Combined.py

# coding: utf-8
# 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 sklearn.cluster import KMeans
from sklearn import neighbors
from sklearn import svm

logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
logging.root.level = logging.INFO

id2word_wiki=gensim.corpora.Dictionary()

# 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)

wiki_bow_path = root_folder+'wiki_bow.mm'
wiki_dict_path = root_folder+'wiki_dict.dict'

# 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")
    

# CSV to XML Download data:

def get_data(ids,output_file):
    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:
            output_file.write(str(si))

def batch_train(file):
    output_file = open(file.replace(".csv",".xml"), 'a', encoding="utf8")
    output_file.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,output_file)
        start = end + 1
        end = start + 50
        if end> totalRecords:
            end=totalRecords
            
    output_file.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")

# 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):
    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
        




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, 8000) 

lda_model = gensim.models.LdaModel(clipped_corpus, num_topics=len(category_list), id2word=id2word_wiki, passes=10, alpha='asymmetric')
print("done")

tfidf_model = gensim.models.TfidfModel(mm_corpus, id2word=id2word_wiki)
lsi_model = lsi_model = gensim.models.LsiModel(tfidf_model[mm_corpus], id2word=id2word_wiki, num_topics=len(category_list))
print("done")

def calculate_centroid(topic_docs):
    test_doc = [tokens for tokens in iter_wiki(topic_docs)]
    part = [lda_model[id2word_wiki.doc2bow(tokens)] for tokens in test_doc]
    
    topic_dic={}
    
    for i in range(len(category_list)):
        topic_dic[i]=0
        
    for doc in part:
        for p in doc:
            topic_dic[p[0]] += p[1]
    
    centroid = [(x, topic_dic[x]/len(part)) for x in range(len(category_list))]
    return centroid

print("Calcuating centroid")

centroids_dict={}
for path, subdirs, files in os.walk(root_folder):
    del subdirs[:]
    for file in files:
        if file.endswith('.xml'):
            doc_path = path + file
            centroid = calculate_centroid(doc_path)
            centroids_dict[file.replace(".xml","")]=centroid

print("Centroid calculation done")

km=None
def get_values(l):
    g=[0]*len(category_list)
    for i in l:
        g[i[0]]=i[1]
    return g

def calculate_kmeans(part):
    km = KMeans(n_clusters=len(category_list), init='k-means++', max_iter=500, n_init=1)
    km.fit(part)
    return km

print("Calculating KMeans ")
for path, subdirs, files in os.walk(root_folder):
    del subdirs[:]
    part=[]
    for file in files:
        if file.endswith('.xml'):
            doc_path = path + file
            test_doc = [tokens for tokens in iter_wiki(doc_path)]
            part+=[get_values(lda_model[id2word_wiki.doc2bow(tokens)]) for tokens in test_doc]
    km = calculate_kmeans(part)

y=[]
X=[]

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)

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)

def draw_graph(x_label,y,file,text_data,topic):
    x = np.arange(len(x_label))  # the x locations for the groups
    width = 0.1       # the width of the bars

    fig, ax = plt.subplots()
    fig.set_figheight(6)
    fig.set_figwidth(8)
    rects = ax.bar(x, y, width, color='blue')
    ax.set_ylabel('Probabilities')
    ax.set_xlabel('Categories')
    ax.set_title('Topic Distribution of: ' + topic)
    ax.title.set_position([.5, 1.2])
    ax.set_ylim([0,1])
    
    def autolabel(rects):
        """
        Attach a text label above each bar displaying its height
        """
        for rect in rects:
            height = rect.get_height()
            ax.text(rect.get_x() - rect.get_width()*2, 1.05 * height,round(height,2))
    
    autolabel(rects)
    plt.tight_layout(pad=6)
    plt.xticks(x,category_list,rotation=90)
    
    plt.savefig(file.replace(".xml","_")+str(len(category_list))+'.png')
    plt.close('all')

# Test LDA - Cosine
model_name="LDA-Cosine"
if not os.path.exists(root_folder+model_name):
    os.mkdir(root_folder+model_name)
    
def get_part(testFile):
    test_doc = [tokens for tokens in iter_wiki(testFile)]
    part = [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
            path=get_part(doc_path)
            results+=file.replace(".xml","")+"\n\n"
            graph_data=[]
            text_data=""
            for topic in category_list:
                
                cos_dis=np.mean([gensim.matutils.cossim(p1, p2) for p1, p2 in zip([centroids_dict[topic]], path)])
                text_data +=topic+":"+str(cos_dis)+"\n"
                
                graph_data.append(np.mean([gensim.matutils.cossim(p1, p2) for p1, p2 in zip([centroids_dict[topic]], path)]))
            results+=text_data+"\n\n\n\n"
                
            draw_graph(list(centroids_dict.keys()),graph_data,root_folder+model_name+'/'+file,text_data,file)
output_file = open(root_folder+model_name+"/"+model_name+str(len(category_list))+".txt", 'w', encoding="utf8")
output_file.write(results)
output_file.close()
print(" Done ")

# Test LDA- KMeans
model_name="LDA-KMeans"
if not os.path.exists(root_folder+model_name):
    os.mkdir(root_folder+model_name)
    
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[km.predict(part)[0]]
            results += "\n\n"
                
output_file = open(root_folder+model_name+"/"+model_name+str(len(category_list))+".txt", 'w', encoding="utf8")
output_file.write(results)
output_file.close()
print(" Done ")

#Test LDA _ KNN
model_name="LDA-KNN"
if not os.path.exists(root_folder+model_name):
    os.mkdir(root_folder+model_name)

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"
                
output_file = open(root_folder+model_name+"/"+model_name+str(len(category_list))+".txt", 'w', encoding="utf8")
output_file.write(results)
output_file.close()
print(" Done ")
    




#LDA Linear Classifier
model_name="LDA-Linear Classifier"

if not os.path.exists(root_folder+model_name):
    os.mkdir(root_folder+model_name)
    
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)
            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"
                
output_file = open(root_folder+model_name+"/"+model_name+str(len(category_list))+".txt", 'w', encoding="utf8")
output_file.write(results)
output_file.close()
print(" Done ")

model_name="LSI-Cosine"

if not os.path.exists(root_folder+model_name):
    os.mkdir(root_folder+model_name)
# Test 
def get_part(testFile):
    test_doc = [tokens for tokens in iter_wiki(testFile)]
    part = [lsi_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
            path=get_part(doc_path)
            results+=file.replace(".xml","")+"\n\n"
            graph_data=[]
            text_data=""
            graph_topic=[]
            for topic in category_list:
                
                cos_dis=np.mean([gensim.matutils.cossim(p1, p2) for p1, p2 in zip([centroids_dict[topic]], path)])
                text_data +=  topic+":"+str(cos_dis)+"\n"
                if(np.mean([gensim.matutils.cossim(p1, p2) for p1, p2 in zip([centroids_dict[topic]], path)])>0):
                    graph_data.append(np.mean([gensim.matutils.cossim(p1, p2) for p1, p2 in zip([centroids_dict[topic]], path)]))
                    graph_topic.append(topic)
            results+=text_data+"\n\n\n\n"
            draw_graph(graph_topic,graph_data,root_folder+model_name+'/'+file,text_data,file)
output_file = open(root_folder+model_name+"/"+model_name+str(len(category_list))+".txt", 'w', encoding="utf8")
output_file.write(results)
output_file.close()
print(" Done ")