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#!/usr/bin/env python
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"""Simple text analysis: word frequencies and co-occurrence graph.
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Usage:
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text_analysis.py [text_file]
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This script will analize a plain text file treating it as a list of
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newline-separated sentences (e.g. a list of paper titles).
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It computes word frequencies (after doing some naive normalization by
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lowercasing and throwing away a few overly common words). It also computes,
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from the most common words, a weighted graph of word co-occurrences and
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displays it, as well as summarizing the graph structure by ranking its nodes in
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descending order of eigenvector centrality.
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This is meant as an illustration of text processing in Python, using matplotlib
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for visualization and NetworkX for graph-theoretical manipulation. It should
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not be considered production-strength code for serious text analysis.
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Author: Fernando Perez <fernando.perez@berkeley.edu>
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"""
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#-----------------------------------------------------------------------------
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# Imports
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#-----------------------------------------------------------------------------
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# From the standard library
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import os
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import re
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import sys
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import urllib2
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# Third-party libraries
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import networkx as nx
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import numpy as np
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from matplotlib import pyplot as plt
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#-----------------------------------------------------------------------------
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# Function definitions
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#-----------------------------------------------------------------------------
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def rescale_arr(arr,amin,amax):
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"""Rescale an array to a new range.
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Return a new array whose range of values is (amin,amax).
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Parameters
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----------
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arr : array-like
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amin : float
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new minimum value
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amax : float
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new maximum value
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Examples
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--------
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>>> a = np.arange(5)
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>>> rescale_arr(a,3,6)
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array([ 3. , 3.75, 4.5 , 5.25, 6. ])
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"""
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# old bounds
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m = arr.min()
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M = arr.max()
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# scale/offset
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s = float(amax-amin)/(M-m)
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d = amin - s*m
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# Apply clip before returning to cut off possible overflows outside the
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# intended range due to roundoff error, so that we can absolutely guarantee
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# that on output, there are no values > amax or < amin.
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return np.clip(s*arr+d,amin,amax)
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def all_pairs(items):
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"""Make all unique pairs (order doesn't matter)"""
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pairs = []
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nitems = len(items)
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for i, wi in enumerate(items):
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for j in range(i+1, nitems):
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pairs.append((wi, items[j]))
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return pairs
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def text_cleanup(text, min_length=3,
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remove = set(['for', 'the', 'and', 'with'])):
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"""Clean up a list of lowercase strings of text for simple analysis.
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Splits on whitespace, removes all 'words' less than `min_length` characters
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long, and those in the `remove` set.
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Returns a list of strings.
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"""
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return [w for w in text.lower().split()
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if len(w)>=min_length and w not in remove]
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def print_vk(lst):
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"""Print a list of value/key pairs nicely formatted in key/value order."""
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# Find the longest key: remember, the list has value/key paris, so the key
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# is element [1], not [0]
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longest_key = max([len(word) for word, count in lst])
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# Make a format string out of it
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fmt = '%'+str(longest_key)+'s -> %s'
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# Do actual printing
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for k,v in lst:
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print fmt % (k,v)
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def word_freq(text):
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"""Return a dictionary of word frequencies for the given text.
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Input text should be given as an iterable of strings."""
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freqs = {}
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for word in text:
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freqs[word] = freqs.get(word, 0) + 1
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return freqs
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def sort_freqs(freqs):
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"""Sort a word frequency histogram represented as a dictionary.
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Parameters
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----------
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freqs : dict
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A dict with string keys and integer values.
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Return
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------
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items : list
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A list of (count, word) pairs.
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"""
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items = freqs.items()
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items.sort(key = lambda wc: wc[1])
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return items
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## words,counts = freqs.keys(),freqs.values()
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## # Sort by count
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## items = zip(counts,words)
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## items.sort()
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## return items
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def summarize_freq_hist(freqs, n=10):
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"""Print a simple summary of a word frequencies dictionary.
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Paramters
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---------
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freqs : dict or list
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Word frequencies, represented either as a dict of word->count, or as a
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list of count->word pairs.
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n : int
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The number of least/most frequent words to print.
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"""
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items = sort_freqs(freqs) if isinstance(freqs, dict) else freqs
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print 'Number of unique words:',len(freqs)
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print
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print '%d least frequent words:' % n
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print_vk(items[:n])
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print
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print '%d most frequent words:' % n
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print_vk(items[-n:])
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def get_text_from_url(url):
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"""Given a url (local file path or remote url), read its contents.
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If it's a remote URL, it downloads the file and leaves it locally cached
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for future runs. If the local matching file is found, no download is made.
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Returns
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-------
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text : string
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The contents of the file.
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"""
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if url.startswith('http'):
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# remote file, fetch only if needed
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fname = os.path.split(url)[1]
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if os.path.isfile(fname):
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with open(fname, 'r') as f:
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text = f.read()
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else:
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with open(fname, 'w') as f:
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text = urllib2.urlopen(url).read()
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f.write(text)
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else:
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with open(url, 'r') as f:
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text = f.read()
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return text
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def co_occurrences(lines, words):
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"""Return histogram of co-occurrences of words in a list of lines.
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Parameters
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----------
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lines : list
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A list of strings considered as 'sentences' to search for co-occurrences.
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words : list
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A list of words from which all unordered pairs will be constructed and
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searched for co-occurrences.
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"""
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wpairs = all_pairs(words)
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# Now build histogram of co-occurrences
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co_occur = {}
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for w1, w2 in wpairs:
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rx = re.compile('%s .*%s|%s .*%s' % (w1, w2, w2, w1))
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co_occur[w1, w2] = sum([1 for line in lines if rx.search(line)])
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return co_occur
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def co_occurrences_graph(word_hist, co_occur, cutoff=0):
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"""Convert a word histogram with co-occurrences to a weighted graph.
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Edges are only added if the count is above cutoff.
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"""
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g = nx.Graph()
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for word, count in word_hist:
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g.add_node(word, count=count)
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for (w1, w2), count in co_occur.iteritems():
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if count<=cutoff:
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continue
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g.add_edge(w1, w2, weight=count)
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return g
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def plot_graph(wgraph, pos=None):
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"""Conveniently summarize graph visually"""
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# Plot nodes with size according to count
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sizes = []
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degrees = []
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for n, d in wgraph.nodes_iter(data=True):
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sizes.append(d['count'])
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degrees.append(wgraph.degree(n))
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sizes = rescale_arr(np.array(sizes, dtype=float), 100, 1000)
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# Compute layout and label edges according to weight
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pos = nx.spring_layout(wgraph) if pos is None else pos
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labels = {}
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width = []
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for n1, n2, d in wgraph.edges_iter(data=True):
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w = d['weight']
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labels[n1, n2] = w
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width.append(w)
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# remap width to 1-10 range
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width = rescale_arr(np.array(width, dtype=float), 1, 15)
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# Create figure
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fig, ax = plt.subplots()
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fig.subplots_adjust(0,0,1)
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nx.draw_networkx_nodes(wgraph, pos, node_size=sizes, node_color=degrees,
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alpha=0.8)
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nx.draw_networkx_labels(wgraph, pos, font_size=15, font_weight='bold')
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nx.draw_networkx_edges(wgraph, pos, width=width, edge_color=width,
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edge_cmap=plt.cm.Blues)
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nx.draw_networkx_edge_labels(wgraph, pos, edge_labels=labels)
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ax.set_title('Node color:degree, size:count, edge: co-occurrence count')
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def plot_word_histogram(freqs, show=10, title=None):
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"""Plot a histogram of word frequencies, limited to the top `show` ones.
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"""
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sorted_f = sort_freqs(freqs) if isinstance(freqs, dict) else freqs
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# Don't show the tail
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if isinstance(show, int):
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# interpret as number of words to show in histogram
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show_f = sorted_f[-show:]
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else:
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# interpret as a fraction
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start = -int(round(show*len(freqs)))
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show_f = sorted_f[start:]
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# Now, extract words and counts, plot
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n_words = len(show_f)
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ind = np.arange(n_words)
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words = [i[0] for i in show_f]
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counts = [i[1] for i in show_f]
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fig, ax = plt.subplots()
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if n_words<=20:
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# Only show bars and x labels for small histograms, they don't make
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# sense otherwise
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ax.bar(ind, counts)
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ax.set_xticks(ind)
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ax.set_xticklabels(words, rotation=45)
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fig.subplots_adjust(bottom=0.25)
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else:
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# For larger ones, do a step plot
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ax.step(ind, counts)
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# If it spans more than two decades, use a log scale
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if float(max(counts))/min(counts) > 100:
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ax.set_yscale('log')
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if title:
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ax.set_title(title)
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return ax
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def summarize_centrality(centrality):
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c = centrality.items()
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c.sort(key=lambda x:x[1], reverse=True)
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print '\nGraph centrality'
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for node, cent in c:
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print "%15s: %.3g" % (node, cent)
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#-----------------------------------------------------------------------------
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# Main script
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#-----------------------------------------------------------------------------
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# if __name__ == '__main__':
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# # Configure user variables here
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# # Specify the url (can be a local file path) of the text file to analyze.
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# # If not given, it's read from the command line as the first argument
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#
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# # 11226 titles of recent articles in arxiv/math/prob
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# default_url = "http://bibserver.berkeley.edu/tmp/titles.txt"
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# # Number of words to display in detailed histogram
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# n_words = 15
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# # Number of words to use as nodes for co-occurrence graph.
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# n_nodes = 15
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#
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# # End of user configuration
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#
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# # Actual code starts here
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# try:
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# url = sys.argv[1]
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# except IndexError:
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# url = default_url
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#
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# # Fetch text and do basic preprocessing
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# text = get_text_from_url(url).lower()
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# lines = text.splitlines()
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# words = text_cleanup(text)
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#
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# # Compute frequency histogram
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# wf = word_freq(words)
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# sorted_wf = sort_freqs(wf)
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#
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# # Build a graph from the n_nodes most frequent words
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# popular = sorted_wf[-n_nodes:]
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# pop_words = [wc[0] for wc in popular]
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# co_occur = co_occurrences(lines, pop_words)
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# wgraph = co_occurrences_graph(popular, co_occur, cutoff=1)
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# centrality = nx.eigenvector_centrality_numpy(wgraph)
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#
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# # Print summaries of single-word frequencies and graph structure
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# summarize_freq_hist(sorted_wf)
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# summarize_centrality(centrality)
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#
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# # Plot histogram and graph
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# plt.close('all')
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# plot_word_histogram(sorted_wf, n_words,
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# "Frequencies for %s most frequent words" % n_words)
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# plot_word_histogram(sorted_wf, 1.0, "Frequencies for entire word list")
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# plot_graph(wgraph)
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#
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# # Display figures
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# plt.show()
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