Wednesday, September 24, 2014

Text summarization with NLTK

The target of the automatic text summarization is to reduce a textual document to a summary that retains the pivotal points of the original document. The research about text summarization is very active and during the last years many summarization algorithms have been proposed.
In this post we will see how to implement a simple text summarizer using the NLTK library (which we also used in a previous post) and how to apply it to some articles extracted from the BBC news feed. The algorithm that we are going to see tries to extract one or more sentences that cover the main topics of the original document using the idea that, if a sentences contains the most recurrent words in the text, it probably covers most of the topics of the text. Here's the Python class that implements the algorithm:
from nltk.tokenize import sent_tokenize,word_tokenize
from nltk.corpus import stopwords
from collections import defaultdict
from string import punctuation
from heapq import nlargest

class FrequencySummarizer:
  def __init__(self, min_cut=0.1, max_cut=0.9):
     Initilize the text summarizer.
     Words that have a frequency term lower than min_cut 
     or higer than max_cut will be ignored.
    self._min_cut = min_cut
    self._max_cut = max_cut 
    self._stopwords = set(stopwords.words('english') + list(punctuation))

  def _compute_frequencies(self, word_sent):
      Compute the frequency of each of word.
       word_sent, a list of sentences already tokenized.
       freq, a dictionary where freq[w] is the frequency of w.
    freq = defaultdict(int)
    for s in word_sent:
      for word in s:
        if word not in self._stopwords:
          freq[word] += 1
    # frequencies normalization and fitering
    m = float(max(freq.values()))
    for w in freq.keys():
      freq[w] = freq[w]/m
      if freq[w] >= self._max_cut or freq[w] <= self._min_cut:
        del freq[w]
    return freq

  def summarize(self, text, n):
      Return a list of n sentences 
      which represent the summary of text.
    sents = sent_tokenize(text)
    assert n <= len(sents)
    word_sent = [word_tokenize(s.lower()) for s in sents]
    self._freq = self._compute_frequencies(word_sent)
    ranking = defaultdict(int)
    for i,sent in enumerate(word_sent):
      for w in sent:
        if w in self._freq:
          ranking[i] += self._freq[w]
    sents_idx = self._rank(ranking, n)    
    return [sents[j] for j in sents_idx]

  def _rank(self, ranking, n):
    """ return the first n sentences with highest ranking """
    return nlargest(n, ranking, key=ranking.get)
The FrequencySummarizer tokenizes the input into sentences then computes the term frequency map of the words. Then, the frequency map is filtered in order to ignore very low frequency and highly frequent words, this way it is able to discard the noisy words such as determiners, that are very frequent but don't contain much information, or words that occur only few times. And finally, the sentences are ranked according to the frequency of the words they contain and the top sentences are selected for the final summary.

To test the summarizer, let's create a function that extract the natural language from a html page using BeautifulSoup:
import urllib2
from bs4 import BeautifulSoup

def get_only_text(url):
  return the title and the text of the article
  at the specified url
 page = urllib2.urlopen(url).read().decode('utf8')
 soup = BeautifulSoup(page)
 text = ' '.join(map(lambda p: p.text, soup.find_all('p')))
 return soup.title.text, text
We can finally apply our summarizer on a set of articles extracted from the BBC news feed:
feed_xml = urllib2.urlopen('').read()
feed = BeautifulSoup(feed_xml.decode('utf8'))
to_summarize = map(lambda p: p.text, feed.find_all('guid'))

fs = FrequencySummarizer()
for article_url in to_summarize[:5]:
  title, text = get_only_text(article_url)
  print '----------------------------------'
  print title
  for s in fs.summarize(text, 2):
   print '*',s
And here are the results:
BBC News - Scottish independence: Campaigns seize on Scotland powers pledge
* Speaking ahead of a visit to apprentices at an engineering firm in Renfrew, Deputy First Minister Nicola Sturgeon said: Only a 'Yes' vote will ensure we have full powers over job creation - enabling us to create more and better jobs across the country.
* Asked if the move smacks of panic, Mr Alexander told BBC Breakfast: I don't think there's any embarrassment about placing policies on the front page of papers with just days two go.
BBC News - US air strike supports Iraqi troops under attack
* Gabriel Gatehouse reports from the front line of Peshmerga-held territory in northern Iraq The air strike south-west of Baghdad was the first taken as part of our expanded efforts beyond protecting our own people and humanitarian missions to hit Isil targets as Iraqi forces go on offence, as outlined in the president's speech last Wednesday, US Central Command said.
* But Iran's Supreme Leader Ayatollah Ali Khamenei said on Monday that the US had requested Iran's co-operation via the US ambassador to Iraq.
BBC News - Passport delay victims deserve refund, say MPs
* British adult passport costs Normal service - £72.50 Check  Send - Post Office staff check application correct and it is sent by Special Delivery - £81.25 Fast-Track - Applicant attends Passport Office in person and passport delivered within one week - £103 Premium - Passport available for collection on same day applicant attends Passport Office - £128 In mid-June it announced that - for people who could prove they were booked to travel within seven days and had submitted passport applications more than three weeks earlier - there would be a free upgrade to its fast-track service.
* The Passport Office has since cut the number of outstanding applications to around 90,000, but the report said: A number of people have ended up out-of-pocket due to HMPO's inability to meet its service standard.
BBC News - UK inflation rate falls to 1.5%
* Howard Archer, chief UK and European economist at IHS Global Insight, said: August's muted consumer price inflation is welcome news for consumers' purchasing power as they currently continue to be hampered by very low earnings growth.
* Consumer Price Index (CPI) inflation fell to 1.5% from 1.6% in August, the Office for National Statistics said.
BBC News - Thailand deaths: Police have 'number of suspects'
* The BBC's Jonathan Head, on Koh Tao, says police are focussing on the island's Burmese community BBC south-east Asia correspondent Jonathan Head said the police's focus on Burmese migrants would be quite controversial as Burmese people were often scapegoated for crimes in Thailand.
* By Jonathan Head, BBC south-east Asia correspondent The shocking death of the two young tourists has cast a pall over this scenic island resort Locals say they can remember nothing like it happening before.
Of course, the evaluation a text summarizer is not an easy task. But, from the results above we note that the summarizer often picked quoted text reported in the original article and that the sentences picked by the summarizer often represent decent insights if we consider the title of the article.

Wednesday, August 27, 2014

Visualizing electricity prices with Plotly

We have already mentioned plotly many times (here are other two posts about it) and this time we'll see how to use it in order to build an interactive visualization of the latest data about the domestic electricity prices provided by International Energy Agency (IEA).

In the chart that we are going to make, we will show the prices of the domestic electricity among the countries monitored by IEA in 2013 with a bar chart where each bar shows the electricity price and the fraction of the price represented by the taxes.

First, we import the data (the full data is available here, in this post we'll use only the Table 5.5.1 in cvs format) using pandas:
import pandas as pd
ieaprices = pd.read_csv('iea_prices.csv',
ieaprices = ieaprices.dropna()
countries = ieaprices.sort('2013_with_tax').index
Then, we arrange the data in order create a plotly bar chart:
from plotly.graph_objs import Bar,Data,Layout,Figure
from plotly.graph_objs import XAxis,YAxis,Marker,Scatter,Legend

prices_bars = []

# computing the taxes
taxes = ieaprices['2013_with_tax']-ieaprices['2013_no_tax']

# adding the prices to the chart
             name='price without taxes'))

# adding the taxes to the chart
And now we are ready to submit the data to the plotly server to render the chart:
import plotly.plotly as py

py.sign_in("SexyUser", "asexykeyforasexyuser")

meadian_line = Scatter(
    marker=Marker(color='rgb(40, 40, 40)'),

data = Data(prices_bars+[meadian_line])

layout = Layout(
    title='Domestic electricity prices in the IEA in 2013',
    yaxis=YAxis(title='Price (Pence per Kwh)'),

fig = Figure(data=data, layout=layout)

# this line will work only in ipython
# use py.plot() in other environments
plot_url = py.iplot(fig, filename='ieaprices2013') 
The result should look like this:

Looking at the chart we note that, during 2013, the average domestic electricity prices, including taxes, in Denmark and Germany were the highest in the IEA. We also note that in Denmark the fraction of taxes paid is higher than the actual electricity price whereas in Germany the actual electricity price and the taxes are almost the same. Interestingly, USA has the lowest price and the lowest taxation.

This post shows how to create one of the charts commented here, where a more insights about the IEA data are provided.

Wednesday, August 20, 2014

Quick HDF5 with Pandas

HDF5 is a format designed to store large numerical arrays of homogenous type. It cames particularly handy when you need to organize your data models in a hierarchical fashion and you also need a fast way to retrieve the data. Pandas implements a quick and intuitive interface for this format and in this post will shortly introduce how it works.

We can create a HDF5 file using the HDFStore class provided by Pandas:
import numpy as np
from pandas import HDFStore,DataFrame
# create (or open) an hdf5 file and opens in append mode
hdf = HDFStore('storage.h5')
Now we can store a dataset into the file we just created:
df = DataFrame(np.random.rand(5,3), columns=('A','B','C'))
# put the dataset in the storage
hdf.put('d1', df, format='table', data_columns=True)
The structure used to represent the hdf file in Python is a dictionary and we can access to our data using the name of the dataset as key:
print hdf['d1'].shape
(5, 3)
The data in the storage can be manipulated. For example, we can append new data to the dataset we just created:
hdf.append('d1', DataFrame(np.random.rand(5,3), 
           format='table', data_columns=True)
hdf.close() # closes the file
There are many ways to open a hdf5 storage, we could use again the constructor of the class HDFStorage, but the function read_hdf makes us also able to query the data:
from pandas import read_hdf
# this query selects the columns A and B
# where the values of A is greather than 0.5
hdf = read_hdf('storage.h5', 'd1',
               where=['A>.5'], columns=['A','B'])
At this point, we have a storage which contains a single dataset. The structure of the storage can be organized using groups. In the following example we add three different datasets to the hdf5 file, two in the same group and another one in a different one:
hdf = HDFStore('storage.h5')
hdf.put('tables/t1', DataFrame(np.random.rand(20,5)))
hdf.put('tables/t2', DataFrame(np.random.rand(10,3)))
hdf.put('new_tables/t1', DataFrame(np.random.rand(15,2)))
Our hdf5 storage now looks like this:
print hdf

File path: storage.h5
/d1             frame_table  (typ->appendable,nrows->10,ncols->3,indexers->[index],dc->[A,B,C])
/new_tables/t1  frame        (shape->[15,2])                                                   
/tables/t1      frame        (shape->[20,5])                                                   
/tables/t2      frame        (shape->[10,3])  
On the left we can see the hierarchy of the groups added to the storage, in the middle we have the type of dataset and on the right there is the list of attributes attached to the dataset. Attributes are pieces of metadata you can stick on objects in the file and the attributes we see here are automatically created by Pandas in order to describe the information required to recover the data from the hdf5 storage system.