UpdateReadME

Author: LucasPotin98

README.md

@@ -9,15 +9,14 @@ Pang is free software: you can redistribute it and/or modify it under the terms
 # Description
 Pang is an algorithm which represents and classifies a collection of graphs according to their frequent patterns (subgraphs).
 
-The data available are in the FOPPA repository, they are extracted from FOPPA, a database of French public procurement notices [[P'22](#references)]. 
-
 
 # Organization
 This repository is composed of the following elements:
 * `requirements.txt` : List of Python packages used in pang.py.
 * `PANG.py` : Python script in order to use the algorithm.
+* `EMCL.py` : Python script in order to compute the results of the experiments of the ECML paper.
 * `ProcessingPattern.py` : Python script in order to compute the number of occurences and the set of induced patterns
-* `data` : folder with the input files needed.
+* `data` : folder with the input data files. There is one folder for each dataset, which are described in the [Datasets](#datasets) section.
 
 
 # Installation
@@ -27,19 +26,17 @@ You first need to install `python` and the required packages:
 2. Download this project from GitHub and unzip.
 3. Execute `pip install -r requirements.txt` to install the required packages (see also the *Dependencies* Section).
 
-The source code of SPMF in order to use gSpan and cgSpan is available [here](https://www.philippe-fournier-viger.com/spmf/index.php?link=download.php)
-
-The VF2 and ISMAGS algortihms are included in the [`Networkx` library](https://networkx.org/)
-
-For the baselines:
-* The WL and WLOA algorithms are included in the Grakel library, available [here](https://ysig.github.io/GraKeL/0.1a8/benchmarks.html)
-* Graph2Vec is included in the karateclub library, available [here](https://karateclub.readthedocs.io/en/latest/)
-* DGCNN is included in the stellargraph library, available [here](https://stellargraph.readthedocs.io/en/stable/).
-* We use the implementation of CORK from Marisa Thoma. This implementation is available in the `CORKcpp.zip` archive.
+The source code of SPMF in order to use gSpan and cgSpan is available [here](https://www.philippe-fournier-viger.com/spmf/index.php?link=download.php).
+SPMF is available in two versions:
+* a jar file that can be run from the command line. Actually, this version can be use with gSpan, but not with cgSpan.
+* a source code. The installation of this version is more complicated, but it allows to use cgSpan. You can find the instructions [here](https://www.philippe-fournier-viger.com/spmf/how_to_install.php).
 
+In order to use Pang, you need to unzip the datasets in each folder of the `data` folder.
 
 # Use
-
+We provide two scripts to use Pang:
+* `ECML.py` : a python script in order to compute the results of the ECML paper.
+* `PANG.py` : a python script in order to use Pang with your own data.
 
 ## To Replicate the Paper Experiments
 In order to use Pang:
@@ -52,19 +49,16 @@ The script will compute the results of the experiments and save the results asso
 ## To Apply PANG to Other Data
 If you want to use Pang with your own data, you need to create an `XXX` folder in the `data` folder and put your data in it. This folder must contain the following files:
 * `XXX_graph.txt` : a file containing the graphs.
-* `XXX_pattern.txt` : a file containing the patterns.
 * `XXX_label.txt` : a file containing the labels of the graphs.
 
 Then you need to run a script to produce the data files that will be used by Pang:
 1. Open the Python console.
-2. Run `ProcessingPattern.py`with the option `-d XXX` in order to create the files `XXX_mono.txt` and `XXX_iso.txt`.
-3. Run `PANG.py` with the option `-d XXX` in order to run Pang on the data `XXX`.
+2. Run the script `Patterns.sh` in order to create the files `XXX_patterns.txt`.
+3. Run `ProcessingPattern.py`with the option `-d XXX` in order to create the files `XXX_mono.txt` and `XXX_iso.txt`.
+4. Run `PANG.py` with the option `-d XXX` in order to run Pang on the data `XXX`.
 
 For each value of the parameter `k`, Pang will create a file `KResults.txt` containing the results of the classification and a file `KPatterns.txt` containing the patterns.
 
- ## To Reconstruct all patterns for a dataset
- If you want to reconstruct all patterns using SPMF for a dataset, you need to run the associated script in the `scripts` folder. This process involves pattern mining and post processing for each pattern and is therefore time consuming. Each file will be saved in the associated `data` folder.
-
 ## Data Format
 We use the same format as SPMF for the graph input files. Each graph is defined as follows:
 
@@ -76,7 +70,13 @@ For the patterns output files, each pattern contains one more line than the grap
 
 4. `x A B C A,B,C` : graphs containing the pattern
 
-
+## Datasets
+The datasets used in the paper are available in the `data` folder. The following datasets are available:
+* `MUTAG` : MUTAG dataset, available [here](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets).
+* `NCI1` : NCI1 dataset, available [here](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets).
+* `PTC` : PTC dataset, available [here](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets).
+* `DD` : DD dataset, available [here](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets).
+* `FOPPA` : dataset extracted from FOPPA, a database of French public procurement notices [[P'22](#references)]. 
 # Dependencies
 Tested with `SPMF` version 2.54, and `python` version 3.8.0 with the following packages:
 * [`pandas`](https://pypi.org/project/pandas/): version 1.3.5
@@ -86,5 +86,15 @@ Tested with `SPMF` version 2.54, and `python` version 3.8.0 with the following p
 * [`matplotlib`](https://pypi.org/project/numpy/): version 3.6.0
 
 
+
+The VF2 and ISMAGS algortihms are included in the [`Networkx` library](https://networkx.org/)
+
+For the baselines:
+* The WL and WLOA algorithms are included in the Grakel library, available [here](https://ysig.github.io/GraKeL/0.1a8/benchmarks.html)
+* Graph2Vec is included in the karateclub library, available [here](https://karateclub.readthedocs.io/en/latest/)
+* DGCNN is included in the stellargraph library, available [here](https://stellargraph.readthedocs.io/en/stable/).
+* We use the implementation of CORK from Marisa Thoma. This implementation is available in the `CORKcpp.zip` archive.
+
+
 # References
 * **[P'22]** L. Potin, V. Labatut, R. Figueiredo, C. Largeron, P.-H. Morand. *FOPPA: A database of French Open Public Procurement Award notices*, Technical Report, Avignon University, 2022.  [⟨hal-03796734⟩](https://hal.archives-ouvertes.fr/hal-03796734)