manifest.json

{
    "title": "Subsequence IF",
    "description": "Isolation Forest on sliding windows to detect subsequence anomalies.",
    "inputDimensionality": "univariate",
    "version": "0.3.1",
    "authors": "Sebastian Schmidl",
    "language": "Python",
    "type": "Detector",
    "mainFile": "algorithm.py",
    "learningType": "unsupervised",
    "executionStep": {
        "parameters": [
            {
                "name": "window_size",
                "type": "int",
                "defaultValue": 100,
                "optional": "true",
                "description": "Size of the sliding windows to extract subsequences as input to LOF."
            },
            {
                "name": "n_trees",
                "type": "int",
                "defaultValue": 100,
                "optional": "true",
                "description": "The number of decision trees (base estimators) in the forest (ensemble)."
            },
            {
                "name": "max_samples",
                "type": "float",
                "defaultValue": null,
                "optional": "true",
                "description": "The number of samples to draw from X to train each base estimator: `max_samples * X.shape[0]`. If unspecified (`null`), then `max_samples=min(256, n_samples)`."
            },
            {
                "name": "max_features",
                "type": "float",
                "defaultValue": 1.0,
                "optional": "true",
                "description": "The number of features to draw from X to train each base estimator: `max_features * X.shape[1]`."
            },
            {
                "name": "bootstrap",
                "type": "boolean",
                "defaultValue": "false",
                "optional": "true",
                "description": "If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed."
            },
            {
                "name": "random_state",
                "type": "int",
                "defaultValue": 42,
                "optional": "true",
                "description": "Seed for random number generation."
            },
            {
                "name": "verbose",
                "type": "int",
                "defaultValue": 0,
                "optional": "true",
                "description": "Controls the verbosity of the tree building process logs."
            },
            {
                "name": "n_jobs",
                "type": "int",
                "defaultValue": 1,
                "optional": "true",
                "description": "The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores."
            }
        ],
        "modelInput": "none"
    }
}