This folder provides the source code used in Experiment B in "Continuous-variable quantum neural networks" arXiv:1806.06871.
The raw data is sourced from the Credit Card Fraud Detection dataset on Kaggle. The creditcard.csv file should be downloaded and placed in this folder. The user can then run:
python3 data_processor.pyThis script creates two datasets for training and testing.
The model is a hybrid classical-quantum classifier, with a number of input classical layers that control the parameters of an input layer in a two-mode CV quantum neural network. The model is trained so that it outputs a photon in one mode for a genuine credit card transaction, and outputs a photon in the other mode for a fraudulent transaction.
Training can be performed with:
python3 fraud_detection.py| WARNING: this script can take a long time to run. On a typical PC, it may take hours to arrive at a well-trained model. |
|---|
The model is periodically saved during training, and progress can be monitored by launching TensorBoard in the terminal:
tensorboard --logdir=outputs/tensorboard/simulation_labelwhere simulation_label is the name used to refer to a particular run of the script fraud_detection.py (this is specified within the file itself; the default is 1).
Testing can be performed with:
python3 testing.py| WARNING: this script can take a long time to run |
|---|
Here, the user must edit testing.py to point to the simulation label and checkpoint of the model which is to be tested. These are specified under the variables simulation_label and ckpt_val in testing.py.
The output of testing is a confusion table, which can be found as a numpy array in outputs/confusion/simulation_label. The confusion table is given for multiple threshold probabilities for a transaction to be considered as genuine.
The performance of the trained model can be investigated with:
python3 roc.pywhich outputs the receiver operating characteristic (ROC) curve and confusion matrix for the optimal threshold probability.