def calcularfrecuencia(self, texto, palabra=[]):
table = TfIdf()
table.add_document("informacion", texto)
resultado = table.similarities(palabra)[0][1]
if resultado > 0.0:
return True
return False
class SearchEngine:
def __init__(self):
self.tfidf = TfIdf()
def load_documents(self, documents):
for doc in documents:
name = doc.name
text = self.doc_to_text(doc)
words = self.text_to_word_array(text)
self.tfidf.add_document(name, words)
def query(self, query):
return self.tfidf.similarities(query)
def doc_to_text(self, doc):
parser = AKParser()
tree = parser.parse(doc)
text = ''
q = deque()
q.append(tree)
while True:
if not q:
break
node = q.pop()
if node.tag:
if 'link' == node.tag.lower():
val = node.children[0].value.split('|')[0]
text += f' {val} '
continue
if node.value:
text += f' {node.value} '
children = node.children
if children:
for c in children:
q.append(c)
return re.sub(' +', ' ', text)
def text_to_word_array(self, text):
regex = re.compile('[^a-zA-Z\s]')
text = regex.sub('', text)
text = re.sub(' +', ' ', text)
return text.lower().split()
def build_tfidf_model(self, files):
'''
It builds the Tf-Idf model
:param files: List of files of the corpora
:return: A Tf-Idf object with the model loaded
'''
tfidf = TfIdf()
for file_path in files:
with open(file_path) as f:
doc_name = file_path.split('/')[-1]
doc_text = f.readline().split()
tfidf.add_document(doc_name, doc_text)
return tfidf
def calculateTFIDFofNew(self, inputTitle, inputBody):
title = self.textToWordsArray(inputTitle)
sentences = self.textArrayToWordsArray(inputBody)
if len(sentences) < 1:
return []
table = TfIdf()
for i in range(0, len(sentences)):
table.add_document("sentences" + str(i), sentences[i])
result = []
similarities = table.similarities(title)
for similarity in similarities:
result.append(similarity[1])
resLen = len(result)
for i in range(resLen, 5):
result.append(0)
return result
def test_similarity(self):
table = TfIdf()
table.add_document("foo", ["a", "b", "c", "d", "e", "f", "g", "h"])
table.add_document("bar", ["a", "b", "c", "i", "j", "k"])
table.add_document("baz", ["k", "l", "m", "n"])
self.assertEqual(
table.similarities(["a", "b", "c"]),
[["foo", 0.6875], ["bar", 0.75], ["baz", 0.0]])
def test_similarity(self):
table = TfIdf()
table.add_document("doc1", [
"The", "game", "of", "life", "is", "a", "game", "of",
"everlasting", "learning"
])
table.add_document(
"doc2",
["The", "unexamined", "life", "is", "not", "worth", "living"])
table.add_document("doc3", ["Never", "stop", "learning"])
table.calculate_tf()
table.calculate_idf()
table.calculate_tf_idf()
"""self.assertEqual(
table.similarities(["life","learning"]),
[["foo", 1.0], ["bar", 0.707106781], ["baz", 0.707106781]])"""
print(table.similarities(["life", "learning"]))
class TestSequenceFunctions(unittest.TestCase):
def setUp(self):
self.unk_cutoff = 2
self.vocab = TfIdf(unk_cutoff=self.unk_cutoff)
def test_vocab(self):
self.vocab.train_seen("a", 300)
self.vocab.train_seen("b")
self.vocab.train_seen("c")
self.vocab.finalize()
# Infrequent words should look the same
self.assertEqual(self.vocab.vocab_lookup("b"),
self.vocab.vocab_lookup("c"))
# Infrequent words should look the same as never seen words
self.assertEqual(self.vocab.vocab_lookup("b"),
self.vocab.vocab_lookup("d"), "")
# The frequent word should be different from the infrequent word
self.assertNotEqual(self.vocab.vocab_lookup("a"),
self.vocab.vocab_lookup("b"))
def test_censor(self):
self.vocab.train_seen("a", 300)
self.vocab.train_seen("b")
self.vocab.train_seen("c")
self.vocab.finalize()
censored_a = [str(x) for x in self.vocab.tokenize("a b d")]
censored_b = [str(x) for x in self.vocab.tokenize("d b a")]
censored_c = [str(x) for x in self.vocab.tokenize("a b d")]
censored_d = [str(x) for x in self.vocab.tokenize("b d a")]
self.assertEqual(censored_a, censored_c)
self.assertEqual(censored_b, censored_d)
# Should add start and end tag
print(censored_a)
self.assertEqual(len(censored_a), 3)
self.assertEqual(censored_a[0], censored_b[2])
self.assertEqual(censored_a[1], censored_b[0])
def test_tf(self):
self.vocab.train_seen("a", 300)
self.vocab.finalize()
self.vocab.add_document("a a b")
# Test MLE
word_a = self.vocab.vocab_lookup("a")
word_b = self.vocab.vocab_lookup("b")
word_c = self.vocab.vocab_lookup("c")
self.assertAlmostEqual(self.vocab.term_freq(word_a), 0.66666666)
self.assertAlmostEqual(self.vocab.term_freq(word_b), 0.33333333)
self.assertAlmostEqual(self.vocab.term_freq(word_c), 0.33333333)
def test_df(self):
self.vocab.train_seen("a", 300)
self.vocab.train_seen("b", 100)
self.vocab.finalize()
self.vocab.add_document("a a b")
self.vocab.add_document("b b c")
self.vocab.add_document("a a a")
self.vocab.add_document("a a a")
# Test MLE
word_a = self.vocab.vocab_lookup("a")
word_b = self.vocab.vocab_lookup("b")
word_c = self.vocab.vocab_lookup("c")
word_d = self.vocab.vocab_lookup("d")
self.assertAlmostEqual(self.vocab.inv_docfreq(word_a),
log10(1.3333333))
self.assertAlmostEqual(self.vocab.inv_docfreq(word_b), log10(2.0))
self.assertAlmostEqual(self.vocab.inv_docfreq(word_c), log10(4.0))
self.assertAlmostEqual(self.vocab.inv_docfreq(word_d), log10(4.0))
for x in results:
f = open(urls[x - 1][0])
obj = json.load(f)
soup = BeautifulSoup(obj["content"],
"html.parser",
from_encoding="iso-8859-1")
joinedText = [
ps.stem(word) for word in ' '.join(
[p.get_text()
for p in soup.find_all("p", text=True)]).split()
]
line = set(
re.sub(r'[^a-zA-Z0-9]', ' ', joinedText.lower()).split())
#response = vectorizer.fit_transform([])
table.add_document(obj["url"], joinedText)
doc = html.fromstring(obj["content"])
for l in doc.iterlinks():
G.add_edge(obj["url"], l)
#print(obj["url"])
tf_idf = table.similarities(query)
for x in tf_idf:
rankDict[x[0]] += x[1]
#G = nx.barabasi_albert_graph(60, 41)
pr = nx.pagerank(G, 0.4)
for key, val in pr.items():
rankDict[key] += val
for y in sorted(rankDict.items(), key=lambda x: x[1], reverse=True):
def menu():
print("Que deseja fazer?")
print("1 - Consultar a informação do site do jornal ABola")
print("2 - Aplicar o algoritmo do TFIDF")
print("3 - Sair")
word = 0
nword = 0
narray = []
j = 0
for line in fileinput.input():
if line.replace("\n", "") == "1":
os.system('python3 web_scraper.py')
print("Que deseja fazer?")
print("1 - Consultar a informação do site do jornal ABola")
print("2 - Aplicar o algoritmo do TFIDF")
print("3 - Sair")
elif (line.replace("\n", "") == "2") or (word > 0):
if word == 0 and j == 0:
if (os.path.isdir("artigos") == False):
print(
'Necessita de gerar primeiro o conteúdo. Escolha a opção 1'
)
print("Que deseja fazer?")
print("1 - Consultar a informação do site do jornal ABola")
print("2 - Aplicar o algoritmo do TFIDF")
print("3 - Sair")
else:
filesA = os.listdir('artigos')
table = TfIdf()
for i in filesA:
with open('artigos/{}'.format(i), 'r') as content:
#print(content.read().split('h2'))
val = content.read().split('h2')
firstVal = val[0]
secondVal = val[1]
table.add_document(
'title{}'.format(i),
re.sub(r'[\W]', ' ', firstVal).lower().split())
table.add_document(
'text{}'.format(i),
re.sub(r'[\W]', ' ',
secondVal).lower().split())
word += 1
print('Indique quantas palavras quer comparar:')
elif (word == 1) and (j == 0):
if (line.replace("\n", "").isnumeric() and int(line) > 1):
nword = int(line)
word += 1
else:
print('Digite um número maior que 1')
elif (word > 1) and (word <= nword) and (j == 0):
if (line.replace("\n", "") != ''):
narray.append(line.replace("\n", "").lower())
word += 1
else:
j = 1
if (j == 1):
if line.replace("\n", "") != '':
narray.append(line.replace("\n", "").lower())
j += 1
if (j == 2):
print(narray)
fTDIDF = open('output' + narray[0] + '.html', 'w+')
fTDIDF.write(
'<h2>Resultados da aplicação do algoritmo:<h2>')
splitArray = {}
for s in table.similarities(narray):
if s[0].startswith('title'):
s[0] = s[0].replace('title', '')
if s[0] in splitArray.keys():
splitArray[s[0]] += s[1] * 0.7
else:
splitArray[s[0]] = s[1] * 0.7
elif s[0].startswith('text'):
s[0] = s[0].replace('text', '')
if s[0] in splitArray.keys():
splitArray[s[0]] += s[1] * 0.3
else:
splitArray[s[0]] = s[1] * 0.3
for elem in splitArray.keys():
fTDIDF.write(
'<p><h5><a href="artigos/{}" >'.format(elem) +
elem + '</a> -> ' + str(splitArray[elem]) +
'</h5></p>')
new = 2 # open in a new tab, if possible
url = "file:///home/ze/SPLN/WebScraper/output" + narray[
0] + ".html"
webbrowser.open(url, new=new)
word = 0
nword = 0
narray = []
print("Que deseja fazer?")
print("1 - Consultar a informação do site do jornal ABola")
print("2 - Aplicar o algoritmo do TFIDF")
print("3 - Sair")
elif (line.replace("\n", "") == "3") and (word == 0):
print("Obrigado pela sua visita")
fileinput.close()
i.lower()
lemmatizer.lemmatize(i)
return list
if __name__ =="__main__":
print("Please enter the URLs saperated by a space" )
urls=input().strip().split()
index(urls)
l1=[]
l2=tagrem.text_parser(urls,l1)
print("Please enter the query saperated by space")
query=input().strip().split()
print("The entered urls are")
for i in range(len(urls)):
print(i +":"+urls[i])
query=word_preprocessing(query)
table = TfIdf()
for i in range(len(urls)):
table.add_document(str(i),l2[i])
x=table.similarities(query)
print("URLs Indexed by cosine rankings are")
for i in x:
a=int(x[i][0])
print(i+":"+urls[a])
Created on 15 Jul 2018
@author: goksukara
'''
from tfidf import TfIdf
def addallfilesinpath(path):
pass
if __name__ == "__main__":
Tf_idf = TfIdf('s')
list=[['human', 'human', 'interface'],['ship', 'human', 'interface']]
list1=[['ship', 'humasn', 'interface']]
list2=[['human', 'human', 'am']]
list3=[['humafn', 'humasn', 'am1']]
#map(unicode,list)
Tf_idf.add_document(list)
Tf_idf.add_document(list1)
Tf_idf.Saverelatedwords()
Tf_idf.add_document(list2)
Tf_idf.add_document(list3)
Tf_idf.SaveCorpusdic()
Tf_idf.loaddictionary()
Tf_idf.buildmodel()
#Tf_idf.listnhighIdfs(10)
Tf_idf.getTF_IDF()
from tfidf import TfIdf
import unittest
from operator import itemgetter
table = TfIdf()
table.add_document("oqueE", [
"Business", "Model", "Generation", "ou", "simplesmente", "Canvas", "É",
"uma", "metodologia", "criada", "em", "meados", "dos", "anos", "2000",
"pelo", "Suíço", "Alex", "Osterwalder", "durante", "sua", "Tese", "de",
"Doutorado", "na", "prestigiada", "HEC", "Lausanne", ",e", "Yves",
"Pigneur.", "O", "Canvas", "é", "um", "esquema", "visual", "que",
"possibilita", "as", "pessoas", "cocriarem", "modelos", "de", "negócios",
"analisando", "9", "elementos", "que", "toda", "empresa", "ou",
"organização", "possuem:", "proposta", "de", "valor", "parcerias",
"chaves", ",atividades", "chaves", ",recursos", "chaves",
",relacionamento", "com", "clientes", ",segmentos", "de", "clientes",
",canais", "de", "distribuição", ",estrutura", "de", "custos", "e",
"fluxo", "de", "receitas", "(HSM", ",2017", "organização", "do",
"empreendedor", "de", "seus", "concorrentes", "ou", "qual", "quer",
"outra", "empresa", "Conforme", "Osterwalder", "e", "Pigneur", "o",
"conceito", "Canvas", "já", "foi", "aplicado", "e", "testado", "em",
"todo", "o", "mundo", "e", "já", "é", "utilizado", "por", "grandes",
"organizações", "como", "IBM", "Ericsson", "Deloitte", "Public", "Works",
"o", "governo", "do", "Canadá", "entre", "outras"
])
table.add_document("ondeUsar", [
"O", "canvas", "pode", "ser", "usado", "em", "companhia", "firma", "casa",
"negócio", "sociedade", "entidade", "estabelecimento", "instituição",
"organização", "empregador", "parceria", "corporação", "cometimento",
"desígnio", "empreendimento", "feito", "intento", "tentativa", "projeto",
from tfidf import TfIdf
import unittest
from operator import itemgetter
table = TfIdf()
table.add_document("oqueE", [
"business", "model", "generation", "ou", "simplesmente", "canvas", "e",
"uma", "metodologia", "criada", "em", "meados", "dos", "anos", "2000",
"pelo", "suico", "alex", "osterwalder", "durante", "sua", "tese", "de",
"doutorado", "na", "prestigiada", "hec", "lausanne", ",e", "yves",
"pigneur.", "o", "canvas", "e", "um", "esquema", "visual", "que",
"possibilita", "as", "pessoas", "cocriarem", "modelos", "de", "negócios",
"analisando", "9", "elementos", "que", "toda", "empresa", "ou",
"organização", "possuem:", "proposta", "de", "valor", "parcerias",
"chaves", ",atividades", "chaves", ",recursos", "chaves",
",relacionamento", "com", "clientes", ",segmentos", "de", "clientes",
"canais", "de", "distribuição", ",estrutura", "de", "custos", "e", "fluxo",
"de", "receitas", "hsm", ",2017", "organização", "do", "empreendedor",
"de", "seus", "concorrentes", "ou", "qual", "quer", "outra", "empresa",
"conforme", "osterwalder", "e", "pigneur", "o", "conceito", "canvas", "ja",
"foi", "aplicado", "e", "testado", "em", "todo", "o", "mundo", "e", "ja",
"e", "utilizado", "por", "grandes", "organizações", "como", "ibm",
"ericsson", "deloitte", "public", "works", "o", "governo", "do", "canada",
"entre", "outras"
])
table.add_document("ondeUsar", [
"O", "canvas", "pode", "ser", "usado", "em", "companhia", "firma", "casa",
"negócio", "sociedade", "entidade", "estabelecimento", "instituição",
"organização", "empregador", "parceria", "corporação", "cometimento",
"desígnio", "empreendimento", "feito", "intento", "tentativa", "projeto",