rerun with smoothing

MOJ5.ipynb

@@ -0,0 +1,372 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "machine_shape": "hm"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3dV_4SJ2xY_C",
+        "outputId": "28f0b228-e536-410e-8d45-a2063a04455b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/gdrive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount(\"/content/gdrive\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# %env DATA_DIR=/content/gdrive/MyDrive/data_gralinski\n",
+        "DATA_DIR=\"/content/gdrive/MyDrive/data_gralinski/\""
+      ],
+      "metadata": {
+        "id": "VwdW1Qm3x9-N"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import lzma\n",
+        "import pickle\n",
+        "from collections import Counter"
+      ],
+      "metadata": {
+        "id": "irsty5KcyYkR"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def clean_line(line: str):\n",
+        "    separated = line.split('\\t')\n",
+        "    prefix = separated[6].replace(r'\\n', ' ')\n",
+        "    suffix = separated[7].replace(r'\\n', ' ')\n",
+        "    return prefix + ' ' + suffix"
+      ],
+      "metadata": {
+        "id": "LXXtiKW3yY5J"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def words(filename):\n",
+        "    with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
+        "        index = 1\n",
+        "        print('Words')\n",
+        "        for line in fid:\n",
+        "            print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
+        "            text = clean_line(line)\n",
+        "            for word in text.split():\n",
+        "                yield word\n",
+        "            index += 1\n",
+        "        print()\n"
+      ],
+      "metadata": {
+        "id": "y9r0wmD3ycIi"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def bigrams(filename, V: dict):\n",
+        "    with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
+        "        index = 1\n",
+        "        print('Bigrams')\n",
+        "        for line in fid:\n",
+        "            print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
+        "            text = clean_line(line)\n",
+        "            first_word = ''\n",
+        "            for second_word in text.split():\n",
+        "                if V.get(second_word) is None:\n",
+        "                    second_word = 'UNK'\n",
+        "                if second_word:\n",
+        "                    yield first_word, second_word\n",
+        "                first_word = second_word\n",
+        "            index += 1\n",
+        "        print()"
+      ],
+      "metadata": {
+        "id": "HE3YfiHkycKt"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def trigrams(filename, V: dict):\n",
+        "    with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
+        "        index = 1\n",
+        "        print('Trigrams')\n",
+        "        for line in fid:\n",
+        "            print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
+        "            text = clean_line(line)\n",
+        "            first_word = ''\n",
+        "            second_word = ''\n",
+        "            for third_word in text.split():\n",
+        "                if V.get(third_word) is None:\n",
+        "                    third_word = 'UNK'\n",
+        "                if first_word:\n",
+        "                    yield first_word, second_word, third_word\n",
+        "                first_word = second_word\n",
+        "                second_word = third_word\n",
+        "            index += 1\n",
+        "        print()"
+      ],
+      "metadata": {
+        "id": "lvHvJV6XycNZ"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def tetragrams(filename, V: dict):\n",
+        "    with lzma.open(filename, mode='rt', encoding='utf-8') as fid:\n",
+        "        index = 1\n",
+        "        print('Tetragrams')\n",
+        "        for line in fid:\n",
+        "            print(f'\\rProgress: {(index / 432022 * 100):2f}%', end='')\n",
+        "            text = clean_line(line)\n",
+        "            first_word = ''\n",
+        "            second_word = ''\n",
+        "            third_word = ''\n",
+        "            for fourth_word in text.split():\n",
+        "                if V.get(fourth_word) is None:\n",
+        "                    fourth_word = 'UNK'\n",
+        "                if first_word:\n",
+        "                    yield first_word, second_word, third_word, fourth_word\n",
+        "                first_word = second_word\n",
+        "                second_word = third_word\n",
+        "                third_word = fourth_word\n",
+        "            index += 1\n",
+        "        print()"
+      ],
+      "metadata": {
+        "id": "sOKeZN9cycP-"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def P(first_word, second_word=None, third_word=None, fourth_word=None):\n",
+        "    try:\n",
+        "        if second_word is None:\n",
+        "            return V_common_dict[first_word] / total\n",
+        "        if third_word is None:\n",
+        "            return V2_dict[(first_word, second_word)] / V_common_dict[first_word]\n",
+        "        if fourth_word is None:\n",
+        "            return V3_dict[(first_word, second_word, third_word)] / V2_dict[(first_word, second_word)]\n",
+        "        else:\n",
+        "            return V4_dict[(first_word, second_word, third_word, fourth_word)] / V3_dict[\n",
+        "                (first_word, second_word, third_word)]\n",
+        "    except KeyError:\n",
+        "        return 0"
+      ],
+      "metadata": {
+        "id": "MN_RftZNycSB"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def smoothed(tetragram):\n",
+        "    first, second, third, fourth = tetragram\n",
+        "    return 0.5 * P(first, second, third, fourth) + 0.25 * P(second, third, fourth) + 0.15 * P(third, fourth) + 0.1 * P(\n",
+        "        fourth)"
+      ],
+      "metadata": {
+        "id": "n9wIsbLEycUd"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def candidates(left_context, right_context):\n",
+        "    cand = {}\n",
+        "    first, second, third = left_context\n",
+        "    fifth, sixth, seventh = right_context\n",
+        "    for word in V_common_dict:\n",
+        "        p1 = smoothed((first, second, third, word))\n",
+        "        p2 = smoothed((second, third, word, fifth))\n",
+        "        p3 = smoothed((third, word, fifth, sixth))\n",
+        "        p4 = smoothed((word, fifth, sixth, seventh))\n",
+        "        cand[word] = p1 * p2 * p3 * p4\n",
+        "    cand = sorted(list(cand.items()), key=lambda x: x[1], reverse=True)[:5]\n",
+        "    norm = [(x[0], float(x[1]) / sum([y[1] for y in cand])) for x in cand]\n",
+        "    for index, elem in enumerate(norm):\n",
+        "        unk = None\n",
+        "        if 'UNK' in elem:\n",
+        "            unk = norm.pop(index)\n",
+        "            norm.append(('', unk[1]))\n",
+        "            break\n",
+        "    if unk is None:\n",
+        "        norm[-1] = ('', norm[-1][1])\n",
+        "    return ' '.join([f'{x[0]}:{x[1]}' for x in norm])"
+      ],
+      "metadata": {
+        "id": "l490B5KFycXj"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def outputs(folder_name):\n",
+        "    print(f'Creating outputs in {folder_name}')\n",
+        "    with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
+        "        with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8') as f:\n",
+        "            for line in fid:\n",
+        "                separated = line.split('\\t')\n",
+        "                prefix = separated[6].replace(r'\\n', ' ').split()\n",
+        "                suffix = separated[7].replace(r'\\n', ' ').split()\n",
+        "                left_context = [x if V_common_dict.get(x) else 'UNK' for x in prefix[-3:]]\n",
+        "                right_context = [x if V_common_dict.get(x) else 'UNK' for x in suffix[:3]]\n",
+        "                w = candidates(left_context, right_context)\n",
+        "                f.write(w + '\\n')"
+      ],
+      "metadata": {
+        "id": "mMC84-OzycZ5"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "WORD_LIMIT = 3000\n",
+        "V = Counter(words(DATA_DIR+'train/in.tsv.xz'))\n",
+        "V_common_dict = dict(V.most_common(WORD_LIMIT))\n",
+        "# UNK = 0\n",
+        "# for key, value in V.items():\n",
+        "#     if V_common_dict.get(key) is None:\n",
+        "#         UNK += value\n",
+        "# V_common_dict['UNK'] = UNK\n",
+        "# with open('V.pickle', 'wb') as handle:\n",
+        "#     pickle.dump(V_common_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+        "\n",
+        "\n",
+        "with open(DATA_DIR+'5/V.pickle', 'rb') as handle:\n",
+        "    V_common_dict = pickle.load(handle)\n",
+        "\n",
+        "total = sum(V_common_dict.values())\n",
+        "\n",
+        "# V2 = Counter(bigrams('train/in.tsv.xz', V_common_dict))\n",
+        "# V2_dict = dict(V2)\n",
+        "# with open('V2.pickle', 'wb') as handle:\n",
+        "#     pickle.dump(V2_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+        "\n",
+        "with open(DATA_DIR+'5/V2.pickle', 'rb') as handle:\n",
+        "    V2_dict = pickle.load(handle)\n",
+        "\n",
+        "# V3 = Counter(trigrams('train/in.tsv.xz', V_common_dict))\n",
+        "# V3_dict = dict(V3)\n",
+        "# with open('V3.pickle', 'wb') as handle:\n",
+        "#     pickle.dump(V3_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+        "\n",
+        "with open(DATA_DIR+'5/V3.pickle', 'rb') as handle:\n",
+        "    V3_dict = pickle.load(handle)\n",
+        "\n",
+        "V4 = Counter(tetragrams(DATA_DIR+'train/in.tsv.xz', V_common_dict))\n",
+        "V4_dict = dict(V4)\n",
+        "with open('V4.pickle', 'wb') as handle:\n",
+        "    pickle.dump(V4_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+        "\n",
+        "# with open('V4.pickle', 'rb') as handle:\n",
+        "#     V4_dict = pickle.load(handle)\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "Fsvv3QJl7kWN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3c8387a4-5ebe-41ae-aafa-2bf3999f0025"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Words\n",
+            "Progress: 100.000000%\n",
+            "Tetragrams\n",
+            "Progress: 100.000000%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "outputs(DATA_DIR+'dev-0')\n",
+        "outputs(DATA_DIR+'test-A')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UK73WsKnB8ZP",
+        "outputId": "2c3d6171-bfb2-4fcd-cfc0-49cabdf9f0a9"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Creating outputs in /content/gdrive/MyDrive/data_gralinski/dev-0\n",
+            "Creating outputs in /content/gdrive/MyDrive/data_gralinski/test-A\n"
+          ]
+        }
+      ]
+    }
+  ]
+}

gonito.yaml

@@ -1,10 +0,0 @@
-description: trigram model
-tags:
-  - neural-network
-  - trigram
-params:
-  epochs: 1
-  learning-rate: 0.0001
-  vocab_size: 40000
-  embed_size: 300
-  hidden_size: 256