End of GSOC

We've reached the end of Google Summer of Code (it was weeks ago now, but I'm writing this blog post anyway).

Although I've written a lot of code and made a lot of plans, I can't really back off of the project until I see it being used in a way that people (outside and inside of OpenWorm) are actually seeing it. When I came into the project, my long-term goal was an "annotated simulation". This is a system for adding annotations to data, carrying them through to model files, and finally exposing the annotations to end-users upon inspection into simulation results. This is to answer our long standing criticism that, in some way or another, our simulations don't line up with the actual science. (Similar concerns are addressed from a computational perspective in this thread) Over this passed summer, I've gotten the first step to a place where I almost like it, but the second step has stalled significantly, and the third was never started.

Cleaning up documentation...

As I've been using PyOpenWorm, I've encountered some cracks in the design. The primary issue is the interaction between configuration, object properties, and database set-up. Some examples of problems that have resulted:

1. Run-time configuration has been set-up such that each object has to receive its configuration anew from the object that creates it or else use a globally set default configuration. This is a very brittle framework that falls apart any time the appropriate configuration is not passed to Configureable objects contained within other Configureable objects which can result in configuration values hanging around between connect() and disconnect(). This isn't a big deal for simple one-off scripts, but can be annoying when testing or when designing more complex scripts.
2. Registration of a Property doesn't take place until the first time its owner is initialized. This prevents us from resolving a Property to an object directly from the graph without having made one of its owners first. This results in properties which are identical having different names in sub-classes due to the dynamic nature of property-name creation.
3. Each DataObject sub-class has to be registered as a separate step from defining the class. This is an minor annoyance when creating new classes.
4. It's necessary to either know the structure of generated namespace URIs (e.g., http://openworm.org/entities/Neuron/) and recreate them or to create an object of the desired type when referencing objects in another namespace. The first approach is sub-optimal because we might have reason to change the structure of the URIs in the future which is already a problem for some auxiliary methods designed to extract information from a URI. The second approach is also problematic since it requires the creation of objects which are never used otherwise, creating overhead for the garbage collector as well as the programmer

My proposed solution to these problems is to push the configuration and class-dependent initialization to the class-definiton phase through the use of Python's decorators which run on library-load. This takes care of the fourth point by having all of the namespaces and namespace sturcture made static by the time any object should be created.

The third point is covered by folding the current registration into the class decoration.

The second point is covered by attaching each Property subclass to the owner's class while retaining the initialization in the __init__() call for accessing owner-instances.

Finally, the first point can be addressed without the proposed solution by not having configuration passed through __init__. Initially I conceived of a tree structure for configuration which could be augmented by internal nodes and passed on to children with specific configuration needs that didn't need communication to higher level users. This structure is exemplified by Data which is Configureable, but also a Configure object suitable for configuring other objects. Unfortunately, this is the only case where the feature gets any use and isn't required for passing augmented configuration to the module as a whole, so it can simply be removed.

I hope to address some of these issues soon after outstanding issues are closed and features for the next release are well-defined.

UPDATE: Most of these issues have been addressed in the new-classes branch. Not yet merged into master.

It seems I have more to learn about rdflib's SPARQL query engine. A persistent issue was the mis-use of indexing for what (I thought) should be simple queries. My error was in including triples of the form ?x rdf:type openworm:type in the query patterns. For reasons I don't yet understand, the engine evaluates such statements before others even if there's a more reductive join it could perform from other patterns. Hopefully I can understand this eventually.

In any case, standard object->property->value queries are effectively O(1) time again--yay!

small update:

The issue from the previous post was one of indexing. The Property object identifiers were being generated differently for every Neuron object which obviously made minimal use of the index. Basing identifiers on the identity of the owner object and the value of the property takes the query time to get a single property back to constant time. For example:

for x in range(1000):
    Neuron().save() # creates and saves a randomly generated neuron object
Neuron().name()

Takes about 10 seconds for the call to name() on rdflib's memory store as well as the SleepyCat store (closer to 11 seconds) bundled with rdflib. That's about .01 seconds for getting the name of a specific neuron regardless of the database size.

I adjusted the query according to the previous post and also added patterns for the cell the property is actually on (oops):

prefix openworm: <http://openworm.org/entities/>
prefix neuron: <http://openworm.org/entities/Neuron/>
prefix sp: <http://openworm.org/entities/SimpleProperty/>
select distinct ?type where
{ 
?Neuron rdf:type openworm:Neuron .

?Neuron neuron:lineageName ?Neuron_lineageName .
?Neuron_lineageName rdf:type openworm:Neuron_lineageName .
?Neuron_lineageName sp:value ?lineageName .

?Neuron neuron:name ?Neuron_name .
?Neuron_name rdf:type openworm:Neuron_name .
?Neuron_name sp:value "AVAL" .

?Neuron neuron:type ?Neuron_type .
?Neuron_type rdf:type openworm:Neuron_type .
?Neuron_type sp:value ?type .

?Neuron neuron:receptor ?Neuron_receptor .
?Neuron_receptor rdf:type openworm:Neuron_receptor .
?Neuron_receptor sp:value ?receptor 
}

For the data stores we've been using, such queries don't seem to execute very efficiently.

I would hope for it to be quick since

1. there should be exactly one statement matching (a, sp:value, "AVAL") to look up in the 'pos' index,
2. and then only one matching (b, neuron:name, a) looked up again in the 'osp' index,
3. and an index lookup on the 'spo' index for the types of 'b' to check it has the correct type should be efficient.
4. The neuron should then have only one neuron:name,
5. and one of all of the other properties which can be looked up in the 'spo' index.

For queries against Sesame, I have to try using the Java API to be more sure these indexes are actually there, otherwise I'm making a lot of assumptions about how well the indexes work. Besides that, I don't have an easy way of understanding how the queries are getting executed without measuring often very long query times. Maybe I can get some sense of how these queries are being handled with some test data.

I've started reading a little about ZODB, a python object storage database. The core of what I've been making for PyOpenWorm is an Object<->RDF mapper. The choice of RDF storage solutions to back PyOpenWorm was based on the work that had already been done, my experience in working with some RDF tools, and an expectation that joining with existing datasets may be easier with RDF than other storage options. It may be useful to re-evaluate going forward.

I've been trying out the KyotoCabinet store for rdflib. Uploading the whole worm under the current parameters takes about 17 minutes.

Timing any of the queries on the full data set is difficult since even 'simple' ones tend to take several minutes. The queries I'm running look something like this for getting the type ('interneuron', 'sensory', 'motor') :

select distinct ?type where { ?Neuron_type0056fa3c098f873e <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://openworm.org/entities/SimpleProperty> .
?Neuron_type0056fa3c098f873e <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://openworm.org/entities/Property> .
?Neuron_type0056fa3c098f873e <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://openworm.org/entities/DataObject> .
?Neuron_type0056fa3c098f873e <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://openworm.org/entities/Neuron_type> .
?Neuron98fc353f03788d3e <http://openworm.org/entities/Neuron/type> ?Neuron_type0056fa3c098f873e .
?Neuron_type0056fa3c098f873e <http://openworm.org/entities/SimpleProperty/value> ?type }

I've adopted a pattern of mirroring the triples into the upload query and the select query. The objective with this is to make it easier to change the data structures without having to update both the upload format and the select with every change. This is the reason for the query above containing 3 rdf:type patterns -- in lieu of RDF inference (intending to include later), I want to be able to utilize the class hierarchy to, for instance, get all of the Neuron objects when querying for Cell objects. To have this, I insert, for each object, all of its super-types. This would let me query for objects of type Cell and get all those of type Neuron (and Muscle). Any object should have all of the super-type tags in addition to its most specific form, so there's no problem, theoretically, querying for the right object.

The main issue is that these queries get executed very slowly. I suspect it's something to do with scanning the table. Interrupting a query (because who has time for that?) we find ourselves here:

  File "build/bdist.linux-x86_64/egg/rdflib/plugins/sparql/evaluate.py", line 47, in evalBGP
  File "build/bdist.linux-x86_64/egg/rdflib/graph.py", line 1373, in triples
  File "build/bdist.linux-x86_64/egg/rdflib_kyotocabinet/KyotoCabinet.py", line 315, in triples

There is an index on the predicates-objects which should make getting the objects of a given type quick:

%ls -lh worm.db
total 193M
-rw-r--r-- 1 markw markw 6.1M Jul 15 13:35 contexts.kch
-rw-r--r-- 1 markw markw  55M Jul 15 13:35 c^o^s^p^.kch

-rw-r--r-- 1 markw markw  55M Jul 15 13:35 c^p^o^s^.kch

-rw-r--r-- 1 markw markw  55M Jul 15 13:52 c^s^p^o^.kch
-rw-r--r-- 1 markw markw  14M Jul 15 13:35 i2k.kch
-rw-r--r-- 1 markw markw  14M Jul 15 13:35 k2i.kch
-rw-r--r-- 1 markw markw 6.1M Jul 15 13:35 namespace.kch
-rw-r--r-- 1 markw markw 6.1M Jul 15 13:35 prefix.kch

I'm thinking that for my query above though, the database engine is doing a join between the results from the index lookup. Adding in an option to toggle between the triples generated for querying versus uploading should make these queries more reasonable while still allowing us to get subtypes from a super-type query. Some of these issues I've run into (there are others...) suggest that the shared query/upload pattern may be inappropriate. I'll have to think more about it.