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 sklearn.utils.validation import check_is_fitted from sklearn.linear_model import LinearRegression # 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 detection_algorithm.core.PCA import PCA import uuid from sklearn.utils import check_array, column_or_1d from sklearn.utils.validation import check_is_fitted from combo.models.score_comb import average, maximization, median, aom, moa from combo.utils.utility import standardizer Inputs = d3m_dataframe Outputs = d3m_dataframe class Params(Params_ODBase): ######## Add more Attributes ####### pass class Hyperparams(Hyperparams_ODBase): ######## Add more Hyperparamters ####### svd_solver = hyperparams.Enumeration[str]( values=['auto', 'full', 'arpack', 'randomized'], default='auto', description='Algorithm of solver.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) n_components = hyperparams.Union[Union[int, None]]( configuration=OrderedDict( init=hyperparams.Hyperparameter[int]( default=1, # {}, ), ninit=hyperparams.Hyperparameter[None]( default=None, ), ), default='ninit', description='Number of components to keep. It should be smaller than the window_size.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], ) # hyperparams.Hyperparameter[int]( # default=1, # description='Number of components to keep. It should be smaller than the window_size.', # semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] # ) n_selected_components = hyperparams.Union[Union[int, None]]( configuration=OrderedDict( init=hyperparams.Hyperparameter[int]( default=1, # {}, ), ninit=hyperparams.Hyperparameter[None]( default=None, ), ), default='ninit', description='Number of selected principal components for calculating the outlier scores. It is not necessarily equal to the total number of the principal components. If not set, use all principal components.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], ) tol = hyperparams.Hyperparameter[float]( default=0., description='Tolerance for singular values computed by svd_solver == `arpack`.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) iterated_power = hyperparams.Union[Union[int, str]]( configuration=OrderedDict( init=hyperparams.Hyperparameter[int]( default=1, # {}, ), ninit=hyperparams.Hyperparameter[str]( default='auto', ), ), default='ninit', description='Number of iterations for the power method computed by svd_solver == `randomized`.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], ) 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/TuningParameter'], ) whiten = hyperparams.UniformBool( default=True, semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], description="If True, the eigenvalues are used in score computation. The eigenvectors with small eigenvalues comes with more importance in outlier score calculation.", ) standardization = hyperparams.UniformBool( default=True, semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], description="If True, perform standardization first to convert data to zero mean and unit variance.", ) pass class PCAODetector(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): """ PCA-based outlier detection with both univariate and multivariate time series data. TS data will be first transformed to tabular format. For univariate data, it will be in shape of [valid_length, window_size]. for multivariate data with d sequences, it will be in the shape of [valid_length, window_size]. Parameters ---------- window_size : int The moving window size. step_size : int, optional (default=1) The displacement for moving window. 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_components : int, float, None or string Number of components to keep. It should be smaller than the window_size. if n_components is not set all components are kept:: n_components == min(n_samples, n_features) if n_components == 'mle' and svd_solver == 'full', Minka\'s MLE is used to guess the dimension if ``0 < n_components < 1`` and svd_solver == 'full', select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components n_components cannot be equal to n_features for svd_solver == 'arpack'. n_selected_components : int, optional (default=None) Number of selected principal components for calculating the outlier scores. It is not necessarily equal to the total number of the principal components. If not set, use all principal components. whiten : bool, optional (default False) When True (False by default) the `components_` vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances. Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions. svd_solver : string {'auto', 'full', 'arpack', 'randomized'} auto : the solver is selected by a default policy based on `X.shape` and `n_components`: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient 'randomized' method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards. full : run exact full SVD calling the standard LAPACK solver via `scipy.linalg.svd` and select the components by postprocessing arpack : run SVD truncated to n_components calling ARPACK solver via `scipy.sparse.linalg.svds`. It requires strictly 0 < n_components < X.shape[1] randomized : run randomized SVD by the method of Halko et al. tol : float >= 0, optional (default .0) Tolerance for singular values computed by svd_solver == 'arpack'. iterated_power : int >= 0, or 'auto', (default 'auto') Number of iterations for the power method computed by svd_solver == 'randomized'. random_state : int, RandomState instance 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`. Used when ``svd_solver`` == 'arpack' or 'randomized'. weighted : bool, optional (default=True) If True, the eigenvalues are used in score computation. The eigenvectors with small eigenvalues comes with more importance in outlier score calculation. standardization : bool, optional (default=True) If True, perform standardization first to convert data to zero mean and unit variance. See http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html 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": "PCAODetector", "python_path": "d3m.primitives.tods.detection_algorithm.PCAODetector", "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_components', 'n_selected_components', 'contamination', 'whiten', 'svd_solver', 'tol', 'iterated_power', 'random_state', 'standardization'], "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'PCAODetector')), }) 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 = PCA(window_size=hyperparams['window_size'], contamination=hyperparams['contamination'], n_components=hyperparams['n_components'], n_selected_components=hyperparams['n_selected_components'], whiten=hyperparams['whiten'], svd_solver=hyperparams['svd_solver'], tol=hyperparams['tol'], iterated_power=hyperparams['iterated_power'], random_state=hyperparams['random_state'], standardization=hyperparams['standardization'], ) 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 produce_score(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 Outlier score of input DataFrame. """ return super().produce_score(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)