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.cof import COF # import uuid 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=5, description='Number of neighbors to use by default for k neighbors queries.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) # method = hyperparams.Enumeration[str]( # values=['largest', 'mean', 'median'], # default='largest', # description='Method to calculate outlier score.', # semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] # ) class PyodCOF(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): """ Connectivity-Based Outlier Factor (COF) COF uses the ratio of average chaining distance of data point and the average of average chaining distance of k nearest neighbor of the data point, as the outlier score for observations. See :cite:`tang2002enhancing` for details. Parameters ---------- 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. Used when fitting to define the threshold on the decision function. n_neighbors : int, optional (default=20) Number of neighbors to use by default for k neighbors queries. Note that n_neighbors should be less than the number of samples. If n_neighbors is larger than the number of samples provided, all samples will be used. 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_``. n_neighbors_: int Number of neighbors to use by default for k neighbors queries. """ __author__ = "Data Lab" metadata = metadata_base.PrimitiveMetadata( { '__author__' : "DATA Lab at Texas A&M University", 'name': "Connectivity-Based Outlier Factor (COF)", 'python_path': 'd3m.primitives.tods.detection_algorithm.pyod_cof', 'source': { 'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 'uris': [ 'https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/purav/anomaly-primitives/anomaly_primitives/PyodCOF.py', ], }, 'algorithm_types': [ metadata_base.PrimitiveAlgorithmType.PYOD_COF, ], 'primitive_family': metadata_base.PrimitiveFamily.ANOMALY_DETECTION, 'id': 'c7259da6-7ce6-42ad-83c6-15238679f5fa', 'hyperparameters_to_tune':['rank','update','objective','max_iter','learning_rate'], 'version': '0.0.1', }, ) 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 = COF(contamination=hyperparams['contamination'], n_neighbors=hyperparams['n_neighbors'], ) 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)