This repository is the work produced by Ludovic De Matteïs and Matias Etcheverry in the course Machine learning with kernel methods given by Michael Arbel, Alessandro Rudi, Jean-Philippe Vert and Julien Mairal at the MVA.
The goal of this repository is to implement machine learning algorithms, for a classification task on graph data.
In order to run this repository, you need to run the following:
# clone the repository
git clone [email protected]:MatiasEtcheve/KM-graph-classification.git
cd KM-graph-classification
# install the dependencies
pip install -r requirements.txt
The Command Line Interface can be used efficiently to compute predictions on the dataset.
In order to use our best model, you can run:
python start.py
This will train 2 kernels:
- a Weisfeiler Lehman kernel on the edges of the graphs
- a Weisfeiler Lehman kernel on the nodes of the graphs A linear combination is then applied between the logits of the 2 models.
However, you can also tune the learning, with the CLI options:
| Option name | Type | Description | Default Value |
|---|---|---|---|
kernels |
list | Kernels to train on the data. If multiple kernels are provided, it will individually train on each kernel, then do a linear combination of the logits. Must be one (or multiples) of "EH", "VH", "SP", "GL", "WL-Edges", "WL-Nodes" |
"[WL-Edges,WL-Nodes]" (care quotes !) |
combination |
list | list of coefficient for the combination of kernels | "[1.59,1.35]" (care quotes !) |
max-alpha |
float | Max value of the alpha coefficient in SVM. Note: multiple alphas can be higher than C, when class_weight=balanced. |
100 |
sigma |
float >= 0 | Sigma in the RBF wrapper. If 0, a linear wrapper is applied instead. | Defaults to 1 |
src |
folder | Path to .pkl datasets. | data/ |
train-val-split |
float or int | Train val split, in ratio or in number of elements, eg 0.7 or 4200. Usefull when training takes time. |
0.7 |
do-predict |
flag | whether to do the prediction on the test set. | True |
predict-filename |
filename | path to the prediction (if do-predict flag is present). |
test_pred.csv |
Example: Examples of working command lines:
- Default command:
python start.py --kernels "[WL-Edges,WL-Nodes]" --combination "[1.59,1.35]" --src data/ --train-val-split 0.7 --do-predict --predict-filename data/predictions.csv - another command:
python start.py --kernels "[EH,VH]" --combination "[1,1]" --max-alpha 1 --sigma 0 --src data/ --train-val-split 0.4 --do-predict --predict-filename data/predictions.csv