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LDA-Linear+Classifier.py
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# coding: utf-8
# In[ ]:
# imports:
import time;
import urllib.request
import csv
import os
import itertools
import logging
import json
import numpy as np
import gensim
from bs4 import BeautifulSoup
from urllib.parse import urlencode
from gensim.utils import smart_open, simple_preprocess
from gensim.corpora.wikicorpus import _extract_pages, filter_wiki
from gensim.parsing.preprocessing import STOPWORDS
from xml.etree.cElementTree import iterparse
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import svm
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
logging.root.level = logging.INFO
model_name="LDA-Linear Classifier"
id2word_wiki=gensim.corpora.Dictionary()
# In[ ]:
# List Categories:
#category_list = ["Mathematics","Technology","Music"]
category_list = ["Mathematics","Technology","Music","History","Geography","Arts","Health","Nature","Religion","Literature"]
test_folder = "./test/"
root_folder = "./Simplex/"
list.sort(category_list)
folder_name=''.join([x[0]+x[1]+x[2] for x in category_list])
root_folder='./'+folder_name+'/'
print("Checking folder : "+folder_name)
if not os.path.exists(root_folder):
os.mkdir(root_folder)
if not os.path.exists(root_folder+model_name):
os.mkdir(root_folder+model_name)
wiki_bow_path = root_folder+'wiki_bow.mm'
wiki_dict_path = root_folder+'wiki_dict.dict'
# In[ ]:
# Download Page Ids:
#https://petscan.wmflabs.org/?language=en&project=wikipedia&depth=1&format=csv&categories=mathematics&doit=Do it!
print ("Scanning CSV")
for cat in category_list:
if not os.path.exists(root_folder+cat+".csv"):
url="https://petscan.wmflabs.org/?language=en&project=wikipedia&depth=1&format=csv&doit=Do%20it!&categories="+cat
urllib.request.urlretrieve(url, root_folder+cat+".csv")
print("Downloading ",cat+".csv")
# In[ ]:
# CSV to XML Download data:
def get_data(ids,outputFile):
url="https://en.wikipedia.org/w/api.php?action=query&prop=revisions&rvprop=content&format=xml&pageids="+ids
req = urllib.request.urlopen(url)
if req.getcode() == 200:
soup = BeautifulSoup(req.read(), 'html.parser')
s = soup.find_all('page')
for si in s:
outputFile.write(str(si))
def batch_train(file):
outputFile = open(file.replace(".csv",".xml"), 'a', encoding="utf8")
outputFile.write("<pages>")
csvReader = csv.reader(open(file,'r'))
totalRecords = sum(1 for row in csv.reader(open(file,'r',encoding="UTF-8")) )
print (file.replace(".csv",".xml"),totalRecords)
start = 0
end = start + 50
while (start <= totalRecords):
pageIds = ""
for row in itertools.islice(csv.reader(open(file,'r',encoding="UTF-8")),start,end):
pageIds = pageIds + row[2] + "|"
get_data(pageIds,outputFile)
start = end + 1
end = start + 50
if end> totalRecords:
end=totalRecords
outputFile.write("</pages>")
for path, subdirs, files in os.walk(root_folder):
for file in files:
if file.endswith('.csv'):
if not os.path.exists(root_folder+file.replace(".csv",".xml")):
print("Downloading ",root_folder+file.replace(".csv",".xml"))
batch_train(path + file)
print ("Done downloading")
# In[ ]:
# Train Model
def head(stream, n=10):
"""Convenience fnc: return the first `n` elements of the stream, as plain list."""
return list(itertools.islice(stream, n))
def my_extract_pages(f):
elems = (elem for _, elem in iterparse(f, events=("end",)))
page_tag = "rev"
for elem in elems:
if elem.tag == page_tag and elem.text != None:
text = elem.text
yield text
elem.clear()
def tokenize(text):
return [token for token in simple_preprocess(text) if token not in STOPWORDS]
def iter_wiki(dump_file):
"""Yield each article from the Wikipedia dump, as a `(title, tokens)` 2-tuple."""
ignore_namespaces = 'Wikipedia Category File Portal Template MediaWiki User Help Book Draft'.split()
for text in my_extract_pages(smart_open(dump_file)):
text = filter_wiki(text)
tokens = tokenize(text)
if len(tokens) < 50:
continue # ignore short articles and various meta-articles
yield tokens
# In[ ]:
print("Training Model")
class WikiCorpus(object):
def __init__(self, dump_file, dictionary, clip_docs=None):
"""
Parse the first `clip_docs` Wikipedia documents from file `dump_file`.
Yield each document in turn, as a list of tokens (unicode strings).
"""
self.dump_file = dump_file
self.dictionary = dictionary
self.clip_docs = clip_docs
def __iter__(self):
for tokens in itertools.islice(iter_wiki(self.dump_file), self.clip_docs):
yield self.dictionary.doc2bow(tokens)
def __len__(self):
return self.clip_docs
if not (os.path.exists(wiki_bow_path) and os.path.exists(wiki_dict_path)):
for path, subdirs, files in os.walk(root_folder):
del subdirs[:]
for file in files:
if file.endswith('.xml'):
doc_path = path + file
stream = iter_wiki(doc_path)
doc_stream = (tokens for tokens in iter_wiki(doc_path))
id2word_wiki.merge_with(gensim.corpora.Dictionary(doc_stream))
id2word_wiki.filter_extremes(no_below=10, no_above=0.1)
# create a stream of bag-of-words vectors
wiki_corpus = WikiCorpus(doc_path, id2word_wiki)
id2word_wiki.save(wiki_dict_path)
gensim.corpora.MmCorpus.serialize(wiki_bow_path, wiki_corpus)
id2word_wiki = gensim.corpora.Dictionary.load(wiki_dict_path)
mm_corpus = gensim.corpora.MmCorpus(wiki_bow_path)
clipped_corpus = gensim.utils.ClippedCorpus(mm_corpus, 4000)
lda_model = gensim.models.LdaModel(clipped_corpus, num_topics=len(category_list), id2word=id2word_wiki, passes=10, alpha='asymmetric')
print("done")
# In[ ]:
y=[]
X=[]
def get_values(l):
g=[0]*len(category_list)
for i in l:
g[i[0]]=i[1]
return g
for path, subdirs, files in os.walk(root_folder):
del subdirs[:]
for file in files:
if file.endswith('.xml'):
doc_path = path + file
topic = file.replace(".xml","")
test_doc = [tokens for tokens in iter_wiki(doc_path)]
X+=[get_values(lda_model[id2word_wiki.doc2bow(tokens)]) for tokens in test_doc]
y+=[category_list.index(topic)]*len(test_doc)
print("Feeding data to SVC")
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X, y)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)
poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)
lin_svc = svm.LinearSVC(C=C).fit(X, y)
# In[ ]:
# Test
results=""
def getPart(testFile):
test_doc = [tokens for tokens in iter_wiki(testFile)]
part = [get_values(lda_model[id2word_wiki.doc2bow(tokens)]) for tokens in test_doc]
return part
print("Testing results")
for path, subdirs, files in os.walk(test_folder):
for file in files:
if file.endswith('.xml'):
doc_path = test_folder + file
results+=file.replace(".xml","")+": "
part=getPart(doc_path)
for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):
#print(category_list[clf.predict(part)[0]])
results+=category_list[clf.predict(part)[0]]
results += "\n\n"
outputFile = open(root_folder+model_name+"/"+model_name+str(len(category_list))+".txt", 'w', encoding="utf8")
outputFile.write(results)
print(" Done ")
# In[ ]: