Learn CF

Repo. for our paper "Predicting Cascading Failures with a Hyperparametric Diffusion Model".

Running Environments

It's currently running on Linux with Python 3.10.10, can be ported to other OS.

Requirements

It supports running on both general CPU and Intel GPU (via OpenCL standard), the latter requires package numba-dpex.

Manual

The main file is run.py or a shell script run.sh. Besides, mle_run.py, features.py, samples.py, simulate_igraph.py can run separately by needs.

NAME
    run.py

SYNOPSIS
    run.py INSTANCE <flags>

POSITIONAL ARGUMENTS
    INSTANCE

FLAGS
    -o, --output=OUTPUT
        Default: ''
        Output directory.
    -v, --verbose=VERBOSE
        Default: 0
    --method=METHOD
        Default: 'lbfgsb'
        Optimization algorithm.
    -g, --gpu=GPU
        Default: False
    --poly_fts=POLY_FTS
        Default: False
        Use polynomial features.
    -d, --dist=DIST
        Default: False
        Use distance features.
    -c, --corr=CORR
        Default: 0.9
        Pearson correlation coefficient.
    --scaler=SCALER
        Default: 'max_abs'
        Feature scaler.
    --block=BLOCK
        Default: 1
        Split data.
    --sel=SEL
        Default: -1
        Select feature dimension.
    --a1=A1
        Default: 0.001
        L1 regularization.
    --a2=A2
        Default: 0.01
        L2 regularization.
    -B, --B=B
        Default: 'inf'
        Box boundary of hyperparameters.
    --tol=TOL
        Default: 1e-06
        Optimization tolerance.
    --maxiter=MAXITER
        Default: 300
        Optimization iteration.
    --pfunc=PFUNC
        Default: 'logistic'
        Influence probability function.
    --theta=THETA
        Default: 0
        Specify value for hyperparameters.
    --test=TEST
        Type: Optional[]
        Default: None
        Test instances.
    --rerun=RERUN
        Default: False
    --fr=FR
        Default: 1
        Feature rank filter.
    --rank=RANK
        Type: Optional[]
        Default: None
    -w, --weight=WEIGHT
        Default: []
        Sample weights.
    -b, --build_only=BUILD_ONLY
        Default: False
        Only build features and samples.
    --fc=FC
        Default: 0.05
        Cascading failures filter.
    -n, --no_mc=NO_MC
        Default: -1
        No. of Monte Carlo simulation.
    --fp=FP
        Default: 1
        Probability filter.
    --resample=RESAMPLE
        Default: False
        Resampling strategy.
    --precision=PRECISION
        Default: 6
    -i, --init_failures=INIT_FAILURES
        Default: ''
    -k, --k=K
        Default: 1
        N-k contingencies.
    --max_workers=MAX_WORKERS
        Default: 1
        Parallelization on CPU.

Datasets

DATA STRUCTURES:

Instances
│
├── 1.00  # power demands factor
│   │
│   ├── generations.csv  # cascading failures
│   │
│   ├── ig_data.mat  # power grid data
│   │
│   ├── res_cnt_size.mat  # benchmarks
│   │
│   └── sub
│       ├── 9 -> ..  # for managing instances, can be any value
│       └── ...
├── ...
│
└── 2.00
    └── ...