LDA-KNN.py

# coding: utf-8

# In[1]:

# 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 neighbors


logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
logging.root.level = logging.INFO
model_name="LDA-KNN"
id2word_wiki=gensim.corpora.Dictionary()


# In[2]:

# List Categories:
#category_list = ["Mathematics","Technology","Music"]
#category_list = ["Mathematics","Technology","Music","History","Geography","Arts","Health","Nature","Religion","Literature"]
category_list = ["1977 introductions", "Language" ,"Arts", "Asia", "Asthma", "Automobiles", "BRICS nation", "Belief", "Climate", "Crime", "Culture", "Dance", "Deserts", "Dolls", "Earth", "Engines", "Fishing", "Folklore", "Games", "Geography", "Glass", "Government agencies", "Health", "History", "Humans", "Hygiene", "India", "Industry", "Internet", "Law", "Life", "Literature", "Mathematics", "Matter", "Millionaires", "Music", "Nature", "Nothing", "Parties", "Peace", "Politics", "Religion", "Sexology", "Society", "Songs", "Space", "Technology", "Television", "Transport", "Water sports"]
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[3]:

# 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[4]:

# 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[5]:

# 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[6]:

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) 

    #wiki_bow_path = root_folder+'wiki_bow.mm'
    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 KNeighborsClassifier")
n_neighbors = 15
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')
clf.fit(X, y)


# In[ ]:

# Test 
def get_part(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

results=""
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=get_part(doc_path)
            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[ ]: