add 03
This commit is contained in:
Jakub Pokrywka
parent
4437a7f71b
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cw/03a_tfidf.ipynb
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cw/03a_tfidf.ipynb
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cw/03a_tfidf_ODPOWIEDZI.ipynb
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cw/03a_tfidf_ODPOWIEDZI.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Ekstrakcja informacji </h1>\n",
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"<h2> 3. <i>tfidf (1)</i> [\u0107wiczenia]</h2> \n",
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"<h3> Jakub Pokrywka (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"def word_to_index(word):\n",
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" vec = np.zeros(len(vocabulary))\n",
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" if word in vocabulary:\n",
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" idx = vocabulary.index(word)\n",
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" vec[idx] = 1\n",
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" else:\n",
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" vec[-1] = 1\n",
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" return vec"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def tf(document):\n",
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" document_vector = None\n",
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" for word in document:\n",
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" if document_vector is None:\n",
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" document_vector = word_to_index(word)\n",
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" else:\n",
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" document_vector += word_to_index(word)\n",
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" return document_vector"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def similarity(query, document):\n",
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" numerator = np.sum(query * document)\n",
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" denominator = np.sqrt(np.sum(query*query)) * np.sqrt(np.sum(document*document)) \n",
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" return numerator / denominator"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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},
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"author": "Jakub Pokrywka",
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"email": "kubapok@wmi.amu.edu.pl",
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"lang": "pl",
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"subtitle": "3.tfidf (1)[\u0107wiczenia]",
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"title": "Ekstrakcja informacji",
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"year": "2021"
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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cw/03b_tfidf_newsgroup.ipynb
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cw/03b_tfidf_newsgroup.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Ekstrakcja informacji </h1>\n",
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"<h2> 3. <i>tfidf (2)</i> [\u0107wiczenia]</h2> \n",
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"<h3> Jakub Pokrywka (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Zajecia 2\n",
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"\n",
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"Przydatne materia\u0142y:\n",
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"\n",
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"https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
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"\n",
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"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Importy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import sklearn.metrics\n",
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"\n",
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"from sklearn.datasets import fetch_20newsgroups\n",
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"\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Zbi\u00f3r danych"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"newsgroups = fetch_20newsgroups()['data']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"11314"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(newsgroups)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"From: lerxst@wam.umd.edu (where's my thing)\n",
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"Subject: WHAT car is this!?\n",
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"Nntp-Posting-Host: rac3.wam.umd.edu\n",
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"Organization: University of Maryland, College Park\n",
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"Lines: 15\n",
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"\n",
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" I was wondering if anyone out there could enlighten me on this car I saw\n",
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"the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
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"early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
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"the front bumper was separate from the rest of the body. This is \n",
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"all I know. If anyone can tellme a model name, engine specs, years\n",
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"of production, where this car is made, history, or whatever info you\n",
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"have on this funky looking car, please e-mail.\n",
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"\n",
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"Thanks,\n",
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"- IL\n",
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" ---- brought to you by your neighborhood Lerxst ----\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"print(newsgroups[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Naiwne przeszukiwanie"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"all_documents = list() \n",
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"for document in newsgroups:\n",
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" if 'car' in document:\n",
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" all_documents.append(document)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"From: lerxst@wam.umd.edu (where's my thing)\n",
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"Subject: WHAT car is this!?\n",
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"Nntp-Posting-Host: rac3.wam.umd.edu\n",
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"Organization: University of Maryland, College Park\n",
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"Lines: 15\n",
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"\n",
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" I was wondering if anyone out there could enlighten me on this car I saw\n",
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"the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
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"early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
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"the front bumper was separate from the rest of the body. This is \n",
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"all I know. If anyone can tellme a model name, engine specs, years\n",
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|
"of production, where this car is made, history, or whatever info you\n",
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|
"have on this funky looking car, please e-mail.\n",
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"\n",
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"Thanks,\n",
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"- IL\n",
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" ---- brought to you by your neighborhood Lerxst ----\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"print(all_documents[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"From: guykuo@carson.u.washington.edu (Guy Kuo)\n",
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"Subject: SI Clock Poll - Final Call\n",
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"Summary: Final call for SI clock reports\n",
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"Keywords: SI,acceleration,clock,upgrade\n",
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"Article-I.D.: shelley.1qvfo9INNc3s\n",
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"Organization: University of Washington\n",
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"Lines: 11\n",
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"NNTP-Posting-Host: carson.u.washington.edu\n",
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"\n",
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"A fair number of brave souls who upgraded their SI clock oscillator have\n",
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"shared their experiences for this poll. Please send a brief message detailing\n",
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"your experiences with the procedure. Top speed attained, CPU rated speed,\n",
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"add on cards and adapters, heat sinks, hour of usage per day, floppy disk\n",
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"functionality with 800 and 1.4 m floppies are especially requested.\n",
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"\n",
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"I will be summarizing in the next two days, so please add to the network\n",
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"knowledge base if you have done the clock upgrade and haven't answered this\n",
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"poll. Thanks.\n",
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"\n",
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"Guy Kuo <guykuo@u.washington.edu>\n",
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"\n"
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]
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}
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],
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"source": [
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"print(all_documents[1])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### jakie s\u0105 problemy z takim podej\u015bciem?\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## TFIDF i odleg\u0142o\u015b\u0107 cosinusowa- gotowe biblioteki"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"vectorizer = TfidfVectorizer()\n",
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"#vectorizer = TfidfVectorizer(use_idf = False, ngram_range=(1,2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"document_vectors = vectorizer.fit_transform(newsgroups)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<11314x130107 sparse matrix of type '<class 'numpy.float64'>'\n",
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"\twith 1787565 stored elements in Compressed Sparse Row format>"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"document_vectors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<1x130107 sparse matrix of type '<class 'numpy.float64'>'\n",
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"\twith 89 stored elements in Compressed Sparse Row format>"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"document_vectors[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"matrix([[0., 0., 0., ..., 0., 0., 0.]])"
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]
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||||||
|
},
|
||||||
|
"execution_count": 12,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"document_vectors[0].todense()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 13,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"matrix([[0., 0., 0., ..., 0., 0., 0.],\n",
|
||||||
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
||||||
|
" [0., 0., 0., ..., 0., 0., 0.],\n",
|
||||||
|
" [0., 0., 0., ..., 0., 0., 0.]])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 13,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"document_vectors[0:4].todense()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 14,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"query_str = 'speed'\n",
|
||||||
|
"#query_str = 'speed car'\n",
|
||||||
|
"#query_str = 'spider man'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 15,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"query_vector = vectorizer.transform([query_str])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 16,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"<11314x130107 sparse matrix of type '<class 'numpy.float64'>'\n",
|
||||||
|
"\twith 1787565 stored elements in Compressed Sparse Row format>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 16,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"document_vectors"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 17,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"<1x130107 sparse matrix of type '<class 'numpy.float64'>'\n",
|
||||||
|
"\twith 1 stored elements in Compressed Sparse Row format>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 17,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"query_vector"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 18,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"similarities = sklearn.metrics.pairwise.cosine_similarity(query_vector,document_vectors)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 19,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([0.26949927, 0.3491801 , 0.44292083, 0.47784165])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 19,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"np.sort(similarities)[0][-4:]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 20,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([4517, 5509, 2116, 9921])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 20,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"similarities.argsort()[0][-4:]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 21,
|
||||||
|
"metadata": {
|
||||||
|
"scrolled": false
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"From: ray@netcom.com (Ray Fischer)\n",
|
||||||
|
"Subject: Re: x86 ~= 680x0 ?? (How do they compare?)\n",
|
||||||
|
"Organization: Netcom. San Jose, California\n",
|
||||||
|
"Distribution: usa\n",
|
||||||
|
"Lines: 36\n",
|
||||||
|
"\n",
|
||||||
|
"dhk@ubbpc.uucp (Dave Kitabjian) writes ...\n",
|
||||||
|
">I'm sure Intel and Motorola are competing neck-and-neck for \n",
|
||||||
|
">crunch-power, but for a given clock speed, how do we rank the\n",
|
||||||
|
">following (from 1st to 6th):\n",
|
||||||
|
"> 486\t\t68040\n",
|
||||||
|
"> 386\t\t68030\n",
|
||||||
|
"> 286\t\t68020\n",
|
||||||
|
"\n",
|
||||||
|
"040 486 030 386 020 286\n",
|
||||||
|
"\n",
|
||||||
|
">While you're at it, where will the following fit into the list:\n",
|
||||||
|
"> 68060\n",
|
||||||
|
"> Pentium\n",
|
||||||
|
"> PowerPC\n",
|
||||||
|
"\n",
|
||||||
|
"060 fastest, then Pentium, with the first versions of the PowerPC\n",
|
||||||
|
"somewhere in the vicinity.\n",
|
||||||
|
"\n",
|
||||||
|
">And about clock speed: Does doubling the clock speed double the\n",
|
||||||
|
">overall processor speed? And fill in the __'s below:\n",
|
||||||
|
"> 68030 @ __ MHz = 68040 @ __ MHz\n",
|
||||||
|
"\n",
|
||||||
|
"No. Computer speed is only partly dependent of processor/clock speed.\n",
|
||||||
|
"Memory system speed play a large role as does video system speed and\n",
|
||||||
|
"I/O speed. As processor clock rates go up, the speed of the memory\n",
|
||||||
|
"system becomes the greatest factor in the overall system speed. If\n",
|
||||||
|
"you have a 50MHz processor, it can be reading another word from memory\n",
|
||||||
|
"every 20ns. Sure, you can put all 20ns memory in your computer, but\n",
|
||||||
|
"it will cost 10 times as much as the slower 80ns SIMMs.\n",
|
||||||
|
"\n",
|
||||||
|
"And roughly, the 68040 is twice as fast at a given clock\n",
|
||||||
|
"speed as is the 68030.\n",
|
||||||
|
"\n",
|
||||||
|
"-- \n",
|
||||||
|
"Ray Fischer \"Convictions are more dangerous enemies of truth\n",
|
||||||
|
"ray@netcom.com than lies.\" -- Friedrich Nietzsche\n",
|
||||||
|
"\n",
|
||||||
|
"0.4778416465020907\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"From: rvenkate@ux4.cso.uiuc.edu (Ravikuma Venkateswar)\n",
|
||||||
|
"Subject: Re: x86 ~= 680x0 ?? (How do they compare?)\n",
|
||||||
|
"Distribution: usa\n",
|
||||||
|
"Organization: University of Illinois at Urbana\n",
|
||||||
|
"Lines: 59\n",
|
||||||
|
"\n",
|
||||||
|
"ray@netcom.com (Ray Fischer) writes:\n",
|
||||||
|
"\n",
|
||||||
|
">dhk@ubbpc.uucp (Dave Kitabjian) writes ...\n",
|
||||||
|
">>I'm sure Intel and Motorola are competing neck-and-neck for \n",
|
||||||
|
">>crunch-power, but for a given clock speed, how do we rank the\n",
|
||||||
|
">>following (from 1st to 6th):\n",
|
||||||
|
">> 486\t\t68040\n",
|
||||||
|
">> 386\t\t68030\n",
|
||||||
|
">> 286\t\t68020\n",
|
||||||
|
"\n",
|
||||||
|
">040 486 030 386 020 286\n",
|
||||||
|
"\n",
|
||||||
|
"How about some numbers here? Some kind of benchmark?\n",
|
||||||
|
"If you want, let me start it - 486DX2-66 - 32 SPECint92, 16 SPECfp92 .\n",
|
||||||
|
"\n",
|
||||||
|
">>While you're at it, where will the following fit into the list:\n",
|
||||||
|
">> 68060\n",
|
||||||
|
">> Pentium\n",
|
||||||
|
">> PowerPC\n",
|
||||||
|
"\n",
|
||||||
|
">060 fastest, then Pentium, with the first versions of the PowerPC\n",
|
||||||
|
">somewhere in the vicinity.\n",
|
||||||
|
"\n",
|
||||||
|
"Numbers? Pentium @66MHz - 65 SPECint92, 57 SPECfp92 .\n",
|
||||||
|
"\t PowerPC @66MHz - 50 SPECint92, 80 SPECfp92 . (Note this is the 601)\n",
|
||||||
|
" (Alpha @150MHz - 74 SPECint92,126 SPECfp92 - just for comparison)\n",
|
||||||
|
"\n",
|
||||||
|
">>And about clock speed: Does doubling the clock speed double the\n",
|
||||||
|
">>overall processor speed? And fill in the __'s below:\n",
|
||||||
|
">> 68030 @ __ MHz = 68040 @ __ MHz\n",
|
||||||
|
"\n",
|
||||||
|
">No. Computer speed is only partly dependent of processor/clock speed.\n",
|
||||||
|
">Memory system speed play a large role as does video system speed and\n",
|
||||||
|
">I/O speed. As processor clock rates go up, the speed of the memory\n",
|
||||||
|
">system becomes the greatest factor in the overall system speed. If\n",
|
||||||
|
">you have a 50MHz processor, it can be reading another word from memory\n",
|
||||||
|
">every 20ns. Sure, you can put all 20ns memory in your computer, but\n",
|
||||||
|
">it will cost 10 times as much as the slower 80ns SIMMs.\n",
|
||||||
|
"\n",
|
||||||
|
"Not in a clock-doubled system. There isn't a doubling in performance, but\n",
|
||||||
|
"it _is_ quite significant. Maybe about a 70% increase in performance.\n",
|
||||||
|
"\n",
|
||||||
|
"Besides, for 0 wait state performance, you'd need a cache anyway. I mean,\n",
|
||||||
|
"who uses a processor that runs at the speed of 80ns SIMMs? Note that this\n",
|
||||||
|
"memory speed corresponds to a clock speed of 12.5 MHz.\n",
|
||||||
|
"\n",
|
||||||
|
">And roughly, the 68040 is twice as fast at a given clock\n",
|
||||||
|
">speed as is the 68030.\n",
|
||||||
|
"\n",
|
||||||
|
"Numbers?\n",
|
||||||
|
"\n",
|
||||||
|
">-- \n",
|
||||||
|
">Ray Fischer \"Convictions are more dangerous enemies of truth\n",
|
||||||
|
">ray@netcom.com than lies.\" -- Friedrich Nietzsche\n",
|
||||||
|
"-- \n",
|
||||||
|
"Ravikumar Venkateswar\n",
|
||||||
|
"rvenkate@uiuc.edu\n",
|
||||||
|
"\n",
|
||||||
|
"A pun is a no' blessed form of whit.\n",
|
||||||
|
"\n",
|
||||||
|
"0.44292082969477664\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"From: ray@netcom.com (Ray Fischer)\n",
|
||||||
|
"Subject: Re: x86 ~= 680x0 ?? (How do they compare?)\n",
|
||||||
|
"Organization: Netcom. San Jose, California\n",
|
||||||
|
"Distribution: usa\n",
|
||||||
|
"Lines: 30\n",
|
||||||
|
"\n",
|
||||||
|
"rvenkate@ux4.cso.uiuc.edu (Ravikuma Venkateswar) writes ...\n",
|
||||||
|
">ray@netcom.com (Ray Fischer) writes:\n",
|
||||||
|
">>040 486 030 386 020 286\n",
|
||||||
|
">\n",
|
||||||
|
">How about some numbers here? Some kind of benchmark?\n",
|
||||||
|
"\n",
|
||||||
|
"Benchmarks are for marketing dweebs and CPU envy. OK, if it will make\n",
|
||||||
|
"you happy, the 486 is faster than the 040. BFD. Both architectures\n",
|
||||||
|
"are nearing then end of their lifetimes. And especially with the x86\n",
|
||||||
|
"architecture: good riddance.\n",
|
||||||
|
"\n",
|
||||||
|
">Besides, for 0 wait state performance, you'd need a cache anyway. I mean,\n",
|
||||||
|
">who uses a processor that runs at the speed of 80ns SIMMs? Note that this\n",
|
||||||
|
">memory speed corresponds to a clock speed of 12.5 MHz.\n",
|
||||||
|
"\n",
|
||||||
|
"The point being the processor speed is only one of many aspects of a\n",
|
||||||
|
"computers performance. Clock speed, processor, memory speed, CPU\n",
|
||||||
|
"architecture, I/O systems, even the application program all contribute \n",
|
||||||
|
"to the overall system performance.\n",
|
||||||
|
"\n",
|
||||||
|
">>And roughly, the 68040 is twice as fast at a given clock\n",
|
||||||
|
">>speed as is the 68030.\n",
|
||||||
|
">\n",
|
||||||
|
">Numbers?\n",
|
||||||
|
"\n",
|
||||||
|
"Look them up yourself.\n",
|
||||||
|
"\n",
|
||||||
|
"-- \n",
|
||||||
|
"Ray Fischer \"Convictions are more dangerous enemies of truth\n",
|
||||||
|
"ray@netcom.com than lies.\" -- Friedrich Nietzsche\n",
|
||||||
|
"\n",
|
||||||
|
"0.3491800997095306\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"From: mb4008@cehp11 (Morgan J Bullard)\n",
|
||||||
|
"Subject: Re: speeding up windows\n",
|
||||||
|
"Keywords: speed\n",
|
||||||
|
"Organization: University of Illinois at Urbana\n",
|
||||||
|
"Lines: 30\n",
|
||||||
|
"\n",
|
||||||
|
"djserian@flash.LakeheadU.Ca (Reincarnation of Elvis) writes:\n",
|
||||||
|
"\n",
|
||||||
|
">I have a 386/33 with 8 megs of memory\n",
|
||||||
|
"\n",
|
||||||
|
">I have noticed that lately when I use programs like WpfW or Corel Draw\n",
|
||||||
|
">my computer \"boggs\" down and becomes really sluggish!\n",
|
||||||
|
"\n",
|
||||||
|
">What can I do to increase performance? What should I turn on or off\n",
|
||||||
|
"\n",
|
||||||
|
">Will not loading wallpapers or stuff like that help when it comes to\n",
|
||||||
|
">the running speed of windows and the programs that run under it?\n",
|
||||||
|
"\n",
|
||||||
|
">Thanx in advance\n",
|
||||||
|
"\n",
|
||||||
|
">Derek\n",
|
||||||
|
"\n",
|
||||||
|
"1) make sure your hard drive is defragmented. This will speed up more than \n",
|
||||||
|
" just windows BTW. Use something like Norton's or PC Tools.\n",
|
||||||
|
"2) I _think_ that leaving the wall paper out will use less RAM and therefore\n",
|
||||||
|
" will speed up your machine but I could very will be wrong on this.\n",
|
||||||
|
"There's a good chance you've already done this but if not it may speed things\n",
|
||||||
|
"up. good luck\n",
|
||||||
|
"\t\t\t\tMorgan Bullard mb4008@coewl.cen.uiuc.edu\n",
|
||||||
|
"\t\t\t\t\t or mjbb@uxa.cso.uiuc.edu\n",
|
||||||
|
"\n",
|
||||||
|
">--\n",
|
||||||
|
">$_ /|$Derek J.P. Serianni $ E-Mail : djserian@flash.lakeheadu.ca $ \n",
|
||||||
|
">$\\'o.O' $Sociologist $ It's 106 miles to Chicago,we've got a full tank$\n",
|
||||||
|
">$=(___)=$Lakehead University $ of gas, half a pack of cigarettes,it's dark,and$\n",
|
||||||
|
">$ U $Thunder Bay, Ontario$ we're wearing sunglasses. -Elwood Blues $ \n",
|
||||||
|
"\n",
|
||||||
|
"0.26949927393886913\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n",
|
||||||
|
"----------------------------------------------------------------------------------------------------\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"for i in range (1,5):\n",
|
||||||
|
" print(newsgroups[similarities.argsort()[0][-i]])\n",
|
||||||
|
" print(np.sort(similarities)[0,-i])\n",
|
||||||
|
" print('-'*100)\n",
|
||||||
|
" print('-'*100)\n",
|
||||||
|
" print('-'*100)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Zadanie domowe\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"- Wybra\u0107 zbi\u00f3r tekstowy, kt\u00f3ry ma conajmniej 10000 dokument\u00f3w (inny ni\u017c w tym przyk\u0142adzie).\n",
|
||||||
|
"- Na jego podstawie stworzy\u0107 wyszukiwark\u0119 bazuj\u0105c\u0105 na OKAPI BM25, tzn. system kt\u00f3ry dla podanej frazy podaje kilka (5-10) posortowanych najbardziej pasuj\u0105cych dokument\u00f3w razem ze scorami. Nale\u017cy wypisywa\u0107 te\u017c ilo\u015b\u0107 zwracanych dokument\u00f3w, czyli takich z niezerowym scorem. Mo\u017cna korzysta\u0107 z gotowych bibliotek do wektoryzacji dokument\u00f3w, nale\u017cy jednak samemu zaimplementowa\u0107 OKAPI BM25. \n",
|
||||||
|
"- Znale\u017a\u0107 fraz\u0119 (query), dla kt\u00f3rej wynik nie jest satysfakcjonuj\u0105cy.\n",
|
||||||
|
"- Poprawi\u0107 wyszukiwark\u0119 (np. poprzez zmian\u0119 preprocessingu tekstu, wektoryzer, zmian\u0119 parametr\u00f3w algorytmu rankuj\u0105cego lub sam algorytm) tak, \u017ceby zwraca\u0142a satysfakcjonuj\u0105ce wyniki dla poprzedniej frazy. Nale\u017cy zrobi\u0107 inn\u0105 zmian\u0119 ni\u017c w tym przyk\u0142adzie, tylko wymy\u015bli\u0107 co\u015b w\u0142asnego.\n",
|
||||||
|
"- prezentowa\u0107 prac\u0119 na nast\u0119pnych zaj\u0119ciach (14.04) odpowiadaj\u0105c na pytania:\n",
|
||||||
|
" - jak wygl\u0105da zbi\u00f3r i system wyszukiwania przed zmianami\n",
|
||||||
|
" - dla jakiej frazy wyniki s\u0105 niesatysfakcjonuj\u0105ce (pokaza\u0107 wyniki)\n",
|
||||||
|
" - jakie zmiany zosta\u0142y naniesione\n",
|
||||||
|
" - jak wygl\u0105daj\u0105 wyniki wyszukiwania po zmianach\n",
|
||||||
|
" - jak zmiany wp\u0142yn\u0119\u0142y na wyniki (1-2 zdania)\n",
|
||||||
|
" \n",
|
||||||
|
"Prezentacja powinna by\u0107 maksymalnie prosta i trwa\u0107 maksymalnie 2-3 minuty.\n",
|
||||||
|
"punkt\u00f3w do zdobycia: 60\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"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": "3.8.3"
|
||||||
|
},
|
||||||
|
"author": "Jakub Pokrywka",
|
||||||
|
"email": "kubapok@wmi.amu.edu.pl",
|
||||||
|
"lang": "pl",
|
||||||
|
"subtitle": "3.tfidf (2)[\u0107wiczenia]",
|
||||||
|
"title": "Ekstrakcja informacji",
|
||||||
|
"year": "2021"
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 4
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user