Thursday, May 11, 2017

Air Quality in Poland #16 - This is the end?

Initial idea


Hello and welcome to the last post published under Get Noticed 2017! tag. But fear nothing, it is just last post submitted to Get Noticed competition, not last post written and published by me. I decided to dedicate this post for Air Quality In Poland project summary. This topic was my main project for this competition and I spent 15 + 1 blog posts for working on it. So, let me discuss it.

What was my original idea for this project? When I started to think about topic to pick for this competition, there was fresh discussion in Polish media about very poor quality of air in many of Polish biggest cities. In general this discussion was narrated as "Breaking News! We have new record of pollution! Are we living in places more polluted than ... (put your favorite polluted place here)?". I am very interested in health and living quality, so I was also interested in following this topic. After some time I started to notice some flaws with TV reports about air quality. There were some misinterpretations and possible mistakes in reasoning. Naturally, my question was: Can I do it better? Can I produce reproducible report which would not have such errors? And that questions formed my general goal for this project - Get raw air quality data and produce as accurate, meaningful and as reproducible report as possible.

Goals


After forming this wide goal I decided to play with general idea and formulate additional goals: Which place is most polluted in Poland? Which is least polluted? Can I visualize pollution? Can I calculate changes of air quality? Can I visualize those changes? Can I interpolate missing values so I could estimate air quality in every place in Poland? Can I predict future values of pollution? Can I predict future values for each place in Poland? Maybe can I build application which will advise best time for outdoor activities? Will there be possibility to scrap GIOS webpage for current pollution measurements?

So many questions and ideas, and so little time. How much did I accomplished? Well, I wasn't able to properly implement much of those ideas, but I believe that I did something useful.

Implementation


First of all, I prepared pipeline which gathers values of main pollutants from various years which was scattered in various csv files. Since this data wasn't always perfectly labeled I had to do some data cleaning in order to gather it in one place. Finally I produced multi-index data frame with date time and location as index and pollutants as columns. You can check it in notebook number one. It doesn't answers any of those questions, but builds fundaments for answering each of them.

When I had developed consistent data structure I focused on finding worst and best place to breathe in Poland. To do that I had to learn how air quality is classified and apply those rules on my data structure. You can find this analysis in notebook number two. When I was preparing it, I found that since there are many data points missing, I cannot tell with full certainty which places are best and worst. But I addressed this problem clearly, so shouldn't be any misunderstandings there. Since my script is capable of calculating overall air quality for all places for selected time ranges, it could be easily used to calculate day to day, week to week (and so on ...) changes of air quality. So it also answers one of my questions. I totally skipped geo-spatial visualizations at this point - I decided it will take me too much time to produce something which wouldn't be useless colorful blob.

Next steps of my projects were directed to interpolation and prediction of pollutants, but in meantime, GIOS made a surprise and released official API which allowed to get current air data. It was pleasant surprise because I didn't had to focus on java-script web scraping with Python. Such scraping seems to be quite mundane, but maybe I'm missing something. Anyway, as you can see in notebook number three, I tested this API and it seems to be working as promised. Very cool feature.

Fourth task which I planned to do was to predict future values based on time series. I picked fresh data from API and tired to predict values for next few hours. Since I don't have experience with time series I decided to try naive regression approach. As you can see in notebook number four, it wasn't successful idea. So I'm assuming that I didn't completed this task and there is still much to do here.

Last task which I was planning to complete, was to interpolate missing data, which, if implemented successfully, would lead me to building pollution estimator for each place in Poland. Most promising idea was to use nearest neighbors regression and apply it on geo-spatial features. And this implementation also failed miserably - you can check analysis code in notebook number five. I'm not sure why it failed so badly. Maybe there is no way to properly interpolate such data based just on geo-spatial coordinates. Or maybe other algorithms would be better suited for such purpose? Who knows? One thing would be very useful in this and previous (prediction) problem - data structure with weather data. I believe that there might be some correlations or even causation related to various weather parameters. I would definitely check them out if I had access to such data.

Leftovers and future

What ideas I didn't touched at all? There are two ideas which I would like to see implemented in this project but I didn't had time to take care of them. One is this advisor web application supposed to help with picking best time for outdoors activity. I didn't bother to start with it because fourth and fifth tasks were completed with unsatisfactory results. Another idea was to rewrite everything using Dask - Python module designed to parallelize array/data frame operations. I tried start with it, but it seems that it is little bit more complicated to use than Pandas. But those ideas are rather minor, so I'm not too much sad about not completing them.

The future? For now I'm not planning to continue to work on this project. I will put it into my maybe/far future projects shelf, so maybe I will go back to it when far future occurs ;). On the other hand if someone will be interested in discussing it or/and using my code I will happily share my thoughts about it and maybe do some additional work on it. Again, who knows?

Thanks for reading my blog and code. I hope that I created something useful and maybe I showed something interesting related to Python, reproducible research and data science. See you soon! 

Wednesday, May 10, 2017

Not enough RAM for data frame? Maybe Dask will help?

What environment do we like?


When we are doing data analysis we would like to do it in as much interactive manner as possible. We would like to get results as soon as possible and test ideas without unnecessary waiting. We like to work in that manner because great proportion of our work leads to dead ends. As soon as we get there we can start thinking about other approach to problems. When you were working with R, Python or Julia backed by Jupyter Notebook, you are familiar with this workflow. And everything works well when your workstation has enough RAM to handle all data, its processing, cleaning, aggregation and transformations. Even Python, which is considered as rather slow language performs nicely here. Especially when you are using strongly optimized modules like NumPy. 

But what with case, where data which we want to process is larger than available RAM? Well, there are three simple outcomes of your attempts: 1) your operating system will start to swap and possibly will be able to complete ordered task, but it will take way more time than expected; 2) you will receive warning or error message from functions which can estimate needed resources - rare case; 3) your script will use all available RAM and swap and will hang your operating system. But are there maybe any other options?

What can we do?


According to this (Rob Story: Python Data Bikeshed) and this (Peader Coyle - The PyData map: Presenting a map of the landscape of PyData tools) presentations, there are many possibilities to build tailored data processing solution. From all of those modules, Dask looks especially interesting to me, because it mimics Pandas naming convention and functions behavior.

Dask offers three data structures - array, data frame and bag. Array structure is designed to implement methods known from NumPy array. So everyone experienced with NumPy will be able to use it without much problems. The same approach was used to implement data frame which is inspired by Pandas data frame structure. Bag on the other hand is designed to be equivalent of json dictionaries or other Python data structures.

What is key difference between Dask data structures and their archetypes? Dask structures are divided into small chunks and every operation on them is evaluated when its needed. It means that when we have data frame and series of transformations, aggregations, searches and similar operations Dask will calculate what and when to do with each chunk and will take care of executions of those operations and do garbage collection immediately. If we would like to do that in original Pandas data frame it will have to fit into RAM entirely, and every of steps in pipeline will also have to store its results in RAM even if some operations could be executed with inplace=True parameter.

How can we use it? As I mentioned, Dask data structures were designed to be "compatible" with NumPy and Pandas data structures. So, if we check data frame API reference we will see that many Pandas methods was re-implemented with the same arguments and results.

But the problem is that not all original methods from NumPy and Pandas are implemented in Dask. So it is not possible to blindly substitute Pandas with Dask and expect that code will work. On the other hand, in cases where you are unable to read your data at all, it might be worth to spend some time to rework your flow and adjust it to Dask.
 
Second problem with Dask it that despite it tries to execute various operations on chunks in parallel, it may take more time to produce final results than in simple all in RAM data frame. But if you know time execution characteristic of your scripts you can try to substitute some heavy time parts of it and compare with Dask execution. Maybe it will make sense.

The future?


What is the future of such modules? It depends. One my ask "Why to bother when we have PySpark?". And this is valid question. I would say, that Dask and similar solutions fits nicely in niche where data is to big for RAM but on the other hand it fits on directly attached hard drives. If data fits nicely in RAM I wouldn't bother to work with Dask and similar modules - I would just stick to plain old and good NumPy and Pandas. Also if I had to deal with such amount of data that wouldn't fit on available attached to workstation disks, I would consider going into big data solutions which also gives me redundancy over hardware failures. But Dask is still very cool, and at least worth to test.

I hope that Dask developers will port more of NumPy and Pandas methods into. I also saw some works towards integrating it with Scikit-Learn, XGBoost and TensorFlow. You should definitely check this out when you will be considering buying more RAM next time ;).

Friday, May 5, 2017

Air Quality in Poland #15 - How to estimate missing pollutants? Part 2

Current status


In last post we prepared data for machine learning process. We selected one date time point of measurements and found actual coordinates of measurement stations. As I mentioned, I planned to use Nearest Neighbor Regressor applied on geographical coordinates.

Approach

We already should have column with tuple of latitude and longitude. I decided to change it to list and split into two columns



Since I have no idea how to tackle number of neighbors to use for prediction I decided to create loop and try to find best answer for that. I also generated different model for each pollutant since measurement stations are not identically distributed. Here is the code:



What is going on here? In line 4 we are selecting only interesting data for each pollutant - measurements values and latitudes and longitudes. Then in line 5 we are selecting points which have no values - we will predict those values in future. In line 6 we are doing the same selection but without "~" which is used for negation. We are taking every point which has measurement data. In lines 8 and 9 I'm splitting data frame into features and target data frames. In line 11 I'm preparing train and test sets with 3:1 size. I'm using constant random state so it should be reproducible. And in line 12 magic is starting to happen.

As I mentioned, I have no clue of how many neighbors should I use for value calculation. So I prepared loop from 1 to (all - 1) points,  which is making fit, and calculates score of its accuracy. Scores are then added to list and plotted. And here is the place where magic turns out to be just cheap trick. Scoring function used for evaluating test set is coefficient of determination. And as far as I know it should give score between 0 and 1. In my cases it gives quite much negative values, so it might mean that this model is very bad. Lets see example of best and worst scenarios:
Best results?
Worst results?

As you can see both of those results are far from something useful. That's quite disappointing, but to be honest, I'm getting used to it ;).

Conclusion?

What can we learn from this failure? We can think about possible causes of it. One cause might be that I'm using wrong scoring function - it was designed to measure effectiveness of linear fit. Maybe our situation is not linear? Other cause might be that in this date time there was not enough meaningful data - nothing was happening, so it was hard to predict anything. Another reason might be related to overall nature of measured data - it might be not to much related to geo-spatial distribution but maybe to weather, industry production, national park presence or something like this. Without this data it might be not possible to predict missing data.

Anyway, I believe I had build quite nice base for further experimentation. When I will have more spare time maybe I will try to scientifically find why my initial approach didn't worked. Or maybe you have any idea?