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Commit d2786b64 by Rosa Delima Mendrofa
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Query Searching

parents 74c49902 13ae1c62
Showing with 1003 additions and 34 deletions
.ipynb_checkpoints/12S4055 - STBI Project-checkpoint.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Kelompok 3 | Search Engine with Inverted Index Simulator Based on Billboard Songs Collection\n",
" - 12S16003 Maria H. Siallagan\n",
" - 12S16026 Yolanda Nainggolan\n",
" - 12S16036 Prima Hutapea\n",
" - 12S16049 Rosa Delima Mendrofa"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'dataset_STBI.xml'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-0410f424fcaa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeature_extraction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCountVectorizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mxml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminidom\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mminidom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdcmnt_xml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminidom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"dataset_STBI.xml\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\Anaconda3\\lib\\xml\\dom\\minidom.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(file, parser, bufsize)\u001b[0m\n\u001b[0;32m 1956\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mparser\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mbufsize\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1957\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mxml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mexpatbuilder\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1958\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mexpatbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1959\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1960\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mxml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpulldom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\xml\\dom\\expatbuilder.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(file, namespaces)\u001b[0m\n\u001b[0;32m 908\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 909\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 910\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 911\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparseFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 912\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dataset_STBI.xml'"
]
}
],
"source": [
"import string\n",
"import re\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"import xml.dom.minidom as minidom\n",
"dcmnt_xml = minidom.parse(\"dataset_STBI.xml\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"all_doc_no = dcmnt_xml.getElementsByTagName('DOCNO')\n",
"all_profile = dcmnt_xml.getElementsByTagName('SONG')\n",
"all_date = dcmnt_xml.getElementsByTagName('ARTIST')\n",
"all_text = dcmnt_xml.getElementsByTagName('LYRICS')\n",
"all_pub = dcmnt_xml.getElementsByTagName('PUB')\n",
"all_page = dcmnt_xml.getElementsByTagName('PAGE')\n",
"\n",
"N_DOC_sample = len(all_doc_no)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"all_sentence_doc_sample = []\n",
"for i in range(N_DOC_sample):\n",
" sentence_doc_sample = ' '+ all_text[i].firstChild.data\n",
" all_sentence_doc_sample.append(sentence_doc_sample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocessing "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"tokens_doc = []"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def remove_punc_tokenize(sentence):\n",
" tokens = []\n",
" for punctuation in string.punctuation:\n",
" sentence = sentence.replace(punctuation,\" \")\n",
" \n",
" sentence = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '', sentence, flags=re.MULTILINE)\n",
" for w in CountVectorizer().build_tokenizer()(sentence):\n",
" tokens.append(w)\n",
" return tokens"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"for i in range(N_DOC):\n",
" tokens_doc.append(remove_punc_tokenize(all_sentence_doc_sample[i]))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from nltk.corpus import stopwords\n",
"stop_words = set(stopwords.words('english'))\n",
"def stop_word_token(tokens):\n",
" tokens = [w for w in tokens if not w in stop_words]\n",
" return tokens\n",
"\n",
"for i in range(N_DOC):\n",
" tokens_doc[i] = stop_word_token(tokens_doc[i])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"for i in range(N_DOC):\n",
" tokens_doc[i] = ([w for w in tokens_doc[i] if not any(j.isdigit() for j in w)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from nltk.stem import PorterStemmer\n",
"stemmer = PorterStemmer()\n",
"def stemming(tokens):\n",
" for i in range(0, len(tokens)):\n",
" if (tokens[i] != stemmer.stem(tokens[i])):\n",
" tokens[i] = stemmer.stem(tokens[i])\n",
" return tokens\n",
"\n",
"\n",
"for i in range(N_DOC):\n",
" tokens_doc[i] = stemming(tokens_doc[i])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_tokens = []\n",
"for i in range(N_DOC):\n",
" for w in tokens_doc[i]:\n",
" all_tokens.append(w)\n",
"\n",
"new_sentence = ' '.join([w for w in all_tokens])\n",
"\n",
"for w in CountVectorizer().build_tokenizer()(new_sentence):\n",
" all_tokens.append(w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_tokens = set(all_tokens)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from itertools import count\n",
"try: \n",
" from itertools import izip as zip\n",
"except ImportError:\n",
" pass\n",
"proximity_index = {}\n",
"for token in all_tokens:\n",
" dict_doc_position = {}\n",
" for n in range(N_DOC):\n",
" if(token in tokens_doc[n]):\n",
" dict_doc_position[all_doc_no[n].firstChild.data] = [i+1 for i, j in zip(count(), tokens_doc[n]) if j == token]\n",
" proximity_index[token] = dict_doc_position"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import collections\n",
"proximity_index = collections.OrderedDict(sorted(proximity_index.items()))\n",
"for key, value in proximity_index.items():\n",
" print (key, value)"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
12S4055 - STBI Project.ipynb
This source diff could not be displayed because it is too large. You can view the blob instead.
SearchEngine-master/SearchEngine/InvertedIndexSimulator/data/dataset_STBI.xml
This source diff could not be displayed because it is too large. You can view the blob instead.
SearchEngine-master/SearchEngine/InvertedIndexSimulator/inverted/main.py
......@@ -3,15 +3,13 @@ resource_package = __name__
import string
import re
from sklearn.feature_extraction.text import CountVectorizer
import string
import re
from sklearn.feature_extraction.text import CountVectorizer
from nltk.corpus import stopwords
from nltk.tokenize import sent_tokenize, word_tokenize
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from itertools import count
import collections
import math
import xml.etree.ElementTree as et
from xml.etree.ElementTree import ElementTree
......@@ -50,20 +48,37 @@ def generate_ngrams(data, n):
return ngram, result
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
def stop_word_token(tokens):
tokens = [w for w in tokens if not w in stop_words]
return tokens
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
def stemming(tokens):
for i in range(0, len(tokens)):
if (tokens[i] != stemmer.stem(tokens[i])):
tokens[i] = stemmer.stem(tokens[i])
return tokens
def main(query):
tree = ElementTree()
tree.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
all_doc_no = []
all_headline = []
all_song = []
all_text = []
for node in tree.iter("DOCNO"):
all_doc_no.append(node.text)
for node in tree.iter("SONG"):
all_headline.append(node.text)
all_song.append(node.text)
for node in tree.iter("LYRICS"):
all_text.append(node.text)
......@@ -72,7 +87,7 @@ def main(query):
all_sentence_doc = []
for i in range(N_DOC):
all_sentence_doc.append(all_headline[i] + all_text[i])
all_sentence_doc.append(all_song[i] + all_text[i])
tokens_doc = []
for i in range(N_DOC):
tokens_doc.append(remove_punc_tokenize(all_sentence_doc[i]))
......@@ -80,7 +95,7 @@ def main(query):
for i in range(N_DOC):
tokens_doc[i] = to_lower(tokens_doc[i])
stop_words = set(stopwords.words('indonesian'))
stop_words = set(stopwords.words('english'))
stopping = []
for i in range(N_DOC):
......@@ -189,7 +204,7 @@ def main(query):
score*=idf[i] #tf * idf
idx = all_doc_no[i]
judul = all_headline[i]
judul = all_song[i]
dic['docno'] = idx
dic['judul'] = judul
......@@ -205,20 +220,25 @@ def main(query):
return hasil
def detail(nomor):
<<<<<<< HEAD:SearchEngine-master/SearchEngine/InvertedIndexSimulator/inverted/main.py
tree = ElementTree()
tree.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
=======
tree = et()
tree.parse("apps/data/dataset_STBI.xml")
>>>>>>> 13ae1c6214da83348ebb0752338974874c4f66ae:SearchEngine/InvertedIndexSimulator/inverted/main.py
all_doc_no = []
all_headline = []
all_song = []
all_text = []
for node in tree.iter("DOCNO"):
all_doc_no.append(node.text)
for node in tree.iter("SONG"):
# all_headline.append(node.text.replace("\n"," "))
all_headline.append(node.text)
head = all_headline
# all_song.append(node.text.replace("\n"," "))
all_song.append(node.text)
head = all_song
for node in tree.iter("LYRICS"):
# all_text.append(node.text.replace("\n"," "))
......@@ -233,5 +253,5 @@ def detail(nomor):
check = all_doc_no[i]
if check == id:
text = all_text[i]
judul = all_headline[i]
judul = all_song[i]
return text,judul
\ No newline at end of file
SearchEngine-master/SearchEngine/InvertedIndexSimulator/static/assets/css/dataframe.min.css
......@@ -55,6 +55,15 @@ footer {
border-radius: 15px;
padding: 20px;
margin-top: 10px;
width: auto;
}
.carda {
box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2);
border-radius: 15px;
padding: 20px;
margin-top: 10px;
width: max-content;
}
.jumbotron {
......
SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/dataframe.html
......@@ -5,35 +5,103 @@
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Song Lyric Search Engine</title>
<link href="../../static/assets/css/dataframe.min.css" rel="stylesheet">
<style>
#leftbox {
float:left;
white-space: nowrap;
}
#middlebox{
float:left;
white-space: nowrap;
}
#middleboxa{
float:left;
white-space: nowrap;
}
#rightbox{
float:right;
white-space: nowrap;
}
</style>
</head>
<body>
<main>
<div id="content">
<article class="card">
<div>
<div>
<button onclick="pageRedirect_prev()" class="button" style="vertical-align:middle"><span>Previous</span></button>
</div>
<div align="right">
<button onclick="pageRedirect()" class="button" style="vertical-align:middle"><span>Next</span></button>
<button onclick="pageRedirect_next()" class="button" style="vertical-align:middle"><span>Next</span></button>
</div>
</div>
<center><h1>Dataset</h1><br>
<table style="width:100%">
<center><h1>Dataset</h1><br></center>
<article class="carda" style="overflow-x:scroll; overflow-y:scroll;">
<div id = "leftbox">
<table>
<tr>
<th>DOCNO</th>
</tr>
{% for i in DOCNO %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</div>
<div id = "middlebox">
<table align="left">
<tr>
<th>SONG</th>
</tr>
{% for i in SONG %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</div>
<div id = "middlebox">
<table>
<tr>
<th>ARTIST</th>
</tr>
{% for i in ARTIST %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</div>
<div id = "middlebox">
<table>
<tr>
<th>LYRICS</th>
</tr>
{% for l in LYRICS %}
{% for i in LYRICS %}
<tr>
<td>{{ i }}</td>
<td>{{ j }}</td>
<td>{{ k }}</td>
<td>{{ l }}</td>
</tr>
{% endfor %}
</table>
</center>
</div>
</article>
</article>
</div>
......@@ -46,7 +114,11 @@
</body>
<script>
function pageRedirect() {
function pageRedirect_prev() {
window.location.href = "/home";
}
function pageRedirect_next() {
window.location.href = "/preprocessing";
}
</script>
......
SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/indexing.html
......@@ -11,23 +11,42 @@
<main>
<div id="content">
<article class="card">
<div>
<div>
<button onclick="pageRedirect_prev()" class="button" style="vertical-align:middle"><span>Previous</span></button>
</div>
<div align="right">
<button onclick="pageRedirect()" class="button" style="vertical-align:middle"><span>Next</span></button>
<button onclick="pageRedirect_next()" class="button" style="vertical-align:middle"><span>Next</span></button>
</div>
</div>
<center><h1>Indexing</h1><br></center>
<p><strong>Dengan Proximity Index</strong></p><br></center>
<table style="width:100%">
<tr>
<th>Apa judulnya ya?</th>
</tr>
{% for i in indexnya %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</article>
</div>
</main>
<footer>
<p>&copy; STBI-2020-03</p>
</footer>
</body>
<script>
function pageRedirect() {
function pageRedirect_prev() {
window.location.href = "/preprocessing4";
}
function pageRedirect_next() {
window.location.href = "/index";
}
</script>
......
SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/preprocessing.html
......@@ -11,24 +11,44 @@
<main>
<div id="content">
<article class="card">
<div>
<div>
<button onclick="pageRedirect_prev()" class="button" style="vertical-align:middle"><span>Previous</span></button>
</div>
<div align="right">
<button onclick="pageRedirect()" class="button" style="vertical-align:middle"><span>Next</span></button>
<button onclick="pageRedirect_next()" class="button" style="vertical-align:middle"><span>Next</span></button>
</div>
</div>
<center><h1>Text Preprocessing</h1><br></center>
<center><p style="font-size:40px;"><strong>Text Preprocessing - 1</strong></p>
<p><strong>After Punctuation Removal and Tokenization</strong></p><br></center>
<table style="width:100%">
<tr>
<th>All tokens for each document</th>
</tr>
{% for i in tokens_doc %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</article>
</div>
</main>
<footer>
<p>&copy; STBI-2020-03</p>
</footer>
</body>
<script>
function pageRedirect() {
window.location.href = "/indexing";
function pageRedirect_prev() {
window.location.href = "/dataframe";
}
function pageRedirect_next() {
window.location.href = "/preprocessing2";
}
</script>
......
SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/preprocessing2.html 0 → 100644
<!DOCTYPE html>
<html lang="en">
<head>
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Song Lyric Search Engine</title>
<link href="../../static/assets/css/dataframe.min.css" rel="stylesheet">
</head>
<body>
<main>
<div id="content">
<article class="card">
<div>
<div>
<button onclick="pageRedirect_prev()" class="button" style="vertical-align:middle"><span>Previous</span></button>
</div>
<div align="right">
<button onclick="pageRedirect_next()" class="button" style="vertical-align:middle"><span>Next</span></button>
</div>
</div>
<center><p style="font-size:40px;"><strong>Text Preprocessing - 2</strong></p>
<p><strong>After Case Folding</strong></p><br></center>
<table style="width:100%">
<tr>
<th>All tokens for each document</th>
</tr>
{% for i in tokens_doc %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</article>
</div>
</main>
</body>
<script>
function pageRedirect_prev() {
window.location.href = "/preprocessing";
}
function pageRedirect_next() {
window.location.href = "/preprocessing3";
}
</script>
</html>
SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/preprocessing3.html 0 → 100644
<!DOCTYPE html>
<html lang="en">
<head>
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Song Lyric Search Engine</title>
<link href="../../static/assets/css/dataframe.min.css" rel="stylesheet">
</head>
<body>
<main>
<div id="content">
<article class="card">
<div>
<div>
<button onclick="pageRedirect_prev()" class="button" style="vertical-align:middle"><span>Previous</span></button>
</div>
<div align="right">
<button onclick="pageRedirect_next()" class="button" style="vertical-align:middle"><span>Next</span></button>
</div>
</div>
<center><p style="font-size:40px;"><strong>Text Preprocessing - 3</strong></p>
<p><strong>After Stopwords Removal</strong></p><br></center>
<table style="width:100%">
<tr>
<th>All tokens for each document</th>
</tr>
{% for i in tokens_doc %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</article>
</div>
</main>
</body>
<script>
function pageRedirect_prev() {
window.location.href = "/preprocessing2";
}
function pageRedirect_next() {
window.location.href = "/preprocessing4";
}
</script>
</html>
SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/preprocessing4.html 0 → 100644
<!DOCTYPE html>
<html lang="en">
<head>
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Song Lyric Search Engine</title>
<link href="../../static/assets/css/dataframe.min.css" rel="stylesheet">
</head>
<body>
<main>
<div id="content">
<article class="card">
<div>
<div>
<button onclick="pageRedirect_prev()" class="button" style="vertical-align:middle"><span>Previous</span></button>
</div>
<div align="right">
<button onclick="pageRedirect_next()" class="button" style="vertical-align:middle"><span>Next</span></button>
</div>
</div>
<center><p style="font-size:40px;"><strong>Text Preprocessing - 4</strong></p>
<p><strong>After Normalization</strong></p><br></center>
<table style="width:100%">
<tr>
<th>All tokens for each document</th>
</tr>
{% for i in tokens_doc %}
<tr>
<td>{{ i }}</td>
</tr>
{% endfor %}
</table>
</article>
</div>
</main>
</body>
<script>
function pageRedirect_prev() {
window.location.href = "/preprocessing3";
}
function pageRedirect_next() {
window.location.href = "/indexing";
}
</script>
</html>
SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/result.html
......@@ -44,7 +44,11 @@
<div class="col-lg-4">
<div class="testimonial-item mx-auto mb-5 mb-lg-0">
<<<<<<< HEAD:SearchEngine-master/SearchEngine/InvertedIndexSimulator/templates/apps/result.html
<img class="img-fluid rounded-circle mb-3" src="../../static/img/billboard-logo-2016-1548-768x433.jpeg" alt="">
=======
<img class="img-fluid rounded-circle mb-3" src="../../static/img/hkbp.jpg" alt="">
>>>>>>> 13ae1c6214da83348ebb0752338974874c4f66ae:SearchEngine/InvertedIndexSimulator/templates/apps/result.html
<h5><a href="/lyric">Lagu No:{{ j.docno }}</a></h5>
<h5>"{{ j.judul }}"</h5>
<p class="font-weight-light mb-0">score :{{ j.score }}</p>
......
SearchEngine-master/SearchEngine/InvertedIndexSimulator/urls.py
......@@ -10,6 +10,12 @@ urlpatterns = [
path('', views.home),
path('dataframe/', views.dataframe),
path('preprocessing/', views.preprocessing),
<<<<<<< HEAD:SearchEngine-master/SearchEngine/InvertedIndexSimulator/urls.py
=======
path('preprocessing2/', views.preprocessing2),
path('preprocessing3/', views.preprocessing3),
path('preprocessing4/', views.preprocessing4),
>>>>>>> 13ae1c6214da83348ebb0752338974874c4f66ae:SearchEngine/InvertedIndexSimulator/urls.py
path('indexing/', views.indexing),
path('index/', views.index),
path('result/', views.result),
......
SearchEngine-master/SearchEngine/SearchEngine/urls.py
......@@ -22,6 +22,12 @@ urlpatterns = [
path('', views.home),
path('dataframe/', views.dataframe),
path('preprocessing/', views.preprocessing),
<<<<<<< HEAD:SearchEngine-master/SearchEngine/SearchEngine/urls.py
=======
path('preprocessing2/', views.preprocessing2),
path('preprocessing3/', views.preprocessing3),
path('preprocessing4/', views.preprocessing4),
>>>>>>> 13ae1c6214da83348ebb0752338974874c4f66ae:SearchEngine/SearchEngine/urls.py
path('indexing/', views.indexing),
path('index/', views.index),
path('result/', views.result),
......
SearchEngine/InvertedIndexSimulator/templates/apps/home.html 0 → 100644
<!DOCTYPE html>
<html lang="en">
<head>
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Song Lyric Search Engine</title>
<link href="../../static/assets/css/landing-page.min.css" rel="stylesheet">
</head>
<body>
<header>
<div class="jumbotron">
<h1>Song Lyric Search Engine<br>- Simulator -</h1>
<p>Search engine yang pake inverted index untuk indexing nya</p>
</div>
</header>
<main>
<div id="content">
<article class="card">
<center><h1>Pilih Dataset</h1><br>
<table>
<tr>
<th><button onclick="pageRedirect()" class="button" style="vertical-align:middle"><span>International Billboard Song </span></button></th>
<td><button class="button" style="vertical-align:middle"><span>Indonesian Song </span></button></td>
</tr>
</table>
</center>
</article>
</div>
</main>
<footer>
<p>&copy; STBI-2020-03</p>
</footer>
</body>
<script>
function pageRedirect() {
window.location.href = "/dataframe";
}
</script>
</html>
SearchEngine/InvertedIndexSimulator/templates/apps/index.html 0 → 100644
<!DOCTYPE html>
<html lang="en">
<head>
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Song Lyric Search Engine</title>
<link href="../../static/assets/css/dataframe.min.css" rel="stylesheet">
</head>
<body>
<main>
<div id="content">
<article class="card">
<div>
<div>
<button onclick="pageRedirect_prev()" class="button" style="vertical-align:middle"><span>Previous</span></button>
</div>
</div>
<div class="row">
<center><h1 style="font-size:45px">Searching!<br></h1>
<p style="font-size:20px"><strong>Silahkan masukkan lirik dari lagu yang ingin Anda temukan</strong></p>
<form method="POST" action="/result/">
{% csrf_token %}
<div class="form-row">
<input type="text" name="querysearch" placeholder="Masukkan Query Anda..."> <br>
<button type="submit">Cari!</button>
</div>
</form>
</div>
</center>
</article>
</div>
</main>
</body>
<script>
function pageRedirect_prev() {
window.location.href = "/indexing";
}
</script>
</html>
SearchEngine/InvertedIndexSimulator/views.py 0 → 100644
from django.shortcuts import render
from django.http import HttpResponse
from InvertedIndexSimulator.inverted import main
import pandas as pd
import xml.etree.ElementTree as et
def home(request):
return render(request, 'apps/home.html')
def dataframe(request):
parse_data = et.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
data = parse_data.getroot()
df_cols = ["DOCNO", "SONG", "ARTIST", "LYRICS"]
rows = []
for node in data:
s_docno = node.find("DOCNO").text if node is not None else None
s_song = node.find("SONG").text if node is not None else None
s_artist = node.find("ARTIST").text if node is not None else None
s_lyrics = node.find("LYRICS").text if node is not None else None
rows.append({"DOCNO": s_docno, "SONG": s_song, "ARTIST": s_artist, "LYRICS": s_lyrics})
DataFrame = pd.DataFrame(rows, columns = df_cols)
dictionary = DataFrame.set_index('DOCNO').T.to_dict('list')
nilai = list(dictionary.values())
nomornya = list(dictionary.keys())
lagunya = [sublist[0] for sublist in nilai]
artisnya = [sublist[1] for sublist in nilai]
liriknya = [sublist[2] for sublist in nilai]
context = {"DOCNO": nomornya, "SONG": lagunya, "ARTIST": artisnya, "LYRICS": liriknya}
return render(request, 'apps/dataframe.html', context)
def preprocessing(request):
from xml.etree.ElementTree import ElementTree
tree = ElementTree()
tree.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
all_doc_no = []
all_song = []
all_text = []
for node in tree.iter("DOCNO"):
all_doc_no.append(node.text)
for node in tree.iter("SONG"):
all_song.append(node.text)
for node in tree.iter("LYRICS"):
all_text.append(node.text)
N_DOC = len(all_text)
all_sentence_doc = []
for i in range(N_DOC):
all_sentence_doc.append(all_song[i] + all_text[i])
tokens_doc = []
for i in range(N_DOC):
tokens_doc.append(main.remove_punc_tokenize(all_sentence_doc[i]))
context = {"tokens_doc": tokens_doc}
return render(request, 'apps/preprocessing.html', context)
def preprocessing2(request):
from xml.etree.ElementTree import ElementTree
tree = ElementTree()
tree.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
all_doc_no = []
all_song = []
all_text = []
for node in tree.iter("DOCNO"):
all_doc_no.append(node.text)
for node in tree.iter("SONG"):
all_song.append(node.text)
for node in tree.iter("LYRICS"):
all_text.append(node.text)
N_DOC = len(all_text)
all_sentence_doc = []
for i in range(N_DOC):
all_sentence_doc.append(all_song[i] + all_text[i])
tokens_doc = []
for i in range(N_DOC):
tokens_doc.append(main.remove_punc_tokenize(all_sentence_doc[i]))
for i in range(N_DOC):
tokens_doc[i] = main.to_lower(tokens_doc[i])
context = {"tokens_doc": tokens_doc}
return render(request, 'apps/preprocessing2.html', context)
def preprocessing3(request):
from xml.etree.ElementTree import ElementTree
tree = ElementTree()
tree.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
all_doc_no = []
all_song = []
all_text = []
for node in tree.iter("DOCNO"):
all_doc_no.append(node.text)
for node in tree.iter("SONG"):
all_song.append(node.text)
for node in tree.iter("LYRICS"):
all_text.append(node.text)
N_DOC = len(all_text)
all_sentence_doc = []
for i in range(N_DOC):
all_sentence_doc.append(all_song[i] + all_text[i])
tokens_doc = []
for i in range(N_DOC):
tokens_doc.append(main.remove_punc_tokenize(all_sentence_doc[i]))
for i in range(N_DOC):
tokens_doc[i] = main.to_lower(tokens_doc[i])
for i in range(N_DOC):
tokens_doc[i] = main.stop_word_token(tokens_doc[i])
for i in range(N_DOC):
tokens_doc[i] = ([w for w in tokens_doc[i] if not any(j.isdigit() for j in w)])
context = {"tokens_doc": tokens_doc}
return render(request, 'apps/preprocessing3.html', context)
def preprocessing4(request):
from xml.etree.ElementTree import ElementTree
tree = ElementTree()
tree.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
all_doc_no = []
all_song = []
all_text = []
for node in tree.iter("DOCNO"):
all_doc_no.append(node.text)
for node in tree.iter("SONG"):
all_song.append(node.text)
for node in tree.iter("LYRICS"):
all_text.append(node.text)
N_DOC = len(all_text)
all_sentence_doc = []
for i in range(N_DOC):
all_sentence_doc.append(all_song[i] + all_text[i])
tokens_doc = []
for i in range(N_DOC):
tokens_doc.append(main.remove_punc_tokenize(all_sentence_doc[i]))
for i in range(N_DOC):
tokens_doc[i] = main.to_lower(tokens_doc[i])
for i in range(N_DOC):
tokens_doc[i] = main.stop_word_token(tokens_doc[i])
for i in range(N_DOC):
tokens_doc[i] = ([w for w in tokens_doc[i] if not any(j.isdigit() for j in w)])
for i in range(N_DOC):
tokens_doc[i] = main.stemming(tokens_doc[i])
context = {"tokens_doc": tokens_doc}
return render(request, 'apps/preprocessing4.html', context)
def indexing(request):
from sklearn.feature_extraction.text import CountVectorizer
from xml.etree.ElementTree import ElementTree
tree = ElementTree()
tree.parse("InvertedIndexSimulator/data/dataset_STBI.xml")
all_doc_no = []
all_song = []
all_text = []
for node in tree.iter("DOCNO"):
all_doc_no.append(node.text)
for node in tree.iter("SONG"):
all_song.append(node.text)
for node in tree.iter("LYRICS"):
all_text.append(node.text)
N_DOC = len(all_text)
all_sentence_doc = []
for i in range(N_DOC):
all_sentence_doc.append(all_song[i] + all_text[i])
tokens_doc = []
for i in range(N_DOC):
tokens_doc.append(main.remove_punc_tokenize(all_sentence_doc[i]))
for i in range(N_DOC):
tokens_doc[i] = main.to_lower(tokens_doc[i])
for i in range(N_DOC):
tokens_doc[i] = main.stop_word_token(tokens_doc[i])
for i in range(N_DOC):
tokens_doc[i] = ([w for w in tokens_doc[i] if not any(j.isdigit() for j in w)])
for i in range(N_DOC):
tokens_doc[i] = main.stemming(tokens_doc[i])
all_tokens =[]
for i in range(N_DOC):
for j in tokens_doc[i]:
all_tokens.append(j)
new_sentences = ' '.join([w for w in all_tokens])
for j in CountVectorizer().build_tokenizer()(new_sentences):
all_tokens.append(j)
all_tokens = set(all_tokens)
from itertools import count
try:
from future_builtins import zip
except ImportError: # not 2.6+ or is 3.x
try:
from itertools import izip as zip # < 2.5 or 3.x
except ImportError:
pass
proximity_index = {}
for token in all_tokens:
dict_doc_position = {}
for n in range(N_DOC):
if(token in tokens_doc[n]):
dict_doc_position[all_doc_no[n].firstChild.data] = [i+1 for i, j in zip(count(), tokens_doc[n]) if j == token]
proximity_index[token] = dict_doc_position
import collections
proximity_index = collections.OrderedDict(sorted(proximity_index.items()))
for key, value in proximity_index.items():
indexnya = (key, value)
context = {"indexnya": indexnya}
return render(request, 'apps/indexing.html', context)
def index(request):
return render(request, 'apps/index.html')
def lyric(request,id):
text, judul = main.detail(id)
content={
'no': id,
'judul':judul,
'text':text
}
return render(request, 'apps/lyric.html', content)
def result(request):
#%%
# proximity_index = collections.OrderedDict(sorted(proximity_index.items()))
# for key, value in proximity_index.items():
# # print (key, value)
if request.method == 'POST':
query = request.POST['querysearch']
hasil= main.main(query)
content={
'hasil':hasil,
'query':query
}
return render(request, 'apps/result.html', content)
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