Label Spreading

This week, I finally managed to get the last lines of code I needed written.  I wanted to apply the label spreading algorithm provided by scikit learn but the documentation provided is next to useless, even bearing in mind how much I’ve learned so far.  There are other ways of grouping data, but my approach from the start has always been to go with the most straightforward, tried and tested methods.  After all, my contribution isn’t about optimising document classification, but the results of document classification, which will reveal what pretty much everyone from one community who writes a blog has been writing about.

The label spreading algorithm works by representing a document as a point in space, and then finding all the other points that are closest to it than, say, another document somewhere else.  I gave the algorithm a set of documents that I’d already decided should be close to each other in the form of a training set of blog posts allocated to one of six categories.  The algorithm can then work out how the rest of the unlabelled blog posts should be labelled based on how close (or distant) they are from the training group.

It’s also possible to give the algorithm a degree of freedom (referred to as clamping) so that it can relax the boundaries and reassign some unlabelled data to an adjacent category that is more appropriate.  I don’t know yet exactly how this works, but it will have something to do with the probability that document  would be a better fit with category a than category b.

I ran the algorithm twice with different clamping parameters, and you can see the results below.

alpha = 0.2, gamma = 20 alpha = 0.1, gamma = 20
Category No. of Posts Category No. of Posts Category No. of Posts
6 21 6 475 6 506
5 98 5 1915 5 1920
4 34 4 1013 4 1044
3 27 3 505 3 516
2 34 2 746 2 712
1 78 1 3132 1 3088
-1 7494 -1 0 -1 0

The first couple of columns are the set of posts with just my labelled training set. -1 represents the unlabelled data.  Thereafter you can see two sets of results, one with a clamping setting of 0.2 (alpha), the other slightly less flexible at 0.1.

alpha : float

Clamping factor. A value in [0, 1] that specifies the relative amount that an instance should adopt the information from its neighbors as opposed to its initial label. alpha=0 means keeping the initial label information; alpha=1 means replacing all initial information (scikit learn).

I’m still trying to find out exactly what the gamma parameter does.  I just went with the value given by all the scikit documentation I could find.

I then went through 50 randomly selected posts that had originally been unlabelled to see what category they had been allocated.   I changed 26 of them, although 10 of these were labelled with a new category which I’m just calling ‘other’ at the moment.  So, in summary, I changed 32% of the sample and added 10% of the sample to a new category.

I always knew from previous explorations of the text data that there would be posts that went into the ‘wrong’ category, but the degree of ‘wrong’ is only according to my personal assessment.  I could be ‘wrong’, and I have absolutely no doubt that others would disagree with how I’ve defined my categories and identified blog posts that ‘fit’, but that’s the joy / frustration of data science.  Context and interpretation are everything.

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Writing Retreat: Cumberland Lodge

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This isn’t even the front.

I spent all of last weekend, from Friday afternoon through to Monday lunch time, at the magnificent Cumberland Lodge.  It really is the most beautiful building, once occupied by the Ranger of Windsor Great Park, a grace-and-favour appointment that’s been held by some well-known names from English aristocracy.  And just to remind you who the boss is, there’s a clear view from a spot in the grounds all the way to Windsor Castle, nine miles away.  In 1947, the lodge was given to an education foundation established by Amy Buller.  Click on the link and read about the book she authored, Darkness Over Germany.  Strangely, the only available Wikipedia link is to the German site.

When we arrived on Friday afternoon, the fires were lit, and it was like something straight out of Gosford Park.  Someone, who shall remain nameless, described the accommodation as ‘nursing home chic’.  Here’s a picture of my room. You decide.  It did get bloody cold at night, though (single glazed windows, high ceilings, what can you expect?) which of course wouldn’t be tolerated in most nursing homes.

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My room.

There were eight of us students, accompanied by Professor Susan Halford and Dr Mark Weal.  The focus, of course, was writing.  For most of us, that was writing a chapter or section of our PhDs, although a couple of people were writing a paper for publication.  We were free to write anywhere, but most people chose to stay sitting around the huge table that was provided for us in a library, although on Sunday afternoon and Monday I chose to go down to one of the drawing rooms and stretch out on a sofa.

We also had two mentors with us for part of the stay – ex-students who had completed their PhDs – to answer any of our questions and talk to use about the last stages of qualifying, including the dreaded viva.

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The view from the library.

The final attraction was a visit from Jen McCall, representing Emerald Publishing, to talk about converting our PhDs into books or monograms, which isn’t as easy as it sounds!  Nevertheless, I think many PhDs could be re-written successfully for a broader audience, and in spite of the work (think another year at least of re-writing and adding new material) I really feel as if I could do this with mine.  Time will tell….

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The drawing room.

The real benefit of all this, though, was the opportunity to write in a quiet, relaxed atmosphere, away from all the usual distractions (dogs, children, washing up, take your pick).   I forgot to make a note of how many words my chapter started out with, and so couldn’t tell you how much I wrote.  It was a lot, though.  And I made several pages worth of notes as I went along; notes to check things, find things, do extra things…. I find that whenever I start writing I think of lots of other things as I’m getting the words down on the page.  I’m hoping this means I’ve done enough preparation that my mind is free to wander and doesn’t have to pay too much attention to writing any more.

It was also a benefit beyond words to have Susan and Mark with us.  As any PhD student knows, getting face time with your supervisor when you most need it is practically impossible.  They’re busy people.  Also, by the time you do get to see them, the problem has either been resolved, or there are now a whole set of problems that need addressing, few of which you’ll have time to address in your meeting.  The fact that they were there, happy to talk through whatever was an issue was priceless.  Thank you.

As a result of this weekend, I’ve booked myself in to as many other writing retreats organised by the Digital Economy Network as I can.    They’re an organisation funded by the Research Councils UK to support post-grad and research students; all the retreats run over two days, and are free.  As well as the chance to write in a purposeful atmosphere, they are also held in different locations across the  UK so there’s the added bonus of seeing somewhere new.

I’m going to miss this in September.

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Coding Resources, or: Things I Wish I’d Known When I Started

Resources2

As some of you know, I’m in the final year of my PhD in Web Science.  For whatever reason, I decided I’d learn whole load of new stuff from the ground up.  In my 50s.  With zero knowledge to start with except some very basic maths.  I needed to learn to write code, and although my MSc year included a module on writing code in Python, it did nothing more than get me familiar with what code actually looks like on the page.

I cried every Sunday night, prior to the workshop on Monday, because I just couldn’t see how to make things work.

Today, over two years on, I get it.  I can write it (although I still have to refer to a book or previous code I’ve written as a reminder) and my ability to think logically and has improved considerably.  During that time, I’ve amassed a range of books and URLs that have been, and still are, incredibly useful.  It’s time to share and provide myself with a post of curated resources at the same time.

First of all, you absolutely need a pencil (preferably with a rubber on the end), some coloured pens if you’re a bit creative, and plenty of A3 paper.  Initially, this is just for taking notes but I found then incredibly useful further along when I wanted to write the task that I needed my code to carry out, step by step.

Post-it notes – as many colours and sizes as you fancy.  Great for scribbling notes as you go, acting as bookmarks, and if you combine them with the coloured pens and A3 paper, you can make a flow chart.

Code Academy is a good place to start.  It takes you through the basics step by step, and helps you to both see what code looks like on screen, and how it should be written.  There are words that act as commands e.g. print, while, for etc.  that appear in different colours so you can see you’ve written something that’s going to do something, and you can see straight away that indents are important as they signal the order in which tasks are carried out (indents act like brackets in maths).

Just about every book that covers writing code includes a basic tutorial, but one that I bought and still keep referring back to is Automate The Boring Stuff With Python.  By the time you get here, you’ll be wanting to start writing your own code.  For that, I recommend you install Anaconda which will give you a suite of excellent tools.  Oh, and I use Python 3.6.
Resources1Once you’ve opened Anaconda, Spyder is the basic code editor.  I also use the Jupyter Notebook a lot.  I like it because it’s much easier to try out code bit by bot, so for example when I’m cleaning up some text data  and want to remove white space, or ‘new line’ commands, I can clear things one step at a time and see the results at the end of each one.  You can do the same using Spyder, but it isn’t as easy.

I’m going to list some books next, but before I do I should mention Futurelearn.  I have done several of the coding courses – current ones include ‘Data MiningWith WEKA’, ‘Advanced Data Mining With WEKA’ and ‘Learning To Code For Data Analysis’.  While these may not cover exactly what you have in mind to do (more on that in a minute), they will all familiarise you with gathering data, doing things with the data by writing code, and visualising the results.  They also help to get you thinking about the whole process.

I had a series of tasks I needed code to do for me.  In fact, I think the easiest way to learn how to write code is to have something in mind that you want it to do.  I needed to be able to gather text from blog posts and store it in a way that would make it easily accessible.  In fact, I needed to store the content of a blog post, the title of the post and the date it was published.  I later added the URL, as I discovered that for various reasons sometimes the title or the date (or both) were missing and that information is usually in the URL.  I then identified various other things I needed to do with the data, which led to identifying more things I needed to do with the data….. and so on.  This is where I find books so useful, so here’s a list:

  • Mining The Social Web, 2nd Edition.  The code examples given in this book are a little dated, and in fact rather than write the code line-by-line to do some things, you’d be better off employing what I’ll call for the sake of simplicity an app to do it for you.  It was the book that got me started, though, and I found the simple explanations for some of the things I needed to achieve very useful.
  • Data Science From Scratch.  I probably should have bought this book earlier, but it’s been invaluable for general information.
  • Python For Data Analysis, 2nd Edition.  Again, good for general stuff, especially how to use Pandas.  Imagine all the things you can do with an Excel spreadsheet, but once your sheet gets large, it becomes very difficult to navigate, and calculations can take forever.  Pandas can handle spreadsheet-style stuff with consummate ease and will only display what you want to see.  I love it.
  • Programming Collective Intelligence.  This book answered pretty much all the other questions I had, but also added a load more.  It takes you through all sorts of interesting algorithms and introduces things like building classifiers, but the main problem for me is that the examples draw on data that has already been supplied for you.  That’s great, but like so many other examples in all sorts of other books (and on the web, see below) that’s all fine until you want to use your own data.
  • This book began to answer the questions about how to gather your own data, and how to apply the models from the books cited above: Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data.  This book has real-world examples which were relatively easy for me to adapt, as well as straightforward explanations as to how the code works.

Finally, some useful web sites.  The first represented a real break-through for me.  Not only did it present a real-world project from the ground up, but the man behind it, Brandon Rose (who also contributed to the last book in my list) is on Twitter and he answered a couple of questions from me when I couldn’t get his code to work with my data.  In fact, he re-wrote bots of my code for me, with explanations, which was incredibly helpful and got me started.  http://brandonrose.org/ is amazing.

This is the one and only video tutorial I’ve found useful.  Very useful, actually.  I find video tutorials impossible to learn anything from on the whole – you can’t beat a book for being able to go back, re-read, bookmark, write notes etc. – but this one was just what I needed to help me write my code to scrape blog posts, which are just web pages https://www.youtube.com/watch?v=BCJ4afDX4L4&t=34s.

https://datasciencelab.wordpress.com/2013/12/12/clustering-with-k-means-in-python/ and other blog posts.

https://www.naftaliharris.com/blog/visualizing-k-means-clustering/ does what it says, and more.

http://www.ritchieng.com/machine-learning-multinomial-naive-bayes-vectorization/ useful walk-through.

http://www.ultravioletanalytics.com/2016/11/18/tf-idf-basics-with-pandas-scikit-learn/ 

The URLs listed above are quite specific to the project I’ve been working on.  I’d also like to add Scikit-Learn which provided all the apps I’ve been using.  The explanations and documentation that is included on the site was less than helpful as it assumed a level of knowledge that was, and to a certain extent still is way above my head.  However, what it gave me was the language to use when I was searching for how to write a piece of code.  Stack Overflow is the best resource there is for this, and most of my bookmarks are links to various questions and  responses.  However, it did take me a while to a) learn what form of words would elicit an answer to my problem, and b) to understand the answers.  I even tried asking a question myself.  Never again.  Unless you’re a fully-fledged computer science geek (and if you were, you wouldn’t be here) it’s hostile territory.

Finally, an excellent site that has been useful again and again: DataVizTools.

Going back to Anaconda for a minute, when you’re feeling a bit more confident, have a look at the Orange application.  I’ve blogged about it several times, and blog on the site is an excellent source of information and example projects.  The help pages are excellent for all the basic apps, although some of the  newer ones don’t have anything yet.

And to finish, a site that I found, courtesy of Facebook, this very morning.  This site lets you see how your code works with a visualiser, something I found myself doing with pencil and paper when my code wasn’t doing what it should and I didn’t know why.

Developing Categories, Part 4

I thought I’d have a quick look at the difference using a lemmatiser instead of a snowball stemmer makes to clustering using k-means and just my group of labelled blogs.  Here’s the silhouette plot based on groups:

SilPlotLemm

Remember, the closer the score is to 0, the more statistically likely it is that the blog could be in a different category.

Here’s the same data, this time with the number of categories set to 6, but grouped according to the category the algorithm has calculated as being the most appropriate.

SilPlotLemmV2

There appear to be blogs that, at least according the k-means, are in a category with a variety of different labels.  The algorithm isn’t learning anything, though, it’s just making decisions based on the scores of tokens in the blog, nothing else.  I simply wanted to see if lemmatising the blogs instead of stemming made much of a difference.

Here’s the same parameters as above, but using the snowball stemmer as before:

SilPlotSnow

And side-by-side (Snowballing / Lemmatising):

SilPlotSnow                                       SilPlotLemmV2

 

The answer is: overall, not that I can see.

Developing Categories, Part 3

stuff8I’ve already said that I wasn’t sure if ‘behaviour’ and ‘feedback, assessment & marking’ (FAM) should be separate categories, and some further analysis has convinced me that I need to drop them both.

One of the many useful features of Orange is the ‘concordance’ app, shown on the left in my workflow.  It allows for a sub-set of documents to be extracted based on a key word.  I chose to have a closer look at ‘marking’.  As you can see from the screenshot below, the app will show you your chosen word as it appears with a selected number of words either side.  The default is 5, which I stuck with.

stuff9

The white and blue bands represent individual documents, which can then be selected and viewed using the ‘corpus viewer’ app.  I browsed through several, deciding that they should best be classed as ‘professional concern’, ‘positioning’, ‘soapboxing’ or ‘reflective practice’.  I selected ‘assessment’ and ‘feedback’ as alternatives to ‘marking’, but a closer look at a few of them suggested the same.  I went back to the posts I’d originally classified as ‘FAM’ and reviewed them, and decided I could easily re-categorise them too.

Here’s an example of a post containing the key word ‘marking’:

Lesson 3 (previous post) had seen my Head of Department sit in with a year 7 group to look at ideas he could apply. His key observation- the need for a grounding in the terminology and symbols see first lesson which has been shared as a flipchart with the department. We move on apace to lesson 4 where pupils start to be involved in setting their own marking criteria linked to SOLO. Still no hexagons, a key aspect in the sequence of lessons now being blogged about by Paul Berry (see previous post). Linking activities between lessons has become very overt in this sequence of lessons. Our starter was a return to annotate the pics from last lesson. Most recall was at a uni-structural stage and some discussion ensued see Year 7 example below. The focus today was to be on marking information onto maps accurately. We have decided as a department to return to more traditional mapping skills as many of our pupils have a lack of sense of place. So we returned to the textbook (Foundations) and a copy of the main map was shared with the class. It limits the amount of information, and hopefully this will develop a stronger use of maps in future work. Before starting though we needed to determine a SOLO based marking criteria which allowed peer marking. The pupils in year 7 in particular had clear ideas already about this. We identified how they would mark and initial and day the marking as Sirdoes so it was clear who the peer marker was. The map task was time limited. I use a variety of flash based timers which I found online- the novelty value of how the timer will end can be a distraction at the end of a task but does promote pupil interest. I circulated the room giving prompts on how seas could include other terms e.g. Channel and ocean. The work rate was very encouraging. The peer marking was successful and invoked quite a lot of table based discussions. We started to identify the idea of feed forward feedback to allow improvement of future pieces of work. Lesson 4 with years 8 and 9 included a return to the SOLO symbols image sheet and sharing recall. Also a key facts based table quiz was used to promote teamwork and remind how we already know a range of facts. These quizzes provided a good opportunity to use the interactive nature of the board to match answers to locations. Writing to compare features in different locations became the focus for Years 8 and 9. We recapped the use of directions in Relational answers. Headings were provided and I circulated to support and/ or prompt as required. Now I need to identify opportunities to use HOT maps as recommended by others including Lucie Golton, John Sayers et al. from Twitters growing #SOLO community. Also the mighty hexagons and linking facts need to enter the arena. Please if commenting, which image size works better as lesson 3 or lesson 4?

This is clearly ‘reflective practice’, as the practitioner is clearly commenting on the successes of using the SOLO taxonomy model  with a variety of year groups.

If I have time, it may well be more appropriate to interrogate a particular category to visualise what sub-categories may emerge e.g.  I would expect ‘professional concern’ to encompass workload, marking, growth mindset, flipped  learning etc. , areas of concern that are ‘product’ as opposed to ‘process’.

Developing Categories, Part 2

So, while I deploy my bespoke python code to scrape the contents of umpteen WordPress and Blogger blogs, I’ve continued trying to classify blogs from my sample according the the categories I outlined in my previous post.

I say ‘trying’ because it’s not as straightforward as it seems.  Some blogs clearly don’t fit into any of the categories, e.g. where a blogger has simply written about their holiday, or for one blogger written a series of posts explaining various aspects of science or physics.  I reckon that this a science teacher writing for the benefit of his or her students, but as the posts are sharing ‘knowledge’ rather than ‘resources’, I can’t classify them.  Fortunately the label propagation algorithm I will eventually be using will allow for new categories to be instigated (or the ‘boundaries’ for existing categories to be softened) so it shouldn’t be a problem.

‘Soapboxing’, ‘professional concern’ and ‘positioning’ have also caused me to think carefully about my definitions.  ‘Soapboxing’ I’m counting as all posts that express an opinion in a strident, one-sided way,  with a strong feeling that the writer is venting frustration, and perhaps with a call to action.  These tend to be short posts, probably written because the blogger simply needs to get something off their chest and (possibly, presumably) get some support from others via the comments.  ‘Professional concern’, then, is a also post expressing a view or concern, but the language will be more measured.  Perhaps evidence from research or other bloggers will be cited, and the post will generally be longer.  The blogger may identify themselves as a teacher of some experience, or perhaps a head of department or other school leader.  As with ‘soapboxing’, a point of view will be expressed, but the call to action will be absent.

‘Positioning’ is a blog post that expresses a belief or method that the blogger holds to be valid above others, and expresses this as a series of statements.  Evidence to support the statements will be present, generally in the form of books or published research by educational theorists or other leading experts in the field of education.

Of course, having made some decisions regarding which blogs fit into these categories, I need to go back through some specific examples and try to identify some specific words or phrases that exemplify my decision.  And I fully expect other people to disagree with me, and be able to articulate excellent reasons why blog A is an example of ‘positioning’ rather than ‘professional concern’, but all I can say in response is that, while it’s possible to get a group of humans to agree around 75% of the time, it’s impossible to get them to agree 100%, and that’s but the joy and the curse of this kind of research.

Given more time, I’d choose some edu-people from Twitter and ask them to categorise a sample of blogs to verify (or otherwise) my decision, but as I don’t have that luxury the best I can do is make my definitions as clear as possible, and provide a range of examples as justification.

The other categories that aren’t proving straightforward are ‘feeedback, assessment and marking’ (‘FAM’) and ‘behaviour’.  I knew this might be the case, though, so I’m keeping an open mind about these.  I have seen examples of blogs discussing ‘behaviour’ that I’ve put into one of the three categories I’ve mentioned above, but that’s because the blogs don’t discuss ‘behaviour’ exclusively.

Anyway, I’ve categorised 284 (out of a total of 7,788) posts so far so I thought I’d have a bit of a look at the data.

stuff1

I used Orange again to get a bit more insight into my data.  Just looking at the top flow, after opening the corpus I selected the rows that had something entered in the ‘group’ column I created.

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Selecting rows.

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Creating classes.

 

 

 

 

 

 

 

 

I then created a class for each group name.  This additional information can be saved, and I’ve dragged the ‘save data’ icon onto the workspace, but I’ve chosen not to save it automatically for now.  If you do, and you give it a file name, every time you open Orange the file will be overwritten, which you may not want.  Then, I pre-processed the 284 blogs using the snowball stemmer, and decided I’d have a look at how just the sample might be clustered using k-means.

“Since it effectively provides a ‘suffix STRIPPER GRAMmar’, I had toyed with the idea of calling it ‘strippergram’, but good sense has prevailed, and so it is ‘Snowball’ named as a tribute to SNOBOL, the excellent string handling language of Messrs Farber, Griswold, Poage and Polonsky from the 1960s.”

Martin Porter

I’m not sure if I’ve explained k-means before, but here’s a nice link that explains it well.

“Clustering is a technique for finding similarity groups in a data, called clusters. It attempts to group individuals in a population together by similarity, but not driven by a specific purpose.”

The data points are generated from the words in the blogs.  These have been reduced to tokens by the stemmer, then a count is made of the number of times each word is used in a post.  The count is subsequently adjusted to take account of the length of the document so that a word used three times in a document of 50 words is not given undue weight compared with the same word used three times in a document of 500.  So, each document generates a score for each word used, with zero for a word not used that appears in another document or documents.  Mathematical things happen and the algorithm coverts each document into a data point in a graph like the ones in the link.  K-means then clusters the documents according to how similar they are.

I already know I have 8 classes, so that’s the number of clusters I’m looking for.  If I deploy the algorithm, I can see the result on a silhouette plot (the matching icon, top far right of the flow diagram above).  The closer to a score of ‘0’, the more likely it is that a blog post is on the border between two clusters.  When I select that the silhouette plot groups each post by cluster, it’s clear that ‘resources’ has a few blogs that are borderline.

Stuff4

stuff5

 

 

 

 

 

 

‘FAM’ and ‘behaviour’ are more clearly demarcated.  If I let the algorithm choose the optimal number of clusters (Orange allows between 2 and 30), the result is 6, although 8 has a score of 0.708 which is reasonable (as you can see, the closer to 1 the score is, the higher the probability that the number suggested is the ‘best fit’ for the total number  of clusters within the data set).

stuff6 As you can see from the screenshot below, cluster 4 is made up of posts from nearly all the groups.  Remember, though, that this algorithm is taking absolutely no notice of my categories, or the actual words as words that convey meaning.  It’s just doing what it does  based on numbers, and providing me with a bit of an insight into my data.

stuff7