pyiron_ontology is a pyiron project built on top of owlready2 to leverage ontologies for guided workflow design and dynamic (use-based) typing.
Philosophically, pyiron_ontology breaks the construction of ontologies down into two parts: a set of classes that are "universal" to all pyiron_ontology ontologies, which describe the core elements of a workflow, and a set of "domain specific" instance declarations (most or all of which) define relationships between different pieces of code.
pyiron_ontology available to clone, pip-install, or on conda (recommended) from Conda Forge via conda install -c conda-forge pyiron_ontology.
pyiron_ontology uses owlready2, which requires java.
In case you try to run and get an owlready2 error about not finding java, you may need to set the java path (cf. owlready2 docs): import owlready2; owlready2.JAVA_EXE = "C:\\path\\to\\java.exe".
Java is also available via conda install -c conda-forge openjdk.
It may be sufficient to install this, and restart your jupyter session from a clean terminal.
We use a graph-based paradigm for representing workflows, such that they are made up of nodes on a directed graph. Each node is thought of to have inputs and outputs, and these may connect to the outputs/inputs of other nodes (or even back on themselves). Data moving through these classes, i.e. anything that is an input or an output, is represented by some child of a generic data class (these are defined and refined during domain-specific declarations).
The structure of these classes is, along with a couple of key methods:
classDiagram
class PyironOntoThing
PyironOntoThing: get_sources()
<<Abstract>> PyironOntoThing
WorkflowThing <|--PyironOntoThing
<<Abstract>> WorkflowThing
Parameter <|--PyironOntoThing
<<Abstract>> Parameter
Function <|--WorkflowThing
IO <|--Parameter
IO <|--WorkflowThing
<<Abstract>> IO
Input <|--IO
Output <|--IO
Generic <|--Parameter
Generic: is_representable_by()
Generic: is_more_specific_than()
With the following key relationships:
erDiagram
Function ||--|{ Input : optional_inputs
Function ||--o{ Input : mandatory_inputs
Function ||--|{ Output : outputs
Input |{--|| Generic : generic
Output |{--|| Generic : generic
Input ||--o{ Generic: req
Input ||--o{ Generic: transitive_req
The method get_sources leverages the ontology to allow an individual to see where in the knowledge space it may come from; the sources of an Output is a Function, the source of a Function is its mandatory Input, and both Input and Generic have as sources Output.
Constraints on sources are carried through the graph as Generic by the direct requirements (additional detail on the generic) and transitive_requirements (Generic requirements that do not inherit from generic and should be passed further upstream).
This allows us to build all possible workflows by guaranteeing that upstream WorkflowThing individuals always satisfy the (transitive) requirements for each piece of input in the workflow.
The main task of domain-specific declarations is to inherit from Generic to create a body of knowledge in the form of classes, and to instantiate the Function, Input, and Output classes to create individuals to represent available computations.
One of the hurdles here is that we often want to describe mutual exclusion in our Generic data types.
E.g. consider an atomic structure.
We may wish to assign it mutually-exclusive properties, like having a grain boundary or a dislocation XOR being bulk-like.
Unfortunately, the OWL framework doesn't handle this sort of mutual-exclusion particularly well.
The best documentation I could find is this W3C post, which outlines two possible routes -- one with individuals and one with classe.
Here we use the class-based approach.
I.e. in our example above your Structure sub-class may inherit from HasGrainBoundary and/or HasDislocation, but these are exclusive to (AllDisjoint with) Bulk.
In the parlance of owlready2, we can use is_a to define a multiple-inheritance scheme to nail down all the details of our Generic, but if we try to inherit from two that are disjoint (mutually exclusive) our reasoner will fail and let us know about the problem.
Instantiating Function, Input, and Output individuals is comparatively straightforward.
These just get their generic, requirements, and transitive_requirements populated by instances of our Generic classes described above, and their other fields populated with each other.
The primary idea is to build your ontology to represent existing functionality in your existing code base and then perform ontological reasoning to search for available workflows, but you might also find it useful to do some "ontologically-driven design" and write the sort of functionality you want first in the ontology and then go code something that matches your ontological design specifications.
Over in ironflow we are working to bring these concepts together, with ontological types explicitly associated with nodes and their sub-components in our graph-based visual scripting.
Here is an example using the Constructor class to build a workflow ontology for baking pizzas.
You can use it interactively in the demo notebook pizza.ipynb
>>> import owlready2 as owl
>>> from pyiron_ontology import Constructor
>>>
>>> c = Constructor('pizza')
>>>
>>> # Knowledge base
>>> with c.onto:
... class Flour(c.onto.Generic): pass
... class Wheat(Flour): pass
... class GlutenFree(Flour): pass
... _ = owl.AllDisjoint([GlutenFree, Wheat])
...
... class Crust(c.onto.Generic): pass
... class Thin(Crust): pass
... class Regular(Crust): pass
... _ = owl.AllDisjoint([Thin, Regular])
... class Stuffed(Regular): pass
...
... class Ingredients(c.onto.Generic): pass
... class HasVegetables(Ingredients): pass
... class HasMushrooms(HasVegetables): pass
... class HasPeppers(HasVegetables): pass
... class HasMeat(Ingredients): pass
... class HasSalami(HasMeat): pass
... class HasBacon(HasMeat): pass
... class Vegetarian(Ingredients):
... equivalent_to = [Ingredients & owl.Not(HasMeat)]
... _ = owl.AllDisjoint([Vegetarian, HasMeat])
...
... class RawPizza(c.onto.Generic): pass
...
... class CookedPizza(c.onto.Generic): pass
...
... _ = owl.AllDisjoint([Flour, Crust, Ingredients, RawPizza, CookedPizza])
>>>
>>> # Code base
>>> buy_wheat_flour = c.onto.Function("buy_wheat_flour")
>>> buy_wheat_flour_out = c.onto.Output(
... "buy_wheat_flour_out",
... output_of=buy_wheat_flour,
... generic=Wheat()
... )
>>>
>>> buy_corn_flour = c.onto.Function("buy_corn_flour")
>>> buy_corn_flour_out = c.onto.Output(
... "buy_corn_flour_out",
... output_of=buy_corn_flour,
... generic=GlutenFree()
... )
>>>
>>> make_crust = c.onto.Function("make_crust")
>>> make_crust_inp_flour = c.onto.Input(
... name="make_crust_inp_flour",
... mandatory_input_of=make_crust,
... generic=Flour(),
... )
>>> make_crust_out = c.onto.Output(
... name="make_crust_out",
... output_of=make_crust,
... generic=Crust(),
... )
>>>
>>> make_thin_crust = c.onto.Function("make_thin_crust")
>>> make_thin_crust_inp_flour = c.onto.Input(
... name="make_thin_crust_inp_flour",
... mandatory_input_of=make_thin_crust,
... generic=Flour(),
... )
>>> make_thin_crust_out = c.onto.Output(
... name="make_thin_crust_out",
... output_of=make_thin_crust,
... generic=Thin(),
... )
>>> make_gluten_free_crust = c.onto.Function("make_gluten_free_crust")
>>> make_gluten_free_crust_inp_flour = c.onto.Input(
... name="make_gluten_free_crust_inp_flour",
... mandatory_input_of=make_gluten_free_crust,
... generic=GlutenFree(),
... )
>>> make_gluten_free_crust_out = c.onto.Output(
... name="make_gluten_free_crust_out",
... output_of=make_gluten_free_crust,
... generic=Crust(),
... )
>>>
>>> add_meat = c.onto.Function("add_meat")
>>> add_meat_inp_ingredients = c.onto.Input(
... name="add_meat_inp_ingredients",
... mandatory_input_of=add_meat,
... generic=HasMeat(),
... )
>>> add_meat_inp_crust = c.onto.Input(
... name="add_meat_inp_crust",
... mandatory_input_of=add_meat,
... generic=Crust(),
... transitive_requirements=[Flour()]
... )
>>> add_meat_out = c.onto.Output(
... name="add_meat_out",
... output_of=add_meat,
... generic=RawPizza()
... )
>>>
>>> add_vegetables = c.onto.Function("add_vegetables")
>>> add_vegetables_inp_ingredients = c.onto.Input(
... name="add_vegetables_inp_ingredients",
... mandatory_input_of=add_vegetables,
... generic=HasVegetables(),
... )
>>> add_vegetables_inp_crust = c.onto.Input(
... name="add_vegetables_inp_crust",
... mandatory_input_of=add_vegetables,
... generic=Crust(),
... transitive_requirements=[Flour()]
... )
>>> add_vegetables_out = c.onto.Output(
... name="add_vegetables_out",
... output_of=add_vegetables,
... generic=RawPizza()
... )
>>>
>>> canadian = c.onto.Function("canadian")
>>> canadian_inp_ingredients = c.onto.Input(
... name="canadian_inp_ingredients",
... mandatory_input_of=canadian,
... generic=Ingredients(is_a=[HasBacon, HasMushrooms]),
... )
>>> canadian_inp_crust = c.onto.Input(
... name="canadian_inp_crust",
... mandatory_input_of=canadian,
... generic=Crust(),
... transitive_requirements=[Flour()]
... )
>>> canadian_out = c.onto.Output(
... name="canadian_out",
... output_of=canadian,
... generic=RawPizza()
... )
>>>
>>> bake_for_omnivor = c.onto.Function("bake_for_omnivor")
>>> bake_for_omnivor_inp = c.onto.Input(
... name="bake_for_omnivor_inp",
... mandatory_input_of=bake_for_omnivor,
... generic=RawPizza(),
... )
>>> bake_for_omnivor_out = c.onto.Output(
... name="bake_for_omnivor_out",
... output_of=bake_for_omnivor,
... generic=CookedPizza()
... )
>>>
>>> bake_for_vegetarian = c.onto.Function("bake_for_vegetarian")
>>> bake_for_vegetarian_inp = c.onto.Input(
... name="bake_for_vegetarian_inp",
... mandatory_input_of=bake_for_vegetarian,
... generic=RawPizza(),
... requirements=[Vegetarian()]
... )
>>> bake_for_vegetarian_out = c.onto.Output(
... name="bake_for_vegetarian_out",
... output_of=bake_for_vegetarian,
... generic=CookedPizza()
... )
>>>
>>> bake_stuffed_crust = c.onto.Function("bake_stuffed_crust")
>>> bake_stuffed_crust_inp = c.onto.Input(
... name="bake_stuffed_crust_inp",
... mandatory_input_of=bake_stuffed_crust,
... generic=RawPizza(),
... requirements=[Stuffed(), Wheat()]
... )
>>> bake_stuffed_crust_out = c.onto.Output(
... name="bake_stuffed_crust_out",
... output_of=bake_stuffed_crust,
... generic=CookedPizza()
... )
>>>
>>> bake_dietary_restrictions = c.onto.Function("bake_dietary_restrictions")
>>> bake_dietary_restrictions_inp = c.onto.Input(
... name="bake_dietary_restrictions_inp",
... mandatory_input_of=bake_dietary_restrictions,
... generic=RawPizza(),
... requirements=[GlutenFree(), Vegetarian()]
... )
>>> bake_dietary_restrictions_out = c.onto.Output(
... name="bake_dietary_restrictions_out",
... output_of=bake_dietary_restrictions,
... generic=CookedPizza()
... )
>>>
>>> c.sync()
>>>
>>> bake_for_vegetarian_out.get_source_tree().render()
bake_for_vegetarian_out
bake_for_vegetarian
bake_for_vegetarian_inp
add_vegetables_out
add_vegetables
add_vegetables_inp_crust
make_crust_out
make_crust
make_crust_inp_flour
buy_corn_flour_out
buy_corn_flour
buy_wheat_flour_out
buy_wheat_flour
make_gluten_free_crust_out
make_gluten_free_crust
make_gluten_free_crust_inp_flour
buy_corn_flour_out
buy_corn_flour
make_thin_crust_out
make_thin_crust
make_thin_crust_inp_flour
buy_corn_flour_out
buy_corn_flour
buy_wheat_flour_out
buy_wheat_flour
add_vegetables_inp_ingredientsNote: Our bake_for_vegetarian has an input requirement Vegetarian(), and sure enough we only get workflows that use add_vegetables when choosing topings.
This function has input specifying the generic as HasVegetables().
If we then go back and look at our knowledge base, we see that there is no direct relationship between Vegetarian and HasVegetables.
Rather, when we called sync(), the reasoner used the equivalence definition of Vegetarian to deduce that an individual HasVegetables() is_a Vegetarian!
Defining all the individuals that map to a (hypothetical) code base is quite verbose (we'll work on that!), and it's just a bit of a silly example, but this nevertheless highlights the power and flexibility of exploiting ontologies to describe the knoweldge base.
pyiron_ontology also comes with an ontology defined for (a small sub-set of) pyiron_atomistics (an optional dependency).
For example, if you have Murnaghan jobs in your pyiron project, the snippet below will return a nice little dataframe of results:
>>> import pyiron_ontology as po
>>> from pyiron_ontology import AtomisticsReasoner
>>> from pyiron_atomistics import Project
>>>
>>> onto = po.dynamic.atomistics()
>>> reasoner = AtomisticsReasoner(onto)
>>> pr = Project('your_project_tree_with_loads_of_data')
>>>
>>> out = reasoner.search_database_for_property(onto.BulkModulus(), pr)
>>> out.columns
Index(['Chemical Formula', 'atomistics.BulkModulus', 'unit', 'Engine'], dtype='object')