manifest.json

{
    "title": "KNN",
    "description": "Implementation of https://doi.org/10.1145/342009.335437.",
    "inputDimensionality": "multivariate",
    "version": "0.3.1",
    "authors": "Sridhar Ramaswamy,Rajeev Rastogi, Kyuseok Shim",
    "language": "Python",
    "type": "Detector",
    "mainFile": "algorithm.py",
    "learningType": "unsupervised",
    "executionStep": {
        "parameters": [
            {
                "name": "n_neighbors",
                "type": "int",
                "defaultValue": 5,
                "optional": "true",
                "description": " Number of neighbors to use by default for `kneighbors` queries."
            },
            {
                "name": "leaf_size",
                "type": "int",
                "defaultValue": 30,
                "optional": "true",
                "description": "Leaf size passed to `BallTree`. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem."
            },
            {
                "name": "method",
                "type": "enum[largest,mean,median]",
                "defaultValue": "largest",
                "optional": "true",
                "description": "'largest': use the distance to the kth neighbor as the outlier score, 'mean': use the average of all k neighbors as the outlier score, 'median': use the median of the distance to k neighbors as the outlier score."
            },
            {
                "name": "radius",
                "type": "float",
                "defaultValue": 1.0,
                "optional": "true",
                "description": " Range of parameter space to use by default for `radius_neighbors` queries."
            },
            {
                "name": "distance_metric_order",
                "type": "int",
                "defaultValue": 2,
                "optional": "true",
                "description": "Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. See http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances."
            },
            {
                "name": "n_jobs",
                "type": "int",
                "defaultValue": 1,
                "optional": "true",
                "description": "The number of parallel jobs to run for neighbors search. If ``-1``, then the number of jobs is set to the number of CPU cores. Affects only kneighbors and kneighbors_graph methods."
            },
            {
                "name": "random_state",
                "type": "int",
                "defaultValue": 42,
                "optional": "true",
                "description": "Seed for random number generation."
            }
        ],
        "modelInput": "none"
    }
}