# 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 Union, Optional from sagemaker import image_uris from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa from sagemaker.amazon.validation import ge, le from sagemaker.deserializers import JSONDeserializer from sagemaker.predictor import Predictor from sagemaker.model import Model from sagemaker.serializers import CSVSerializer 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 IPInsights(AmazonAlgorithmEstimatorBase): """An unsupervised learning algorithm that learns the usage patterns for IPv4 addresses. It is designed to capture associations between IPv4 addresses and various entities, such as user IDs or account numbers. """ repo_name: str = "ipinsights" repo_version: str = "1" MINI_BATCH_SIZE: int = 10000 num_entity_vectors: hp = hp( "num_entity_vectors", (ge(1), le(250000000)), "An integer in [1, 250000000]", int ) vector_dim: hp = hp("vector_dim", (ge(4), le(4096)), "An integer in [4, 4096]", int) batch_metrics_publish_interval: hp = hp( "batch_metrics_publish_interval", (ge(1)), "An integer greater than 0", int ) epochs: hp = hp("epochs", (ge(1)), "An integer greater than 0", int) learning_rate: hp = hp("learning_rate", (ge(1e-6), le(10.0)), "A float in [1e-6, 10.0]", float) num_ip_encoder_layers: hp = hp( "num_ip_encoder_layers", (ge(0), le(100)), "An integer in [0, 100]", int ) random_negative_sampling_rate: hp = hp( "random_negative_sampling_rate", (ge(0), le(500)), "An integer in [0, 500]", int ) shuffled_negative_sampling_rate: hp = hp( "shuffled_negative_sampling_rate", (ge(0), le(500)), "An integer in [0, 500]", int ) weight_decay: hp = hp("weight_decay", (ge(0.0), le(10.0)), "A float in [0.0, 10.0]", float) def __init__( self, role: Optional[Union[str, PipelineVariable]] = None, instance_count: Optional[Union[int, PipelineVariable]] = None, instance_type: Optional[Union[str, PipelineVariable]] = None, num_entity_vectors: Optional[int] = None, vector_dim: Optional[int] = None, batch_metrics_publish_interval: Optional[int] = None, epochs: Optional[int] = None, learning_rate: Optional[float] = None, num_ip_encoder_layers: Optional[int] = None, random_negative_sampling_rate: Optional[int] = None, shuffled_negative_sampling_rate: Optional[int] = None, weight_decay: Optional[float] = None, **kwargs ): """This estimator is for IP Insights. An unsupervised algorithm that learns usage patterns of IP addresses. This Estimator may be fit via calls to :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`. It requires CSV data to be stored in S3. 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.IPInsightPredictor` object that can be used for inference calls using the trained model hosted in the SageMaker Endpoint. IPInsights Estimators can be configured by setting hyperparamters. The available hyperparamters are documented below. For further information on the AWS IPInsights algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/ip-insights-hyperparameters.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.m5.xlarge'. num_entity_vectors (int): Required. The number of embeddings to train for entities accessing online resources. We recommend 2x the total number of unique entity IDs. vector_dim (int): Required. The size of the embedding vectors for both entity and IP addresses. batch_metrics_publish_interval (int): Optional. The period at which to publish metrics (batches). epochs (int): Optional. Maximum number of passes over the training data. learning_rate (float): Optional. Learning rate for the optimizer. num_ip_encoder_layers (int): Optional. The number of fully-connected layers to encode IP address embedding. random_negative_sampling_rate (int): Optional. The ratio of random negative samples to draw during training. Random negative samples are randomly drawn IPv4 addresses. shuffled_negative_sampling_rate (int): Optional. The ratio of shuffled negative samples to draw during training. Shuffled negative samples are IP addresses picked from within a batch. weight_decay (float): Optional. Weight decay coefficient. Adds L2 regularization. **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(IPInsights, self).__init__(role, instance_count, instance_type, **kwargs) self.num_entity_vectors = num_entity_vectors self.vector_dim = vector_dim self.batch_metrics_publish_interval = batch_metrics_publish_interval self.epochs = epochs self.learning_rate = learning_rate self.num_ip_encoder_layers = num_ip_encoder_layers self.random_negative_sampling_rate = random_negative_sampling_rate self.shuffled_negative_sampling_rate = shuffled_negative_sampling_rate self.weight_decay = weight_decay def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs): """Create a model for the latest s3 model 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 IPInsightsModel constructor. Returns: :class:`~sagemaker.amazon.IPInsightsModel`: references the latest s3 model data produced by this estimator. """ return IPInsightsModel( 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 not None and (mini_batch_size < 1 or mini_batch_size > 500000): raise ValueError("mini_batch_size must be in [1, 500000]") super(IPInsights, self)._prepare_for_training( records, mini_batch_size=mini_batch_size, job_name=job_name ) class IPInsightsPredictor(Predictor): """Returns dot product of entity and IP address embeddings as a score for compatibility. The implementation of :meth:`~sagemaker.predictor.Predictor.predict` in this `Predictor` requires a numpy ``ndarray`` as input. The array should contain two columns. The first column should contain the entity ID. The second column should contain the IPv4 address in dot notation. """ def __init__( self, endpoint_name, sagemaker_session=None, serializer=CSVSerializer(), deserializer=JSONDeserializer(), ): """Creates object to be used to get dot product of entity nad IP address. 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 text/csv. deserializer (callable): Optional. Default parses JSON responses using ``json.load(...)``. """ super(IPInsightsPredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=deserializer, ) class IPInsightsModel(Model): """Reference IPInsights s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns a Predictor that calculates anomaly scores for data points. """ def __init__( self, model_data: Union[str, PipelineVariable], role: Optional[str] = None, sagemaker_session: Optional[Session] = None, **kwargs ): """Creates object to get insights on S3 model data. 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( IPInsights.repo_name, sagemaker_session.boto_region_name, version=IPInsights.repo_version, ) pop_out_unused_kwarg("predictor_cls", kwargs, IPInsightsPredictor.__name__) pop_out_unused_kwarg("image_uri", kwargs, image_uri) super(IPInsightsModel, self).__init__( image_uri, model_data, role, predictor_cls=IPInsightsPredictor, sagemaker_session=sagemaker_session, **kwargs )