{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NLTK: Brown & Reuters\n", "\n", "https://www.nltk.org/book/ch02.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Contents\n", "\n", "* [Brown](#brown)\n", "* [Reuters](#reuters)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# General\n", "\n", "import json\n", "import pandas as pd\n", "import numpy as np\n", "import string\n", "import sys\n", "import tqdm\n", "\n", "from collections import (\n", " Counter,\n", " defaultdict,\n", ")\n", "\n", "# NLP\n", "\n", "import nltk\n", "import spacy\n", "\n", "from nltk.corpus import (\n", " brown,\n", " reuters,\n", " stopwords,\n", ")\n", "\n", "# Plots\n", "\n", "from matplotlib import pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package brown to /home/alekseev/nltk_data...\n", "[nltk_data] Package brown is already up-to-date!\n", "[nltk_data] Downloading package reuters to /home/alekseev/nltk_data...\n", "[nltk_data] Package reuters is already up-to-date!\n", "[nltk_data] Downloading package stopwords to\n", "[nltk_data] /home/alekseev/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nltk.download('brown')\n", "nltk.download('reuters')\n", "nltk.download('stopwords')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /data/topicnet/lib/python3.6/site-packages (2.2.5)\n", "Requirement already satisfied: spacy>=2.2.2 in /data/topicnet/lib/python3.6/site-packages (from en_core_web_sm==2.2.5) (2.2.4)\n", "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.3)\n", "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.45.0)\n", "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.6.0)\n", "Requirement already satisfied: thinc==7.4.0 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n", "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n", "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n", "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n", "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n", "Requirement already satisfied: setuptools in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (46.1.3.post20200330)\n", "Requirement already satisfied: numpy>=1.15.0 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.18.2)\n", "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n", "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.2)\n", "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /data/topicnet/lib/python3.6/site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n", "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /data/topicnet/lib/python3.6/site-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.6.0)\n", "Requirement already satisfied: idna<3,>=2.5 in /data/topicnet/lib/python3.6/site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.9)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /data/topicnet/lib/python3.6/site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.25.9)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /data/topicnet/lib/python3.6/site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n", "Requirement already satisfied: certifi>=2017.4.17 in /data/topicnet/lib/python3.6/site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.4.5.1)\n", "Requirement already satisfied: zipp>=0.5 in /data/topicnet/lib/python3.6/site-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.1.0)\n", "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", "You can now load the model via spacy.load('en_core_web_sm')\n", "\u001b[38;5;2m✔ Linking successful\u001b[0m\n", "/data/topicnet/lib/python3.6/site-packages/en_core_web_sm -->\n", "/data/topicnet/lib/python3.6/site-packages/spacy/data/en\n", "You can now load the model via spacy.load('en')\n" ] } ], "source": [ "! {sys.executable} -m spacy download en" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Initializing spacy 'en' model, keeping only tagger component needed for lemmatization\n", "\n", "SPACY_MODEL = spacy.load('en', disable=['parser', 'ner'])\n", "SPACY_PRON = '-PRON-'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'the stripe bat be hang on -PRON- foot for good'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# https://webdevblog.ru/podhody-lemmatizacii-s-primerami-v-python/\n", "\n", "\n", "test_sentence = 'The striped bats are hanging on their feet for best'\n", "\n", "# Parse the sentence using the loaded 'en' model object `nlp`\n", "doc = SPACY_MODEL(test_sentence)\n", "\n", "# Extract the lemma for each token and join\n", "' '.join([token.lemma_ for token in doc])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "STOPWORDS = set(stopwords.words('english'))\n", "PUNCTUATION = string.punctuation" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PUNCTUATION" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/topicnet/demos/Making-Decorrelation-and-Topic-Selection-Friends.ipynb\n", "\n", "\n", "COLOR = (176/255, 0, 0)\n", "FIGSIZE = (15, 10)\n", "LINEWIDTH = 5\n", "LABELPAD = 10\n", "\n", "\n", "# https://en.wikipedia.org/wiki/Zipf%27s_law\n", "\n", "def draw_zipfs_plot(\n", " texts,\n", " low_outlier_percentile_bound=1,\n", " upper_outlier_percentile_bound=99,\n", " ax=None):\n", "\n", " counter = Counter(' '.join(texts).split())\n", " \n", " if ax is None:\n", " fig, ax = plt.subplots(1, 1, figsize=FIGSIZE)\n", " else:\n", " fig = None\n", " \n", " frequencies = [freq for word, freq in counter.most_common()]\n", " low_outlier_bound = np.percentile(frequencies, low_outlier_percentile_bound)\n", " upper_outlier_bound = np.percentile(frequencies, upper_outlier_percentile_bound)\n", " frequencies = [freq for freq in frequencies if freq > low_outlier_bound and freq <= upper_outlier_bound]\n", " \n", " ranks = range(1, len(frequencies) + 1)\n", " \n", " ax.plot(ranks, frequencies, lw=LINEWIDTH, color=COLOR)\n", " \n", " ax.set_xlabel('Rank', labelpad=LABELPAD)\n", " ax.set_ylabel('Frequency', labelpad=LABELPAD)\n", " ax.set_title('Zipf\\'s law')\n", " \n", " if fig is not None:\n", " plt.show()\n", "\n", " return ax\n", "\n", "\n", "# https://en.wikipedia.org/wiki/Heaps%27_law\n", "\n", "def draw_heaps_plot(\n", " texts,\n", " in_symbols=True,\n", " ax=None):\n", "\n", " if in_symbols is True:\n", " lengths = [len(t) for t in texts]\n", " length_units = 'symbols'\n", " else:\n", " lengths = [len(t.split()) for t in texts]\n", " length_units = 'words'\n", "\n", " num_uniques = [len(set(t.split())) for t in texts]\n", " \n", " if ax is None:\n", " fig, ax = plt.subplots(1, 1, figsize=FIGSIZE)\n", " else:\n", " fig = None\n", " \n", " ax.scatter(lengths, num_uniques, s=4 * LINEWIDTH ** 2, color=COLOR)\n", " \n", " ax.set_xlabel(f'Document length ({length_units})', labelpad=LABELPAD)\n", " ax.set_ylabel('Number of unique tokens', labelpad=LABELPAD)\n", " ax.set_title('Heaps\\' law')\n", " \n", " if fig is not None:\n", " plt.show()\n", "\n", " return ax\n", "\n", "\n", "def draw_document_lengths_histogram(\n", " texts,\n", " in_symbols=False,\n", " low_outlier_percentile_bound=1,\n", " upper_outlier_percentile_bound=99,\n", " bins=200,\n", " ax=None):\n", "\n", " if in_symbols is True:\n", " lengths = [len(t) for t in texts]\n", " length_units = 'symbols'\n", " else:\n", " lengths = [len(t.split()) for t in texts]\n", " length_units = 'words'\n", "\n", " low_outlier_bound = np.percentile(lengths, low_outlier_percentile_bound)\n", " upper_outlier_bound = np.percentile(lengths, upper_outlier_percentile_bound)\n", " lengths = [l for l in lengths if l > low_outlier_bound and l <= upper_outlier_bound]\n", " \n", " if ax is None:\n", " fig, ax = plt.subplots(1, 1, figsize=FIGSIZE)\n", " else:\n", " fig = None\n", " \n", " ax.hist(lengths, bins=bins, color=COLOR)\n", " \n", " ax.set_xlabel(f'Document length ({length_units})', labelpad=LABELPAD)\n", " ax.set_ylabel('Number of documents', labelpad=LABELPAD)\n", " ax.set_title('Document lengths distribution')\n", " \n", " if fig is not None:\n", " plt.show()\n", "\n", " return ax\n", "\n", "\n", "def draw_statistics(texts, figsize=(20, 15), font_size=16):\n", " old_font_size = plt.rcParams['font.size']\n", " plt.rcParams.update({'font.size': font_size})\n", "\n", " fig, axes = plt.subplots(2, 2, figsize=figsize)\n", " \n", " draw_zipfs_plot(\n", " texts,\n", " ax=axes[0][0]\n", " )\n", " draw_document_lengths_histogram(\n", " texts,\n", " ax=axes[0][1]\n", " )\n", " draw_heaps_plot(\n", " texts,\n", " ax=axes[1][0],\n", " in_symbols=True\n", " )\n", " draw_heaps_plot(\n", " texts,\n", " ax=axes[1][1],\n", " in_symbols=False\n", " )\n", " \n", " plt.show()\n", " \n", " plt.rcParams.update({'font.size': old_font_size})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Brown\n", "\n", "
Back to Contents
" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BROWN CORPUS\n", "\n", "A Standard Corpus of Present-Day Edited American\n", "English, for use with Digital Computers.\n", "\n", "by W. N. Francis and H. Kucera (1964)\n", "Department of Linguistics, Brown University\n", "Providence, Rhode Island, USA\n", "\n", "Revised 1971, Revised and Amplified 1979\n", "\n", "http://www.hit.uib.no/icame/brown/bcm.html\n", "\n", "Distributed with the permission of the copyright holder,\n", "redistribution permitted.\n", "\n" ] } ], "source": [ "print(brown.readme())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['adventure',\n", " 'belles_lettres',\n", " 'editorial',\n", " 'fiction',\n", " 'government',\n", " 'hobbies',\n", " 'humor',\n", " 'learned',\n", " 'lore',\n", " 'mystery',\n", " 'news',\n", " 'religion',\n", " 'reviews',\n", " 'romance',\n", " 'science_fiction']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brown.categories()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "500" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# num docs\n", "\n", "len(brown.fileids())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ca01', 'ca02', 'ca03', 'ca04', 'ca05']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# doc names\n", "\n", "brown.fileids()[:5]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['cr05', 'cr06', 'cr07', 'cr08', 'cr09']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brown.fileids()[-5:]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2242" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(brown.words('ca01'))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['The',\n", " 'Fulton',\n", " 'County',\n", " 'Grand',\n", " 'Jury',\n", " 'said',\n", " 'Friday',\n", " 'an',\n", " 'investigation',\n", " 'of']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brown.words('ca01')[:10]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The/at Fulton/np-tl County/nn-tl Grand/jj-tl Ju'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# doc's raw text\n", "\n", "brown.raw('ca01')[:50].strip()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['Austin', ',', 'Texas'],\n", " ['--',\n", " 'Committee',\n", " 'approval',\n", " 'of',\n", " 'Gov.',\n", " 'Price',\n", " \"Daniel's\",\n", " '``',\n", " 'abandoned',\n", " 'property',\n", " \"''\",\n", " 'act',\n", " 'seemed',\n", " 'certain',\n", " 'Thursday',\n", " 'despite',\n", " 'the',\n", " 'adamant',\n", " 'protests',\n", " 'of',\n", " 'Texas',\n", " 'bankers',\n", " '.']]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brown.sents('ca02')[:2]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Austin/np-hl ,/,-hl Texas/np-hl \\n--/-- Committee/n'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brown.raw('ca02')[:50].strip()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Austin/np-hl ,/,-hl Texas/np-hl --/-- Committee/n'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "' '.join(brown.raw('ca02')[:50].split())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['humor', 'news']" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brown.categories(['ca01', 'cr05'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|██████████| 500/500 [01:38<00:00, 5.08it/s]\n" ] } ], "source": [ "DATASET_NAME = 'Brown'\n", "DATASET_FILE_NAME = DATASET_NAME + '.csv'\n", "\n", "DATA_HANDLER = brown\n", "FILE_NAMES = DATA_HANDLER.fileids()\n", "\n", "file2topics = defaultdict(list)\n", "topic2files = defaultdict(list)\n", "\n", "\n", "with open(DATASET_FILE_NAME, 'w') as f:\n", " f.write('id,vw_text,raw_text')\n", " f.write('\\n')\n", "\n", "\n", "for file_name in tqdm.tqdm(FILE_NAMES, total=len(FILE_NAMES), file=sys.stdout):\n", " raw_text = DATA_HANDLER.raw(file_name)\n", " raw_text = raw_text.replace('\\\"', '\\'') # for .csv safety (\"\\n\", \",\" and so on)\n", " \n", " words = DATA_HANDLER.words(file_name)\n", " \n", " ### Building VW Text ###\n", " \n", " modality2words = dict()\n", " modality2words['word'] = list()\n", " \n", " for word in words:\n", " if word in PUNCTUATION:\n", " continue\n", " if word in ['lt', 'gt']: # < >\n", " continue\n", " \n", " modality2words['word'].append(word)\n", "\n", " modality2text = dict()\n", "\n", " for modality, modality_words in modality2words.items():\n", " modality_words = [\n", " w.lower() for w in modality_words\n", " ]\n", " doc = SPACY_MODEL(' '.join(modality_words))\n", " words = [\n", " w.lemma_.lower()\n", " for w in doc\n", " if w.lemma_ != SPACY_PRON\n", " ]\n", " words = [\n", " w for w in words\n", " if w not in STOPWORDS\n", " if all([s not in w for s in PUNCTUATION])\n", " \n", " ]\n", " \n", " modality2text[modality] = ' '.join(words)\n", " \n", " vw_text = f'{file_name}'\n", " \n", " for modality, modality_text in modality2text.items():\n", " vw_text += f' |@{modality} {modality_text}'\n", " \n", " ### VW Text Done ###\n", " \n", " with open(DATASET_FILE_NAME, 'a') as f:\n", " f.write(f'\\\"{file_name}\\\",\\\"{vw_text}\\\",\\\"{raw_text}\\\"')\n", " f.write('\\n')\n", " \n", " topics = DATA_HANDLER.categories(file_name)\n", " \n", " file2topics[file_name] = list(topics)\n", " \n", " for topic in topics:\n", " topic2files[topic].append(file_name)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "with open(f'{DATASET_NAME}_topic2docs.json', 'w') as f:\n", " f.write(json.dumps(topic2files))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "with open(f'{DATASET_NAME}_doc2topics.json', 'w') as f:\n", " f.write(json.dumps(file2topics))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(topic2files)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "500" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(file2topics)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(DATASET_FILE_NAME)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'ca03 |@word several defendant summerdale police burglary trial make statement indicate guilt time arrest judge james parsons tell criminal court yesterday disclosure charles bellow chief defense counsel startle observer view prelude quarrel six attorney represent eight former policeman trial bellow make disclosure ask judge parson grant client alan clement 30 separate trial bellow make request woman jury courtroom fear prejudicial aspect statement may highly prejudicial client bellow tell court '" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['vw_text'][2][:500]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "draw_statistics(df['vw_text'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reuters\n", "\n", "
Back to Contents
" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " The Reuters-21578 benchmark corpus, ApteMod version\n", "\n", "This is a publically available version of the well-known Reuters-21578\n", "\"ApteMod\" corpus for text categorization. It has been used in\n", "publications like these:\n", "\n", " * Yiming Yang and X. Liu. \"A re-examination of text categorization\n", " methods\". 1999. Proceedings of 22nd Annual International SIGIR.\n", " http://citeseer.nj.nec.com/yang99reexamination.html\n", "\n", " * Thorsten Joachims. \"Text categorization with support vector\n", " machines: learning with many relevant features\". 1998. Proceedings\n", " of ECML-98, 10th European Conference on Machine Learning.\n", " http://citeseer.nj.nec.com/joachims98text.html\n", "\n", "ApteMod is a collection of 10,788 documents from the Reuters financial\n", "newswire service, partitioned into a training set with 7769 documents\n", "and a test set with 3019 documents. The total size of the corpus is\n", "about 43 MB. It is also available for download from\n", "http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html ,\n", "which includes a more extensive history of the data revisions.\n", "\n", "The distribution of categories in the ApteMod corpus is highly skewed,\n", "with 36.7% of the documents in the most common category, and only\n", "0.0185% (2 documents) in each of the five least common categories.\n", "In fact, the original data source is even more skewed---in creating\n", "the corpus, any categories that did not contain at least one document\n", "in the training set and one document in the test set were removed from\n", "the corpus by its original creator.\n", "\n", "In the ApteMod corpus, each document belongs to one or more\n", "categories. There are 90 categories in the corpus. The average\n", "number of categories per document is 1.235, and the average number of\n", "documents per category is about 148, or 1.37% of the corpus.\n", "\n", " -Ken Williams\n", " ken@mathforum.org\n", "\n", " Copyright & Notification \n", "\n", "(extracted from the README at the UCI address above)\n", "\n", "The copyright for the text of newswire articles and Reuters\n", "annotations in the Reuters-21578 collection resides with Reuters Ltd.\n", "Reuters Ltd. and Carnegie Group, Inc. have agreed to allow the free\n", "distribution of this data *for research purposes only*. \n", "\n", "If you publish results based on this data set, please acknowledge\n", "its use, refer to the data set by the name \"Reuters-21578,\n", "Distribution 1.0\", and inform your readers of the current location of\n", "the data set (see \"Availability & Questions\").\n", "\n", "\n" ] } ], "source": [ "print(reuters.readme())" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['acq',\n", " 'alum',\n", " 'barley',\n", " 'bop',\n", " 'carcass',\n", " 'castor-oil',\n", " 'cocoa',\n", " 'coconut',\n", " 'coconut-oil',\n", " 'coffee']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuters.categories()[:10]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10788" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# num docs\n", "\n", "len(reuters.fileids())" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['test/14826',\n", " 'test/14828',\n", " 'test/14829',\n", " 'test/14832',\n", " 'test/14833',\n", " 'test/14839',\n", " 'test/14840',\n", " 'test/14841',\n", " 'test/14842',\n", " 'test/14843']" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuters.fileids()[:10]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['training/9982',\n", " 'training/9984',\n", " 'training/9985',\n", " 'training/9988',\n", " 'training/9989',\n", " 'training/999',\n", " 'training/9992',\n", " 'training/9993',\n", " 'training/9994',\n", " 'training/9995']" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuters.fileids()[-10:]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "899" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(reuters.words('test/14826'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> The first handful of words in each of the texts are the **titles, which by convention are stored as upper case**." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ASIAN',\n", " 'EXPORTERS',\n", " 'FEAR',\n", " 'DAMAGE',\n", " 'FROM',\n", " 'U',\n", " '.',\n", " 'S',\n", " '.-',\n", " 'JAPAN',\n", " 'RIFT',\n", " 'Mounting',\n", " 'trade',\n", " 'friction',\n", " 'between',\n", " 'the',\n", " 'U',\n", " '.',\n", " 'S',\n", " '.',\n", " 'And',\n", " 'Japan',\n", " 'has',\n", " 'raised',\n", " 'fears',\n", " 'among',\n", " 'many',\n", " 'of',\n", " 'Asia',\n", " \"'\"]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuters.words('test/14826')[:30]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['U',\n", " '.',\n", " 'K',\n", " '.',\n", " 'MONEY',\n", " 'MARKET',\n", " 'SHORTAGE',\n", " 'FORECAST',\n", " 'REVISED',\n", " 'DOWN',\n", " 'The',\n", " 'Bank',\n", " 'of',\n", " 'England',\n", " 'said',\n", " 'it',\n", " 'had',\n", " 'revised',\n", " 'its',\n", " 'forecast']" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuters.words('training/999')[:20]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'U.K. MONEY MARKET SHORTAGE FORECAST REVISED DOWN\\n The Bank '" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuters.raw('training/999')[:60]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> !!! Unlike the Brown Corpus, **categories in the Reuters corpus overlap with each other**, simply because a news story often covers multiple topics. We can ask for the topics covered by one or more documents, or for the documents included in one or more categories. For convenience, the corpus methods accept a single fileid or a list of fileids." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['barley', 'corn', 'grain', 'wheat']" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuters.categories('training/9865')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|██████████| 10788/10788 [03:18<00:00, 54.37it/s]\n" ] } ], "source": [ "DATASET_NAME = 'Reuters'\n", "DATASET_FILE_NAME = DATASET_NAME + '.csv'\n", "\n", "DATA_HANDLER = reuters\n", "FILE_NAMES = DATA_HANDLER.fileids()\n", "\n", "file2topics = defaultdict(list)\n", "topic2files = defaultdict(list)\n", "\n", "\n", "with open(DATASET_FILE_NAME, 'w') as f:\n", " f.write('id,vw_text,raw_text')\n", " f.write('\\n')\n", "\n", " \n", "for file_name in tqdm.tqdm(FILE_NAMES, total=len(FILE_NAMES), file=sys.stdout):\n", " raw_text = DATA_HANDLER.raw(file_name)\n", " raw_text = raw_text.replace('\\\"', '\\'') # making VW with care\n", " \n", " words = DATA_HANDLER.words(file_name)\n", "\n", " ### VW Text ##\n", " \n", " modality2words = dict()\n", " modality2words['title'] = list()\n", " modality2words['word'] = list()\n", " is_title = True\n", " \n", " for word in words:\n", " if word in PUNCTUATION:\n", " continue\n", " if word in ['lt', 'gt']:\n", " continue\n", "\n", " if not is_title:\n", " modality2words['word'].append(word)\n", " elif word.upper() != word:\n", " modality2words['word'].append(word)\n", " is_title = False\n", " else:\n", " modality2words['title'].append(word) \n", "\n", " modality2text = dict()\n", "\n", " for modality, modality_words in modality2words.items():\n", " modality_words = [\n", " w.lower() for w in modality_words\n", " ]\n", " doc = SPACY_MODEL(' '.join(modality_words))\n", " words = [\n", " w.lemma_.lower()\n", " for w in doc\n", " if w.lemma_ != SPACY_PRON\n", " ]\n", " words = [\n", " w for w in words\n", " if w not in STOPWORDS\n", " if all([s not in w for s in PUNCTUATION])\n", " \n", " ]\n", " \n", " modality2text[modality] = ' '.join(words)\n", " \n", " vw_text = f'{file_name}'\n", " \n", " for modality, modality_text in modality2text.items():\n", " vw_text += f' |@{modality} {modality_text}'\n", " \n", " ### VW Text Done ###\n", " \n", " with open(DATASET_FILE_NAME, 'a') as f:\n", " f.write(f'\\\"{file_name}\\\",\\\"{vw_text}\\\",\\\"{raw_text}\\\"')\n", " f.write('\\n')\n", " \n", " topics = DATA_HANDLER.categories(file_name)\n", " \n", " file2topics[file_name] = list(topics)\n", " \n", " for topic in topics:\n", " topic2files[topic].append(file_name)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "with open(f'{DATASET_NAME}_topic2docs.json', 'w') as f:\n", " f.write(json.dumps(topic2files))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "with open(f'{DATASET_NAME}_doc2topics.json', 'w') as f:\n", " f.write(json.dumps(file2topics))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "90" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(topic2files)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10788" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(file2topics)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(DATASET_FILE_NAME)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'test/14829 |@title japan revise long term energy demand downwards |@word ministry international trade industry miti revise long term energy supply demand outlook august meet forecast downtrend japanese energy demand ministry official say miti expect lower projection primary energy supply year 2000 550 mln kilolitre kl 600 mln say decision follow emergence structural change japanese industry follow rise value yen decline domestic electric power demand miti plan work revise energy supply demand ou'" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['vw_text'][2][:500]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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