def addTestLyrics(urls): db = dBDelegate.getDBConnection() for url in urls: song = GeniusScraper.scrapeSongByURL(url) song_lyrics = positionalIndex.tokenizeText(song.lyrics) lyrics = positionalIndex.tokenizeLyrics(song.lyrics) song_id = dBDelegate.addSongMetadata(db, song.url, song.artist, song.title) song_positional = positionalIndex.createPositionalIndex(song_lyrics) dBDelegate.addPositionalIndex(db, song_positional, song_id)
def test_main(base, cap): artist_list = GeniusScraper.scrapeLyricsByArtist(base, cap) if artist_list != None: for song_list in artist_list: for song in song_list: db = dBDelegate.getDBConnection() song_lyrics = positionalIndex.tokenizeText(song.lyrics) song_id = dBDelegate.addSongMetadata(db, song.url, song.artist, song.title) song_positional = positionalIndex.createPositionalIndex(song_lyrics) dBDelegate.addPositionalIndex(db, song_positional, song_id)
def detectASample(query):
db = dBDelegate.getDBConnection()
query = query
print "****************************************************************"
print "Songs which contain your query, in perfect order:"
relevant_positional_index = createPositionalIndex(db, query)
songs_that_contain_all_query_words = getIntersectingPositionalIndex(db, query)
sampled_songs = compareLists(query, relevant_positional_index, songs_that_contain_all_query_words, db)
average_song_length = db.songs.find_one({'average_length' : {'$exists' : True}})['average_length']
tfidf_values = calculateWeightedTfidf(sampled_songs, query, relevant_positional_index, songs_that_contain_all_query_words, average_song_length, db)
tfidf.sortTfidfValues(tfidf_values)
from __future__ import division
import re, string, math, random
import dBDelegate
from bson.objectid import ObjectId
from collections import Counter
from itertools import islice
import itertools
import collections
import crawler
appropriate_punctuation = '!"#$%&()*+,./:;<=>?@[\\]^_`{|}~'
punct = re.compile(r'[\s{}]+'.format(re.escape(appropriate_punctuation)))
k = 2
db = dBDelegate.getDBConnection()
def remove_stopwords():
stopwords_file = open('stopwords.txt', 'r')
stopwords = stopwords_file.read()
stopwords = stopwords.split('\n')
stopwords_file.close()
return stopwords
def calculateTfidf(query, positional_index, song, avg_songlength, collection_length, weight, tfidf_values):
f = open('tfidf_samples.txt', 'a')
# for song in list_of_matching_documents:
similarity = 0
for word in set(query):
querytf = query.count(word)
raw_tf = len(positional_index[word]['document_dict'][song])
songtf = len(positional_index[word]['document_dict'])
