FactorizationMachines: A supervised learning algorithm used in classification and...
In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon
FactorizationMachines R Documentation
A supervised learning algorithm used in classification and regression.
Description
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.
Super classes
sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::AmazonAlgorithmEstimatorBase -> FactorizationMachines
Public fields
repo_namesagemaker repo name for framework
repo_versionversion of framework
.modulemimic python module
Active bindings
num_factorsDimensionality of factorization.
predictor_typeType of predictor 'binary_classifier' or 'regressor'.
epochsNumber of training epochs to run.
clip_gradientClip the gradient by projecting onto the box [-clip_gradient, +clip_gradient]
epsSmall value to avoid division by 0.
rescale_gradIf set, multiplies the gradient with rescale_grad before updating
bias_lrNon-negative learning rate for the bias term.
linear_lrNon-negative learning rate for linear terms.
factors_lrNon-negative learning rate for factorization terms.
bias_wdNon-negative weight decay for the bias term.
linear_wdNon-negative weight decay for linear terms.
factors_wdNon-negative weight decay for factorization terms.
bias_init_methodInitialization method for the bias term:
'normal', 'uniform' or 'constant'.
bias_init_scaleNon-negative range for initialization of
the bias term that takes effect when bias_init_method parameter
is 'uniform'
bias_init_sigmaNon-negative standard deviation for
initialization of the bias term that takes effect when
bias_init_method parameter is 'normal'.
bias_init_valueInitial value of the bias term that takes
effect when bias_init_method parameter is 'constant'.
linear_init_methodInitialization method for linear term:
normal', 'uniform' or 'constant'.
linear_init_scaleon-negative range for initialization of
linear terms that takes effect when linear_init_method parameter
is 'uniform'.
linear_init_sigmaNon-negative standard deviation for
initialization of linear terms that takes effect when
linear_init_method parameter is 'normal'.
linear_init_valueInitial value of linear terms that takes
effect when linear_init_method parameter is 'constant'.
factors_init_methodInitialization method for
factorization term: 'normal', 'uniform' or 'constant'.
factors_init_scaleNon-negative range for initialization of
factorization terms that takes effect when factors_init_method
parameter is 'uniform'.
factors_init_sigmaNon-negative standard deviation for
initialization of factorization terms that takes effect when
factors_init_method parameter is 'normal'.
factors_init_valueInitial value of factorization terms
that takes effect when factors_init_method parameter is
constant'.
Methods
Public methods
Inherited methods
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sagemaker.mlcore::EstimatorBase$latest_job_debugger_artifacts_path()
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sagemaker.mlcore::EstimatorBase$latest_job_profiler_artifacts_path()
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sagemaker.mlcore::EstimatorBase$latest_job_tensorboard_artifacts_path()
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sagemaker.mlcore::AmazonAlgorithmEstimatorBase$hyperparameters()
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sagemaker.mlcore::AmazonAlgorithmEstimatorBase$prepare_workflow_for_training()
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sagemaker.mlcore::AmazonAlgorithmEstimatorBase$training_image_uri()
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Method new()
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
Usage
FactorizationMachines$new(
role,
instance_count,
instance_type,
num_factors,
predictor_type,
epochs = NULL,
clip_gradient = NULL,
eps = NULL,
rescale_grad = NULL,
bias_lr = NULL,
linear_lr = NULL,
factors_lr = NULL,
bias_wd = NULL,
linear_wd = NULL,
factors_wd = NULL,
bias_init_method = NULL,
bias_init_scale = NULL,
bias_init_sigma = NULL,
bias_init_value = NULL,
linear_init_method = NULL,
linear_init_scale = NULL,
linear_init_sigma = NULL,
linear_init_value = NULL,
factors_init_method = NULL,
factors_init_scale = NULL,
factors_init_sigma = NULL,
factors_init_value = NULL,
...
)
Arguments
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): Number of Amazon EC2 instances to use
for training.
instance_type(str): 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): Non-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(string): 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(string): 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(string): 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'.
...: base class keyword argument values. You can find additional
parameters for initializing this class at
:class:'~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase' and
:class:'~sagemaker.estimator.EstimatorBase'.
Method create_model()
Return a :class:'~sagemaker.amazon.FactorizationMachinesModel'
referencing the latest s3 model data produced by this Estimator.
Usage
FactorizationMachines$create_model(
vpc_config_override = "VPC_CONFIG_DEFAULT",
...
)
Arguments
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.
...: Additional kwargs passed to the FactorizationMachinesModel constructor.
Method clone()
The objects of this class are cloneable with this method.
Usage
FactorizationMachines$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.
R Package Documentation
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| FactorizationMachines | R Documentation |
A supervised learning algorithm used in classification and regression.
Description
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.
Super classes
sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::AmazonAlgorithmEstimatorBase -> FactorizationMachines
Public fields
repo_namesagemaker repo name for framework
repo_versionversion of framework
.modulemimic python module
Active bindings
num_factorsDimensionality of factorization.
predictor_typeType of predictor 'binary_classifier' or 'regressor'.
epochsNumber of training epochs to run.
clip_gradientClip the gradient by projecting onto the box [-clip_gradient, +clip_gradient]
epsSmall value to avoid division by 0.
rescale_gradIf set, multiplies the gradient with rescale_grad before updating
bias_lrNon-negative learning rate for the bias term.
linear_lrNon-negative learning rate for linear terms.
factors_lrNon-negative learning rate for factorization terms.
bias_wdNon-negative weight decay for the bias term.
linear_wdNon-negative weight decay for linear terms.
factors_wdNon-negative weight decay for factorization terms.
bias_init_methodInitialization method for the bias term: 'normal', 'uniform' or 'constant'.
bias_init_scaleNon-negative range for initialization of the bias term that takes effect when bias_init_method parameter is 'uniform'
bias_init_sigmaNon-negative standard deviation for initialization of the bias term that takes effect when bias_init_method parameter is 'normal'.
bias_init_valueInitial value of the bias term that takes effect when bias_init_method parameter is 'constant'.
linear_init_methodInitialization method for linear term: normal', 'uniform' or 'constant'.
linear_init_scaleon-negative range for initialization of linear terms that takes effect when linear_init_method parameter is 'uniform'.
linear_init_sigmaNon-negative standard deviation for initialization of linear terms that takes effect when linear_init_method parameter is 'normal'.
linear_init_valueInitial value of linear terms that takes effect when linear_init_method parameter is 'constant'.
factors_init_methodInitialization method for factorization term: 'normal', 'uniform' or 'constant'.
factors_init_scaleNon-negative range for initialization of factorization terms that takes effect when factors_init_method parameter is 'uniform'.
factors_init_sigmaNon-negative standard deviation for initialization of factorization terms that takes effect when factors_init_method parameter is 'normal'.
factors_init_valueInitial value of factorization terms that takes effect when factors_init_method parameter is constant'.
Methods
Public methods
Inherited methods
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sagemaker.mlcore::EstimatorBase$latest_job_debugger_artifacts_path() -
sagemaker.mlcore::EstimatorBase$latest_job_profiler_artifacts_path() -
sagemaker.mlcore::EstimatorBase$latest_job_tensorboard_artifacts_path() -
sagemaker.mlcore::AmazonAlgorithmEstimatorBase$hyperparameters() -
sagemaker.mlcore::AmazonAlgorithmEstimatorBase$prepare_workflow_for_training() -
sagemaker.mlcore::AmazonAlgorithmEstimatorBase$training_image_uri()
Method new()
Factorization Machines is :class:'Estimator' for general-purpose supervised learning. Amazon SageMaker Factorization Machines is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to parsimoniously capture interactions between features within high dimensional sparse datasets. This Estimator may be fit via calls to :meth:'~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit'. It requires Amazon :class:'~sagemaker.amazon.record_pb2.Record' protobuf serialized data to be stored in S3. There is an utility :meth:'~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.record_set' that can be used to upload data to S3 and creates :class:'~sagemaker.amazon.amazon_estimator.RecordSet' to be passed to the 'fit' call. To learn more about the Amazon protobuf Record class and how to prepare bulk data in this format, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html After this Estimator is fit, model data is stored in S3. The model may be deployed to an Amazon SageMaker Endpoint by invoking :meth:'~sagemaker.amazon.estimator.EstimatorBase.deploy'. As well as deploying an Endpoint, deploy returns a :class:'~sagemaker.amazon.pca.FactorizationMachinesPredictor' object that can be used for inference calls using the trained model hosted in the SageMaker Endpoint. FactorizationMachines Estimators can be configured by setting hyperparameters. The available hyperparameters for FactorizationMachines are documented below. For further information on the AWS FactorizationMachines algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html
Usage
FactorizationMachines$new( role, instance_count, instance_type, num_factors, predictor_type, epochs = NULL, clip_gradient = NULL, eps = NULL, rescale_grad = NULL, bias_lr = NULL, linear_lr = NULL, factors_lr = NULL, bias_wd = NULL, linear_wd = NULL, factors_wd = NULL, bias_init_method = NULL, bias_init_scale = NULL, bias_init_sigma = NULL, bias_init_value = NULL, linear_init_method = NULL, linear_init_scale = NULL, linear_init_sigma = NULL, linear_init_value = NULL, factors_init_method = NULL, factors_init_scale = NULL, factors_init_sigma = NULL, factors_init_value = NULL, ... )
Arguments
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): Number of Amazon EC2 instances to use for training.
instance_type(str): 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): Non-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(string): 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(string): 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(string): 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'.
...: base class keyword argument values. You can find additional parameters for initializing this class at :class:'~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase' and :class:'~sagemaker.estimator.EstimatorBase'.
Method create_model()
Return a :class:'~sagemaker.amazon.FactorizationMachinesModel' referencing the latest s3 model data produced by this Estimator.
Usage
FactorizationMachines$create_model( vpc_config_override = "VPC_CONFIG_DEFAULT", ... )
Arguments
vpc_config_override(dict[str, list[str]]): Optional override for VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids.
...: Additional kwargs passed to the FactorizationMachinesModel constructor.
Method clone()
The objects of this class are cloneable with this method.
Usage
FactorizationMachines$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
R Package Documentation
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