File size: 11,508 Bytes
4021124 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 | # 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: str,
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: str,
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
)
|