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"""Placeholder docstring""" from __future__ import absolute_import from typing import Union, Optional, 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 gt, isin, 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 KMeans(AmazonAlgorithmEstimatorBase): """An unsupervised learning algorithm that attempts to find discrete groupings within data. As the result of KMeans, members of a group are as similar as possible to one another and as different as possible from members of other groups. You define the attributes that you want the algorithm to use to determine similarity. """ repo_name: str = "kmeans" repo_version: str = "1" k: hp = hp("k", gt(1), "An integer greater-than 1", int) init_method: hp = hp( "init_method", isin("random", "kmeans++"), 'One of "random", "kmeans++"', str ) max_iterations: hp = hp("local_lloyd_max_iter", gt(0), "An integer greater-than 0", int) tol: hp = hp("local_lloyd_tol", (ge(0), le(1)), "An float in [0, 1]", float) num_trials: hp = hp("local_lloyd_num_trials", gt(0), "An integer greater-than 0", int) local_init_method: hp = hp( "local_lloyd_init_method", isin("random", "kmeans++"), 'One of "random", "kmeans++"', str ) half_life_time_size: hp = hp( "half_life_time_size", ge(0), "An integer greater-than-or-equal-to 0", int ) epochs: hp = hp("epochs", gt(0), "An integer greater-than 0", int) center_factor: hp = hp("extra_center_factor", gt(0), "An integer greater-than 0", int) eval_metrics: hp = hp( name="eval_metrics", validation_message='A comma separated list of "msd" or "ssd"', data_type=list, ) def __init__( self, role: Optional[Union[str, PipelineVariable]] = None, instance_count: Optional[Union[int, PipelineVariable]] = None, instance_type: Optional[Union[str, PipelineVariable]] = None, k: Optional[int] = None, init_method: Optional[str] = None, max_iterations: Optional[int] = None, tol: Optional[float] = None, num_trials: Optional[int] = None, local_init_method: Optional[str] = None, half_life_time_size: Optional[int] = None, epochs: Optional[int] = None, center_factor: Optional[int] = None, eval_metrics: Optional[List[Union[str, PipelineVariable]]] = None, **kwargs ): """A k-means clustering class :class:`~sagemaker.amazon.AmazonAlgorithmEstimatorBase`. Finds k clusters of data in an unlabeled dataset. This Estimator may be fit via calls to :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit_ndarray` or :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`. The former allows a KMeans model to be fit on a 2-dimensional numpy array. The latter requires Amazon :class:`~sagemaker.amazon.record_pb2.Record` protobuf serialized data to be stored in S3. 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.kmeans.KMeansPredictor` object that can be used to k-means cluster assignments, using the trained k-means model hosted in the SageMaker Endpoint. KMeans Estimators can be configured by setting hyperparameters. The available hyperparameters for KMeans are documented below. For further information on the AWS KMeans algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/k-means.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'. k (int): The number of clusters to produce. init_method (str): How to initialize cluster locations. One of 'random' or 'kmeans++'. max_iterations (int): Maximum iterations for Lloyds EM procedure in the local kmeans used in finalize stage. tol (float): Tolerance for change in ssd for early stopping in local kmeans. num_trials (int): Local version is run multiple times and the one with the best loss is chosen. This determines how many times. local_init_method (str): Initialization method for local version. One of 'random', 'kmeans++' half_life_time_size (int): The points can have a decayed weight. When a point is observed its weight, with regard to the computation of the cluster mean is 1. This weight will decay exponentially as we observe more points. The exponent coefficient is chosen such that after observing ``half_life_time_size`` points after the mentioned point, its weight will become 1/2. If set to 0, there will be no decay. epochs (int): Number of passes done over the training data. center_factor (int): The algorithm will create ``num_clusters * extra_center_factor`` as it runs and reduce the number of centers to ``k`` when finalizing eval_metrics (list[str] or list[PipelineVariable]): JSON list of metrics types to be used for reporting the score for the model. Allowed values are "msd" Means Square Error, "ssd": Sum of square distance. 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(KMeans, self).__init__(role, instance_count, instance_type, **kwargs) self.k = k self.init_method = init_method self.max_iterations = max_iterations self.tol = tol self.num_trials = num_trials self.local_init_method = local_init_method self.half_life_time_size = half_life_time_size self.epochs = epochs self.center_factor = center_factor self.eval_metrics = eval_metrics def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs): """Return a :class:`~sagemaker.amazon.kmeans.KMeansModel`. 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 KMeansModel constructor. """ return KMeansModel( self.model_data, self.role, self.sagemaker_session, vpc_config=self.get_vpc_config(vpc_config_override), **kwargs ) def _prepare_for_training(self, records, mini_batch_size=5000, job_name=None): """Placeholder docstring""" super(KMeans, self)._prepare_for_training( records, mini_batch_size=mini_batch_size, job_name=job_name ) def hyperparameters(self): """Return the SageMaker hyperparameters for training this KMeans Estimator.""" hp_dict = dict(force_dense="True") # KMeans requires this hp to fit on Record objects hp_dict.update(super(KMeans, self).hyperparameters()) return hp_dict class KMeansPredictor(Predictor): """Assigns input vectors to their closest cluster in a KMeans model. 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. ``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 ``ndarray``. The nearest cluster is stored in the ``closest_cluster`` key of the ``Record.label`` field. """ def __init__( self, endpoint_name, sagemaker_session=None, serializer=RecordSerializer(), deserializer=RecordDeserializer(), ): """Initialization for KMeansPredictor 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(KMeansPredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=deserializer, ) class KMeansModel(Model): """Reference KMeans s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and return a Predictor to performs k-means cluster assignment. """ def __init__( self, model_data: Union[str, PipelineVariable], role: Optional[str] = None, sagemaker_session: Optional[Session] = None, **kwargs ): """Initialization for KMeansModel 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( KMeans.repo_name, sagemaker_session.boto_region_name, version=KMeans.repo_version, ) pop_out_unused_kwarg("predictor_cls", kwargs, KMeansPredictor.__name__) pop_out_unused_kwarg("image_uri", kwargs, image_uri) super(KMeansModel, self).__init__( image_uri, model_data, role, predictor_cls=KMeansPredictor, sagemaker_session=sagemaker_session, **kwargs )