How to write trained Word2Vec model to CSV with DeepLearning4j

I used DeepLearning4j to train word2vec model. Then I had to save the dictionary to CSV so I can run some clustering algorithms on it.

Sounded like a simple task, but it took a while, and here is the code to do this:

 

   private void writeIndexToCsv(String csvFileName, Word2Vec model) {

        CSVWriter writer = null;
        try {
            writer = new CSVWriter(new FileWriter(csvFileName));
        } catch (IOException e) {
            e.printStackTrace();
        }

        VocabCache<VocabWord> vocCache =  model.vocab();
        Collection<VocabWord> wrds = vocCache.vocabWords();

        for(VocabWord w : wrds) {
            String s = w.getWord();
            System.out.println("Looking into the word:");
            System.out.println(s);
            StringBuilder sb = new StringBuilder();
            sb.append(s).append(",");
            double[] wordVector = model.getWordVector(s);
            for(int i = 0; i < wordVector.length; i++) {
                sb.append(wordVector[i]).append(",");
            }

            writer.writeNext(sb.toString().split(","), false);
        }

        try {
            writer.close();
        } catch (IOException e) {
            e.printStackTrace();
        }

    }

How to parse dynamic HTML content using Python

In the previous tutorial we learning how to parse HTML in Python. In the Python tutorial we are going to learn to to parse dynamic HTML content generated by JavaScript, jQuery, Ajax, Angular or other dynamic pages technology.

What’s the problem with parsing dynamic HTML content in Python and in general?

The problem is that when you request contents of a HTML page, you are presented HTML, CSS and scripts returned from the server. If the page is dynamic, what you get is only a couple of scripts that are meant to be interpreted by your browser that, in its turn, will eventually display HTML content for a user.

That leads us to the idea that we should first render the page and then grab its HTML. Also it should take some time to render the page since sometimes the content is quite “heavy” and it takes some time to load it.

So, along with pure Python we should use some kind of UI component and in particular a Web View or some kind of Web frame.

One of the options is to use Qt for Python and to handle page rendering events and another one (which I honestly prefer more) is to use selenium for python.

So, let’s get down to writing some code but before that let’s elaborate and approach.

  1. Open web view with URL.
  2. Wait untill the page is loaded. Often the criteria here is a loaded div of some class.
  3. Grab the rendered HTML.
  4. Process it further using beautiful soup

You will need Chrome Web Driver to run the web view.

Also you will have to install selenium as well as libs from previous tutorial:

pip install selenium

So here is the Python code to parse dynamic content:

#import selenium compnents, urllib, beautiful soup
from bs4 import BeautifulSoup
from selenium import webdriver
from urllib import urlopen
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.common.by import By


#url - the url to fetch dynamic content from.
#delay - second for web view to wait
#block_name - id of the tag to be loaded as criteria for page loaded state.
def fetchHtmlForThePage(url, delay, block_name):
	#supply the local path of web driver.
	#in this example we use chrome driver
	browser = webdriver.Chrome('/Applications/chromedriver')
	#open the browser with the URL
	#a browser windows will appear for a little while
	browser.get(url)
	try:
	#check for presence of the element you're looking for
		element_present = EC.presence_of_element_located((By.ID, block_name))
		WebDriverWait(browser, delay).until(element_present)

	#unless found, catch the exception
	except TimeoutException:
		print "Loading took too much time!"	

	#grab the rendered HTML
	html = browser.page_source
	#close the browser
	browser.quit()
	#return html
	return html


#call the fetching function we created
html = fetchHtmlForThePage(url, 5, 're-Searchresult')
#grab HTML document
soup = BeautifulSoup(html)
#process it further as you wish.....
#.....
processFetchedUrls(soup, path)
	

So here how to parse dynamic HTML content generated with JavaScript with the of Python.

Visit us to get help with your Python challenge of let us know if can help you with your digital needs.

How to parse emails from HTML in Python

In this tutorial we are going to get an idea of how to parse emails from HTML using Python.

Python is a scripting language easy to get started and is perfect for tasks like parsing emails.

So let’s elaborate an approach of how parsing works:

  1. Initialize a queue of URLs. The first item will be the initial URL.
  2. Initialize a set of already visited URL to avoid repetitions.
  3. Start parsing the current URL from the queue.
  4. Add the URL to processed URLs.
  5. Extract the whole HTML, search for an email pattern using a regex.
  6. If one or multiple emails were found, write to CSV.
  7. Loop through <a> tags found.
  8. Check if URL is relative or absolute.
  9. Check if URL is already in the processed URLs set. If not, add to the processing queue
  10. Repeat from step 3.

Before launching the script don’t forget to install proper libraries.

Using command line do:

pip install requests
pip install urlparse
pip install csv
pip install beautifulsoup4

Once you have the libraries installed, you’ll be able to check the script.

from bs4 import BeautifulSoup
import requests
import requests.exceptions
from urlparse import urlparse
from urlparse import urlsplit
from collections import deque
import re
import csv

#initialize CSV writer and filename
cw = csv.writer(open("Singa.csv",'a'), delimiter=',')
# a queue of urls, start
new_urls = deque(['https://foundersgrid.com/50-singapore-startups/'])

# a set of urls that we have already crawled
processed_urls = set()

# a set of crawled emails
emails = set()

# process urls one by one until we exhaust the queue
while len(new_urls):

    #extract the last one from queue
	url = new_urls.popleft()
	#mark as visited by adding to proccessed URLs
	processed_urls.add(url)

    # break down the extract the base url to resolve relative links
	parts = urlsplit(url)
	base_url = "{0.scheme}://{0.netloc}".format(parts)
	path = url[:url.rfind('/')+1] if '/' in parts.path else url

    # get url's content
	#handle exception if any
	try:
		response = requests.get(url)
	except (requests.exceptions.MissingSchema, requests.exceptions.ConnectionError):
        # skip pages with errors
		continue

    # extract all email addresses and add them into the resulting set
	new_emails = set(re.findall(r"[a-z0-9\.\-+_][email protected][a-z0-9\.\-+_]+\.[a-z]+", response.text, re.I))
	emails.update(new_emails)
	print new_emails
	#write to CSV the new mails.
	#alternatively you can write the emails set to CSV after parsing
	for em in new_emails:
		cw.writerow([em,])

    # create a beutiful soup object as representation of the html page
	soup = BeautifulSoup(response.text)

    # walk through a anchords
	for anchor in soup.find_all("a"):
        # extract link url from the anchor
		link = anchor.attrs["href"] if "href" in anchor.attrs else ''
        # resolve relative links
		if link.startswith('/'):
			link = base_url + link
		elif not link.startswith('http'):
			link = path + link
        # add the new url to the queue if it was not enqueued nor processed yet
		if not link in new_urls and not link in processed_urls:
			new_urls.append(link)

As you can see, parsing emails in Python is rather a simple task.

If you have any questions on this tutorial, you can contact us [email protected]

Also, if you need assistance with data collection or any other digital service, please let us know.

Don’t forget to share the tutorial and visit us at https://cyberwhale.tech

PS. In the next tutorial we will discuss how to parse dynamic HTML content using Python.

Update XML node in Python

I like python because it’s minimalistic and elegant.
Let’s see how to update an XML node using ElementTree.

We use CD catalog in XML as a datasource.

<?xml version="1.0" encoding="iso-8859-1" ?>
<?xml-stylesheet type="text/xsl" href="cdcatalog.xsl"?>
 <catalog>
<cd>
  <title>empire burlesque</title> 
  <artist>bob dylan</artist> 
  <country>usa</country> 
  <company>columbia</company> 
  <price>10.90</price> 
  <year>1985</year> 
  </cd>
 <cd>
  <title>hide your heart</title> 
  <artist>bonnie tyler</artist> 
  <country>uk</country> 
  <company>cbs records</company> 
  <price>9.90</price> 
  <year>1988</year> 
  </cd>
 <cd>
  <title>greatest hits</title> 
  <artist>dolly parton</artist> 
  <country>usa</country> 
  <company>rca</company> 
  <price>9.90</price> 
  <year>1982</year> 
  </cd>
</catalog>

Here is the python script itself.

import xml.etree.ElementTree as ET	

#parse XML file
tree = ET.parse('catalog_.xml')

#get root
root = tree.getroot()
#iterate over each price node (which is subchild of cd node)
for price in root.iter('price'):
	#get the price of CD, multiply 10
	new_price = float(price.text) * 10
	#update the text (value) of the node
	price.text = str(new_price)
	#add 'updated' attribute to mark node updated=yes
	price.set('updated', 'yes')

#can also use the same file if you want to directly update file.
tree.write('catalog_new.xml')

And the output is the following:

<catalog>
<cd>
  <title>empire burlesque</title> 
  <artist>bob dylan</artist> 
  <country>usa</country> 
  <company>columbia</company> 
  <price updated="yes">109.0</price> 
  <year>1985</year> 
  </cd>
 <cd>
  <title>hide your heart</title> 
  <artist>bonnie tyler</artist> 
  <country>uk</country> 
  <company>cbs records</company> 
  <price updated="yes">99.0</price> 
  <year>1988</year> 
  </cd>
 <cd>
  <title>greatest hits</title> 
  <artist>dolly parton</artist> 
  <country>usa</country> 
  <company>rca</company> 
  <price updated="yes">99.0</price> 
  <year>1982</year> 
  </cd>
</catalog>

Remove duplicate lines from a file in Scala

How to remove duplicate lines from csv or txt file?

The answer is quite straightforward: you basically need BufferedReader and BufferedWriter, and this also works for large files quite well.

 

 def removeDuplicatesFromFile(fileName : String) {

    val reader = new BufferedReader(new FileReader(fileName))
    val lines = new mutable.HashSet[String]()
    var line: String = null
    while ({line = reader.readLine; line != null}) {
      lines.add(line)
    }
    reader.close

    val writer = new BufferedWriter(new FileWriter(fileName))
    for (unique <- lines) {
      writer.write(unique)
      writer.newLine()
    }
    writer.close

  }

Python networking example

Here is a small example demonstrating get requests in Python.

pip install requests

And the code itself

import library
import requests

#prepare paramteters
parameters = {'date:':'2000:2010', 'format':'xml'}

#prepare URL
url = 'http://api.worldbank.org/countries/br/indicators/SP.POP.TOTL'

#call get method and save data into the response
r = requests.get(url, params=parameters)

#print the url considering the params
print r.url

#check for status code
statusCode = int(r.status_code)

#if failed request print the mesage, else print response headers and text
if statusCode != 200:
print 'Request Failed'
else:
print r.headers['content-type']
#print r.json() for json requests
print r.text