File size: 17,770 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 | # 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.common import RecordSerializer, RecordDeserializer
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.amazon.validation import gt, isin, ge
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 FactorizationMachines(AmazonAlgorithmEstimatorBase):
"""A supervised learning algorithm used in classification and regression.
Factorization Machines combine the advantages of Support Vector Machines
with factorization models. It is an extension of a linear model that is
designed to capture interactions between features within high dimensional
sparse datasets economically.
"""
repo_name: str = "factorization-machines"
repo_version: str = "1"
num_factors: hp = hp("num_factors", gt(0), "An integer greater than zero", int)
predictor_type: hp = hp(
"predictor_type",
isin("binary_classifier", "regressor"),
'Value "binary_classifier" or "regressor"',
str,
)
epochs: hp = hp("epochs", gt(0), "An integer greater than 0", int)
clip_gradient: hp = hp("clip_gradient", (), "A float value", float)
eps: hp = hp("eps", (), "A float value", float)
rescale_grad: hp = hp("rescale_grad", (), "A float value", float)
bias_lr: hp = hp("bias_lr", ge(0), "A non-negative float", float)
linear_lr: hp = hp("linear_lr", ge(0), "A non-negative float", float)
factors_lr: hp = hp("factors_lr", ge(0), "A non-negative float", float)
bias_wd: hp = hp("bias_wd", ge(0), "A non-negative float", float)
linear_wd: hp = hp("linear_wd", ge(0), "A non-negative float", float)
factors_wd: hp = hp("factors_wd", ge(0), "A non-negative float", float)
bias_init_method: hp = hp(
"bias_init_method",
isin("normal", "uniform", "constant"),
'Value "normal", "uniform" or "constant"',
str,
)
bias_init_scale: hp = hp("bias_init_scale", ge(0), "A non-negative float", float)
bias_init_sigma: hp = hp("bias_init_sigma", ge(0), "A non-negative float", float)
bias_init_value: hp = hp("bias_init_value", (), "A float value", float)
linear_init_method: hp = hp(
"linear_init_method",
isin("normal", "uniform", "constant"),
'Value "normal", "uniform" or "constant"',
str,
)
linear_init_scale: hp = hp("linear_init_scale", ge(0), "A non-negative float", float)
linear_init_sigma: hp = hp("linear_init_sigma", ge(0), "A non-negative float", float)
linear_init_value: hp = hp("linear_init_value", (), "A float value", float)
factors_init_method: hp = hp(
"factors_init_method",
isin("normal", "uniform", "constant"),
'Value "normal", "uniform" or "constant"',
str,
)
factors_init_scale: hp = hp("factors_init_scale", ge(0), "A non-negative float", float)
factors_init_sigma: hp = hp("factors_init_sigma", ge(0), "A non-negative float", float)
factors_init_value: hp = hp("factors_init_value", (), "A float value", float)
def __init__(
self,
role: str,
instance_count: Optional[Union[int, PipelineVariable]] = None,
instance_type: Optional[Union[str, PipelineVariable]] = None,
num_factors: Optional[int] = None,
predictor_type: Optional[str] = None,
epochs: Optional[int] = None,
clip_gradient: Optional[float] = None,
eps: Optional[float] = None,
rescale_grad: Optional[float] = None,
bias_lr: Optional[float] = None,
linear_lr: Optional[float] = None,
factors_lr: Optional[float] = None,
bias_wd: Optional[float] = None,
linear_wd: Optional[float] = None,
factors_wd: Optional[float] = None,
bias_init_method: Optional[str] = None,
bias_init_scale: Optional[float] = None,
bias_init_sigma: Optional[float] = None,
bias_init_value: Optional[float] = None,
linear_init_method: Optional[str] = None,
linear_init_scale: Optional[float] = None,
linear_init_sigma: Optional[float] = None,
linear_init_value: Optional[float] = None,
factors_init_method: Optional[str] = None,
factors_init_scale: Optional[float] = None,
factors_init_sigma: Optional[float] = None,
factors_init_value: Optional[float] = None,
**kwargs
):
"""Factorization Machines is :class:`Estimator` for general-purpose supervised learning.
Amazon SageMaker Factorization Machines is a general-purpose
supervised learning algorithm that you can use for both classification
and regression tasks. It is an extension of a linear model that is
designed to parsimoniously capture interactions between features within
high dimensional sparse datasets.
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.pca.FactorizationMachinesPredictor` object
that can be used for inference calls using the trained model hosted in
the SageMaker Endpoint.
FactorizationMachines Estimators can be configured by setting
hyperparameters. The available hyperparameters for FactorizationMachines
are documented below.
For further information on the AWS FactorizationMachines algorithm,
please consult AWS technical documentation:
https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.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_factors (int): Dimensionality of factorization.
predictor_type (str): Type of predictor 'binary_classifier' or
'regressor'.
epochs (int): Number of training epochs to run.
clip_gradient (float): Optimizer parameter. Clip the gradient by
projecting onto the box [-clip_gradient, +clip_gradient]
eps (float): Optimizer parameter. Small value to avoid division by
0.
rescale_grad (float): Optimizer parameter. If set, multiplies the
gradient with rescale_grad before updating. Often choose to be
1.0/batch_size.
bias_lr (float): Non-negative learning rate for the bias term.
linear_lr (float): Non-negative learning rate for linear terms.
factors_lr (float): Noon-negative learning rate for factorization
terms.
bias_wd (float): Non-negative weight decay for the bias term.
linear_wd (float): Non-negative weight decay for linear terms.
factors_wd (float): Non-negative weight decay for factorization
terms.
bias_init_method (str): Initialization method for the bias term:
'normal', 'uniform' or 'constant'.
bias_init_scale (float): Non-negative range for initialization of
the bias term that takes effect when bias_init_method parameter
is 'uniform'
bias_init_sigma (float): Non-negative standard deviation for
initialization of the bias term that takes effect when
bias_init_method parameter is 'normal'.
bias_init_value (float): Initial value of the bias term that takes
effect when bias_init_method parameter is 'constant'.
linear_init_method (str): Initialization method for linear term:
'normal', 'uniform' or 'constant'.
linear_init_scale (float): Non-negative range for initialization of
linear terms that takes effect when linear_init_method parameter
is 'uniform'.
linear_init_sigma (float): Non-negative standard deviation for
initialization of linear terms that takes effect when
linear_init_method parameter is 'normal'.
linear_init_value (float): Initial value of linear terms that takes
effect when linear_init_method parameter is 'constant'.
factors_init_method (str): Initialization method for
factorization term: 'normal', 'uniform' or 'constant'.
factors_init_scale (float): Non-negative range for initialization of
factorization terms that takes effect when factors_init_method
parameter is 'uniform'.
factors_init_sigma (float): Non-negative standard deviation for
initialization of factorization terms that takes effect when
factors_init_method parameter is 'normal'.
factors_init_value (float): Initial value of factorization terms
that takes effect when factors_init_method parameter is
'constant'.
**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(FactorizationMachines, self).__init__(role, instance_count, instance_type, **kwargs)
self.num_factors = num_factors
self.predictor_type = predictor_type
self.epochs = epochs
self.clip_gradient = clip_gradient
self.eps = eps
self.rescale_grad = rescale_grad
self.bias_lr = bias_lr
self.linear_lr = linear_lr
self.factors_lr = factors_lr
self.bias_wd = bias_wd
self.linear_wd = linear_wd
self.factors_wd = factors_wd
self.bias_init_method = bias_init_method
self.bias_init_scale = bias_init_scale
self.bias_init_sigma = bias_init_sigma
self.bias_init_value = bias_init_value
self.linear_init_method = linear_init_method
self.linear_init_scale = linear_init_scale
self.linear_init_sigma = linear_init_sigma
self.linear_init_value = linear_init_value
self.factors_init_method = factors_init_method
self.factors_init_scale = factors_init_scale
self.factors_init_sigma = factors_init_sigma
self.factors_init_value = factors_init_value
def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
"""Return a :class:`~sagemaker.amazon.FactorizationMachinesModel`.
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 FactorizationMachinesModel constructor.
"""
return FactorizationMachinesModel(
self.model_data,
self.role,
sagemaker_session=self.sagemaker_session,
vpc_config=self.get_vpc_config(vpc_config_override),
**kwargs
)
class FactorizationMachinesPredictor(Predictor):
"""Performs binary-classification or regression prediction from input vectors.
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 ``ndarray``. The prediction is stored in the ``"score"`` key of
the ``Record.label`` field. Please refer to the formats details described:
https://docs.aws.amazon.com/sagemaker/latest/dg/fm-in-formats.html
"""
def __init__(
self,
endpoint_name,
sagemaker_session=None,
serializer=RecordSerializer(),
deserializer=RecordDeserializer(),
):
"""Initialization for FactorizationMachinesPredictor 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(FactorizationMachinesPredictor, self).__init__(
endpoint_name,
sagemaker_session,
serializer=serializer,
deserializer=deserializer,
)
class FactorizationMachinesModel(Model):
"""Reference S3 model data created by FactorizationMachines estimator.
Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and
returns :class:`FactorizationMachinesPredictor`.
"""
def __init__(
self,
model_data: Union[str, PipelineVariable],
role: str,
sagemaker_session: Optional[Session] = None,
**kwargs
):
"""Initialization for FactorizationMachinesModel 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(
FactorizationMachines.repo_name,
sagemaker_session.boto_region_name,
version=FactorizationMachines.repo_version,
)
pop_out_unused_kwarg("predictor_cls", kwargs, FactorizationMachinesPredictor.__name__)
pop_out_unused_kwarg("image_uri", kwargs, image_uri)
super(FactorizationMachinesModel, self).__init__(
image_uri,
model_data,
role,
predictor_cls=FactorizationMachinesPredictor,
sagemaker_session=sagemaker_session,
**kwargs
)
|