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#
# 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: str,
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: str,
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
)
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