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This is Ian Ozsvald's blog (@IanOzsvald), I'm an entrepreneurial geek, a Data Science/ML/NLP/AI consultant, author of O'Reilly's High Performance Python book, co-organiser of PyDataLondon, a Pythonista, co-founder of ShowMeDo and also a Londoner. Here's a little more about me.

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7 June 2017 - 18:10Kaggle’s Quora Question Pairs Competition

Kaggle‘s Quora Question Pairs competition has just closed, I’m pleased to say that with 10 days effort I ranked in the top 39th percentile (rank 1346 of 3396 in the private leaderboard). Having just run and spoken at PyDataLondon 2017, taught ML in Romania and worked on several client projects I only freed up time right at the end of this competition. Despite joining at the end I had immense fun – this was my first ‘proper’ Kaggle competition.

I figured a short retrospective here might be a useful reminder to myself in the future. Things that worked well:

  • Use of github, Jupyter Notebooks, my research module template
  • Python 3.6, scikit-learn, pandas
  • RandomForests (some XGBoost but ultimately just RFs)
  • Dask (great for using all cores when feature engineering with Pandas apply)
  • Lots of text similarity measures, word2vec, some Part of Speech tagging
  • Some light text clean-up (punctuation, whitespace, some mixed case normalisation)
  • Spacy for PoS noun extraction, some NLTK
  • Splitting feature generation and ML exploitation into different Notebooks
  • Lots of visualisation of each distance measure by class (mainly matplotlib histograms on single features)
  • Fully reproducible Notebooks with fixed seeds
  • Debugging code to diagnose the most-wrong guesses from the model (pulling out features and the raw questions was often enough to get a feel for “what it missed” which lead to thoughts on new features that might help)

Things that I didn’t get around to trying due to lack of time:

  • PoS named entities in Spacy, my own entity recogniser
  • GloVe, wordrank, fasttext
  • Clustering around topics
  • Text clean-up (synonyms, weights & measures normalisation)
  • Use of external corpus (e.g. Stackoverflow) for TF-IDF counts
  • Dask on EC2

Things that didn’t work so well:

  • Fully reproducible Notebooks (great!) to generate features with no caching of no-need-to-rebuild-yet-again features, so I did a lot of recalculating features (which really hurt in the last 2 days) – possible solution below with named columns
  • Notebooks are still a PITA for debugging, attaching a console with –existing works ok until things start to crash and then it gets sticky
  • Running out of 32GB of RAM several times on my laptop and having a semi-broken system whilst trying to persist partial models to disk – I should have started with an AWS deployment earlier so I could easily turn on more cores+RAM as needed
  • I barely checked the Kaggle forums (only reading the Notebooks concerning the negative resampling requirement) so I missed a whole pile of tricks shared by others, some I folded in on the last day but there’s a huge pile that I missed – I think I might have snuck into the top 20% of rankings if I’d have used this public information
  • Calibrating RandomForests (I’m pretty convinced I did this correctly but it didn’t improve things, I’m not sure why)

Dask definitely made parallelisation easier with only a few lines of overhead in a function beyond a normal call to apply. The caching, if using something like luigi, would add a lot of extra engineered overhead – not so useful in a rapidly iterating 10 day competition.

I think next time I’ll try using version-named columns in my DataFrames. Rather than having e.g. “unigram_distance_raw_sentences” I might add “_v0”, if that calculation process is never updated then I can just use a pre-built version of the column. This is a poor-mans caching strategy. If any dependencies existed then I guess luigi/airflow would be the next step. For now at least I think a version number will solve my most immediate time-sink in recent days.

I hope to enter another competition soon. I’m also hoping to attend the London Kaggle meetup at some point to learn from others.


Ian applies Data Science as an AI/Data Scientist for companies in ModelInsight, sign-up for Data Science tutorials in London. Historically Ian ran Mor Consulting. He also founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

1 Comment | Tags: ArtificialIntelligence, Python

1 June 2017 - 15:30PyDataLondon 2017 Conference write-up

Several weeks back we ran our 4th PyDataLondon (2017) conference – it was another smashing success! This builds on our previous 3 years of effort (2016, 2015, 2014) building both the conference and our over-subscribed monthly meetup. We’re grateful to our host Bloomberg for providing the lovely staff, venue and catering.

Really got inspired by @genekogan’s great talk on AI & the visual arts at @pydatalondon @annabellerol

Each year we try some new ideas – this year we tried:

pros: Great selection of talks for all levels and pub quiz cons: on a weekend, pub quiz (was hard). Overall would recommend 9/10 @harpal_sahota

We’re very thankful to all our sponsors for their financial support and to all our speakers for donating their time to share their knowledge. Personally I say a big thank-you to Ruby (co-chair) and Linda (review committee lead) – I resigned both of these roles this year after 3 years and I’m very happy to have been replaced so effectively (ahem – Linda – you really have shown how much better the review committee could be run!). Ruby joined Emlyn as co-chair for the conference, I took a back-seat on both roles and supported where I could. Our volunteer team great again – thanks Agata for pulling this together.

I believe we had 20% female attendees – up from 15% or so last year. Here’s a write-up from Srjdan and another from FullFact (and one from Vincent as chair at PyDataAmsterdam earlier this year) – thanks!

#PyDataLdn thank you for organising a great conference. My first one & hope to attend more. Will recommend it to my fellow humanists! @1208DL

For this year I’ve been collaborating with two colleagues – Dr Gusztav Belteki and Giles Weaver – to automate the analysis of baby ventilator data with the NHS. I was very happy to have the 3 of us present to speak on our progress, we’ve been using RandomForests to segment time-series breath data to (mostly) correctly identify the start of baby breaths on 100Hz single-channel air-flow data. This is the precursor step to starting our automated summarisation of a baby’s breathing quality.

Slides here and video below:

This updates our talk at the January PyDataLondon meetup. This collaboration came about after I heard of Dr. Belteki’s talk at PyConUK last year, whilst I was there to introduce RandomForests to Python engineers. You’re most welcome to come and join our monthly meetup if you’d like.

Many thanks to all of our sponsors again including Bloomberg for the excellent hosting and Continuum for backing the series from the start and NumFOCUS for bringing things together behind the scenes (and for supporting lots of open source projects – that’s where the money we raise goes to!).

There are plenty of other PyData and related conferences and meetups listed on the PyData website – if you’re interested in data then you really should get along. If you don’t yet contribute back to open source (and really – you should!) then do consider getting involved as a local volunteer. These events only work because of the volunteered effort of the core organising committees and extra hands (especially new members to the community) are very welcome indeed.

I’ll also note – if you’re in London or the south-east of the UK and you want to get a job in data science you should join my data scientist jobs email list, a set of companies who attended the conference have added their jobs for the next posting. Around 600 people are on this list and around 7 jobs are posted out every 2 weeks. Your email is always kept private.


Ian applies Data Science as an AI/Data Scientist for companies in ModelInsight, sign-up for Data Science tutorials in London. Historically Ian ran Mor Consulting. He also founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

No Comments | Tags: Data science, Life, pydata, Python