manifest.json

{
  "title": "SR-CNN",
  "description": "Implementation of https://doi.org/10.1145/3292500.3330680",
  "inputDimensionality": "univariate",
  "version": "0.3.0",
  "authors": "Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang",
  "language": "Python",
  "type": "Detector",
  "mainFile": "algorithm.py",
  "learningType": "semi-supervised",
  "trainingStep": {
    "parameters": [
      {
        "name": "window_size",
        "type": "int",
        "defaultValue": 128,
        "optional": "true",
        "description": "Sliding window size"
      },
      {
        "name": "random_state",
        "type": "int",
        "defaultValue": 42,
        "optional": "true",
        "description": "Seed for random number generators"
      },
      {
        "name": "step",
        "type": "int",
        "defaultValue": 64,
        "optional": "true",
        "description": "stride size for training data generation"
      },
      {
        "name": "num",
        "type": "int",
        "defaultValue": 10,
        "optional": "true",
        "description": "Max value for generated data"
      },
      {
        "name": "learning_rate",
        "type": "float",
        "defaultValue": 1e-06,
        "optional": "true",
        "description": "Gradient factor during SGD training"
      },
      {
        "name": "epochs",
        "type": "int",
        "defaultValue": 1,
        "optional": "true",
        "description": "Number of training passes over entire dataset"
      },
      {
        "name": "batch_size",
        "type": "int",
        "defaultValue": 256,
        "optional": "true",
        "description": "Number of data points trained in parallel"
      },
      {
        "name": "n_jobs",
        "type": "int",
        "defaultValue": 1,
        "optional": "true",
        "description": "Number of processes used during training"
      },
      {
        "name": "split",
        "type": "float",
        "defaultValue": 0.9,
        "optional": "true",
        "description": "Train-validation split for early stopping"
      },
      {
        "name": "early_stopping_delta",
        "type": "float",
        "defaultValue": 0.05,
        "optional": "true",
        "description": "If 1 - (loss / last_loss) is less than `delta` for `patience` epochs, stop"
      },
      {
        "name": "early_stopping_patience",
        "type": "int",
        "defaultValue": 10,
        "optional": "true",
        "description": "If 1 - (loss / last_loss) is less than `delta` for `patience` epochs, stop"
      }
    ],
    "modelInput": "none"
  },
  "executionStep": {
    "parameters": [
      {
        "name": "random_state",
        "type": "int",
        "defaultValue": 42,
        "optional": "true",
        "description": "Seed for random number generators"
      },
      {
        "name": "window_size",
        "type": "int",
        "defaultValue": 128,
        "optional": "true",
        "description": "Sliding window size"
      }
    ],
    "modelInput": "required"
  }
}