class_docstr: An unsupervised algorithm for detecting anomalous data points within a data set.
  
  These are observations which diverge from otherwise well-structured or patterned data.
  Anomalies can manifest as unexpected spikes in time series data, breaks in periodicity,
  or unclassifiable data points.
docstr: An `Estimator` class implementing a Random Cut Forest.
  
  Typically used for anomaly detection, this Estimator may be fit via calls to
  :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`.
  It requires Amazon :class:`~sagemaker.amazon.record_pb2.Record` protobuf
  serialized data to be stored in S3. There is an utility
  :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.record_set`
  that can be used to upload data to S3 and creates
  :class:`~sagemaker.amazon.amazon_estimator.RecordSet` to be passed to
  the `fit` call.
  
  To learn more about the Amazon protobuf Record class and how to
  prepare bulk data in this format, please consult AWS technical
  documentation:
  https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html
  
  After this Estimator is fit, model data is stored in S3. The model
  may be deployed to an Amazon SageMaker Endpoint by invoking
  :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as
  deploying an Endpoint, deploy returns a
  :class:`~sagemaker.amazon.ntm.RandomCutForestPredictor` object that can
  be used for inference calls using the trained model hosted in the
  SageMaker Endpoint.
  
  RandomCutForest Estimators can be configured by setting
  hyperparameters. The available hyperparameters for RandomCutForest are
  documented below.
  
  For further information on the AWS Random Cut Forest algorithm,
  please consult AWS technical documentation:
  https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html
  
  Args:
      role (str): An AWS IAM role (either name or full ARN). The Amazon
          SageMaker training jobs and APIs that create Amazon SageMaker
          endpoints use this role to access training data and model
          artifacts. After the endpoint is created, the inference code
          might use the IAM role, if accessing AWS resource.
      instance_count (int or PipelineVariable): Number of Amazon EC2 instances to use
          for training.
      instance_type (str or PipelineVariable): Type of EC2 instance to use for training,
          for example, 'ml.c4.xlarge'.
      num_samples_per_tree (int): Optional. The number of samples used to
          build each tree in the forest. The total number of samples drawn
          from the train dataset is num_trees * num_samples_per_tree.
      num_trees (int): Optional. The number of trees used in the forest.
      eval_metrics (list): Optional. JSON list of metrics types to be used
          for reporting the score for the model. Allowed values are
          "accuracy", "precision_recall_fscore": positive and negative
          precision, recall, and f1 scores. If test data is provided, the
          score shall be reported in terms of all requested metrics.
      **kwargs: base class keyword argument values.
  
  .. tip::
  
      You can find additional parameters for initializing this class at
      :class:`~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase` and
      :class:`~sagemaker.estimator.EstimatorBase`.
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code: def __init__(
      self,
      role: Optional[Union[str, PipelineVariable]] = None,
      instance_count: Optional[Union[int, PipelineVariable]] = None,
      instance_type: Optional[Union[str, PipelineVariable]] = None,
      num_samples_per_tree: Optional[int] = None,
      num_trees: Optional[int] = None,
      eval_metrics: Optional[List] = None,
      **kwargs,
  ):
      """An `Estimator` class implementing a Random Cut Forest.
  
      Typically used for anomaly detection, this Estimator may be fit via calls to
      :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`.
      It requires Amazon :class:`~sagemaker.amazon.record_pb2.Record` protobuf
      serialized data to be stored in S3. There is an utility
      :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.record_set`
      that can be used to upload data to S3 and creates
      :class:`~sagemaker.amazon.amazon_estimator.RecordSet` to be passed to
      the `fit` call.
  
      To learn more about the Amazon protobuf Record class and how to
      prepare bulk data in this format, please consult AWS technical
      documentation:
      https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html
  
      After this Estimator is fit, model data is stored in S3. The model
      may be deployed to an Amazon SageMaker Endpoint by invoking
      :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as
      deploying an Endpoint, deploy returns a
      :class:`~sagemaker.amazon.ntm.RandomCutForestPredictor` object that can
      be used for inference calls using the trained model hosted in the
      SageMaker Endpoint.
  
      RandomCutForest Estimators can be configured by setting
      hyperparameters. The available hyperparameters for RandomCutForest are
      documented below.
  
      For further information on the AWS Random Cut Forest algorithm,
      please consult AWS technical documentation:
      https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html
  
      Args:
          role (str): An AWS IAM role (either name or full ARN). The Amazon
              SageMaker training jobs and APIs that create Amazon SageMaker
              endpoints use this role to access training data and model
              artifacts. After the endpoint is created, the inference code
              might use the IAM role, if accessing AWS resource.
          instance_count (int or PipelineVariable): Number of Amazon EC2 instances to use
              for training.
          instance_type (str or PipelineVariable): Type of EC2 instance to use for training,
              for example, 'ml.c4.xlarge'.
          num_samples_per_tree (int): Optional. The number of samples used to
              build each tree in the forest. The total number of samples drawn
              from the train dataset is num_trees * num_samples_per_tree.
          num_trees (int): Optional. The number of trees used in the forest.
          eval_metrics (list): Optional. JSON list of metrics types to be used
              for reporting the score for the model. Allowed values are
              "accuracy", "precision_recall_fscore": positive and negative
              precision, recall, and f1 scores. If test data is provided, the
              score shall be reported in terms of all requested metrics.
          **kwargs: base class keyword argument values.
  
      .. tip::
  
          You can find additional parameters for initializing this class at
          :class:`~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase` and
          :class:`~sagemaker.estimator.EstimatorBase`.
      """
  
      super(RandomCutForest, self).__init__(
          role, instance_count, instance_type, **kwargs
      )
      self.num_samples_per_tree = num_samples_per_tree
      self.num_trees = num_trees
      self.eval_metrics = eval_metrics
  
imports: from __future__ import absolute_import
  from typing import Optional, Union, List
  from sagemaker import image_uris
  from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
  from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import ge, le
  from sagemaker.predictor import Predictor
  from sagemaker.model import Model
  from sagemaker.session import Session
  from sagemaker.utils import pop_out_unused_kwarg
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 1 to create a new RandomCutForest object from inherited base classes: AmazonAlgorithmEstimatorBase with example: obj = RandomCutForest(role, instance_count, instance_type, num_samples_per_tree, num_trees, eval_metrics)