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.iforest import IForest from typing import Union import uuid Inputs = d3m_dataframe Outputs = d3m_dataframe class Params(Params_ODBase): ######## Add more Attributes ####### pass class Hyperparams(Hyperparams_ODBase): ######## Add more Hyperparamters ####### n_estimators = hyperparams.Hyperparameter[int]( default=100, description='The number of base estimators in the ensemble.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) max_samples = hyperparams.Enumeration[str]( values=['auto', 'int', 'float'], default='auto', # 'box-cox', # description='The number of samples to draw from X to train each base estimator.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) max_features = hyperparams.Hyperparameter[float]( default=1., description='The number of features to draw from X to train each base estimator.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) bootstrap = hyperparams.UniformBool( default=False, description='If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) behaviour = hyperparams.Enumeration[str]( values=['old', 'new'], default='new', description='Refer to https://github.com/yzhao062/pyod/blob/master/pyod/models/iforest.py.', 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/ControlParameter'], ) verbose = hyperparams.Hyperparameter[int]( default=0, description='Controls the verbosity of the tree building process.', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) pass class IsolationForest(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): """ Wrapper of Pyod Isolation Forest with more functionalities. The IsolationForest 'isolates' observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. See :cite:`liu2008isolation,liu2012isolation` for details. Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length, averaged over a forest of such random trees, is a measure of normality and our decision function. Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies. Parameters ---------- n_estimators : int, optional (default=100) The number of base estimators in the ensemble. max_samples : int or float, optional (default="auto") The number of samples to draw from X to train each base estimator. - If int, then draw `max_samples` samples. - If float, then draw `max_samples * X.shape[0]` samples. - If "auto", then `max_samples=min(256, n_samples)`. If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling). 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. max_features : int or float, optional (default=1.0) The number of features to draw from X to train each base estimator. - If int, then draw `max_features` features. - If float, then draw `max_features * X.shape[1]` features. bootstrap : bool, optional (default=False) If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed. behaviour : str, default='old' Behaviour of the ``decision_function`` which can be either 'old' or 'new'. Passing ``behaviour='new'`` makes the ``decision_function`` change to match other anomaly detection algorithm API which will be the default behaviour in the future. As explained in details in the ``offset_`` attribute documentation, the ``decision_function`` becomes dependent on the contamination parameter, in such a way that 0 becomes its natural threshold to detect outliers. 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`. verbose : int, optional (default=0) Controls the verbosity of the tree building process. 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.IsolationForest", "python_path": "d3m.primitives.tods.detection_algorithm.pyod_iforest", "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', 'uris': ['https://gitlab.com/lhenry15/tods.git']}, "algorithm_types": [metadata_base.PrimitiveAlgorithmType.ISOLATION_FOREST, ], "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION, "version": "0.0.1", "hyperparams_to_tune": ['n_estimators', 'contamination'], "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'IsolationForest')) }) 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 = IForest(contamination=hyperparams['contamination'], n_estimators=hyperparams['n_estimators'], max_samples=hyperparams['max_samples'], max_features=hyperparams['max_features'], bootstrap=hyperparams['bootstrap'], behaviour=hyperparams['behaviour'], random_state=hyperparams['random_state'], verbose=hyperparams['verbose'], ) 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)