# mlconcepts
This library is a wrapper around [libmlconcepts](https://github/xeredent/libmlconcepts), namely
a `c++` library which implements a series of (interpretable) machine learning algorithms
based on FCA, e.g. [[3]](https://www.sciencedirect.com/science/article/pii/S0167923624000290).
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
```bash
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.
```python
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
```python
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](https://arxiv.org/abs/2210.17330)
[[2] A Meta-Learning Algorithm for Interrogative Agendas](https://arxiv.org/abs/2301.01837)
[[3] Outlier detection using flexible categorisation and interrogative agendas](https://www.sciencedirect.com/science/article/pii/S0167923624000290)
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