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Mark Needham
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Thoughts on Software Development
Updated: 1 hour 28 min ago

Python: scikit-learn: ImportError: cannot import name __check_build

Sat, 01/10/2015 - 10:48

In part 3 of Kaggle’s series on text analytics I needed to install scikit-learn and having done so ran into the following error when trying to use one of its classes:

>>> from sklearn.feature_extraction.text import CountVectorizer
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/sklearn/", line 37, in <module>
    from . import __check_build
ImportError: cannot import name __check_build

This error doesn’t reveal very much but I found that when I exited the REPL and tried the same command again I got a different error which was a bit more useful:

>>> from sklearn.feature_extraction.text import CountVectorizer
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/sklearn/", line 38, in <module>
    from .base import clone
  File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/sklearn/", line 10, in <module>
    from scipy import sparse
ImportError: No module named scipy

The fix for this is now obvious:

$ pip install scipy

And I can now load CountVectorizer without any problem:

$ python
Python 2.7.5 (default, Aug 25 2013, 00:04:04)
[GCC 4.2.1 Compatible Apple LLVM 5.0 (clang-500.0.68)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from sklearn.feature_extraction.text import CountVectorizer
Categories: Blogs

Python: gensim – clang: error: unknown argument: ‘-mno-fused-madd’ [-Wunused-command-line-argument-hard-error-in-future]

Sat, 01/10/2015 - 10:39

While working through part 2 of Kaggle’s bag of words tutorial I needed to install the gensim library and initially ran into the following error:

$ pip install gensim
cc -fno-strict-aliasing -fno-common -dynamic -arch x86_64 -arch i386 -g -Os -pipe -fno-common -fno-strict-aliasing -fwrapv -mno-fused-madd -DENABLE_DTRACE -DMACOSX -DNDEBUG -Wall -Wstrict-prototypes -Wshorten-64-to-32 -DNDEBUG -g -fwrapv -Os -Wall -Wstrict-prototypes -DENABLE_DTRACE -arch x86_64 -arch i386 -pipe -I/Users/markneedham/projects/neo4j-himym/himym/build/gensim/gensim/models -I/System/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7 -I/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/numpy/core/include -c ./gensim/models/word2vec_inner.c -o build/temp.macosx-10.9-intel-2.7/./gensim/models/word2vec_inner.o
clang: error: unknown argument: '-mno-fused-madd' [-Wunused-command-line-argument-hard-error-in-future]
clang: note: this will be a hard error (cannot be downgraded to a warning) in the future
command 'cc' failed with exit status 1
an integer is required
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/Users/markneedham/projects/neo4j-himym/himym/build/gensim/", line 166, in <module>
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/", line 152, in setup
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/", line 953, in run_commands
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/", line 972, in run_command
  File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/setuptools/command/", line 59, in run
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/command/", line 573, in run
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/", line 326, in run_command
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/", line 972, in run_command
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/command/", line 127, in run
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/", line 326, in run_command
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/", line 972, in run_command
  File "/Users/markneedham/projects/neo4j-himym/himym/build/gensim/", line 71, in run
    "There was an issue with your platform configuration - see above.")
TypeError: an integer is required
Cleaning up...
Command /Users/markneedham/projects/neo4j-himym/himym/bin/python -c "import setuptools, tokenize;__file__='/Users/markneedham/projects/neo4j-himym/himym/build/gensim/';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /var/folders/sb/6zb6j_7n6bz1jhhplc7c41n00000gn/T/pip-i8aeKR-record/install-record.txt --single-version-externally-managed --compile --install-headers /Users/markneedham/projects/neo4j-himym/himym/include/site/python2.7 failed with error code 1 in /Users/markneedham/projects/neo4j-himym/himym/build/gensim
Storing debug log for failure in /Users/markneedham/.pip/pip.log

The exception didn’t make much sense to me but I came across a blog post which explained it:

The Apple LLVM compiler in Xcode 5.1 treats unrecognized command-line options as errors. This issue has been seen when building both Python native extensions and Ruby Gems, where some invalid compiler options are currently specified.

The author suggests this only became a problem with XCode 5.1 so I’m surprised I hadn’t come across it sooner since I haven’t upgraded XCode in a long time.

We can work around the problem by telling the compiler to treat extra command line arguments as a warning rather than an error

export ARCHFLAGS=-Wno-error=unused-command-line-argument-hard-error-in-future

Now it installs with no problems.

Categories: Blogs

Python NLTK/Neo4j: Analysing the transcripts of How I Met Your Mother

Sat, 01/10/2015 - 03:22

After reading Emil’s blog post about dark data a few weeks ago I became intrigued about trying to find some structure in free text data and I thought How I met your mother’s transcripts would be a good place to start.

I found a website which has the transcripts for all the episodes and then having manually downloaded the two pages which listed all the episodes, wrote a script to grab each of the transcripts so I could use them on my machine.

I wanted to learn a bit of Python and my colleague Nigel pointed me towards the requests and BeautifulSoup libraries to help me with my task. The script to grab the transcripts looks like this:

import requests
from bs4 import BeautifulSoup
from soupselect import select
episodes = {}
for i in range(1,3):
    page = open("data/transcripts/page-" + str(i) + ".html", 'r')
    soup = BeautifulSoup(
    for row in select(soup, "td.topic-titles a"):
        parts = row.text.split(" - ")
        episodes[parts[0]] = {"title": parts[1], "link": row.get("href")}
for key, value in episodes.iteritems():
    parts = key.split("x")
    season = int(parts[0])
    episode = int(parts[1])
    filename = "data/transcripts/S%d-Ep%d" %(season, episode)
    print filename
    with open(filename, 'wb') as handle:
        headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
        response = requests.get("" + value["link"], headers = headers)
        if response.ok:
            for block in response.iter_content(1024):
                if not block:

the files containing the lists of episodes are named ‘page-1′ and ‘page-2′

The code is reasonably simple – we find all the links inside the table, put them in a dictionary and then iterate through the dictionary and download the files to disk. The code to save the file is a bit of a monstrosity but there didn’t seem to be a ‘save’ method that I could use.

Having downloaded the files, I thought through all sorts of clever things I could do, including generating a bag of words model for each episode or performing sentiment analysis on each sentence which I’d learnt about from a Kaggle tutorial.

In the end I decided to start simple and extract all the words from the transcripts and count many times a word occurred in a given episode.

I ended up with the following script which created a dictionary of (episode -> words + occurrences):

import csv
import nltk
import re
from bs4 import BeautifulSoup
from soupselect import select
from nltk.corpus import stopwords
from collections import Counter
from nltk.tokenize import word_tokenize
def count_words(words):
    for elem in words:
        tally[elem] += 1
    return tally
episodes_dict = {}
with open('data/import/episodes.csv', 'r') as episodes:
    reader = csv.reader(episodes, delimiter=',')
    for row in reader:
        print row
        transcript = open("data/transcripts/S%s-Ep%s" %(row[3], row[1])).read()
        soup = BeautifulSoup(transcript)
        rows = select(soup, "table.tablebg tr div.postbody")
        raw_text = rows[0]
        [ad.extract() for ad in select(raw_text, "")]
        [ad.extract() for ad in select(raw_text, "div.t-foot-links")]
        text = re.sub("[^a-zA-Z]", " ", raw_text.text.strip())
        words = [w for w in nltk.word_tokenize(text) if not w.lower() in stopwords.words("english")]
        episodes_dict[row[0]] = count_words(words)

Next I wanted to explore the data a bit to see which words occurred across episodes or which word occurred most frequently and realised that this would be a much easier task if I stored the data somewhere.

s/somewhere/in Neo4j

Neo4j’s query language, Cypher, has a really nice ETL-esque tool called ‘LOAD CSV’ for loading in CSV files (as the name suggests!) so I added some code to save my words to disk:

with open("data/import/words.csv", "w") as words:
    writer = csv.writer(words, delimiter=",")
    writer.writerow(["EpisodeId", "Word", "Occurrences"])
    for episode_id, words in episodes_dict.iteritems():
        for word in words:
            writer.writerow([episode_id, word, words[word]])

This is what the CSV file contents look like:

$ head -n 10 data/import/words.csv

Now we need to write some Cypher to get the data into Neo4j:

// words
LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-himym/data/import/words.csv" AS row
MERGE (word:Word {value: row.Word})
// episodes
LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-himym/data/import/words.csv" AS row
MERGE (episode:Episode {id: TOINT(row.EpisodeId)})
// words to episodes
LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-himym/data/import/words.csv" AS row
MATCH (word:Word {value: row.Word})
MATCH (episode:Episode {id: TOINT(row.EpisodeId)})
MERGE (word)-[:USED_IN_EPISODE {times: TOINT(row.Occurrences) }]->(episode);

Having done that we can write some simple queries to explore the words used in How I met your mother:

MATCH (word:Word)-[r:USED_IN_EPISODE]->(episode) 
RETURN word.value, COUNT(episode) AS episodes, SUM(r.times) AS occurrences
ORDER BY occurrences DESC
==> +-------------------------------------+
==> | word.value | episodes | occurrences |
==> +-------------------------------------+
==> | "Ted"      | 207      | 11437       |
==> | "Barney"   | 208      | 8052        |
==> | "Marshall" | 208      | 7236        |
==> | "Robin"    | 205      | 6626        |
==> | "Lily"     | 207      | 6330        |
==> | "m"        | 208      | 4777        |
==> | "re"       | 208      | 4097        |
==> | "know"     | 208      | 3489        |
==> | "Oh"       | 197      | 3448        |
==> | "like"     | 208      | 2498        |
==> +-------------------------------------+
==> 10 rows

The main 5 characters occupy the top 5 positions which is probably what you’d expect. I’m not sure why ‘m’ and ‘re’ are in the next two position s – I expect that might be scraping gone wrong!

Our next query might focus around checking which character is referred to the post in each episode:

WITH ["Ted", "Barney", "Robin", "Lily", "Marshall"] as mainCharacters
MATCH (word:Word) WHERE word.value IN mainCharacters
MATCH (episode:Episode)<-[r:USED_IN_EPISODE]-(word)
WITH episode, word, r
ORDER BY, r.times DESC
WITH episode, COLLECT({word: word.value, times: r.times})[0] AS topWord
RETURN, topWord.word AS word, topWord.times AS occurrences
==> +---------------------------------------+
==> | | word       | occurrences |
==> +---------------------------------------+
==> | 72         | "Barney"   | 75          |
==> | 143        | "Ted"      | 16          |
==> | 43         | "Lily"     | 74          |
==> | 156        | "Ted"      | 12          |
==> | 206        | "Barney"   | 23          |
==> | 50         | "Marshall" | 51          |
==> | 113        | "Ted"      | 76          |
==> | 178        | "Barney"   | 21          |
==> | 182        | "Barney"   | 22          |
==> | 67         | "Ted"      | 84          |
==> +---------------------------------------+
==> 10 rows

If we dig into it further there’s actually quite a bit of variety in the number of times the top character in each episode is mentioned which again probably says something about the data:

WITH ["Ted", "Barney", "Robin", "Lily", "Marshall"] as mainCharacters
MATCH (word:Word) WHERE word.value IN mainCharacters
MATCH (episode:Episode)<-[r:USED_IN_EPISODE]-(word)
WITH episode, word, r
ORDER BY, r.times DESC
WITH episode, COLLECT({word: word.value, times: r.times})[0] AS topWord
RETURN MIN(topWord.times), MAX(topWord.times), AVG(topWord.times), STDEV(topWord.times)
==> +-------------------------------------------------------------------------------------+
==> | MIN(topWord.times) | MAX(topWord.times) | AVG(topWord.times) | STDEV(topWord.times) |
==> +-------------------------------------------------------------------------------------+
==> | 3                  | 259                | 63.90865384615385  | 42.36255207691068    |
==> +-------------------------------------------------------------------------------------+
==> 1 row

Obviously this is a very simple way of deriving structure from text, here are some of the things I want to try out next:

  • Detecting common phrases/memes/phrases used in the show (e.g. the yellow umbrella) – this should be possible by creating different length n-grams and then searching for those phrases across the corpus.
  • Pull out scenes – some of the transcripts use the keyword ‘scene’ to denote this although some of them don’t. Depending how many transcripts contain scene demarkations perhaps we could train a classifier to detect where scenes should be in the transcripts which don’t have scenes.
  • Analyse who talks to each other or who talks about each other most frequently
  • Create a graph of conversations as my colleagues Max and Michael have previously blogged about.
Categories: Blogs