text: 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 .UODBasePrimitive import Params_ODBase, Hyperparams_ODBase, UnsupervisedOutlierDetectorBase
  from pyod.models.ocsvm import OCSVM
  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 #######
  
      kernel = hyperparams.Enumeration[str](
          values=['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'],
          default='rbf',
          description='Specifies the kernel type to be used in the algorithm.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      nu = hyperparams.Uniform(
          lower=0.,
          upper=1.,
          default=0.5,
          description='An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      degree = hyperparams.Hyperparameter[int](
          default=3,
          description='Degree of the polynomial kernel function (poly). Ignored by all other kernels.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      gamma = hyperparams.Union[Union[float, str]](
          configuration=OrderedDict(
              init=hyperparams.Hyperparameter[float](
                  default=0.,
              ),
              ninit=hyperparams.Hyperparameter[str](
                  default='auto',
              ),
          ),
          default='ninit',
          description='Kernel coefficient for rbf, poly and sigmoid. If gamma is auto then 1/n_features will be used instead.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
      )
  
      coef0 = hyperparams.Hyperparameter[float](
          default=0.,
          description='Independent term in kernel function. It is only significant in poly and sigmoid.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      tol = hyperparams.Hyperparameter[float](
          default=0.001,
          description='Tolerance for stopping criterion.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      shrinking = hyperparams.UniformBool(
          default=True,
          description='Whether to use the shrinking heuristic.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      cache_size = hyperparams.Hyperparameter[int](
          default=200,
          description='Specify the size of the kernel cache (in MB).',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      verbose = hyperparams.UniformBool(
          default=False,
          description='Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      max_iter = hyperparams.Hyperparameter[int](
          default=-1,
          description='Hard limit on iterations within solver, or -1 for no limit.',
          semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
      )
  
      pass
  
  
  class OCSVMPrimitive(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]):
      """
      Wrapper of scikit-learn one-class SVM Class with more functionalities.
      Unsupervised Outlier Detection.
      Estimate the support of a high-dimensional distribution.
      The implementation is based on libsvm.
      See http://scikit-learn.org/stable/modules/svm.html#svm-outlier-detection
      and :cite:`scholkopf2001estimating`.
  
      Parameters
      ----------
      kernel : string, optional (default='rbf')
           Specifies the kernel type to be used in the algorithm.
           It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
           a callable.
           If none is given, 'rbf' will be used. If a callable is given it is
           used to precompute the kernel matrix.
  
      nu : float, optional
          An upper bound on the fraction of training
          errors and a lower bound of the fraction of support
          vectors. Should be in the interval (0, 1]. By default 0.5
          will be taken.
  
      degree : int, optional (default=3)
          Degree of the polynomial kernel function ('poly').
          Ignored by all other kernels.
  
      gamma : float, optional (default='auto')
          Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
          If gamma is 'auto' then 1/n_features will be used instead.
  
      coef0 : float, optional (default=0.0)
          Independent term in kernel function.
          It is only significant in 'poly' and 'sigmoid'.
  
      tol : float, optional
          Tolerance for stopping criterion.
  
      shrinking : bool, optional
          Whether to use the shrinking heuristic.
  
      cache_size : float, optional
          Specify the size of the kernel cache (in MB).
  
      verbose : bool, default: False
          Enable verbose output. Note that this setting takes advantage of a
          per-process runtime setting in libsvm that, if enabled, may not work
          properly in a multithreaded context.
  
      max_iter : int, optional (default=-1)
          Hard limit on iterations within solver, or -1 for no limit.
  
      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.OCSVMPrimitive",
          "python_path": "d3m.primitives.tods.detection_algorithm.pyod_ocsvm",
          "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu',
                     'uris': ['https://gitlab.com/lhenry15/tods.git']},
          "algorithm_types": [metadata_base.PrimitiveAlgorithmType.MARGIN_CLASSIFIER, ],
          "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION,
          "version": "0.0.1",
          "hyperparams_to_tune": ['contamination', 'kernel', 'nu', 'gamma', 'degree'],
          "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'OCSVMPrimitive'))
      })
  
      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 = OCSVM(contamination=hyperparams['contamination'],
                              kernel=hyperparams['kernel'],
                              nu=hyperparams['nu'],
                              degree=hyperparams['degree'],
                              gamma=hyperparams['gamma'],
                              coef0=hyperparams['coef0'],
                              tol=hyperparams['tol'],
                              shrinking=hyperparams['shrinking'],
                              cache_size=hyperparams['cache_size'],
                              verbose=hyperparams['verbose'],
                              max_iter=hyperparams['max_iter'],
                              )
  
          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)
  
  
  