# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """Placeholder docstring""" 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 as hp # noqa 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 class RandomCutForest(AmazonAlgorithmEstimatorBase): """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. """ repo_name: str = "randomcutforest" repo_version: str = "1" MINI_BATCH_SIZE: int = 1000 eval_metrics: hp = hp( name="eval_metrics", validation_message='A comma separated list of "accuracy" or "precision_recall_fscore"', data_type=list, ) num_trees: hp = hp("num_trees", (ge(50), le(1000)), "An integer in [50, 1000]", int) num_samples_per_tree: hp = hp( "num_samples_per_tree", (ge(1), le(2048)), "An integer in [1, 2048]", int ) feature_dim: hp = hp("feature_dim", (ge(1), le(10000)), "An integer in [1, 10000]", int) 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 def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs): """Return a :class:`~sagemaker.amazon.RandomCutForestModel`. It references the latest s3 model data produced by this Estimator. Args: vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids. **kwargs: Additional kwargs passed to the RandomCutForestModel constructor. """ return RandomCutForestModel( self.model_data, self.role, sagemaker_session=self.sagemaker_session, vpc_config=self.get_vpc_config(vpc_config_override), **kwargs ) def _prepare_for_training(self, records, mini_batch_size=None, job_name=None): """Placeholder docstring""" if mini_batch_size is None: mini_batch_size = self.MINI_BATCH_SIZE elif mini_batch_size != self.MINI_BATCH_SIZE: raise ValueError( "Random Cut Forest uses a fixed mini_batch_size of {}".format(self.MINI_BATCH_SIZE) ) super(RandomCutForest, self)._prepare_for_training( records, mini_batch_size=mini_batch_size, job_name=job_name ) class RandomCutForestPredictor(Predictor): """Assigns an anomaly score to each of the datapoints provided. The implementation of :meth:`~sagemaker.predictor.Predictor.predict` in this `Predictor` requires a numpy ``ndarray`` as input. The array should contain the same number of columns as the feature-dimension of the data used to fit the model this Predictor performs inference on. :meth:`predict()` returns a list of :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default recordio-protobuf ``deserializer`` is used), one for each row in the input. Each row's score is stored in the key ``score`` of the ``Record.label`` field. """ def __init__( self, endpoint_name, sagemaker_session=None, serializer=RecordSerializer(), deserializer=RecordDeserializer(), ): """Initialization for RandomCutForestPredictor class. Args: endpoint_name (str): Name of the Amazon SageMaker endpoint to which requests are sent. sagemaker_session (sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain. serializer (sagemaker.serializers.BaseSerializer): Optional. Default serializes input data to x-recordio-protobuf format. deserializer (sagemaker.deserializers.BaseDeserializer): Optional. Default parses responses from x-recordio-protobuf format. """ super(RandomCutForestPredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=deserializer, ) class RandomCutForestModel(Model): """Reference RandomCutForest s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns a Predictor that calculates anomaly scores for datapoints. """ def __init__( self, model_data: Union[str, PipelineVariable], role: Optional[str] = None, sagemaker_session: Optional[Session] = None, **kwargs ): """Initialization for RandomCutForestModel class. Args: model_data (str or PipelineVariable): The S3 location of a SageMaker model data ``.tar.gz`` file. 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 it needs to access an AWS resource. sagemaker_session (sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain. **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer. """ sagemaker_session = sagemaker_session or Session() image_uri = image_uris.retrieve( RandomCutForest.repo_name, sagemaker_session.boto_region_name, version=RandomCutForest.repo_version, ) pop_out_unused_kwarg("predictor_cls", kwargs, RandomCutForestPredictor.__name__) pop_out_unused_kwarg("image_uri", kwargs, image_uri) super(RandomCutForestModel, self).__init__( image_uri, model_data, role, predictor_cls=RandomCutForestPredictor, sagemaker_session=sagemaker_session, **kwargs )