L5Z3-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from collections import defaultdict as dd\n",
    "from typing import List, Dict, Set\n",
    "import nltk\n",
    "from math import log, ceil\n",
    "import time\n",
    "import sys\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"Dane/wiki_list\", \"rb\") as ifile:\n",
    "    wiki_list = pickle.load(ifile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_lemma_mapping():\n",
    "    all_lemmas = dd(list)\n",
    "    for line in open('Dane/polimorfologik-2.1.txt', encoding='utf-8'):\n",
    "        L = line.split(';')[:2]\n",
    "        all_lemmas[L[1].lower()].append(L[0].lower())\n",
    "    return all_lemmas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmas = to_lemma_mapping()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"Dane/polish.stopwords.txt\", \"r\") as ifile:\n",
    "    stopwords = [line[:-1] for line in ifile]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Dane/nkjp+wiki-forms-all-100-cbow-hs.txt\", sep = \" \", skiprows=1, names=np.arange(101))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_article(wiki_list, stopwords):\n",
    "    article_vectors = []\n",
    "    for title, article in wiki_list:\n",
    "        article_vector = np.zeros(100)\n",
    "        for i, line in enumerate(article[:2]):\n",
    "            if i != 0:\n",
    "                line = line[:10]\n",
    "            for w in line:\n",
    "                w = w.lower()\n",
    "                if not w.isalpha():\n",
    "                    continue\n",
    "                if w in stopwords:\n",
    "                    continue\n",
    "                if w not in lemmas:\n",
    "                    lemmas[w] = [w]\n",
    "                for el in lemmas[w]:\n",
    "                    if el not in df.columns:\n",
    "                        continue\n",
    "                    article_vector += df[el].to_numpy()\n",
    "        article_vectors.append(article_vector)\n",
    "    return article_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = preprocess_article(wiki_list, stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}