ipinsights.py

# Copyright 2017-2020 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 sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
from sagemaker.amazon.validation import ge, le
from sagemaker.predictor import Predictor, csv_serializer, json_deserializer
from sagemaker.model import Model
from sagemaker.session import Session
from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT


class IPInsights(AmazonAlgorithmEstimatorBase):
    """Placeholder docstring"""

    repo_name = "ipinsights"
    repo_version = 1
    MINI_BATCH_SIZE = 10000

    num_entity_vectors = hp(
        "num_entity_vectors", (ge(1), le(250000000)), "An integer in [1, 250000000]", int
    )
    vector_dim = hp("vector_dim", (ge(4), le(4096)), "An integer in [4, 4096]", int)

    batch_metrics_publish_interval = hp(
        "batch_metrics_publish_interval", (ge(1)), "An integer greater than 0", int
    )
    epochs = hp("epochs", (ge(1)), "An integer greater than 0", int)
    learning_rate = hp("learning_rate", (ge(1e-6), le(10.0)), "A float in [1e-6, 10.0]", float)
    num_ip_encoder_layers = hp(
        "num_ip_encoder_layers", (ge(0), le(100)), "An integer in [0, 100]", int
    )
    random_negative_sampling_rate = hp(
        "random_negative_sampling_rate", (ge(0), le(500)), "An integer in [0, 500]", int
    )
    shuffled_negative_sampling_rate = hp(
        "shuffled_negative_sampling_rate", (ge(0), le(500)), "An integer in [0, 500]", int
    )
    weight_decay = hp("weight_decay", (ge(0.0), le(10.0)), "A float in [0.0, 10.0]", float)

    def __init__(
        self,
        role,
        train_instance_count,
        train_instance_type,
        num_entity_vectors,
        vector_dim,
        batch_metrics_publish_interval=None,
        epochs=None,
        learning_rate=None,
        num_ip_encoder_layers=None,
        random_negative_sampling_rate=None,
        shuffled_negative_sampling_rate=None,
        weight_decay=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.
            train_instance_count (int): Number of Amazon EC2 instances to use
                for training.
            train_instance_type (str): 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, train_instance_count, train_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):
        """
        Args:
            records:
            mini_batch_size:
            job_name:
        """
        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):
        """
        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.
        """
        super(IPInsightsPredictor, self).__init__(
            endpoint_name,
            sagemaker_session,
            serializer=csv_serializer,
            deserializer=json_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, role, sagemaker_session=None, **kwargs):
        """
        Args:
            model_data:
            role:
            sagemaker_session:
            **kwargs:
        """
        sagemaker_session = sagemaker_session or Session()
        repo = "{}:{}".format(IPInsights.repo_name, IPInsights.repo_version)
        image_uri = "{}/{}".format(
            registry(sagemaker_session.boto_session.region_name, IPInsights.repo_name), repo
        )

        super(IPInsightsModel, self).__init__(
            image_uri,
            model_data,
            role,
            predictor_cls=IPInsightsPredictor,
            sagemaker_session=sagemaker_session,
            **kwargs
        )