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.cblof import CBLOF 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_clusters = hyperparams.Hyperparameter[int]( default=8, description='The number of clusters to form as well as the number of centroids to generate.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) # clustering_estimator = hyperparams.Choice( # choices={ # 'auto': hyperparams.Hyperparams.define( # configuration=OrderedDict({}) # ), # 'full': hyperparams.Hyperparams.define( # configuration=OrderedDict({}) # ), # }, # default='auto', # description='The base clustering algorithm for performing data clustering. A valid clustering algorithm should be passed in. The estimator should have standard sklearn APIs, fit() and predict(). The estimator should have attributes ``labels_`` and ``cluster_centers_``.', # semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] # ) alpha = hyperparams.Uniform( lower=0.5, upper=1., default=0.9, description='Coefficient for deciding small and large clusters. The ratio of the number of samples in large clusters to the number of samples in small clusters.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) beta = hyperparams.Hyperparameter[int]( default=5, description='Coefficient for deciding small and large clusters.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) use_weights = hyperparams.UniformBool( default=False, semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], description="If set to True, the size of clusters are used as weights in outlier score calculation." ) check_estimator = hyperparams.UniformBool( default=False, semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], description="If set to True, check whether the base estimator is consistent with sklearn standard." ) random_state = hyperparams.Union[Union[int, None]]( configuration=OrderedDict( init=hyperparams.Hyperparameter[int]( default=0, ), ninit=hyperparams.Hyperparameter[None]( default=None, ), ), default='ninit', description='the seed used by the random number generator.', semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], ) pass class CBLOFPrimitive(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): """ The CBLOF operator calculates the outlier score based on cluster-based local outlier factor. CBLOF takes as an input the data set and the cluster model that was generated by a clustering algorithm. It classifies the clusters into small clusters and large clusters using the parameters alpha and beta. The anomaly score is then calculated based on the size of the cluster the point belongs to as well as the distance to the nearest large cluster. Use weighting for outlier factor based on the sizes of the clusters as proposed in the original publication. Since this might lead to unexpected behavior (outliers close to small clusters are not found), it is disabled by default.Outliers scores are solely computed based on their distance to the closest large cluster center. By default, kMeans is used for clustering algorithm instead of Squeezer algorithm mentioned in the original paper for multiple reasons. See :cite:`he2003discovering` for details. Parameters ---------- n_clusters : int, optional (default=8) The number of clusters to form as well as the number of centroids to generate. 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. clustering_estimator : Estimator, optional (default=None) The base clustering algorithm for performing data clustering. A valid clustering algorithm should be passed in. The estimator should have standard sklearn APIs, fit() and predict(). The estimator should have attributes ``labels_`` and ``cluster_centers_``. If ``cluster_centers_`` is not in the attributes once the model is fit, it is calculated as the mean of the samples in a cluster. If not set, CBLOF uses KMeans for scalability. See https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html alpha : float in (0.5, 1), optional (default=0.9) Coefficient for deciding small and large clusters. The ratio of the number of samples in large clusters to the number of samples in small clusters. beta : int or float in (1,), optional (default=5). Coefficient for deciding small and large clusters. For a list sorted clusters by size `|C1|, \|C2|, ..., |Cn|, beta = |Ck|/|Ck-1|` use_weights : bool, optional (default=False) If set to True, the size of clusters are used as weights in outlier score calculation. check_estimator : bool, optional (default=False) If set to True, check whether the base estimator is consistent with sklearn standard. .. warning:: check_estimator may throw errors with scikit-learn 0.20 above. random_state : int, RandomState or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. 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.CBLOFPrimitive", "python_path": "d3m.primitives.tods.detection_algorithm.pyod_cblof", "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": ['contamination'], "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'CBLOFPrimitive')), }) 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 = CBLOF(contamination=hyperparams['contamination'], n_clusters=hyperparams['n_clusters'], alpha=hyperparams['alpha'], beta=hyperparams['beta'], use_weights=hyperparams['use_weights'], check_estimator=hyperparams['check_estimator'], random_state=hyperparams['random_state'], ) 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)