Ecml

README.md

@@ -1,26 +1,116 @@
-# Pang
-Pattern Mining for the Classification of Public Procurement Fraud
-
-Copyright 2022 Lucas Potin, Rosa Figueiredo, Christine Largeron, Vincent Labatut
+Pang
+=======
+*Pattern-Based Anomaly Detection in Graphs*
 
 Pang is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation. For source availability and license information see licence.txt
 
-* Contact: Lucas Potin lucas.potin@univ-avignon.fr
+-----------------------------------------------------------------------
+
+# Description
+Pang is an algorithm which represents and classifies a collection of graphs according to their frequent patterns (subgraphs).
+
+
+# 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 data files. There is one folder for each dataset, which are described in the [Datasets](#datasets) section.
+
+
+# Installation
+You first need to install `python` and the required packages:
+
+1. Install the [`python` language](https://www.python.org)
+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).
+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 each dataset in its own folder in 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:
+1. Open the Python console.
+2. Run `EMCL.py`
+
+The script will compute the results of the experiments and save the results associated with Table 2, 5 and 6 in the `results` folder.
+
+
+## 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_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 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.
+
+## Data Format
+We use the same format as SPMF for the graph input files. Each graph is defined as follows:
+
+1. `t # N  N`: graph id
+2. `v M L  M`: node id, L: node label
+3. `e P Q L P`: source node id, Q: destination node id, L: edge label
+
+For the patterns output files, each pattern contains one more line than the graphs:
+
+4. `x A B C A,B,C` : graphs containing the pattern
 
-Description
+## Datasets
+The datasets used in the paper are available in the `data` folder. The following datasets are available:
+* `MUTAG` : MUTAG dataset, representing chemical compounds and their mutagenic properties [[D'91](#references)],
+* `NCI1` : NCI1 dataset, representing molecules and classified according to carcinogenicity [[W'06](#references)],
+* `PTC` : PTC dataset, representing molecules and classified according to carcinogenicity  [[T'03](#references)], 
+* `DD` : DD dataset, representing amino acids and their interactions [[D'03](#references)],
 
-## Organization
-TBC
+Each of these datasets can be found [here](https://www.philippe-fournier-viger.com/spmf/index.php?link=datasets.php).
+* `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 3.6.13 with the following packages:
+* [`pandas`](https://pypi.org/project/pandas/): version 1.1.5
+* [`numpy`](https://pypi.org/project/numpy/): version 1.19.5
+* [`networkx`](https://pypi.org/project/numpy/): version 2.5.1
+* [`sklearn`](https://pypi.org/project/numpy/): version 0.24.2
+* [`matplotlib`](https://pypi.org/project/numpy/): version 3.3.4
+* [`grakel`](https://pypi.org/project/numpy/): version 0.1.8
+* [`karateclub`](https://pypi.org/project/numpy/): version 1.3.3
+* [`stellargraph`](https://pypi.org/project/numpy/): version 1.2.1
 
-## Installation
-TBC
 
-## Use
-TBC
+The VF2 and ISMAGS algortihms are included in the [`Networkx` library](https://networkx.org/)
 
-## Dependencies
-TBC
+For the baselines:
+* The WL and WLOA algorithms are included in the Grakel library, documentation available [here](https://ysig.github.io/GraKeL/0.1a8/benchmarks.html)
+* Graph2Vec is included in the karateclub library, documentation available [here](https://karateclub.readthedocs.io/en/latest/)
+* DGCNN is included in the stellargraph library, documentation 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
 
-[XX'19] X. Yyyyyy & A. Bbbbbb, My title, My journal, X(X)/XX-XX, 201X. doi: XXXXXXXXXX - ⟨hal-XXXXXXXX⟩
+# 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)
+* **[D'91]** A.S. Debnath, R.L. Lopez, G. Debnath, A. Shusterman, C. Hansch. *Structure-
+activity relationship of mutagenic aromatic and heteroaromatic nitro compounds.
+correlation with molecular orbital energies and hydrophobicity*, Journal of Medic-
+inal Chemistry 34(2), 786–797, 1991.
+* **[W'06]** N.Wale, G. Karypis. *Comparison of descriptor spaces for chemical compound
+retrieval and classification*, 6th International Conference on Data Mining, pp.
+678–689, 2006.
+* **[T'03]** H . Toivonen, A. Srinivasan, R.D. King, S. Kramer, C. Helma.*Statistical eval-
+uation of the predictive toxicology challenge 2000-2001*, Bioinformatics 19(10),
+1183–1193, 2003.
+* **[D'03]** P.D. Dobson, A.J. Doig. *Distinguishing enzyme structures from non-enzymes
+without alignments*, Journal of Molecular Biology 330(4), 771–783 ,2003.