xflr6/concepts

Formal Concept Analysis with Python

concepts's Issues

Access to concepts themselves

It seems that currently, context's concepts are accessible only through the lattice representation, through the (implicitly protected) attribute _concepts.

Accessing the Concept instances is necessary for various reasons, including the mapping of a concept with its node in the lattice representation.

Hack: use _concepts even if it's not public API.

Scalable method for deserializing lattice objects?

Is there a scalable way to deserialize lattice objects to disk? Especially when concept lattices are large, I want to avoid recomputing them. However, for large lattices the max recursion depth is hit quickly when pickling. Increasing the max recursion depth may help in border cases, but is not a scalable solution. I was thinking about printing the lattice information to disk print(lattice, file=lattice.txt), but I do not know how I could possibly reconstruct a lattice object from that information.

Is a feature like this available?

Saving lattice visualization to a file

Hello!

I'm trying to improve my FCA libraries performance benchmark (https://github.com/EgorDudyrev/FCApy/tree/feature/benchmarking/notebooks/Performance_Benchmark). But I have some problems saving concepts lattice visualization to a file.

I use the function context.lattice.graphviz('concepts', render=True, ) to save a visualization into a file 'concepts.pdf'. It works for a small context (like 'animal_movement'). But when applied to 'Bob Ross elements-by-episode' dataset it results in a very wide and totally white pdf file.

What is the proper way to save (big) lattice visualization to a file? Can it be png file instead of pdf?

The code to reproduce the error:

import concepts
import pandas as pd

fname = 'bob_ross.csv'
!wget -O {fname} -q https://raw.githubusercontent.com/fivethirtyeight/data/master/bob-ross/elements-by-episode.csv 

df = pd.read_csv(fname)
df['EPISODE_TITLE'] = df['EPISODE']+' '+df['TITLE']
df = df.drop(['EPISODE','TITLE'],1).set_index('EPISODE_TITLE').astype(bool)

context = concepts.Context(df.index, df.columns, df.values)
context.lattice.graphviz('concepts', render=True, );

Concept from pandas dataframe

Hello,

Have you consider to allow the creation of a formal context based on a pandas DataFrame, which could be a sparse matrix (with empty cells and cells filled with 'X' or 1) or a binary matrix (filled with 0/1) ?

In my current work, I use a pandas dataframe filled with 'X' and blank cells, as needed by the package, but I have to export the file in a csv file before re-opening it with the concepts.Context.fromfile() method, which is not practical.

Thanks.

lattice_memebers.Concept vs _common.Concept API

Hi,

I found quite counter-intuitive (for me) API naming.

In case of _common.Concept we have

@property
def objects(self) -> typing.Tuple[str]:
    """The objects subsumed by the concept."""
    return self.extent.members()

In case of lattice_members.Concept we have

@property
def extent(self) -> typing.Tuple[str, ...]:
    """The objects subsumed by the concept.

    Example:
        >>> import concepts
        >>> lattice = concepts.Context.fromstring(concepts.EXAMPLE).lattice
        >>> lattice['+1',].extent
        ('1sg', '1pl')
    """
    return self._extent.members()

and Concept.objects works differently.

I would prefer to have same API, that is, Vector extent can be hidden in concept._extent property, public list of members names can be concept.extent or concept.objects and original concept.objects can be renamed.

You cant have a context with only one Object.

To recreate the error try making a context with only one object.
The issue also arises when there is only one feature.

missing middle level labels ?

Don't know is this bug or a feature... but when drawing the lattice the labels at middle layers are missing !!!
Not like here : https://concepts.readthedocs.io/en/stable/_images/relations.svg
here is the csv..

,does_carry_transport,has_wheels,does_drive_is_driven,has_a_seat_seats,made_of_metal
bus0,X,X,X,,
bus1,,X,X,X,
bus2,X,X,X,,
bus3,X,,X,X,
bus4,,X,X,X,
bus5,X,,X,X,
bus6,,X,,X,X
bus7,,X,X,,X
bus8,,X,X,,X
bus9,X,X,,,X
bus10,X,X,X,,
bus11,X,,,X,X
bus12,,X,,X,X
bus13,X,X,X,,
bus14,X,X,,X,
bus15,,X,X,X,
bus16,X,X,,,X
bus17,,X,X,X,
bus18,X,X,,,X
bus19,X,X,,X,

Plans for lattice reduction?

Many thanks for your work on this! After playing around with this for a while I inevitably wound up with some messy lattices, so I was wondering whether you have any plans to include simple lattice reduction techniques based on stability, support and the like in concepts.

I came across this repository with an MIT-licensed Python implementation of lattice reduction, and if it's of interest I could try to integrate that in this library.