Over this summer as a part of Google Summer of Code 2015, daru received a lot of upgrades and new features which have made a pretty robust tool for data analysis in pure ruby. Of course, a lot of work still remains for bringing daru at par with the other data analysis solutions on offer today, but I feel the work done this summer has put daru on that path.
The new features led to the inclusion of daru in many of SciRuby’s gems, which use daru’s data storage, access and indexing features for storing and carrying around data. Statsample, statsample-glm, statsample-timeseries, statsample-bivariate-extensions are all now compatible with daru and use Vector and DataFrame as their primary data structures. Daru’s plotting functionality, that interfaced with nyaplot for creating interactive plots directly from the data was also significantly overhauled.
Also, new gems developed by other GSOC students, notably Ivan’s GnuplotRB gem and Alexej’s mixed_models gem both accept data from daru data structures. Do see their repo pages for seeing interesting ways of using daru.
The work on daru is also proving to be quite useful for other people, which led a talk/presentation at DeccanRubyConf 2015, which is one of the three major ruby conferences in India. You can see the slides and notebooks presented at the talk here. Given the current interest in data analysis and the need for a viable solution in ruby, I plan to take daru much further. Keep watching the repo for interesting updates :)
In the rest of this post I’ll elaborate on all the work done this summer.
Pre-mid term submissions
Daru as a gem before GSOC was not exactly user friendly. There were many cases, particularly the iterators, that required some thinking before anybody used them. This is against the design philosophy of daru, or even ruby general, where surprising programmers with ubiqtuos constructs is usually frowned down upon by the community. So the first thing that I did mainly concerned overhauling the daru’s many iterators for both
For example, the
#map iterator from
Enumerable returns an
Array no matter object you call it on. This was not the case before, where
#map would a
Daru::DataFrame. This behaviour was changed, and now
#map returns an
Array. If you want a
Vector or a
DataFrame of the modified values, you should call
Each of these iterators also accepts an optional argument,
:vector, which will define the axis over which iteration is supposed to be carried out. So now there are the
#keep_row_if. To iterate over elements along with their respective indexes (or labels), you can likewise use
#each_index. I urge you to go over the docs of each of these methods to utilize the full power of daru.
Apart from this there was also quite a bit of refactoring involved for many methods (courtesy Alexej). This has made daru much faster than previous versions.
The next (major) thing to do was making daru compatible with statsample. This was very essential since statsample is very important tool for statistics in ruby and it was using its own
Dataset classes, which weren’t very robust as computation tools and very difficult to use when it came to cleaning or munging data. So I replaced statsample’s Vector and Dataset clases with Daru::Vector and Daru::DataFrame. It involved a significant amount of work on both statsample and daru. Statsample because many constructs had to changed to make them compatible with daru, and daru because there was a lot of essential functionality in these classes that had to be ported to daru.
Porting code from statsample to daru improved daru significantly. There were a whole of statistics methods in statsample that were imported into daru and you can now use all them from daru. Statsample also works well with rubyvis, a great tool for visualization. You can now do that with daru as well.
Many new methods for reading and writing data to and from files were also added to daru. You can now read and write data to and from CSV, Excel, plain text files or even SQL databases.
In effect, daru is now completely compatible with statsample (and all the other statsample extensions). You can use daru data structures for storing data and pass them to statsample for performing computations. The biggest advantage of this approach is that the analysed data can be passed around to other scientific ruby libraries (some of which listed above) that use daru as well. Since daru offers in-built functions to better ‘see’ your data, better visualization is possible.
Also see the notebooks in the statsample README for using daru with statsample.
Post-mid term submissions
Most of time post the mid term submissions was spent in implementing the time series functions for daru.
I implemented a new index, the DateTimeIndex, which can used for indexing data on time stamps. It enables users to query data based on time stamps. Time stamps can either be specified with precise ruby DateTime objects or can be specified as strings, which will lead to retrival of all the data falling under that time. For example specifying ‘2012’ returns all data that falls in the year 2012. See detailed usage of
DateTimeIndex in conjunction with other daru constructs in the daru README.
An essential utility in implementing
DateOffset, which is a new set of classes that offsets dates based on certain rules or business logic. It can advance or lag a ruby
DateTime to the nearest day or any day of the week or the end or beginning of the month etc.
DateOffset is an essential part of
DateTimeIndex and can also be used as a standalone utility for advancing/lagging
DateTime objects. This blog post elaborates more on the nuances of
DateOffset and its usage.
The last thing done during the post mid term was complete compatibility with statsample-timeseries, which was created by Ankur Goel during GSOC 2013. It offers many uesful functions for analysis of time series data. It now works with daru containers. See some use cases here.
Thats all, as far as I can remember.