from typing import Any, Callable, List, Dict, Union, Optional, Sequence, Tuple from numpy import ndarray from collections import OrderedDict from scipy import sparse import os import sklearn import numpy import typing # Custom import commands if any import warnings import numpy as np from sklearn.utils import check_array from sklearn.exceptions import NotFittedError # from numba import njit from pyod.utils.utility import argmaxn from d3m.container.numpy import ndarray as d3m_ndarray from d3m.container import DataFrame as d3m_dataframe from d3m.metadata import hyperparams, params, base as metadata_base from d3m import utils from d3m.base import utils as base_utils from d3m.exceptions import PrimitiveNotFittedError from d3m.primitive_interfaces.base import CallResult, DockerContainer # from d3m.primitive_interfaces.supervised_learning import SupervisedLearnerPrimitiveBase from d3m.primitive_interfaces.unsupervised_learning import UnsupervisedLearnerPrimitiveBase from d3m.primitive_interfaces.transformer import TransformerPrimitiveBase from d3m.primitive_interfaces.base import ProbabilisticCompositionalityMixin, ContinueFitMixin from d3m import exceptions import pandas from d3m import container, utils as d3m_utils from detection_algorithm.UODBasePrimitive import Params_ODBase, Hyperparams_ODBase, UnsupervisedOutlierDetectorBase from pyod.models.lof import LOF import uuid # from typing import Union Inputs = d3m_dataframe Outputs = d3m_dataframe class Params(Params_ODBase): ######## Add more Attributes ####### pass class Hyperparams(Hyperparams_ODBase): ######## Add more Hyperparamters ####### n_neighbors = hyperparams.Hyperparameter[int]( default=20, description='Number of neighbors to use by default for `kneighbors` queries. If n_neighbors is larger than the number of samples provided, all samples will be used.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) algorithm = hyperparams.Enumeration[str]( values=['auto', 'ball_tree', 'kd_tree', 'brute'], default='auto', description='Algorithm used to compute the nearest neighbors.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) leaf_size = hyperparams.Hyperparameter[int]( default=30, description='Leaf size passed to `BallTree` or `KDTree`. 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.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) metric = hyperparams.Enumeration[str]( values=['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan', 'braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'], default='minkowski', description='metric used for the distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) p = hyperparams.Hyperparameter[int]( default=2, description='Parameter for the Minkowski metric from.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) metric_params = hyperparams.Union[Union[Dict, None]]( configuration=OrderedDict( init=hyperparams.Hyperparameter[Dict]( default={}, ), ninit=hyperparams.Hyperparameter[None]( default=None, ), ), default='ninit', description='Additional keyword arguments for the metric function.', semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], ) pass class LOFPrimitive(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): """ Wrapper of Pyod LOF Class with more functionalities. Unsupervised Outlier Detection using Local Outlier Factor (LOF). The anomaly score of each sample is called Local Outlier Factor. It measures the local deviation of density of a given sample with respect to its neighbors. It is local in that the anomaly score depends on how isolated the object is with respect to the surrounding neighborhood. More precisely, locality is given by k-nearest neighbors, whose distance is used to estimate the local density. By comparing the local density of a sample to the local densities of its neighbors, one can identify samples that have a substantially lower density than their neighbors. These are considered outliers. See :cite:`breunig2000lof` for details. Parameters ---------- n_neighbors : int, optional (default=20) Number of neighbors to use by default for `kneighbors` queries. If n_neighbors is larger than the number of samples provided, all samples will be used. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional Algorithm used to compute the nearest neighbors: - 'ball_tree' will use BallTree - 'kd_tree' will use KDTree - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, optional (default=30) Leaf size passed to `BallTree` or `KDTree`. 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. metric : string or callable, default 'minkowski' metric used for the distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If 'precomputed', the training input X is expected to be a distance matrix. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics: http://docs.scipy.org/doc/scipy/reference/spatial.distance.html p : integer, optional (default = 2) 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 metric_params : dict, optional (default = None) Additional keyword arguments for the metric function. contamination : float in (0., 0.5), optional (default=0.1) The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the decision function. n_jobs : int, optional (default = 1) 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. Attributes ---------- decision_scores_ : numpy array of shape (n_samples,) The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher scores. This value is available once the detector is fitted. threshold_ : float The threshold is based on ``contamination``. It is the ``n_samples * contamination`` most abnormal samples in ``decision_scores_``. The threshold is calculated for generating binary outlier labels. labels_ : int, either 0 or 1 The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies. It is generated by applying ``threshold_`` on ``decision_scores_``. """ metadata = metadata_base.PrimitiveMetadata({ "name": "TODS.anomaly_detection_primitives.LOFPrimitive", "python_path": "d3m.primitives.tods.detection_algorithm.pyod_lof", "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', 'uris': ['https://gitlab.com/lhenry15/tods.git']}, "algorithm_types": [metadata_base.PrimitiveAlgorithmType.LOCAL_OUTLIER_FACTOR, ], "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION, "version": "0.0.1", "hyperparams_to_tune": ['n_neighbors', 'algorithm', 'leaf_size', 'p', 'contamination'], "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'LOFPrimitive')), }) def __init__(self, *, hyperparams: Hyperparams, # random_seed: int = 0, docker_containers: Dict[str, DockerContainer] = None) -> None: super().__init__(hyperparams=hyperparams, random_seed=random_seed, docker_containers=docker_containers) self._clf = LOF(contamination=hyperparams['contamination'], n_neighbors=hyperparams['n_neighbors'], algorithm=hyperparams['algorithm'], leaf_size=hyperparams['leaf_size'], metric=hyperparams['metric'], p=hyperparams['p'], metric_params=hyperparams['metric_params'], ) return def set_training_data(self, *, inputs: Inputs) -> None: """ Set training data for outlier detection. Args: inputs: Container DataFrame Returns: None """ super().set_training_data(inputs=inputs) def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: """ Fit model with training data. Args: *: Container DataFrame. Time series data up to fit. Returns: None """ return super().fit() def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]: """ Process the testing data. Args: inputs: Container DataFrame. Time series data up to outlier detection. Returns: Container DataFrame 1 marks Outliers, 0 marks normal. """ return super().produce(inputs=inputs, timeout=timeout, iterations=iterations) def get_params(self) -> Params: """ Return parameters. Args: None Returns: class Params """ return super().get_params() def set_params(self, *, params: Params) -> None: """ Set parameters for outlier detection. Args: params: class Params Returns: None """ super().set_params(params=params)