Reproduce, Replicate, Reevaluate. The Long but Safe Way to Extend Machine Learning Methods.

This is a work by Luisa Werner, Nabil Layaïda, Pierre Genevès, Jérôme Euzenat, Damien Graux.

This repository contains our re-implementation of the experiments conducted with Knowledge Enhanced Neural Networks (KENN) on the Citeseer Dataset, including the re-implementation of the Experiments in PyTorch and PyTorch Geometric. We also extended the experiments to the datasets Cora and PubMed.

Name	Description	#nodes	#edges	#features	#Classes	Task
CiteSeer	from Planetoid, Citation Network	3,327	9,104	3,703	6	Node classification
Cora	from Planetoid, Citation Network	2,708	10,556	1,433	7	Node Classification
PubMed	from Planetoid, Citation Network	19,717	88,648	500	3	Node Classification

Overview of this repository

This repository contains to sub-directories that refer to the experiments conducted with the initial implementation and the re-implementation.

The initial_implementation contains code from initial experiments here, extended to Cora and PubMed
The re_implementation of the experiments.

The results of both approaches are stored in the respective /results subdirectory.

The implementation of KENN layers in PyTorch is used from here.

Before running the experiments in both implementations

In order to make sure that the right environment is used, the necessary Python packages and their versions are specified in requirements.txt. We use Python 3.9. To install them go in the project directory and create a conda environment with the following packages.

pip install -r requirements.txt

The full list of packages in our environment including the dependencies is specified in system_packages.txt.

While the Citeseer dataset used by the initial implementation is already stored in the directory CiteSeer_reproduce, the datasets Cora and PubMed have to be loaded from PyTorch Geometric [Source] and preprocessed. Run the following command from the project directory to get PubMed and Cora.

python create_datasets.py

To run the initial experiments.

To run the initial experiments with the specified parameters in the paper on all three datasets, run the following command from the project directory:

cd initial_implementation
python test.py

To get an overview of the results of the initial implementation. It is important that all experiments are finished before.

cd initial_implementation
python inspect_results.py

To run the replicated experiment.

We use Weights and Biases (WandB) as experiment tracking tool. The experiments can be run WITHOUT or WITH the use of WandB.

To run the experiments without WandB, run the following command.

cd re-implementation
python train_and_evaluate.py conf.json

(By default, "wandb_use" : false is set in re-implementation/conf.json)

If you want to use weights and biases specify the following parameters in re-implementation/conf.json.

"wandb_use" : true,
"wandb_label": "<your label>",
"wandb_project" : "<your project>",
"wandb_entity": "<your entity>"

Then use the following command to run the experiments:

cd re-implementation
python run_experiments.py conf.json

Interprete the results To get an overview of the results of the re-implementation, run the following command

cd re_implementation
python inspect_results.py

Comparison between both implementations

To compare the results of both approaches (comparison type 2 reproducibility), go to the project folder and run

python compare_results.py

Hyperparameters

In the file re-implementation/conf.json, the hyperparameters and settings of the runs are configured and saved. By default, the conf.json file contains parameters mentioned in the paper. The last column indicates whether the parameter can be modified in this implementation and to which values it should be set.

Parameter	description	default	state
adam_beta1	Adam optimizer parameter	0.9	modifiable
adam_beta2	Adam optimizer parameter	0.999	modifiable
adam_eps	Adam optimizer parameter	1e-07	modifiable
bias init	Initialization of bias in NN	"zeroes"
binary_preactivation	constant value for activation of binary predicate	500.0	modifiable
clause_weight	initialization of clause weight	0.5	modifiable
dataset	dataset	CiteSeer_reproduce	modifiable: [Cora_reproduce, PubMed_reproduce, CiteSeer_reproduce]
device	gpu device for gpu computing	1	modifiable
dropout	dropout rate	0.0	modifiable (0.0, 1.0)
epochs	number of epochs in training	300	modifiable (1, ...)
es_enabled	early stopping activated flag	false	modifiable [true, false]
es_min_delta	early stopping delta threshold	0.001	modifiable (0, ...)
es_patience	early stopping patience	10	modifiable (0, ...)
eval_steps	prints each n steps during training	10	modifiable (1, ...)
hidden_channels	hidden layer dimension	50	modifiable (1, ...)
loss function	loss function	categorical cross-entropy
lr	learning rate	0.001	modifiable (0.0, 1.0)
min_weight	clause weight clipping minimum value	0.0	modifiable (..., max_weight)
max_weight	clause weight clipping maximum value	500.0	modifiable (min_weight, ...)
mode	training mode	"transductive"
model	standard or KENN_Standard (for base NN Standard)	Standard	modifiable: [Standard, KENN_Standard]
num_kenn_layers	number of KENN layers	3	modifiable (0, ...)
num_layers	number of layers of base NN	3	modifiable (1, ...)
optimizer	optimizer	adam
runs	number of runs	30	modifiable (1, ...)
seed	random seed	0	modifiable (0,...)
training_dimension	training dimension	0.1	modifiable: [0.1, 0.25, 0.5, 0.75, 0.9]
valid_dim	validation set dimension	0.2	modifiable in: (0.0, 1.0)
wandb_use	if weights and biases should be used	false	modifiable: [true, false]
wandb_label	label for weights and biases	"None"	modifiable depending on custom WandB settings
wandb_project	project name for weights and biases	"None"	modifiable depending on custom WandB settings
wandb_entity	entity name for weights and biases	"None"	modifiable depending on custom WandB settings
weight init	initialization of weights in NN	xavier uniform

How to Cite

This work can be cited as follows:

@article{Werner_Layaïda_Genevès_Euzenat_Graux_2024, title={Reproduce, Replicate, Reevaluate. The Long but Safe Way to Extend Machine Learning Methods}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29515}, DOI={10.1609/aaai.v38i14.29515}, abstractNote={Reproducibility is a desirable property of scientific research. On the one hand, it increases confidence in results. On the other hand, reproducible results can be extended on a solid basis. In rapidly developing fields such as machine learning, the latter is particularly important to ensure the reliability of research. In this paper, we present a systematic approach to reproducing (using the available implementation), replicating (using an alternative implementation) and reevaluating (using different datasets) state-of-the-art experiments. This approach enables the early detection and correction of deficiencies and thus the development of more robust and transparent machine learning methods. We detail the independent reproduction, replication, and reevaluation of the initially published experiments with a method that we want to extend. For each step, we identify issues and draw lessons learned. We further discuss solutions that have proven effective in overcoming the encountered problems. This work can serve as a guide for further reproducibility studies and generally improve reproducibility in machine learning.}, number={14}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Werner, Luisa and Layaïda, Nabil and Genevès, Pierre and Euzenat, Jérôme and Graux, Damien}, year={2024}, month={Mar.}, pages={15850-15858} }

The works of KENN can be cited as follows:

# Knowledge Enhanced Neural Networks for Relational Domains
@InProceedings{10.1007/978-3-031-27181-6_7,
author="Daniele, Alessandro
and Serafini, Luciano",
editor="Dovier, Agostino
and Montanari, Angelo
and Orlandini, Andrea",
title="Knowledge Enhanced Neural Networks for Relational Domains",
booktitle="AIxIA 2022 -- Advances in Artificial Intelligence",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="91--109",
isbn="978-3-031-27181-6"
}

# Knowledge Enhanced Neural Networks
@InProceedings{10.1007/978-3-030-29908-8_43,
author="Daniele, Alessandro
and Serafini, Luciano",
editor="Nayak, Abhaya C.
and Sharma, Alok",
title="Knowledge Enhanced Neural Networks",
booktitle="PRICAI 2019: Trends in Artificial Intelligence",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="542--554",
isbn="978-3-030-29908-8"
}