tods/detection_algorithm/PyodLOF.py

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)