mlconcepts 0.0.1a3

A small library implementing several interpretable machine learning algorithms based on FCA.

mlconcepts

This library is a wrapper around libmlconcepts, namely a c++ library which implements a series of (interpretable) machine learning algorithms based on FCA, e.g. [3].

Installing mlconcepts from a source distribution only requires a c++23 compiler, all the other dependencies are automatically fetched. If cmake is not able to find the compiler during the installation process, please set the environment variable CXX as follows

CXX = /path/to/c++/compiler

Basic example

Assuming that a dataset containing a column outlier is stored in the file dataset.csv, a basic model could be trained as follows.

import mlconcepts

model = mlconcepts.SODModel() #creates the model
model.fit("dataset.csv", labels = "outlier") #trains the model on the dataset
model.save("model.bin") #compresses and serializes the model to file

A slightly more involved example

import mlconcepts
import mlconcepts.data
import sklearn.metrics
import sklearn.model_selection

#Loads the dataset.
data = mlconcepts.data.load("dataset.csv", labels = "outlier")

#data.split takes as an input any splits generator, such as the ones of sklearn
skf = sklearn.model_selection.StratifiedKFold(n_splits = 4, shuffle = True)
for train, test in data.split(skf):
	model = mlconcepts.SODModel(
		    n = 32, #number of bins for quantization
            epochs = 1000, #number of training iterations
            show_training = False #whether to show training info
	)
	model.fit(train)
	predictions = model.predict(test)
	print("AUC: ", sklearn.metrics.roc_auc_score(test.y, predictions))

References

[1] Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory

[2] A Meta-Learning Algorithm for Interrogative Agendas

[3] Outlier detection using flexible categorisation and interrogative agendas