",
        "* Negative ssampling -> In our above mentioned example, we try to maximize the probability P(1|<orange, juice>) and minimize the probability of our negative samples P(0|<orange, apple>), P(0|<orange, dinner>), P(0|<orange,dog>), P(0|<orange, chair>), P(0|<orange, house>)\n",
        "\n",
        "* Skg or CBOW -> P(<orange | apple>)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLxTCByi0BIe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f091a04-f61b-4129-d3ff-3f5b81cb0c2a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/Jash\")\n",
        "\n",
        "import numpy as np\n",
        "!pip install nltk\n",
        "import nltk"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /Jash\n",
            "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) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j10JeR6A_sV3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83f56435-5ae8-4e0e-e9fb-d5d66835e26e"
      },
      "source": [
        "nltk.download('reuters')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package reuters to /root/nltk_data...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWhyfbBABX0l"
      },
      "source": [
        "from nltk.corpus import reuters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhxc_eafXp2o"
      },
      "source": [
        "# Generating a training corpus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQJwrOLuKRd-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3245e993-3e13-4133-8e56-9b8a6ffbf980"
      },
      "source": [
        "print(reuters.fileids())\n",
        "print(\"\\n\")\n",
        "print(reuters.categories())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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'training/985', 'training/9850', 'training/9851', 'training/9852', 'training/9853', 'training/9855', 'training/9857', 'training/9858', 'training/986', 'training/9860', 'training/9861', 'training/9862', 'training/9864', 'training/9865', 'training/9866', 'training/9867', 'training/9868', 'training/9869', 'training/9871', 'training/9872', 'training/9874', 'training/9875', 'training/9878', 'training/988', 'training/9880', 'training/9884', 'training/9891', 'training/9892', 'training/9893', 'training/9894', 'training/9896', 'training/9897', 'training/99', 'training/9901', 'training/9902', 'training/9903', 'training/9904', 'training/9905', 'training/9906', 'training/9907', 'training/9909', 'training/991', 'training/9912', 'training/9913', 'training/9914', 'training/9915', 'training/9918', 'training/9919', 'training/9920', 'training/9923', 'training/9925', 'training/9926', 'training/9927', 'training/9928', 'training/9929', 'training/9930', 'training/9933', 'training/9934', 'training/9936', 'training/9937', 'training/9939', 'training/9940', 'training/9941', 'training/9942', 'training/9943', 'training/9946', 'training/9947', 'training/995', 'training/9952', 'training/9953', 'training/9954', 'training/9955', 'training/9956', 'training/9957', 'training/9958', 'training/9959', 'training/9961', 'training/9963', 'training/9964', 'training/9965', 'training/9967', 'training/9970', 'training/9971', 'training/9972', 'training/9973', 'training/9974', 'training/9975', 'training/9976', 'training/9977', 'training/9978', 'training/998', 'training/9981', 'training/9982', 'training/9984', 'training/9985', 'training/9988', 'training/9989', 'training/999', 'training/9992', 'training/9993', 'training/9994', 'training/9995']\n",
            "\n",
            "\n",
            "['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', 'dmk', 'earn', 'fuel', 'gas', 'gnp', 'gold', 'grain', 'groundnut', 'groundnut-oil', 'heat', 'hog', 'housing', 'income', 'instal-debt', 'interest', 'ipi', 'iron-steel', 'jet', 'jobs', 'l-cattle', 'lead', 'lei', 'lin-oil', 'livestock', 'lumber', 'meal-feed', 'money-fx', 'money-supply', 'naphtha', 'nat-gas', 'nickel', 'nkr', 'nzdlr', 'oat', 'oilseed', 'orange', 'palladium', 'palm-oil', 'palmkernel', 'pet-chem', 'platinum', 'potato', 'propane', 'rand', 'rape-oil', 'rapeseed', 'reserves', 'retail', 'rice', 'rubber', 'rye', 'ship', 'silver', 'sorghum', 'soy-meal', 'soy-oil', 'soybean', 'strategic-metal', 'sugar', 'sun-meal', 'sun-oil', 'sunseed', 'tea', 'tin', 'trade', 'veg-oil', 'wheat', 'wpi', 'yen', 'zinc']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnPgkF0x1Mru",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5ceb0d2-4ac3-4d15-f625-9aa4c8f10e6e"
      },
      "source": [
        "# Prinitnig random values\n",
        "print(reuters.categories('training/9865'))\n",
        "print(\"\\n\")\n",
        "print(reuters.fileids('barley'))\n",
        "print(\"\\n\")\n",
        "print(reuters.words('training/9865'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['barley', 'corn', 'grain', 'wheat']\n",
            "\n",
            "\n",
            "['test/15618', 'test/15649', 'test/15676', 'test/15728', 'test/15871', 'test/15875', 'test/15952', 'test/17767', 'test/17769', 'test/18024', 'test/18263', 'test/18908', 'test/19275', 'test/19668', 'training/10175', 'training/1067', 'training/11208', 'training/11316', 'training/11885', 'training/12428', 'training/13099', 'training/13744', 'training/13795', 'training/13852', 'training/13856', 'training/1652', 'training/1970', 'training/2044', 'training/2171', 'training/2172', 'training/2191', 'training/2217', 'training/2232', 'training/3132', 'training/3324', 'training/395', 'training/4280', 'training/4296', 'training/5', 'training/501', 'training/5467', 'training/5610', 'training/5640', 'training/6626', 'training/7205', 'training/7579', 'training/8213', 'training/8257', 'training/8759', 'training/9865', 'training/9958']\n",
            "\n",
            "\n",
            "['FRENCH', 'FREE', 'MARKET', 'CEREAL', 'EXPORT', ...]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VojxgBIq2IVM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "038f9db1-9971-4a1a-d173-32c8bf359a51"
      },
      "source": [
        "# train_corpus = []\n",
        "# for files in reuters.fileids():\n",
        "#   if (files[0:4] == \"test\"):\n",
        "#     train_corpus.append(np.array(reuters.words(files)))\n",
        "\n",
        "# corpus = np.array(train_corpus)      # Generating a numpy array of the corpus\n",
        "\n",
        "# print(corpus)\n",
        "\n",
        "corpus = np.load('/Jash/My Drive/training_corpus.npy', allow_pickle=True)\n",
        "print(corpus)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array(['U', '.', 'K', '.', 'WHEAT', 'AND', 'BARLEY', 'EXPORTS',\n",
            "       'ADJUSTED', 'UPWARDS', 'The', 'U', '.', 'K', '.', 'Exported',\n",
            "       '535', ',', '460', 'tonnes', 'of', 'wheat', 'and', '336', ',',\n",
            "       '750', 'tonnes', 'of', 'barley', 'in', 'January', ',', 'the',\n",
            "       'Home', 'Grown', 'Cereals', 'Authority', '(', 'HGCA', ')', 'said',\n",
            "       ',', 'quoting', 'adjusted', 'Customs', 'and', 'Excise', 'figures',\n",
            "       '.', 'Based', 'on', 'the', 'previous', 'January', 'figures',\n",
            "       'issued', 'on', 'February', '9', ',', 'wheat', 'exports',\n",
            "       'increased', 'by', 'nearly', '64', ',', '000', 'tonnes', 'and',\n",
            "       'barley', 'by', 'about', '7', ',', '000', 'tonnes', '.', 'The',\n",
            "       'new', 'figures', 'bring', 'cumulative', 'wheat', 'exports', 'for',\n",
            "       'the', 'period', 'July', '1', '/', 'February', '13', 'to', '2',\n",
            "       '.', '99', 'mln', 'tonnes', ',', 'and', 'barley', 'to', '2', '.',\n",
            "       '96', 'mln', 'compared', 'with', '1', '.', '25', 'and', '1', '.',\n",
            "       '89', 'mln', 'tonnes', 'respectively', 'a', 'year', 'ago', '.',\n",
            "       'January', 'wheat', 'exports', 'comprised', '251', ',', '000',\n",
            "       'tonnes', 'to', 'European', 'Community', 'destinations', 'and',\n",
            "       '284', ',', '000', 'tonnes', 'to', 'third', 'countries', '.',\n",
            "       'The', 'Soviet', 'Union', 'was', 'prominent', 'in', 'third',\n",
            "       'country', 'destinations', ',', 'taking', '167', ',', '700',\n",
            "       'tonnes', 'while', 'Poland', 'was', 'credited', 'with', '54', ',',\n",
            "       '500', 'and', 'South', 'Korea', '50', ',', '000', 'tonnes', '.',\n",
            "       'Italy', 'was', 'the', 'largest', 'EC', 'recipient', 'with', '75',\n",
            "       ',', '000', 'tonnes', 'followed', 'by', 'West', 'Germany', 'with',\n",
            "       '55', ',', '200', 'and', 'France', '52', ',', '000', 'tonnes', '.',\n",
            "       'Barley', 'exports', 'for', 'January', 'comprised', '103', ',',\n",
            "       '700', 'tonnes', 'to', 'the', 'EC', 'and', '233', ',', '000', 'to',\n",
            "       'third', 'countries', '.', 'The', 'Soviet', 'Union', 'was', 'the',\n",
            "       'largest', 'single', 'importer', 'with', '133', ',', '265',\n",
            "       'tonnes', 'followed', 'by', 'Saudi', 'Arabia', 'with', '53', ',',\n",
            "       '800', 'tonnes', '.'], dtype='<U12')\n",
            " array(['U', '.', 'K', '.', 'WHEAT', 'AND', 'BARLEY', 'EXPORTS',\n",
            "       'ADJUSTED', 'UPWARDS', 'The', 'U', '.', 'K', '.', 'Exported',\n",
            "       '612', ',', '000', 'tonnes', 'of', 'wheat', 'and', '498', ',',\n",
            "       '800', 'tonnes', 'of', 'barley', 'in', 'February', ',', 'the',\n",
            "       'Home', 'Grown', 'Cereals', 'Authority', '(', 'HGCA', ')', 'said',\n",
            "       '.', 'Based', 'on', 'the', 'previous', 'provisional', 'figures',\n",
            "       'issued', 'for', 'February', ',', 'wheat', 'exports', 'were',\n",
            "       'increased', 'by', '480', ',', '200', 'tonnes', 'and', 'barley',\n",
            "       'by', '283', ',', '800', 'tonnes', '.', 'The', 'new', 'figures',\n",
            "       'bring', 'cumulative', 'wheat', 'exports', 'for', 'the', 'period',\n",
            "       'July', '1', '-', 'March', '13', 'to', '3', '.', '66', 'mln',\n",
            "       'tonnes', 'and', 'barley', 'to', '3', '.', '50', 'mln', ',',\n",
            "       'compared', 'with', '1', '.', '47', 'and', '2', '.', '09', 'mln',\n",
            "       'tonnes', 'respectively', 'last', 'season', '.'], dtype='<U12')\n",
            " array(['SOVIET', 'UNION', 'FEATURES', 'IN', 'U', '.', 'K', '.', 'GRAIN',\n",
            "       'EXPORTS', 'The', 'Soviet', 'Union', 'featured', 'prominently',\n",
            "       'in', 'U', '.', 'K', '.', 'Grain', 'exports', 'outside', 'the',\n",
            "       'EC', 'for', 'the', 'period', 'July', '1', '/', 'March', '13', ',',\n",
            "       'taking', 'a', 'combined', 'total', 'of', '1', '.', '10', 'mln',\n",
            "       'tonnes', 'of', 'wheat', 'and', 'barley', 'out', 'of', 'all', '-',\n",
            "       'destination', 'U', '.', 'K', '.', 'Exports', 'of', '7', '.', '16',\n",
            "       'mln', 'tonnes', ',', 'the', 'Home', 'Grown', 'Cereals',\n",
            "       'Authority', 'said', ',', 'quoting', 'provisional', 'Customs',\n",
            "       'and', 'Excise', 'figures', '.', 'The', 'Soviet', 'total',\n",
            "       'comprises', '634', ',', '000', 'tonnes', 'of', 'wheat', 'and',\n",
            "       '472', ',', '000', 'tonnes', 'of', 'barley', '.', 'Grain',\n",
            "       'traders', 'said', 'the', 'figures', 'understate', 'shipments',\n",
            "       'already', 'made', 'by', 'several', 'thousand', 'tonnes', 'and',\n",
            "       'they', 'expect', 'total', 'U', '.', 'K', '.', 'Grain', 'exports',\n",
            "       'to', 'the', 'USSR', 'this', 'season', 'to', 'reach', '2', '.',\n",
            "       '5', 'mln', 'tonnes', ',', 'comprising', '1', '.', '5', 'mln',\n",
            "       'wheat', '/', '1', '.', '0', 'mln', 'barley', '.'], dtype='<U11')\n",
            " array(['TEXAS', 'EASTERN', '&', 'lt', ';', 'TET', '>', 'BUYS',\n",
            "       'PETROLEUM', 'TERMINAL', 'Texas', 'Eastern', 'Corp', 'said', 'it',\n",
            "       'has', 'completed', 'the', 'purchase', 'of', 'an', 'idle',\n",
            "       'petroleum', 'products', 'terminal', 'near', 'Norris', 'City', ',',\n",
            "       'Ill', '.,', 'from', '&', 'lt', ';', 'Growmark', 'Inc', '>', 'for',\n",
            "       'undisclosed', 'terms', 'and', 'will', 'reopen', 'it', 'in',\n",
            "       'September', 'after', 'remodeling', '.', 'The', 'company', 'said',\n",
            "       'the', 'terminal', 'will', 'handle', 'leaded', 'and', 'unleaded',\n",
            "       'regular', 'gasolines', ',', 'number', 'two', 'fuel', 'oil', ',',\n",
            "       'ethanol', 'and', 'natural', 'gasoline', '.'], dtype='<U11')\n",
            " array(['EIA', 'SAYS', 'DISTILLATE', ',', 'GAS', 'STOCKS', 'OFF', 'IN',\n",
            "       'WEEK', 'Distillate', 'fuel', 'stocks', 'held', 'in', 'primary',\n",
            "       'storage', 'fell', 'by', '3', '.', '4', 'mln', 'barrels', 'in',\n",
            "       'the', 'week', 'ended', 'Feb', '27', 'to', '128', '.', '4', 'mln',\n",
            "       'barrels', ',', 'the', 'Energy', 'Information', 'Administration',\n",
            "       '(', 'EIA', ')', 'said', '.', 'In', 'its', 'weekly', 'petroleum',\n",
            "       'status', 'report', ',', 'the', 'Department', 'of', 'Energy',\n",
            "       'agency', 'said', 'gasoline', 'stocks', 'were', 'off', '100', ',',\n",
            "       '000', 'barrels', 'in', 'the', 'week', 'to', '251', '.', '5',\n",
            "       'mln', 'barrels', 'and', 'refinery', 'crude', 'oil', 'stocks',\n",
            "       'were', 'up', '3', '.', '2', 'mln', 'barrels', 'to', '333', '.',\n",
            "       '0', 'mln', '.', 'The', 'EIA', 'said', 'residual', 'fuel',\n",
            "       'stocks', 'fell', '2', '.', '2', 'mln', 'barrels', 'to', '37', '.',\n",
            "       '9', 'mln', 'barrels', 'and', 'crude', 'oil', 'stocks', 'in',\n",
            "       'the', 'Strategic', 'Petroleum', 'Reserve', '(', 'SPR', ')',\n",
            "       'were', 'up', '700', ',', '000', 'barrels', 'to', '516', '.', '5',\n",
            "       'mln', '.', 'The', 'total', 'of', 'all', 'crude', ',', 'refined',\n",
            "       'product', 'and', 'SPR', 'stocks', 'were', 'unchanged', 'at', '1',\n",
            "       ',', '575', '.', '1', 'mln', 'barrels', ',', 'it', 'said', '.'],\n",
            "      dtype='<U14')\n",
            " array(['ECUADOR', 'ADOPTS', 'AUSTERITY', 'MEASURES', 'AFTER', 'QUAKE',\n",
            "       'OPEC', 'member', 'Ecuador', 'adopted', 'austerity', 'measures',\n",
            "       'to', 'conserve', 'fuel', 'after', 'oil', 'production', 'was',\n",
            "       'paralyzed', 'by', 'a', 'strong', 'earthquake', '.', 'Energy',\n",
            "       'and', 'Mines', 'Minister', 'Javier', 'Espinosa', 'announced',\n",
            "       'on', 'television', 'the', 'country', 'would', 'cut', 'domestic',\n",
            "       'fuel', 'sales', 'by', '30', 'pct', '.', 'A', 'ministry',\n",
            "       'statement', 'had', 'earlier', 'announced', 'indefinite',\n",
            "       'suspension', 'of', 'crude', 'oil', 'exports', ',', 'declaring',\n",
            "       'force', 'majeure', '.', 'Deputy', 'Energy', 'Minister',\n",
            "       'Fernando', 'Santos', 'Alvite', 'told', 'a', 'television',\n",
            "       'interviewer', 'that', 'it', 'could', 'possibly', 'take', 'more',\n",
            "       'than', 'a', 'month', 'to', 'repair', 'Ecuador', \"'\", 's', 'main',\n",
            "       'pipeline', 'linking', 'Amazon', 'basin', 'oil', 'fields', 'to',\n",
            "       'the', 'Pacific', 'Ocean', 'coast', '.', 'The', 'quake', 'on',\n",
            "       'Thursday', ',', 'which', 'registered', 'six', 'on', 'the', '12',\n",
            "       '-', 'point', 'Mercalli', 'scale', ',', 'killed', 'at', 'least',\n",
            "       'six', 'people', 'and', 'was', 'centred', 'near', 'the',\n",
            "       'Reventador', 'volcano', 'about', '90', 'km', '(', '50', 'miles',\n",
            "       ')', 'east', 'of', 'Quito', '.', 'Ecuador', 'had', 'recently',\n",
            "       'been', 'pumping', '260', ',', '000', 'barrels', 'per', 'day', '.',\n",
            "       'The', 'government', \"'\", 's', 'austerity', 'measures', 'ban',\n",
            "       'the', 'sale', 'of', 'aviation', 'fuel', 'to', 'foreign',\n",
            "       'airliners', 'on', 'international', 'routes', 'and', 'no', 'fuel',\n",
            "       'will', 'be', 'available', 'for', 'ships', 'owned', 'by',\n",
            "       'foreign', 'lines', '.', 'Ecuador', 'also', 'banned', 'the',\n",
            "       'sale', 'of', 'petrol', 'on', 'weekends', 'and', 'holidays', 'and',\n",
            "       'limited', 'sales', 'on', 'weekdays', ',', 'an', 'Energy', 'and',\n",
            "       'Mines', 'Ministry', 'communique', 'said', '.'], dtype='<U13')\n",
            " array(['PHILIPPINE', 'COCONUT', 'AGENCY', 'GETS', 'NEW', 'ADMINISTRATOR',\n",
            "       'Newly', '-', 'installed', 'Philippine', 'Coconut', 'Authority',\n",
            "       'Chairman', 'Jose', 'Romero', 'has', 'announced', 'the',\n",
            "       'appointment', 'of', 'lawyer', 'Leandro', 'Garcia', 'as',\n",
            "       'administrator', ',', 'replacing', 'Colonel', 'Felix', 'Duenas',\n",
            "       'who', 'is', 'returning', 'to', 'military', 'duty', '.', 'The',\n",
            "       'new', 'constitution', 'does', 'not', 'allow', 'military', 'men',\n",
            "       'to', 'hold', 'positions', 'in', 'civilian', 'agencies', '.',\n",
            "       'Duenas', 'and', 'four', 'other', 'military', 'men', 'who', 'were',\n",
            "       'assigned', 'to', 'the', 'authority', 'in', '1978', ',', 'will',\n",
            "       'return', 'to', 'the', 'ministry', 'of', 'defence', 'where',\n",
            "       'they', 'worked', 'prior', 'to', 'their', 'appointments', 'at',\n",
            "       'the', 'coconut', 'agency', '.'], dtype='<U13')\n",
            " array(['PHILIPPINE', 'COCONUT', 'INDUSTRY', 'WORRIED', 'BY', 'EC', 'TAX',\n",
            "       'Philippine', 'coconut', 'oil', 'exports', 'to', 'Europe', 'would',\n",
            "       'be', 'virtually', 'wiped', 'out', 'if', 'the', 'European',\n",
            "       'Community', '(', 'EC', ')', 'implements', 'a', 'new', 'tax', 'on',\n",
            "       'vegetable', 'oils', ',', 'Philippine', 'Coconut', 'Authority',\n",
            "       '(', 'PCA', ')', 'chairman', 'Jose', 'Romero', 'said', '.', 'But',\n",
            "       'he', 'told', 'reporters', 'he', 'did', 'not', 'think', 'the',\n",
            "       'EC', 'would', 'impose', 'the', 'tax', 'because', 'of',\n",
            "       'objections', 'from', 'the', 'U', '.', 'S', '.', '\"', 'There', \"'\",\n",
            "       's', 'just', 'so', 'much', 'flak', 'coming', 'from', 'many',\n",
            "       'countries', ',', 'spearheaded', 'by', 'the', 'United', 'States',\n",
            "       ',', 'whose', 'soybean', 'exports', 'would', 'be', 'adversely',\n",
            "       'affected', '.', 'This', 'would', 'spark', 'a', 'trade', 'war',\n",
            "       ',\"', 'he', 'said', '.', 'The', 'tax', ',', 'to', 'be', 'imposed',\n",
            "       'from', 'July', ',', 'would', 'add', 'about', '375', 'dlrs', 'a',\n",
            "       'tonne', 'to', 'vegetable', 'oils', 'entering', 'the', 'EC', '.',\n",
            "       'The', 'Philippines', 'exported', '43', ',', '540', 'tonnes', 'of',\n",
            "       'coconut', 'oil', 'worth', '11', '.', '7', 'mln', 'dlrs', 'to',\n",
            "       'Europe', 'in', 'January', ',', 'against', 'total', 'exports',\n",
            "       'of', '86', ',', '959', 'tonnes', 'worth', '23', '.', '8', 'mln',\n",
            "       '.', 'It', 'also', 'exports', 'copra', 'and', 'copra', 'meal', '.',\n",
            "       'Agriculture', 'Secretary', 'Carlos', 'Dominguez', 'has', 'also',\n",
            "       'raised', 'objections', 'to', 'the', 'proposed', 'EC', 'tax', '.',\n",
            "       'He', 'said', 'it', 'could', 'cause', 'the', 'collapse', 'of',\n",
            "       'world', 'demand', 'and', 'prices', 'and', 'destroy', 'the',\n",
            "       'domestic', 'industry', '.'], dtype='<U11')\n",
            " array(['SRI', 'LANKA', 'TO', 'UPGRADE', 'QUALITY', 'OF', 'COCONUT',\n",
            "       'PRODUCTS', 'The', 'Sri', 'Lankan', 'cabinet', 'approved',\n",
            "       'recommendations', 'to', 'upgrade', 'the', 'quality', 'of',\n",
            "       'coconut', 'fibre', 'products', ',', 'the', 'government', 'said',\n",
            "       '.', 'It', 'said', 'the', 'recommendations', 'also', 'suggested',\n",
            "       'that', 'encouragement', 'be', 'given', 'for', 'the',\n",
            "       'manufacture', 'of', 'value', '-', 'added', 'products', 'from',\n",
            "       'coconut', 'fibre', 'and', 'a', 'market', 'development',\n",
            "       'programme', 'be', 'launched', 'for', 'traditional', 'and',\n",
            "       'value', '-', 'added', 'products', '.', 'It', 'also', 'suggested',\n",
            "       'the', 'setting', 'up', 'of', 'a', 'marketing', 'mission', 'on',\n",
            "       'coconut', 'fibre', 'to', 'be', 'sent', 'this', 'month', 'to',\n",
            "       'principal', 'target', 'markets', '.', 'Sri', 'Lanka', 'is', 'the',\n",
            "       'world', \"'\", 's', 'second', 'largest', 'exporter', 'of',\n",
            "       'dessicated', 'coconut', 'after', 'the', 'Philippines', '.'],\n",
            "      dtype='<U15')\n",
            " array(['CERTIFICATED', 'COTTON', 'STOCKS', 'Certificated', 'cotton',\n",
            "       'stocks', 'deliverable', 'on', 'the', 'New', 'York', 'Cotton',\n",
            "       'Exchange', 'No', '2', 'cotton', 'futures', 'contract', 'as', 'of',\n",
            "       'March', '31', 'were', 'reported', 'at', '36', ',', '659', 'bales',\n",
            "       ',', 'unchanged', 'from', 'the', 'previous', 'day', \"'\", 's',\n",
            "       'figure', '.', 'There', 'were', 'no', 'bales', 'awaiting',\n",
            "       'review', 'and', '985', 'bales', 'awaiting', 'decertification',\n",
            "       '.'], dtype='<U15')\n",
            " array(['ARGENTINE', 'SUBPRODUCTS', 'SHIPMENTS', 'IN', '1986', 'Argentine',\n",
            "       'subproducts', 'shipments', 'during', 'January', '/', 'December',\n",
            "       '1986', 'totalled', '5', ',', '618', ',', '315', 'tonnes', ',',\n",
            "       'against', '4', ',', '815', ',', '188', 'tonnes', 'in', 'the',\n",
            "       'same', 'period', 'of', '1985', ',', 'the', 'Argentine', 'Grain',\n",
            "       'Board', 'said', '.', 'Breakdown', 'was', 'as', 'follows', ':',\n",
            "       'cotton', '26', ',', '992', '(', '41', ',', '933', '),',\n",
            "       'sunflower', '1', ',', '403', ',', '230', '(', '1', ',', '190',\n",
            "       ',', '862', '),', 'linseed', '261', ',', '600', '(', '309', ',',\n",
            "       '191', '),', 'groundnutseed', '23', ',', '595', '(', '22', ',',\n",
            "       '809', '),', 'soybean', '3', ',', '275', ',', '225', '(', '2', ',',\n",
            "       '415', ',', '492', '),', 'bran', '/', 'pollards', 'wheat', '606',\n",
            "       ',', '352', '(', '659', ',', '271', '),', 'fiber', 'cotton', '3',\n",
            "       ',', '256', '(', '107', ',', '752', '),', 'wheat', 'flour', '18',\n",
            "       ',', '065', '(', '67', ',', '878', '),', 'rape', 'nil', '(', 'nil',\n",
            "       '),', 'the', 'board', 'added', '.', 'Shipments', 'during',\n",
            "       'December', '1986', 'amounted', 'to', '418', ',', '755', 'tonnes',\n",
            "       ',', 'against', '257', ',', '844', 'tonnes', 'in', 'the', 'same',\n",
            "       'month', 'of', '1985', '.', 'The', 'breakdown', ',', 'was', 'as',\n",
            "       'follows', ',', 'in', 'tonnes', ':', 'Cotton', '3', ',', '002',\n",
            "       '(', '6', ',', '234', '),', 'sunflower', '86', ',', '612', '(',\n",
            "       '38', ',', '347', '),', 'linseed', '23', ',', '954', '(', '38',\n",
            "       ',', '290', '),', 'groundnutseed', 'nil', '(', 'nil', '),',\n",
            "       'soybean', '264', ',', '650', '(', '104', ',', '571', '),', 'bran',\n",
            "       '/', 'polards', 'wheat', '37', ',', '724', '(', '49', ',', '946',\n",
            "       '),', 'fiber', 'cotton', '987', '(', '2', ',', '121', '),',\n",
            "       'wheat', 'flour', '1', ',', '826', '(', '18', ',', '335', '),',\n",
            "       'rapeseed', 'nil', '(', 'nil', '),', 'the', 'board', 'said', '.',\n",
            "       'The', 'ten', 'principal', 'destinations', 'during', 'January',\n",
            "       '/', 'December', '1986', ',', 'with', 'comparative', 'figures',\n",
            "       'for', 'the', 'same', 'period', 'of', '1985', 'in', 'brackets',\n",
            "       ',', 'were', 'as', 'follows', ',', 'in', 'tonnes', ':', 'Holland',\n",
            "       '2', ',', '444', ',', '260', '(', '2', ',', '234', ',', '049',\n",
            "       '),', 'Belgium', '546', ',', '423', '(', '595', ',', '635', '),',\n",
            "       'Italy', '430', ',', '029', '(', '338', ',', '766', '),',\n",
            "       'Czechoslovakia', '365', ',', '897', '(', '236', ',', '836', '),',\n",
            "       'Cuba', '253', ',', '067', '(', '222', ',', '842', '),', 'Iran',\n",
            "       '250', ',', '646', '(', '192', ',', '430', '),', 'West', 'Germany',\n",
            "       '232', ',', '049', ')', '(', '158', ',', '491', '),', 'Bulgaria',\n",
            "       '207', ',', '030', '(', '300', ',', '488', '),', 'Spain', 'and',\n",
            "       'Canary', 'Islands', '176', ',', '287', '(', '113', ',', '751',\n",
            "       '),', 'Corea', '163', ',', '304', '(', '37', ',', '416', '),',\n",
            "       'the', 'board', 'added', '.'], dtype='<U14')\n",
            " array(['U', '.', 'S', '.', 'HOUSE', 'PANEL', 'EXTENDS', 'EEP', ',',\n",
            "       'URGES', 'USSR', 'OFFER', 'The', 'U', '.', 'S', '.', 'House',\n",
            "       'Agriculture', 'Committee', 'approved', 'proposals', 'to',\n",
            "       'extend', 'the', 'life', 'of', 'the', 'Export', 'Enhancement',\n",
            "       'Program', ',', 'EEP', ',', 'through', 'fiscal', '1990', 'and',\n",
            "       'urged', 'the', 'Reagan', 'administration', 'offer', 'EEP',\n",
            "       'wheat', 'to', 'the', 'Soviet', 'Union', '.', 'The', 'proposals',\n",
            "       'were', 'approved', 'as', 'amendments', 'to', 'a', 'comprehensive',\n",
            "       'trade', 'bill', 'moving', 'through', 'Congress', 'this', 'year',\n",
            "       '.', 'In', 'addition', 'to', 'the', 'amendments', 'on', 'EEP', ',',\n",
            "       'the', 'committee', 'approved', 'several', 'proposals', 'which',\n",
            "       'could', 'restrict', 'imports', 'of', 'lamb', ',', 'casein', ',',\n",
            "       'sugar', '-', 'containing', 'products', 'and', 'tobacco', '.',\n",
            "       'Those', 'amendments', 'affecting', 'imports', 'face', 'an',\n",
            "       'uncertain', 'future', 'because', 'the', 'House', 'Ways', 'and',\n",
            "       'Means', 'Committee', ',', 'which', 'has', 'overall',\n",
            "       'jurisdiction', 'over', 'trade', 'legislation', ',', 'will',\n",
            "       'oppose', 'them', ',', 'Congressional', 'sources', 'said', '.',\n",
            "       'The', 'effect', 'of', 'the', 'EEP', 'amendments', 'would', 'be',\n",
            "       'to', 'extend', 'the', 'life', 'of', 'the', 'program', 'five',\n",
            "       'years', 'through', 'fiscal', '1990', 'rather', 'than', 'the',\n",
            "       'current', 'three', 'years', 'through', 'fiscal', '1988', '.',\n",
            "       'The', 'amendments', ',', 'offered', 'by', 'Rep', '.', 'Dan',\n",
            "       'Glickman', ',', 'D', '-', 'Kan', '.,', 'also', 'would',\n",
            "       'increase', 'funding', 'for', 'the', 'program', 'to', '2', '.',\n",
            "       '5', 'billion', 'dlrs', 'from', '1', '.', '5', 'billion', 'now',\n",
            "       '.', 'Furthermore', ',', 'the', 'committee', 'passed', 'an',\n",
            "       'amendment', 'offered', 'by', 'Rep', '.', 'Glickman', 'which',\n",
            "       'instructs', 'the', 'U', '.', 'S', '.', 'Agriculture',\n",
            "       'Department', 'to', 'value', 'EEP', 'bonus', 'commodities', 'at',\n",
            "       'market', 'value', ',', 'not', 'acquisition', 'value', '.',\n",
            "       'Glickman', 'said', 'the', 'change', 'would', 'make', 'the',\n",
            "       'program', '30', 'pct', 'less', 'expensive', 'to', 'operate', '.',\n",
            "       'The', 'provision', 'on', 'EEP', 'wheat', 'to', 'the', 'Soviet',\n",
            "       'Union', ',', 'offered', 'by', 'Rep', '.', 'Bob', 'Smith', ',',\n",
            "       'R', '-', 'Ore', '.,', 'does', 'not', 'require', 'the',\n",
            "       'administration', 'make', 'an', 'offer', ',', 'but', 'urges',\n",
            "       'such', 'action', '.', 'The', 'committee', 'approved', 'an',\n",
            "       'amendment', ',', 'offered', 'by', 'Rep', '.', 'Glenn', 'English',\n",
            "       ',', 'D', '-', 'Okla', '.,', 'requiring', 'the', 'Secretary', 'of',\n",
            "       'Agriculture', 'to', 'begin', 'discussions', 'with', 'other',\n",
            "       'major', 'grain', 'producing', 'countries', 'aimed', 'at',\n",
            "       'jointly', 'reducing', 'world', 'grain', 'production', '.',\n",
            "       'Trade', 'Representative', 'Clayton', 'Yeutter', 'yesterday',\n",
            "       'opposed', 'the', 'amendment', ',', 'saying', 'such', 'commodity',\n",
            "       'agreements', 'do', 'not', 'work', '.', 'Among', 'the', 'host',\n",
            "       'of', 'amendments', 'to', 'restrict', 'imports', 'approved', 'by',\n",
            "       'the', 'panel', ',', 'the', 'most', 'significant', 'would',\n",
            "       'require', 'quotas', 'on', 'imports', 'of', 'goods', 'containing',\n",
            "       'more', 'than', '25', 'pct', 'of', 'a', 'bulk', 'farm', 'product',\n",
            "       'that', 'is', 'subject', 'to', 'U', '.', 'S', '.', 'quotas', '.',\n",
            "       'The', 'amendment', ',', 'offered', 'by', 'Rep', '.', 'Arlan',\n",
            "       'Stangeland', ',', 'R', '-', 'Minn', '.,', 'is', 'aimed',\n",
            "       'primarily', 'at', 'curbing', 'imports', 'from', 'Canada', 'of',\n",
            "       'products', 'containing', 'sugar', 'and', 'foreign', 'foods',\n",
            "       'containing', 'dairy', 'products', '.', 'It', 'also', 'may',\n",
            "       'affect', 'peanut', ',', 'cotton', 'and', 'tobacco', 'imports',\n",
            "       ',', 'Committee', 'sources', 'said', '.', 'Another', 'amendment',\n",
            "       'would', 'place', 'a', 'quota', 'on', 'U', '.', 'S', '.',\n",
            "       'imports', 'of', 'casein', ',', 'a', 'dairy', 'product', 'shipped',\n",
            "       'to', 'the', 'U', '.', 'S', '.', 'primarily', 'by', 'New',\n",
            "       'Zealand', 'and', 'Ireland', '.', 'The', 'panel', 'also', 'voted',\n",
            "       'to', 'apply', 'to', 'lamb', 'imports', 'the', 'same',\n",
            "       'countercyclical', 'import', 'quota', 'law', 'which', 'is',\n",
            "       'operating', 'for', 'U', '.', 'S', '.', 'beef', 'imports', '.',\n",
            "       'Other', 'miscellaneous', 'amendments', 'included', ':', '--',\n",
            "       'Urging', 'the', 'administration', 'consider', 'retaliating',\n",
            "       'against', 'Japan', 'and', 'South', 'Korea', 'if', 'those',\n",
            "       'countries', 'do', 'not', 'remove', 'restrictions', 'on', 'beef',\n",
            "       'imports', '.', '--', 'Boosting', 'the', 'amount', 'of', 'U', '.',\n",
            "       'S', '.', 'grain', 'which', 'must', 'be', 'shipped', 'each',\n",
            "       'year', 'under', 'a', 'food', 'aid', 'program', 'called',\n",
            "       'Section', '416', 'to', '800', ',', '000', 'tonnes', 'from', '500',\n",
            "       ',', '000', 'tonnes', 'now', '.', '--', 'Requiring', 'the',\n",
            "       'Agriculture', 'Secretary', 'conduct', 'a', 'study', 'of', 'the',\n",
            "       'Canadian', 'Wheat', 'Board', 'import', 'licensing', 'system',\n",
            "       'for', 'wheat', 'to', 'determine', 'if', 'it', 'is', 'a', 'non',\n",
            "       '-', 'tariff', 'trade', 'barrier', '.', '--', 'Requiring', 'the',\n",
            "       'Agriculture', 'Secretary', 'reimburse', 'the', 'National', 'Corn',\n",
            "       'Growers', 'Association', 'up', 'to', '500', ',', '000', 'dlrs',\n",
            "       'for', 'the', 'costs', 'of', 'defending', 'the', 'U', '.', 'S',\n",
            "       '.', 'feedgrains', 'program', 'against', 'a', 'Canadian',\n",
            "       'countervailing', 'duty', 'case', 'this', 'year', '.', '--',\n",
            "       'Urging', 'the', 'administration', 'oppose', 'the', 'Canadian',\n",
            "       'decision', 'to', 'apply', 'a', 'duty', 'on', 'U', '.', 'S', '.',\n",
            "       'corn', 'imports', ',', 'and', 'a', 'proposal', 'by', 'the',\n",
            "       'European', 'Community', 'to', 'apply', 'a', 'vegetable', 'oils',\n",
            "       'tax', '.', '--', 'USDA', 'conduct', 'a', 'study', 'of', 'the',\n",
            "       'findings', 'of', 'a', 'National', 'Commission', 'on',\n",
            "       'Agricultural', 'Export', 'Policy', ',', 'which', 'recommended',\n",
            "       'a', 'reorganization', 'of', 'USDA', \"'\", 's', 'trade', 'policy',\n",
            "       'apparatus', '.'], dtype='<U15')\n",
            " array(['CHILE', ',', 'CHINA', 'TO', 'ESTABLISH', 'PEKING', 'COPPER',\n",
            "       'PLANT', 'The', 'Chilean', 'state', 'copper', 'corporation', ',',\n",
            "       'Codelco', ',', 'the', 'privately', '-', 'owned', 'Chilean',\n",
            "       'copper', 'fabricator', 'Madeco', 'and', 'Chinese', 'interests',\n",
            "       'are', 'to', 'establish', 'a', 'copper', 'tube', 'plant', 'in',\n",
            "       'Peking', ',', 'Codelco', \"'\", 's', 'U', '.', 'K', '.',\n",
            "       'Subsidiary', 'Chile', 'Copper', 'Ltd', 'said', '.', 'The',\n",
            "       'plant', 'is', 'designed', 'to', 'produce', '5', ',', '000',\n",
            "       'tonnes', 'in', 'the', 'first', 'five', 'years', 'and', '8', ',',\n",
            "       '000', 'tonnes', 'as', 'from', 'year', 'six', '.', 'Total',\n",
            "       'investment', 'is', 'estimated', 'at', '10', 'mln', 'dlrs', ',',\n",
            "       'of', 'which', 'the', 'Chileans', 'will', 'contribute', 'two',\n",
            "       'mln', '.', 'Codelco', 'and', 'Madeco', 'have', 'formed', 'the',\n",
            "       'Chilean', 'Wrought', 'Copper', 'Company', 'to', 'take', 'a', '50',\n",
            "       'pct', 'stake', 'in', 'a', 'new', 'company', 'called', 'Peking',\n",
            "       'Santiago', 'Tube', 'Company', 'with', 'the', 'Chinese', 'holding',\n",
            "       'the', 'other', 'half', '.'], dtype='<U11')\n",
            " array(['NORANDA', 'MINE', 'SEEN', 'BELOW', 'FULL', 'OUTPUT', 'FOR',\n",
            "       'MONTHS', 'Noranda', 'Inc', 'said', 'production', 'will', 'remain',\n",
            "       'shut', 'down', 'at', 'its', 'fire', '-', 'damaged', 'copper',\n",
            "       'mine', 'here', 'until', 'it', 'can', 'completely', 'examine',\n",
            "       'the', 'mine', '.', 'If', 'it', 'decides', 'to', 'keep', 'the',\n",
            "       'mine', 'open', 'it', 'would', 'take', 'four', 'or', 'five',\n",
            "       'months', 'before', 'it', 'could', 'resume', 'full', 'production',\n",
            "       '.', 'Company', 'spokesman', 'Dale', 'Coffin', 'said', 'the',\n",
            "       'investigation', 'could', 'take', 'from', 'a', 'few', 'days', 'to',\n",
            "       'several', 'weeks', 'but', 'would', 'not', 'be', 'more',\n",
            "       'specific', '.', 'Noranda', 'said', 'when', 'it', 'resumes',\n",
            "       'production', 'it', 'plans', 'to', 'operate', 'the', 'mine', 'at',\n",
            "       'about', 'one', '-', 'third', 'of', 'the', 'normal', '72', ',',\n",
            "       '000', 'tonnes', 'annual', 'finished', 'capacity', '.', 'The',\n",
            "       'fire', ',', 'which', 'started', 'Wednesday', 'and', 'burned',\n",
            "       'itself', 'out', 'late', 'yesterday', 'weakened', 'part', 'of',\n",
            "       'the', 'mine', \"'\", 's', 'support', 'structure', ',', 'Coffin',\n",
            "       'said', '.', 'One', 'miner', 'was', 'killed', 'and', '56',\n",
            "       'others', 'were', 'trapped', 'underground', 'for', 'about', '24',\n",
            "       'hours', 'before', 'being', 'brought', 'safely', 'out', '.'],\n",
            "      dtype='<U13')\n",
            " array(['NORANDA', 'SETS', 'TEMPORARY', 'MINE', 'SHUTDOWN', '&', 'lt', ';',\n",
            "       'Noranda', 'Inc', '>', 'said', 'production', 'will', 'remain',\n",
            "       'shut', 'down', 'at', 'its', 'fire', '-', 'damaged', 'copper',\n",
            "       'mine', 'here', 'until', 'it', 'can', 'completely', 'examine',\n",
            "       'the', 'mine', '.', 'The', 'fire', ',', 'which', 'started',\n",
            "       'Wednesday', 'and', 'burned', 'itself', 'out', 'late', 'yesterday',\n",
            "       ',', 'killed', 'one', 'miner', 'and', 'trapped', '56', 'others',\n",
            "       'underground', 'for', 'about', '24', 'hours', '.', 'The', '56',\n",
            "       'were', 'eventually', 'brought', 'safely', 'out', 'of', 'the',\n",
            "       'mine', '.', 'Company', 'spokesman', 'Dale', 'Coffin', 'said',\n",
            "       'the', 'investigation', 'could', 'take', 'from', 'a', 'few',\n",
            "       'days', 'to', 'several', 'weeks', ',', 'but', 'would', 'not', 'be',\n",
            "       'more', 'specific', '.', 'Noranda', 'said', 'that', ',', 'when',\n",
            "       'it', 'resumes', 'production', ',', 'it', 'plans', 'to', 'operate',\n",
            "       'the', 'mine', 'at', 'about', 'one', '-', 'third', 'of', 'the',\n",
            "       'normal', '72', ',', '000', 'metric', 'tons', 'annual', 'finished',\n",
            "       'capacity', '.', 'The', 'fire', 'weakened', 'part', 'of', 'the',\n",
            "       'mine', \"'\", 's', 'support', 'structure', ',', 'Coffin', 'said',\n",
            "       '.', 'Noranda', 'said', 'if', 'it', 'decides', 'to', 'keep', 'the',\n",
            "       'mine', 'open', ',', 'it', 'would', 'take', 'four', 'or', 'five',\n",
            "       'months', 'before', 'it', 'could', 'resume', 'full', 'production',\n",
            "       '.'], dtype='<U13')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DZ9Un7qX_iY"
      },
      "source": [
        "## Note for Shivam\n",
        "\n",
        "In the next block, keep the window = 2\n",
        "sub_sampling = 0\n",
        "epochs = 5\n",
        "choice = NSSKG  (capital letters)\n",
        "\n",
        "Do not leave any space any where\n",
        "\n",
        "# Also, make a folder named \"NLP\" in your drive."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3k9CmPNddkU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48b8b3c3-e9cf-48c8-d067-5ad3e2153880"
      },
      "source": [
        "# Model Parameters\n",
        "\n",
        "window = int(input(\"Enter the Window size. Eg - if window size is 2, max of 4 words can be used as context or target words \" ))\n",
        "# learning_rate = float(input( \"Enter the Learning Rate for the training process\"))\n",
        "# vector_size = int(input(\" Enter the vector/embedding dimmension\"))\n",
        "sub_sampling = int(input(\" Enter the sub_sampling rate if you want to sub sample. Otherwise press 0 \"))\n",
        "epochs = int(input(\" Enter the number of epochs to train the corpus \"))\n",
        "choice = input(\" Enter a choice : SKG, CBOW, NSSKG \")\n",
        "if choice == 'NSSKG':\n",
        "  num_neg = int(input(\"How many negative samples to be used per positive sample in Negative Sampling - \"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the Window size. Eg - if window size is 2, max of 4 words can be used as context or target words 2\n",
            " Enter the sub_sampling rate if you want to sub sample. Otherwise press 0 0\n",
            " Enter the number of epochs to train the corpus 5\n",
            " Enter a choice : SKG, CBOW, NSSKG NSSKG\n",
            "How many negative samples to be used per positive sample in Negative Sampling - 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRKWbFA5J7IE"
      },
      "source": [
        "List of funtions that will be used\n",
        "\n",
        "* Softmax\n",
        "* word2onehot\n",
        "* Negative sampling\n",
        "* Cost function and gradient function\n",
        "* Normalization function to normalize rows of a matrix\n",
        "* Skip-gram model\n",
        "* CBOW\n",
        "* SGD Optimizer\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_puVU8becvz"
      },
      "source": [
        "def Softmax(np_list_inp):\n",
        "  ''' \n",
        "    This function takes a numpy list as input that will contains Z_js. This data structure's name is np_list_inp.\n",
        "    The function will return the softmax output of each entry as a form of numpy list. This data structure's name is np_list_out.\n",
        "\n",
        "    Using instructions of the function :\n",
        "      Consider the function has 10 inputs or values and we need Softjax of 4th element i.e. Z_4,\n",
        "      so 4 element in np_list_out would be sigma(Z_4) i.e. softmax of Z_4\n",
        "  '''\n",
        "  np_list_inp = np.array(np_list_inp)  # In case the input vector is not an numpy array but a simple list\n",
        "  exp_list = np.exp(np_list_inp) \n",
        "  #exp_list = np.exp(np_list_inp - np.max(np_list_inp))   # This version is complex but numerically more stable.\n",
        "  sum_of_exp = np.sum(exp_list)\n",
        "  \n",
        "  return exp_list / sum_of_exp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IvYffukg9iD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c854e811-76b0-4f11-c9bb-b03af03d4f58"
      },
      "source": [
        "# Example block\n",
        "\n",
        "print(Softmax([1,1]))\n",
        "print(Softmax([1,2,3]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.5 0.5]\n",
            "[0.09003057 0.24472847 0.66524096]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nM2ymjwpYj6G"
      },
      "source": [
        "* word_count -> dictionary containing the words along with their frequencies\n",
        "* word_data -> Helper dictionary for generating onehot vectors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oV-C2I-uijSM"
      },
      "source": [
        "word_count = {}\n",
        "for row in corpus:\n",
        "  for word in row:\n",
        "    if word not in word_count:\n",
        "      word_count[word] = 1\n",
        "    else:\n",
        "      word_count[word] += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvGuddeOIZKB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acbf8696-e4da-4975-eb9c-19fd32113d03"
      },
      "source": [
        "# The adjusted_sampling function will be used to get the probabilities for picking up a random negative sample.\n",
        "\n",
        "total = 0\n",
        "for freq in word_count:\n",
        "  total += word_count[freq]\n",
        "print(total)\n",
        "\n",
        "def adjusted_sampling(word):\n",
        "  prob = float( word_count[word] )\n",
        "  prob /= total\n",
        "  prob = prob ** 0.75\n",
        "  return prob"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2886\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzMIZ8ytywqc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4d4adb7-2d81-49d3-da29-8f6af3cdcb9e"
      },
      "source": [
        "word_data = {}\n",
        "\n",
        "index = 0\n",
        "for i in word_count:\n",
        "  if (word_count[i] < sub_sampling):\n",
        "    del word_count[i]\n",
        "  else:\n",
        "    word_count[i] = word_count[i]**0.75\n",
        "    word_data[i] = index\n",
        "    index = index + 1\n",
        "\n",
        "# if not choice == \"NSSKG\":\n",
        "#   del word_count\n",
        "\n",
        "v_count = len(word_data.keys())\n",
        "print(v_count)\n",
        "del i"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1013\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WlbboYKUR1CB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61dabfbd-aa2c-4dda-ea85-2360922ce67b"
      },
      "source": [
        "word_choice = {}\n",
        "sum_p = float(0)\n",
        "\n",
        "for i in word_count:\n",
        "  word_choice[i] = adjusted_sampling(i)\n",
        "  sum_p += word_choice[i]\n",
        "\n",
        "for i in word_choice:\n",
        "  word_choice[i] /= sum_p\n",
        "\n",
        "x = float(0)\n",
        "for i in word_choice:\n",
        "  x += word_choice[i]\n",
        "\n",
        "print(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.000000000000002\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v34qb5DBQvqb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea9391de-887d-4f0b-ab42-fe8ecae392f8"
      },
      "source": [
        "j = 1\n",
        "for i in word_data:\n",
        "  print(i, word_data[i])\n",
        "  j += 1\n",
        "  if j > 50:\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "U 0\n",
            ". 1\n",
            "K 2\n",
            "WHEAT 3\n",
            "AND 4\n",
            "BARLEY 5\n",
            "EXPORTS 6\n",
            "ADJUSTED 7\n",
            "UPWARDS 8\n",
            "The 9\n",
            "Exported 10\n",
            "535 11\n",
            ", 12\n",
            "460 13\n",
            "tonnes 14\n",
            "of 15\n",
            "wheat 16\n",
            "and 17\n",
            "336 18\n",
            "750 19\n",
            "barley 20\n",
            "in 21\n",
            "January 22\n",
            "the 23\n",
            "Home 24\n",
            "Grown 25\n",
            "Cereals 26\n",
            "Authority 27\n",
            "( 28\n",
            "HGCA 29\n",
            ") 30\n",
            "said 31\n",
            "quoting 32\n",
            "adjusted 33\n",
            "Customs 34\n",
            "Excise 35\n",
            "figures 36\n",
            "Based 37\n",
            "on 38\n",
            "previous 39\n",
            "issued 40\n",
            "February 41\n",
            "9 42\n",
            "exports 43\n",
            "increased 44\n",
            "by 45\n",
            "nearly 46\n",
            "64 47\n",
            "000 48\n",
            "about 49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXQfObqETaLQ"
      },
      "source": [
        "def negative_samples():\n",
        "  \"\"\"\n",
        "    This function returns num_neg samples from the corpus for Negative Sampling\n",
        "  \"\"\"\n",
        "  rand = np.random.choice(list(word_choice.keys()), size=num_neg, p=list(word_choice.values()))\n",
        "  samples = []\n",
        "\n",
        "  for i in rand:\n",
        "    samples.append(np.array(word2onehot(i)))\n",
        "\n",
        "  return np.array(samples)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZumNX3jWOpw"
      },
      "source": [
        "def word2onehot(word):\n",
        "  \"\"\"\n",
        "    This function takes a word( string ) as an input and outputs the onehot encoded vector of the word as a form of a numpy array. \n",
        "  \"\"\"\n",
        "  emptyOnehot = np.zeros(v_count)\n",
        "  print(emptyOnehot.shape)\n",
        "  i = word_data[word]\n",
        "  print(i)\n",
        "  emptyOnehot[i] = 1\n",
        "  return emptyOnehot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lYjwHvR7T7xC"
      },
      "source": [
        "Training Data Prepairation for CBOW and SKG models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9-buVmFFIug"
      },
      "source": [
        "![](https://media.geeksforgeeks.org/wp-content/uploads/Skip-gram-architecture-2.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPBHUKW0YZwv"
      },
      "source": [
        "def skg(inp, labels, w1, w2, loss, learning_rate):\n",
        "  \"\"\"\n",
        "      inp -> Onehot vector of the input or the target word in nunmpy array format.\n",
        "      labels -> list of all the onehot vectors of the context words.\n",
        "  \"\"\"\n",
        "  h = np.dot(w1.T, inp)\n",
        "  u = np.dot(w2.T, h)\n",
        "  y_pred = Softmax(u)\n",
        "\n",
        "  error = np.array([-label + y_pred.T for label in labels])\n",
        "  dw2 = np.outer(h, np.sum(error, axis=0))\n",
        "  dw1 = np.outer(inp, np.dot(w2, np.sum(error, axis=0)))\n",
        "\n",
        "  new_W1, new_W2 = SGD(w1,w2,dw1,dw2, learning_rate)\n",
        "\n",
        "  loss += -np.sum([u[label == 1] for label in labels]) + len(labels) * np.log(np.sum(np.exp(u)))\n",
        "\n",
        "  return new_W1, new_W2, loss\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPF3LH__Y3N9"
      },
      "source": [
        "def cbow(inp, label, w1, w2, loss, learning_rate):\n",
        "  \"\"\"\n",
        "    inp -> list of onehot vectors of context words\n",
        "    label -> onehot vector of centre word.\n",
        "  \"\"\"\n",
        "  \n",
        "  x = np.mean(inp, axis=0)\n",
        "  h = np.dot(w1.T, x)\n",
        "  u = np.dot(w2.T, h)\n",
        "  y_pred = Softmax(u)\n",
        "\n",
        "  error = -label + y_pred\n",
        "  dw2 = np.outer(h, error)\n",
        "  dw1 = np.outer(x, np.dot(w2, error))\n",
        "\n",
        "  new_W1, new_W2 = SGD(w1,w2,dw1,dw2, learning_rate)\n",
        "\n",
        "  loss += -float(u[label == 1]) + np.log(np.sum(np.exp(u)))\n",
        "\n",
        "  return new_W1, new_W2, loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tI3Wk10-Zgw_"
      },
      "source": [
        "def sigmoid(x):\n",
        "  return 1 / (1 + np.exp(-x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbcsKg6TS6PA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f66265f4-4554-4e2b-a607-609cbb642778"
      },
      "source": [
        "a = np.array([[1,2],[3,4]])\n",
        "sigmoid(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73105858, 0.88079708],\n",
              "       [0.95257413, 0.98201379]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8kAAq0eZAoF"
      },
      "source": [
        "def SGD(w1,w2,dw1,dw2, learning_rate):\n",
        "  \"\"\"\n",
        "    Each epoch or iteration accounts for updation due single training cycle.\n",
        "  \"\"\"\n",
        "  new_w1 = w1 - (learning_rate * dw1)\n",
        "  new_w2 = w2 - (learning_rate * dw2)\n",
        "\n",
        "  return new_w1, new_w2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9bSxmAKcUEv"
      },
      "source": [
        "def nsskg(inp, label, w1, w2, rate):\n",
        "  h = np.dot(w1.T, inp)\n",
        "  u = np.dot(w2.T, h)\n",
        "\n",
        "  neg_words = negative_samples()\n",
        "\n",
        "  u = sigmoid(u)\n",
        "  mask = inp\n",
        "\n",
        "  for i in neg_words:\n",
        "    mask += i\n",
        "\n",
        "  u = np.dot(u, mask)\n",
        "  u = u - label\n",
        "\n",
        "  dw1 = np.dot(w1.T, u)\n",
        "  dw1 = np.outer(inp, dw1)\n",
        "  dw2 = np.outer(h, u)\n",
        "\n",
        "  return SGD(w1, w2, dw1, dw2, rate)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-47uSVzWIHB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "105f46ac-f7cd-4987-d08e-fd57ee5080ac"
      },
      "source": [
        "'''\n",
        "# def skg(inp, labels, w1, w2, loss, learning_rate):\n",
        "#   \"\"\"\n",
        "#       inp -> Onehot vector of the input or the target word in nunmpy array format.\n",
        "#       labels -> list of all the onehot vectors of the context words.\n",
        "#   \"\"\"\n",
        "#   h = np.dot(w1.T, inp)\n",
        "#   u = np.dot(w2.T, h)\n",
        "#   y_pred = Softmax(u)\n",
        "\n",
        "#   error = np.array([-label + y_pred.T for label in labels])\n",
        "#   dw2 = np.outer(h, np.sum(error, axis=0))\n",
        "#   dw1 = np.outer(inp, np.dot(w2, np.sum(error, axis=0)))\n",
        "\n",
        "#   new_W1, new_W2 = SGD(w1,w2,dw1,dw2, learning_rate)\n",
        "\n",
        "#   loss += -np.sum([u[label == 1] for label in labels]) + len(labels) * np.log(np.sum(np.exp(u)))\n",
        "\n",
        "#   return new_W1, new_W2, loss\n",
        "\n",
        "        # W1 = np.random.rand(v_count, size)\n",
        "        # W2 = np.random.rand(size, v_count)\n",
        "        # inp = label = np.random.rand(v_count, 1)\n",
        "\n",
        "def nsskg(inp, label, w1, w2, rate):\n",
        "  # inp = np.array([inp]).T\n",
        "  # label = np.array([label]).T\n",
        "  h = np.dot(w1.T, inp)     # (size, 1)\n",
        "  u = np.dot(w2.T, h)       # (v_count, 1)\n",
        "\n",
        "  neg_words = negative_samples()\n",
        "\n",
        "  u = sigmoid(u)\n",
        "  mask = inp\n",
        "  # print('mask.shape = ', mask.shape)\n",
        "  for i in neg_words:\n",
        "    # print('i.shape = ', i.shape)\n",
        "    mask += i\n",
        "\n",
        "  u = np.dot(u, mask)\n",
        "  u = u - label\n",
        "\n",
        "  dw1 = np.dot(w1.T, u)     # (size, v_count) * (v_count, 1)  = (size, 1)\n",
        "  # dw1 = np.array([dw1])\n",
        "  # print('>>> NSSKG COMPUTATION\\n', dw1.T.shape)       # (size, 1)\n",
        "  # print(inp.shape)                                  # (v_count, 1)\n",
        "  dw1 = np.outer(inp, dw1)  # (v_count, size)\n",
        "  dw2 = np.outer(h, u)      # (v_count, size)\n",
        "  # dw2 = np.array([dw2])\n",
        "  # dw2 = dw2.T\n",
        "\n",
        "  return SGD(w1, w2, dw1, dw2, rate)\n",
        "\n",
        "  # for c_pos in labels:\n",
        "  #   c_pos_d = c_pos\n",
        "  #   c_pos = np.dot(c_pos.T, w1)\n",
        "  #   W_neg = negative_samples()\n",
        "  #   grad_v_output_pos = sigmoid(np.dot(c_pos.T, h) - 1) * h\n",
        "  #   grad_v_input = (sigmoid(np.dot(c_pos.T, h) - 1)) * c_pos\n",
        "  #   grad_v_output_neg_list = []\n",
        "\n",
        "  #   for c_neg in W_neg:\n",
        "  #     c_neg_d = c_neg\n",
        "  #     c_neg = np.dot(c_neg.T, w1)\n",
        "  #     grad_v_output_neg_list.append(sigmoid(np.dot(c_neg, h)) * h)\n",
        "  #     grad_v_input += sigmoid(np.dot(c_neg, h)) * c_neg\n",
        "\n",
        "  #   w1[word_data[inp]] = w1[word_data[inp]] - rate * grad_v_input\n",
        "\n",
        "  #   w2 = w2.T\n",
        "  #   w2[word_data[c_pos_d]] = w2[word_data[c_pos_d]] - rate * grad_v_output_pos\n",
        "    \n",
        "  #   ind = 0\n",
        "  #   for c_neg in W_neg:\n",
        "  #     w2[word_data[c_neg_d]] = w2[word_data[c_neg_d]] - rate * grad_v_output_neg_list[ind]\n",
        "  #     ind += 1\n",
        "\n",
        "  #   w2 = w2.T\n",
        "\n",
        "  # return new_w1, new_w2\n",
        "  '''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n# def skg(inp, labels, w1, w2, loss, learning_rate):\\n#   \"\"\"\\n#       inp -> Onehot vector of the input or the target word in nunmpy array format.\\n#       labels -> list of all the onehot vectors of the context words.\\n#   \"\"\"\\n#   h = np.dot(w1.T, inp)\\n#   u = np.dot(w2.T, h)\\n#   y_pred = Softmax(u)\\n\\n#   error = np.array([-label + y_pred.T for label in labels])\\n#   dw2 = np.outer(h, np.sum(error, axis=0))\\n#   dw1 = np.outer(inp, np.dot(w2, np.sum(error, axis=0)))\\n\\n#   new_W1, new_W2 = SGD(w1,w2,dw1,dw2, learning_rate)\\n\\n#   loss += -np.sum([u[label == 1] for label in labels]) + len(labels) * np.log(np.sum(np.exp(u)))\\n\\n#   return new_W1, new_W2, loss\\n\\n        # W1 = np.random.rand(v_count, size)\\n        # W2 = np.random.rand(size, v_count)\\n        # inp = label = np.random.rand(v_count, 1)\\n\\ndef nsskg(inp, label, w1, w2, rate):\\n  # inp = np.array([inp]).T\\n  # label = np.array([label]).T\\n  h = np.dot(w1.T, inp)     # (size, 1)\\n  u = np.dot(w2.T, h)       # (v_count, 1)\\n\\n  neg_words = negative_samples()\\n\\n  u = sigmoid(u)\\n  mask = inp\\n  # print(\\'mask.shape = \\', mask.shape)\\n  for i in neg_words:\\n    # print(\\'i.shape = \\', i.shape)\\n    mask += i\\n\\n  u = np.dot(u, mask)\\n  u = u - label\\n\\n  dw1 = np.dot(w1.T, u)     # (size, v_count) * (v_count, 1)  = (size, 1)\\n  # dw1 = np.array([dw1])\\n  # print(\\'>>> NSSKG COMPUTATION\\n\\', dw1.T.shape)       # (size, 1)\\n  # print(inp.shape)                                  # (v_count, 1)\\n  dw1 = np.outer(inp, dw1)  # (v_count, size)\\n  dw2 = np.outer(h, u)      # (v_count, size)\\n  # dw2 = np.array([dw2])\\n  # dw2 = dw2.T\\n\\n  return SGD(w1, w2, dw1, dw2, rate)\\n\\n  # for c_pos in labels:\\n  #   c_pos_d = c_pos\\n  #   c_pos = np.dot(c_pos.T, w1)\\n  #   W_neg = negative_samples()\\n  #   grad_v_output_pos = sigmoid(np.dot(c_pos.T, h) - 1) * h\\n  #   grad_v_input = (sigmoid(np.dot(c_pos.T, h) - 1)) * c_pos\\n  #   grad_v_output_neg_list = []\\n\\n  #   for c_neg in W_neg:\\n  #     c_neg_d = c_neg\\n  #     c_neg = np.dot(c_neg.T, w1)\\n  #     grad_v_output_neg_list.append(sigmoid(np.dot(c_neg, h)) * h)\\n  #     grad_v_input += sigmoid(np.dot(c_neg, h)) * c_neg\\n\\n  #   w1[word_data[inp]] = w1[word_data[inp]] - rate * grad_v_input\\n\\n  #   w2 = w2.T\\n  #   w2[word_data[c_pos_d]] = w2[word_data[c_pos_d]] - rate * grad_v_output_pos\\n    \\n  #   ind = 0\\n  #   for c_neg in W_neg:\\n  #     w2[word_data[c_neg_d]] = w2[word_data[c_neg_d]] - rate * grad_v_output_neg_list[ind]\\n  #     ind += 1\\n\\n  #   w2 = w2.T\\n\\n  # return new_w1, new_w2\\n  '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxRHTDsKAL8y"
      },
      "source": [
        "## Main Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56C4vDm85zY1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54452941-2e85-498e-d2ef-96190cd040c8"
      },
      "source": [
        "vector_size = [200,300, 350, 400, 500]\n",
        "learning_rate = [0.001, 0.01, 0.1, 0.5]\n",
        "\n",
        "for size in vector_size: \n",
        "  print(\"Starting the training with embedding size = \", size, \"\\n\\n\")\n",
        "  np.random.seed(100)\n",
        "  W1 = np.random.rand(v_count, size)\n",
        "  W2 = np.random.rand(size, v_count)\n",
        "  loss = 0.00\n",
        "\n",
        "# For SKG\n",
        "  if (choice == \"SKG\"):\n",
        "    for rate in learning_rate: \n",
        "      itr = 0\n",
        "      print(\"Starting the training with learning rate = \", rate, \"\\n\\n\")\n",
        "      for itr in range(epochs):\n",
        "        \n",
        "        for sentence_skg in corpus:\n",
        "          sent_skg_len = len(sentence_skg)\n",
        "          for i_skg, word_skg in enumerate(sentence_skg):\n",
        "            w_skg_target = word2onehot(sentence_skg[i_skg])\n",
        "            inp = np.array(w_skg_target)\n",
        "\n",
        "            w_skg_context = []\n",
        "            for j in range(i_skg - window, i_skg+ window+1):\n",
        "              if j!=i_skg and j<= sent_skg_len - 1 and j>=0:\n",
        "                w_skg_context.append(word2onehot(sentence_skg[j]))\n",
        "            \n",
        "            labels = np.array(w_skg_context)\n",
        "            W1, W2, loss = skg(inp, labels, W1, W2, rate)\n",
        "\n",
        "            del w_skg_context\n",
        "            del w_skg_target\n",
        "            del labels\n",
        "            del inp\n",
        "        \n",
        "        print(\" Result after epoch number \",itr, \": loss = \", loss, \" for rate, size = \", rate, \"  \",size)\n",
        "        print(\"\\n\\n\\n --------------------------------------------------------------------------------------------------------------------------------------\")\n",
        "    \n",
        "      path = \"/Jash/My Drive/NLP/\" + \"GPU_Window_\" + window + \"/\" + choice + \"/\" + str(rate) + \"_\" + str(size) + \"weight_numpy\" \n",
        "      np.save(path, W1)\n",
        "\n",
        "# For CBOW\n",
        "  elif (choice == \"CBOW\"):\n",
        "    for rate in learning_rate: \n",
        "      print(\"Starting the training with learning rate = \", rate, \"\\n\\n\")\n",
        "      for itr in range(epochs):\n",
        "        for sentence_cbow in corpus:\n",
        "          sent_cbow_len = len(sentence_cbow)\n",
        "\n",
        "          for i_cbow, word_cbow in enumerate(sentence_cbow):\n",
        "            w_cbow_target = word2onehot(sentence_cbow[i_cbow])\n",
        "            label = np.array(w_cbow_target)\n",
        "        \n",
        "            w_cbow_context = []\n",
        "            for j in range(i_cbow - window, i_cbow + window + 1):\n",
        "              if j!=i_cbow and j<=sent_cbow_len-1 and j>=0:\n",
        "                w_cbow_context.append(word2onehot(sentence_cbow[j]))\n",
        "            \n",
        "            inp = np.array(w_cbow_context)\n",
        "            W1, W2, loss = cbow(inp, label, W1, W2, loss, rate) \n",
        "\n",
        "            del w_cbow_context\n",
        "            del w_cbow_target\n",
        "            del label\n",
        "            del inp\n",
        "\n",
        "        print(\" Result after epoch number \",itr, \": loss = \", loss, \" for rate, size = \", rate, \"  \",size)\n",
        "        print(\"\\n\\n\\n --------------------------------------------------------------------------------------------------------------------------------------\")\n",
        "    \n",
        "      path = \"/Jash/My Drive/NLP_GPU_CBOW_Window1/\" + choice + str(rate) + \"_\" + str(size) + \"weight_numpy\" \n",
        "      np.save(path, W1)\n",
        "\n",
        "# For Negative Samapling SKG\n",
        "  elif (choice == \"NSSKG\"):\n",
        "    print('NSSKG')\n",
        "    for rate in learning_rate: \n",
        "      print(\"Starting the training with learning rate = \", rate, \"\\n\\n\")\n",
        "      for itr in range(epochs):\n",
        "        for sentence_ns in corpus:\n",
        "          sent_ns_len = len(sentence_ns)\n",
        "\n",
        "          for i_ns, word_ns in enumerate(sentence_ns):\n",
        "            inp_a = word2onehot(sentence_ns[i_ns])\n",
        "            inp = np.array(inp_a)\n",
        "\n",
        "            for i in range(i_ns-window, i_ns + window + 1):\n",
        "              if i != i_ns and i < sent_ns_len and i >= 0:\n",
        "\n",
        "                label_a = word2onehot(sentence_ns[i])\n",
        "                label = np.array(label_a)\n",
        "                W1, W2 = nsskg(inp, label, W1, W2, rate)\n",
        "\n",
        "            del label\n",
        "            del inp\n",
        "            del label_a\n",
        "            del inp_a\n",
        "\n",
        "        print(\" Result after epoch number \",itr, \": loss = \", loss, \" for rate, size = \", rate, \"  \",size)\n",
        "        print(\"\\n\\n\\n --------------------------------------------------------------------------------------------------------------------------------------\")\n",
        "\n",
        "      path = \"/Jash/My Drive/NLP/GPU_\" + choice + str(rate) + \"_\" + str(size) + \"weight_numpy\" \n",
        "      np.save(path, W1)\n",
        "\n",
        "  else:\n",
        "    print(\" You entered wrong information !!!! \")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting the training with embedding size =  200 \n",
            "\n",
            "\n",
            "NSSKG\n",
            "Starting the training with learning rate =  0.001 \n",
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            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
            "  \n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py:906: RuntimeWarning: invalid value encountered in multiply\n",
            "  return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py:906: RuntimeWarning: overflow encountered in multiply\n",
            "  return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RTe6jn-CYNn"
      },
      "source": [
        "import pickle\n",
        "pickle.dump(word_data, open(\"/Jash/My Drive/NLP/word_data.p\", \"wb\"))\n",
        "#pickle.dump(word_count, open(\"/Jash/My Drive/NLP/word_count.p\", \"wb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2n8r8EimBPH6"
      },
      "source": [
        "%reset -f"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5SLAKTilueE"
      },
      "source": [
        "## In Next section, I will clear the runtime and re-load all the saved embeddings for displaying the results. "
      ]
    }
  ]
}