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torchtext_pentreebank_gt.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "torchtext_pentreebank_gt.ipynb", | |
| "provenance": [], | |
| "toc_visible": true, | |
| "authorship_tag": "ABX9TyMlVa5PZsSW2UIquH1Ob9xu", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/shihono/646d11d176fef923a806e893706636af/torchtext_pentreebank_gt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import math\n", | |
| "from collections import defaultdict\n", | |
| "from itertools import islice\n", | |
| "from pprint import pprint \n", | |
| "\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "# pytorch 関連\n", | |
| "import torch\n", | |
| "import torch.nn as nn\n", | |
| "import torch.nn.functional as F\n", | |
| "\n", | |
| "import torchtext\n", | |
| "from torchtext.data.utils import get_tokenizer\n", | |
| "from torchtext.vocab import build_vocab_from_iterator" | |
| ], | |
| "metadata": { | |
| "id": "0gK3HKyKDGCZ" | |
| }, | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 前処理\n", | |
| "\n", | |
| "### Tokenizer, Vocab\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "RoatFah_-ksB" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tokenizer = get_tokenizer(None)\n", | |
| "\n", | |
| "def get_vocab(data_iter):\n", | |
| " vocab = build_vocab_from_iterator(map(tokenizer, data_iter), specials=['<unk>'])\n", | |
| " # 未知語は全部 `<unk>` 扱いにする\n", | |
| " vocab.set_default_index(vocab['<unk>'])\n", | |
| " return vocab" | |
| ], | |
| "metadata": { | |
| "id": "3lM26l2FDSRu" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### text2index" | |
| ], | |
| "metadata": { | |
| "id": "RnK87wqpFYve" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def text2index(text, vocab):\n", | |
| " return vocab(tokenizer(text))" | |
| ], | |
| "metadata": { | |
| "id": "Ca0hdopXFU0K" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ヘルドアウト推定\n", | |
| "\n", | |
| "bigramで考える\n", | |
| "\n", | |
| "### 準備\n", | |
| "\n", | |
| "- 訓練データにおける頻度 C1(w1, ..., wn)\n", | |
| "- ヘルドアウトデータにおける頻度 C2(w1, ..., wn)\n", | |
| "- 頻度 r の n-gramのタイプ数 Nr\n", | |
| "- 訓練テキスト中のn-gramの頻度がr回のn-gramがヘルドアウトデータで出現した数 Tr\n", | |
| "\n", | |
| "\n", | |
| "train を半分に分割して 訓練データとヘルドアウトデータとする" | |
| ], | |
| "metadata": { | |
| "id": "QJ02t92KE5e3" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def iter_ngram(items, n=2):\n", | |
| " for idx in range(len(items)-n+1):\n", | |
| " yield items[idx:idx+n]\n", | |
| "\n", | |
| "def get_ngram_count(data_iter, vocab, n=2):\n", | |
| " ngram_count = defaultdict(int)\n", | |
| " for data in data_iter:\n", | |
| " indexes = text2index(data, vocab)\n", | |
| " for ngram in iter_ngram(indexes, n=n):\n", | |
| " ngram_count[tuple(ngram)] +=1\n", | |
| " return dict(ngram_count)" | |
| ], | |
| "metadata": { | |
| "id": "5VusiyXrF6WW" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tmp = [data.strip() for data in torchtext.datasets.PennTreebank(split='train')]\n", | |
| "\n", | |
| "train_data = tmp[:len(tmp)//2]\n", | |
| "heldout_data = tmp[len(tmp)//2:]\n", | |
| "len(train_data), len(heldout_data)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "dkRypAuOQEbq", | |
| "outputId": "f8027396-aec9-403b-a87e-ad1253715165" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "5.10MB [00:00, 19.7MB/s]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(21034, 21034)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 訓練データにおける頻度 c1\n", | |
| "c1_vocab = get_vocab(train_data)\n", | |
| "print(len(c1_vocab))\n", | |
| "c1_bigram = get_ngram_count(train_data, c1_vocab, 2)\n", | |
| "print(len(c1_bigram))\n", | |
| "\n", | |
| "# ヘルドアウトデータにおける頻度 c2\n", | |
| "c2_bigram = get_ngram_count(heldout_data, c1_vocab, 2)\n", | |
| "print(len(c2_bigram))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "1JY6Les5EwdW", | |
| "outputId": "6ee4a258-2c51-4e21-86de-4cefcc66d460" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "9661\n", | |
| "157629\n", | |
| "150070\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 頻度 r の n-gramのタイプ数 Nr\n", | |
| "nr_types = defaultdict(int)\n", | |
| "for freq in c1_bigram.values():\n", | |
| " nr_types[freq] += 1\n", | |
| "\n", | |
| "# r=0 は存在しないので vocab_sizeを利用\n", | |
| "nr_types[0] = len(c1_vocab) * len(c1_vocab)" | |
| ], | |
| "metadata": { | |
| "id": "V4_woCaDHErZ" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for i in range(10):\n", | |
| " print(i, nr_types[i])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "GYgHhpAxIB2H", | |
| "outputId": "edfdbc75-5727-4779-c0a0-a9ace7f6742f" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0 93334921\n", | |
| "1 111266\n", | |
| "2 20831\n", | |
| "3 8446\n", | |
| "4 4404\n", | |
| "5 2769\n", | |
| "6 2010\n", | |
| "7 1304\n", | |
| "8 911\n", | |
| "9 710\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Nはデータのサンプル数\n", | |
| "n_sum = 0\n", | |
| "for r, nr in nr_types.items():\n", | |
| " n_sum += r*nr\n", | |
| "\n", | |
| "print(n_sum)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_swx-wQlHl3S", | |
| "outputId": "09c9cd00-9bd2-4909-997a-9aab5bedacfd" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "426278\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 訓練テキスト中のn-gramの頻度がr回のn-gramがヘルドアウトデータで出現した数 Tr\n", | |
| "\n", | |
| "tr_num = defaultdict(int)\n", | |
| "t_sum = 0\n", | |
| "\n", | |
| "for ngram, r in c2_bigram.items():\n", | |
| " c1_r = c1_bigram.get(ngram, 0)\n", | |
| " tr_num[c1_r] += r\n", | |
| " t_sum += r\n", | |
| "\n", | |
| "print(t_sum)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "HnsJFaVuIJSD", | |
| "outputId": "0bb438cb-a2bf-4769-deb3-e2debc14547c" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "419175\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for i in range(10):\n", | |
| " print(i, tr_num[i])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "5C2K2DZ0JLyL", | |
| "outputId": "7a786e19-1a84-41f2-9d63-817f34ed01dd" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0 114926\n", | |
| "1 38323\n", | |
| "2 23052\n", | |
| "3 16426\n", | |
| "4 12254\n", | |
| "5 10315\n", | |
| "6 9390\n", | |
| "7 7274\n", | |
| "8 5967\n", | |
| "9 5315\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### 確率推定値\n", | |
| "\n", | |
| "訓練データで r 回出現するngramについて、ヘルドアウトデータで合計 Tr 回出現する。\n", | |
| "よって1つのngramは、r 回出現するngramのタイプ数で割った Tr/Nr 回出現することになる。\n", | |
| "\n", | |
| "よって確率推定値はヘルドアウトの合計 T で割った以下\n", | |
| "\n", | |
| "$ P_{ho}(w_1,...,w_n) = \\frac{T_r}{N_r T} $\n", | |
| "\n", | |
| "- `w_1,...,w_n` の学習データでの頻度が r \n", | |
| "- T は T_r の総和" | |
| ], | |
| "metadata": { | |
| "id": "3z5etTO7Jd50" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for i in range(10):\n", | |
| " print(f\"{i}\\t{nr_types[i]}\\t{tr_num[i]}\\t{tr_num[i]/nr_types[i]:3f}\\t{-math.log2(tr_num[i]/(nr_types[i]*t_sum)):.3f}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Ei0BrJQBVCiB", | |
| "outputId": "014df6cd-817c-465a-812d-bf6e243f876c" | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0\t93334921\t114926\t0.001231\t28.343\n", | |
| "1\t111266\t38323\t0.344427\t20.215\n", | |
| "2\t20831\t23052\t1.106620\t18.531\n", | |
| "3\t8446\t16426\t1.944826\t17.718\n", | |
| "4\t4404\t12254\t2.782470\t17.201\n", | |
| "5\t2769\t10315\t3.725172\t16.780\n", | |
| "6\t2010\t9390\t4.671642\t16.453\n", | |
| "7\t1304\t7274\t5.578221\t16.197\n", | |
| "8\t911\t5967\t6.549945\t15.966\n", | |
| "9\t710\t5315\t7.485915\t15.773\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "vocab_itos = c1_vocab.get_itos()\n", | |
| "\n", | |
| "for data in islice(torchtext.datasets.PennTreebank(split='valid'), 1):\n", | |
| " print(data)\n", | |
| " indexes = text2index(data, c1_vocab)\n", | |
| " for ngram in iter_ngram(indexes):\n", | |
| " r = c1_bigram.get(tuple(ngram), 0)\n", | |
| " nr = nr_types[r]\n", | |
| " tr = tr_num.get(r, 0)\n", | |
| " print(ngram, [vocab_itos[i] for i in ngram], math.log2(tr/(nr*t_sum)))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "x9vUHKvVJZsb", | |
| "outputId": "ca66eeb7-5656-487b-e30c-85541c658eab" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "400kB [00:00, 17.1MB/s] " | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " consumers may want to move their telephones a little closer to the tv set \n", | |
| "\n", | |
| "[1104, 89] ['consumers', 'may'] -28.342761082872915\n", | |
| "[89, 372] ['may', 'want'] -18.53103332348117\n", | |
| "[372, 4] ['want', 'to'] -12.312620717862819\n", | |
| "[4, 274] ['to', 'move'] -14.307959340492955\n", | |
| "[274, 51] ['move', 'their'] -28.342761082872915\n", | |
| "[51, 8572] ['their', 'telephones'] -28.342761082872915\n", | |
| "[8572, 5] ['telephones', 'a'] -28.342761082872915\n", | |
| "[5, 308] ['a', 'little'] -13.547910133213708\n", | |
| "[308, 2308] ['little', 'closer'] -28.342761082872915\n", | |
| "[2308, 4] ['closer', 'to'] -15.022041334722774\n", | |
| "[4, 1] ['to', 'the'] -8.97848848338833\n", | |
| "[1, 736] ['the', 'tv'] -15.633596761006274\n", | |
| "[736, 396] ['tv', 'set'] -28.342761082872915\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "sample_text = \"this wikipedia is written in english\"\n", | |
| "tokens = text2index(sample_text, c1_vocab)\n", | |
| "for t in iter_ngram(tokens, 2):\n", | |
| " r = c1_bigram.get(tuple(t),0)\n", | |
| " nr = nr_types[r]\n", | |
| " tr = tr_num.get(r, 0)\n", | |
| " print(t, [vocab_itos[i] for i in t], r, -math.log2(tr/(nr*t_sum)))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_qKx8k3Dk47C", | |
| "outputId": "0a1f71c1-66d0-4afb-c92c-bc84fdf7cf0d" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[36, 0] ['this', '<unk>'] 51 13.057139984969156\n", | |
| "[0, 11] ['<unk>', 'is'] 212 10.867692956269513\n", | |
| "[11, 1932] ['is', 'written'] 0 28.342761082872915\n", | |
| "[1932, 6] ['written', 'in'] 4 17.200826767745713\n", | |
| "[6, 2404] ['in', 'english'] 5 16.779886287751758\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### エントロピー" | |
| ], | |
| "metadata": { | |
| "id": "RjRfnvezN7rY" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# エントロピーを求める\n", | |
| "word_sum = 0\n", | |
| "h = 0\n", | |
| "\n", | |
| "test_iter = torchtext.datasets.WikiText2(split='test')\n", | |
| "for t in test_iter:\n", | |
| " tokens = text2index(t, c1_vocab)\n", | |
| " word_sum += len(tokens)\n", | |
| " for ngram in iter_ngram(indexes):\n", | |
| " r = c1_bigram.get(tuple(ngram), 0)\n", | |
| " nr = nr_types[r]\n", | |
| " tr = tr_num.get(r, 0)\n", | |
| " p = tr/(nr * t_sum)\n", | |
| " h += -math.log2(p)\n", | |
| "\n", | |
| "print(f\"word_sum: {word_sum}\\nentropy: {h/word_sum}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ovPwK7QyN91D", | |
| "outputId": "7576e51c-51cc-4cc3-816f-c5f74d86d7f1" | |
| }, | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "100%|██████████| 4.48M/4.48M [00:01<00:00, 2.72MB/s]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "word_sum: 241211\n", | |
| "entropy: 4.849051510527865\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Good-Turing estimator\n", | |
| "\n", | |
| "確率推定値は\n", | |
| "\n", | |
| "$ P_{GT} = \\frac{r^{*}}{N} $\n", | |
| "\n", | |
| "r* は推定頻度\n", | |
| "\n", | |
| "$ r^* = (r+1) \\frac{N_{r+1}}{N_r} $ \n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "9hgzY5eIPGEy" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_iter = torchtext.datasets.PennTreebank(split='train')\n", | |
| "train_vocab = get_vocab(train_iter)\n", | |
| "print(len(train_vocab))\n", | |
| "\n", | |
| "train_iter = torchtext.datasets.PennTreebank(split='train')\n", | |
| "gt_unigram = get_ngram_count(train_iter, train_vocab)\n", | |
| "\n", | |
| "gt_nr = defaultdict(int)\n", | |
| "for r in gt_unigram.values():\n", | |
| " gt_nr[r] += 1\n", | |
| "\n", | |
| "gt_n_sum = sum([r*nr for r, nr in gt_nr.items()])\n", | |
| "print(gt_n_sum)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "zd-ffsSZbE7s", | |
| "outputId": "fb89d39b-0fcc-4a9f-cd36-53fa0626438e" | |
| }, | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "9999\n", | |
| "845453\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "max(list(gt_nr.keys()))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "18JryZY_c0lT", | |
| "outputId": "d1f89634-25bb-4e93-f263-fd985e9af1ed" | |
| }, | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "7475" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(f\"{0}\\t{1*gt_nr[1]/ gt_n_sum:.4f}\")\n", | |
| "for i in range(1, 10):\n", | |
| " print(f\"{i}\\t{(i+1)*gt_nr[i+1]/gt_nr[i]:.4f}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "iGM5CGNYcR6_", | |
| "outputId": "1495560f-f4a2-4eb1-ad4d-bff2493b8a43" | |
| }, | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0\t0.2050\n", | |
| "1\t0.4020\n", | |
| "2\t1.2747\n", | |
| "3\t2.1785\n", | |
| "4\t3.1855\n", | |
| "5\t4.0148\n", | |
| "6\t5.3132\n", | |
| "7\t5.6534\n", | |
| "8\t7.1004\n", | |
| "9\t8.5488\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# N_r の r が大きくなると、 N_r もしくは N_r+1 がなくなる\n", | |
| "\n", | |
| "print(\"r, N_r, N_r+1\")\n", | |
| "for r in range(1, 300):\n", | |
| " if gt_nr.get(r) is not None:\n", | |
| " continue\n", | |
| " else:\n", | |
| " print(f\"{r}, {gt_nr.get(r)}, {gt_nr.get(r+1)}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "u1-uX8GOgBPK", | |
| "outputId": "27374e9e-6ad7-4062-8ea0-e5786005e2ea" | |
| }, | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "r, N_r, N_r+1\n", | |
| "125, None, 4\n", | |
| "136, None, 2\n", | |
| "163, None, 5\n", | |
| "171, None, 3\n", | |
| "185, None, 2\n", | |
| "189, None, 2\n", | |
| "200, None, 1\n", | |
| "208, None, None\n", | |
| "209, None, 4\n", | |
| "212, None, 1\n", | |
| "215, None, 3\n", | |
| "217, None, 1\n", | |
| "220, None, 2\n", | |
| "227, None, 1\n", | |
| "231, None, 1\n", | |
| "236, None, 1\n", | |
| "238, None, 1\n", | |
| "240, None, None\n", | |
| "241, None, None\n", | |
| "242, None, 1\n", | |
| "244, None, 1\n", | |
| "248, None, 1\n", | |
| "257, None, None\n", | |
| "258, None, 2\n", | |
| "260, None, None\n", | |
| "261, None, 1\n", | |
| "265, None, 2\n", | |
| "270, None, None\n", | |
| "271, None, None\n", | |
| "272, None, 1\n", | |
| "274, None, 2\n", | |
| "280, None, None\n", | |
| "281, None, None\n", | |
| "282, None, 1\n", | |
| "286, None, 1\n", | |
| "288, None, None\n", | |
| "289, None, None\n", | |
| "290, None, None\n", | |
| "291, None, 1\n", | |
| "294, None, 2\n", | |
| "296, None, None\n", | |
| "297, None, 1\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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