From e74b5055eef71c4f63ed37942ccd59da7b67e52b Mon Sep 17 00:00:00 2001 From: Austen Adler Date: Thu, 9 Feb 2023 00:08:47 -0500 Subject: [PATCH] Add initial wordlist and code --- .gitignore | 3 + design-workflow/DESIGN_WORKFLOW.adoc | 3 +- design-workflow/annotated_words.ods | Bin 0 -> 568922 bytes design-workflow/wordlist.ipynb | 385 +++++++++++++++++++++++++++ 4 files changed, 390 insertions(+), 1 deletion(-) create mode 100644 design-workflow/annotated_words.ods create mode 100644 design-workflow/wordlist.ipynb diff --git a/.gitignore b/.gitignore index ae7c358..8c71fdf 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,5 @@ /target +/design-workflow/*.html /design-workflow/*.svg +/design-workflow/frequency-all.txt.gz +**/.ipynb_checkpoints diff --git a/design-workflow/DESIGN_WORKFLOW.adoc b/design-workflow/DESIGN_WORKFLOW.adoc index 308c850..f22aa00 100644 --- a/design-workflow/DESIGN_WORKFLOW.adoc +++ b/design-workflow/DESIGN_WORKFLOW.adoc @@ -1,4 +1,5 @@ -// echo DESIGN.adoc | entr sh -c "asciidoctor DESIGN.adoc; printf 'Done\n'" +// echo DESIGN_WORKFLOW.adoc | entr sh -c "podman run --rm -it --network none -v "${PWD}:/documents/" asciidoctor/docker-asciidoctor asciidoctor -r asciidoctor-mathematical -a mathematical-format=svg DESIGN_WORKFLOW.adoc; printf 'Done ($(date -Isecond))\n'" + :toc: :nofooter: :!webfonts: diff --git a/design-workflow/annotated_words.ods b/design-workflow/annotated_words.ods new file mode 100644 index 0000000000000000000000000000000000000000..264dbb086601c5544f32c32866de765a4e8aaf27 GIT binary patch literal 568922 zcmb5V2T)Vr);1oR6hV3uk***e6jVy2SE(ABNDTr40qIhLNbgmubd+l7NDWE{MJzNa zL3-~HNc|u0@4oLh-^`u+pD#1aKiKncP+kVvUsl^MU?1tE>MZ zmM6^9*Yj_{{}S!mf3kZ4d-ekA?fu{L{7(_Ree8UE|9|ZM)2R5sU~d2K(f(R_+Ic|T z{>$_6{D?=k>$EF`hQc%1Q9%*we?70(VP9b@e%e2s)lnya2 ztJ*63wfsR-7M0YLw`+H8D88ZM)1%uCB0?kD&dr;m_d6uo%4hHYo^BE4$>lzDeO+F7 ztKHw@);*gyk>c{SZr>V9gadMq6%9N2KTX{A(xx3~N}{5^o~Y__x)OZ#-q;eglbNy4 zc2P*wnd zMcV_>q;Jy;F|_YipDuRQ?LB#o3eI$Xx##fG_hxK5DxM&w*V 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--git a/design-workflow/wordlist.ipynb b/design-workflow/wordlist.ipynb new file mode 100644 index 0000000..1f1a65b --- /dev/null +++ b/design-workflow/wordlist.ipynb @@ -0,0 +1,385 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0b00342f-7b19-49cc-bc6c-21019f8cc7dc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: nltk in /opt/conda/lib/python3.10/site-packages (3.8.1)\n", + "Requirement already satisfied: odfpy in /opt/conda/lib/python3.10/site-packages (1.4.1)\n", + "Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk) (8.1.3)\n", + "Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk) (2022.10.31)\n", + "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk) (4.64.1)\n", + "Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk) (1.2.0)\n", + "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.10/site-packages (from odfpy) (0.7.1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package wordnet to /home/jovyan/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n" + ] + } + ], + "source": [ + "try:\n", + " _initialized\n", + "except:\n", + " !pip install nltk odfpy\n", + " import nltk\n", + " \n", + " nltk.download(\"wordnet\")\n", + " _initialized=True\n", + " \n", + "from nltk.stem.wordnet import WordNetLemmatizer\n", + "import pandas as pd\n", + "import gzip\n", + "import re\n", + "\n", + "WORDLIST_SIZE=8192 +3" + ] + }, + { + "cell_type": "markdown", + "id": "d5bf26fa-0aab-403a-9a6f-b2a37dc4892e", + "metadata": {}, + "source": [ + "## First, get the list of excluded words" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "926d0d84-0d7e-4939-b87f-1a170f870a8f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "annotated_words=pd.read_excel(\"annotated_words.ods\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8b0d26e4-051c-4669-b566-bbd5ddbbe02b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'i', 's', 'p', 'c', 'b', 'american', 'york', 'd', 'john']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "excluded_words = list(annotated_words[annotated_words[\"keep\"] != \"Yes\"][\"word\"].str.lower())\n", + "excluded_words[0:10]" + ] + }, + { + "cell_type": "markdown", + "id": "5f855ec9-eea5-4e15-bc07-96cdd414f36a", + "metadata": { + "tags": [] + }, + "source": [ + "## Next, get the list of custom mappings" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2eea14b2-82bf-4353-8982-76a6c7f46d22", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('be', 'bee'),\n", + " ('by', 'bye'),\n", + " ('corps', 'core'),\n", + " ('ore', 'oar'),\n", + " ('ore', ' or'),\n", + " ('vary', 'very'),\n", + " ('com', 'calm'),\n", + " ('filing', 'filling'),\n", + " ('fax', 'facts'),\n", + " ('theatre', 'theater'),\n", + " ('par', 'parse'),\n", + " ('honour', 'honor'),\n", + " ('harry', 'hairy'),\n", + " ('brings', 'bring'),\n", + " ('organisation', 'organization'),\n", + " ('simultaneously', 'simultaneous'),\n", + " ('aluminum', 'aluminium'),\n", + " ('knight', 'night'),\n", + " ('electronics', 'electronic'),\n", + " ('organisations', 'organizations'),\n", + " ('fortunately', 'fortunate'),\n", + " ('corp', 'core'),\n", + " ('chile', 'chilly'),\n", + " ('chile', ' chili'),\n", + " ('owe', 'oh'),\n", + " ('capitol', 'capital'),\n", + " ('weary', 'wary'),\n", + " ('berry', 'barry'),\n", + " ('lecturer', 'lecture'),\n", + " ('aluminium', 'aluminum'),\n", + " ('isle', 'aisle'),\n", + " ('boulder', 'bolder'),\n", + " ('ads', 'adds'),\n", + " ('honours', 'honors'),\n", + " ('bot', 'bought'),\n", + " ('dew', 'do'),\n", + " ('dew', ' due'),\n", + " ('theatres', 'theaters'),\n", + " ('thru', 'through'),\n", + " ('monies', 'moneys'),\n", + " ('cue', 'queue'),\n", + " ('hairy', 'harry'),\n", + " ('hem', 'him'),\n", + " ('nun', 'none'),\n", + " ('organisational', 'organizational'),\n", + " ('aux', 'ox'),\n", + " ('rap', 'wrap'),\n", + " ('filings', 'filling'),\n", + " ('sew', 'so'),\n", + " ('pars', 'parse'),\n", + " ('fillings', 'filling')]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "custom_maps = annotated_words[annotated_words[\"maps_to\"].notna()][[\"word\",\"maps_to\"]].assign(maps_to=lambda x: x[\"maps_to\"].map(lambda y: y.split(\",\")))\n", + "\n", + "custom_maps = [\n", + " (m[1][\"word\"].lower(), mapping.lower())\n", + " for m in custom_maps.iterrows()\n", + " for mapping in m[1][\"maps_to\"]\n", + "]\n", + "custom_maps" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dc52697b-2a30-4e6c-ab74-b77edce3607c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# all_words: 21323\n", + "sample: ['the', 'of', 'and', 'to', 'in', 'is', 'that', 'for', 'as', 'it']\n", + "\n", + "# lemmatize_mappings: 21374\n", + "sample: [('the', 'the'), ('of', 'of'), ('and', 'and'), ('to', 'to'), ('in', 'in'), ('is', 'is'), ('that', 'that'), ('for', 'for'), ('as', 'a'), ('it', 'it')]\n", + "\n", + "# distinct_words: 17585\n", + "sample:\n" + ] + }, + { + "data": { + "text/plain": [ + "['the', 'of', 'and', 'to', 'in', 'is', 'that', 'for', 'as', 'it']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_lines(filename):\n", + " with gzip.open(filename, 'r') as f:\n", + " ret = []\n", + " for l in f:\n", + " if len(ret) > 30_000:\n", + " return ret\n", + " ret.append(str(l).lower())\n", + " return ret\n", + " \n", + "lemmatizer = WordNetLemmatizer()\n", + "word_re = re.compile(r\"^[A-Za-z]+$\")\n", + "\n", + "# Start parsing the wordlist\n", + "all_words = get_lines(\"frequency-all.txt.gz\")\n", + "\n", + "# Delete header line\n", + "all_words = all_words[1:]\n", + "\n", + "# Get only the word (fixed width)\n", + "all_words = [w[13:36].strip() for w in all_words]\n", + "\n", + "# Remove special characters\n", + "all_words = [w for w in all_words if word_re.search(w)]\n", + "\n", + "# Remove all removed words\n", + "all_words = [w for w in all_words if w not in excluded_words]\n", + "\n", + "# Lemmatize all words (plural -> singular)\n", + "lemmatize_mappings = [\n", + " (w, lemmatizer.lemmatize(w)) \n", + " for w in all_words\n", + " # if w != lemmatizer.lemmatize(w)\n", + "]\n", + "\n", + "# Remove all words that lemmatize to another word\n", + "#all_words = [w for w in all_words if w not in ]\n", + "\n", + "# Add custom lemmatizations\n", + "for l in custom_maps:\n", + " if l in lemmatize_mappings:\n", + " print(f\"Warning: {l} is already lemmatized\")\n", + " else:\n", + " lemmatize_mappings.append(l)\n", + "\n", + "distinct_words_lemmatized = set()\n", + "distinct_words = []\n", + "for w in lemmatize_mappings:\n", + " if w[1] not in distinct_words_lemmatized:\n", + " distinct_words_lemmatized.add(w[1])\n", + " distinct_words.append(w[0])\n", + "del distinct_words_lemmatized\n", + "\n", + "# Generate a wordlist of word[0] being the word, and w[1] being what that word maps to, or None if it is a distinct word\n", + "#wordlist = [(w[0], None if w[0] == w[1] else w[1]) if w[0] == w[1] else w for w in wl]\n", + "\n", + "# Get a list of words that map to other words\n", + "# A word was lemmatized if wordnet mapped it to another word (not None) that was different\n", + "#only_lemmatized_words = [w for w in wordlist if w[1] is not None and w[0] != w[1]]\n", + "\n", + "# Get a list of distinct lemmatized words\n", + "#distinct_lemmatized_words = [w[1] for w in wordlist if w[1] is not None]\n", + "#distinct_lemmatized_words = [w for w in pd.unique(distinct_lemmatized_words)]\n", + "\n", + "print(f\"# all_words: {len(all_words)}\")\n", + "print(f\"sample: {all_words[0:10]}\")\n", + "print()\n", + "print(f\"# lemmatize_mappings: {len(lemmatize_mappings)}\")\n", + "print(f\"sample: {lemmatize_mappings[0:10]}\")\n", + "print()\n", + "print(f\"# distinct_words: {len(distinct_words)}\")\n", + "print(f\"sample:\")\n", + "distinct_words[0:10]\n" + ] + }, + { + "cell_type": "markdown", + "id": "6acea424-d538-4981-a4b9-0d9224f8efb3", + "metadata": {}, + "source": [ + "## Generate the final wordlist" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "52d0573e-aefd-4c4e-b682-47d1ff8c676b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final wordlist size: 11212\n" + ] + } + ], + "source": [ + "# The final wordlist map. Maps a word to its numeric value\n", + "# Starting at 1\n", + "final_wordlist = {\n", + " w: idx + 1\n", + " for idx, w in enumerate(distinct_words[0:WORDLIST_SIZE])\n", + "}\n", + "\n", + "reverse_lemmatize_idx = {\n", + " lemmatizer.lemmatize(w): w\n", + " for w in final_wordlist.keys()\n", + "}\n", + "\n", + "# Add the lemmatized numbers\n", + "for w, lem_w in lemmatize_mappings:\n", + " if lem_w not in reverse_lemmatize_idx:\n", + " # This word is not in the reverse list\n", + " # This happens when the index of the lemmatized word we're working with is too large\n", + " continue\n", + " \n", + " final_wordlist[w] = final_wordlist[reverse_lemmatize_idx[lem_w]]\n", + "\n", + "assert final_wordlist[\"its\"] == final_wordlist[\"its\"]\n", + "assert final_wordlist[\"its\"] >= 0\n", + "\n", + "print(f\"Final wordlist size: {len(final_wordlist.keys())}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d1a06597-4ad5-4566-a716-8bbad416b7ab", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with open(\"final_wordlist.csv\", \"w\") as f:\n", + " sorted_final_wordlist = [(k, final_wordlist[k]) for k in final_wordlist.keys()]\n", + " \n", + " for w in sorted(sorted_final_wordlist, key=lambda w: w[1]):\n", + " lemmatized = \"\" if not w[1] else w[1]\n", + " f.write(f\"{w[0]},{lemmatized}\")\n", + " f.write(\"\\n\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}