LinearLearner: A supervised learning algorithms used for solving...
In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon
LinearLearner R Documentation
A supervised learning algorithms used for solving classification or regression problems.
Description
For input, you give the model labeled examples (x, y). x is a high-dimensional vector and
y is a numeric label. For binary classification problems, the label must be either 0 or 1.
For multiclass classification problems, the labels must be from 0 to num_classes - 1. For
regression problems, y is a real number. The algorithm learns a linear function, or, for
classification problems, a linear threshold function, and maps a vector x to an approximation
of the label y
Super classes
sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::AmazonAlgorithmEstimatorBase -> LinearLearner
Public fields
repo_namesagemaker repo name for framework
repo_versionversion of framework
DEFAULT_MINI_BATCH_SIZEThe size of each mini-batch to use when training.
.modulemimic python module
Active bindings
predictor_typeThe type of predictor to learn. Either
"binary_classifier" or "multiclass_classifier" or "regressor".
binary_classifier_model_selection_criteriaOne of 'accuracy', 'f1', 'f_beta', 'precision_at_target_recall', 'recall_at_target_precision', 'cross_entropy_loss', 'loss_function'
target_recallOnly applicable if binary_classifier_model_selection_criteria is precision_at_target_recall
target_precisionOnly applicable if binary_classifier_model_selection_criteria is recall_at_target_precision.
positive_example_weight_multThe importance weight of positive examples is multiplied by this constant.
epochsThe maximum number of passes to make over the training data.
use_biasWhether to include a bias field
num_modelsNumber of models to train in parallel
num_calibration_samplesNumber of observations to use from validation dataset for doing model calibration
init_methodFunction to use to set the initial model weights.
init_scaleFor "uniform" init, the range of values.
init_sigmaFor "normal" init, the standard-deviation.
init_biasInitial weight for bias term
optimizerOne of 'sgd', 'adam', 'rmsprop' or 'auto'
lossOne of 'logistic', 'squared_loss', 'absolute_loss',
'hinge_loss', 'eps_insensitive_squared_loss', 'eps_insensitive_absolute_loss',
'quantile_loss', 'huber_loss' or
'softmax_loss' or 'auto'.
wdL2 regularization parameter
l1L1 regularization parameter.
momentumMomentum parameter of sgd optimizer.
learning_rateThe SGD learning rate
beta_1Exponential decay rate for first moment estimates.
beta_2Exponential decay rate for second moment estimates.
bias_lr_multAllows different learning rate for the bias term.
bias_wd_multAllows different regularization for the bias term.
use_lr_schedulerIf true, we use a scheduler for the learning rate.
lr_scheduler_stepThe number of steps between decreases of the learning rate
lr_scheduler_factorEvery lr_scheduler_step the learning rate will decrease by this quantity.
lr_scheduler_minimum_lrEvery lr_scheduler_step the learning rate will decrease by this quantity.
normalize_dataNormalizes the features before training to have standard deviation of 1.0.
normalize_labelNormalizes the regression label to have a standard deviation of 1.0.
unbias_dataIf true, features are modified to have mean 0.0.
unbias_labelIf true, labels are modified to have mean 0.0.
num_point_for_scalerThe number of data points to use for calculating the normalizing and unbiasing terms.
marginThe margin for hinge_loss.
quantileQuantile for quantile loss.
loss_insensitivityParameter for epsilon insensitive loss type.
huber_deltaParameter for Huber loss.
early_stopping_patienceThe number of epochs to wait before ending training if no improvement is made.
early_stopping_toleranceRelative tolerance to measure an improvement in loss.
num_classesThe number of classes for the response variable.
accuracy_top_kThe value of k when computing the Top K
f_betaThe value of beta to use when calculating F score
metrics for binary or multiclass classification.
balance_multiclass_weightsWhether to use class weights which give each class equal importance
in the loss function.
Methods
Public methods
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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()
An :class:'Estimator' for binary classification and regression.
Amazon SageMaker Linear Learner provides a solution for both
classification and regression problems, allowing for exploring different
training objectives simultaneously and choosing the best solution from a
validation set. It allows the user to explore a large number of models
and choose the best, which optimizes either continuous objectives such
as mean square error, cross entropy loss, absolute error, etc., or
discrete objectives suited for classification such as F1 measure,
precision@recall, accuracy. The implementation provides a significant
speedup over naive hyperparameter optimization techniques and an added
convenience, when compared with solutions providing a solution only to
continuous objectives.
This Estimator may be fit via calls to
:meth:'~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit_ndarray'
or
:meth:'~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit'.
The former allows a LinearLearner model to be fit on a 2-dimensional
numpy array. The latter requires Amazon
:class:'~sagemaker.amazon.record_pb2.Record' protobuf serialized data to
be stored in S3.
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.linear_learner.LinearLearnerPredictor' object
that can be used to make class or regression predictions, using the
trained model.
LinearLearner Estimators can be configured by setting
hyperparameters. The available hyperparameters for LinearLearner are
documented below. For further information on the AWS LinearLearner
algorithm, please consult AWS technical documentation:
https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html
Usage
LinearLearner$new(
role,
instance_count,
instance_type,
predictor_type,
binary_classifier_model_selection_criteria = NULL,
target_recall = NULL,
target_precision = NULL,
positive_example_weight_mult = NULL,
epochs = NULL,
use_bias = NULL,
num_models = NULL,
num_calibration_samples = NULL,
init_method = NULL,
init_scale = NULL,
init_sigma = NULL,
init_bias = NULL,
optimizer = NULL,
loss = NULL,
wd = NULL,
l1 = NULL,
momentum = NULL,
learning_rate = NULL,
beta_1 = NULL,
beta_2 = NULL,
bias_lr_mult = NULL,
bias_wd_mult = NULL,
use_lr_scheduler = NULL,
lr_scheduler_step = NULL,
lr_scheduler_factor = NULL,
lr_scheduler_minimum_lr = NULL,
normalize_data = NULL,
normalize_label = NULL,
unbias_data = NULL,
unbias_label = NULL,
num_point_for_scaler = NULL,
margin = NULL,
quantile = NULL,
loss_insensitivity = NULL,
huber_delta = NULL,
early_stopping_patience = NULL,
early_stopping_tolerance = NULL,
num_classes = NULL,
accuracy_top_k = NULL,
f_beta = NULL,
balance_multiclass_weights = 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'.
predictor_type(str): The type of predictor to learn. Either
"binary_classifier" or "multiclass_classifier" or "regressor".
binary_classifier_model_selection_criteria(str): One of 'accuracy',
'f1', 'f_beta', 'precision_at_target_recall', 'recall_at_target_precision',
'cross_entropy_loss', 'loss_function'
target_recall(float): Target recall. Only applicable if
binary_classifier_model_selection_criteria is
precision_at_target_recall.
target_precision(float): Target precision. Only applicable if
binary_classifier_model_selection_criteria is
recall_at_target_precision.
positive_example_weight_mult(float): The importance weight of
positive examples is multiplied by this constant. Useful for
skewed datasets. Only applies for classification tasks.
epochs(int): The maximum number of passes to make over the training
data.
use_bias(bool): Whether to include a bias field
num_models(int): Number of models to train in parallel. If not set,
the number of parallel models to train will be decided by the
algorithm itself. One model will be trained according to the
given training parameter (regularization, optimizer, loss) and
the rest by close by parameters.
num_calibration_samples(int): Number of observations to use from
validation dataset for doing model calibration (finding the best threshold).
init_method(str): Function to use to set the initial model weights.
One of "uniform" or "normal"
init_scale(float): For "uniform" init, the range of values.
init_sigma(float): For "normal" init, the standard-deviation.
init_bias(float): Initial weight for bias term
optimizer(str): One of 'sgd', 'adam', 'rmsprop' or 'auto'
loss(str): One of 'logistic', 'squared_loss', 'absolute_loss',
'hinge_loss', 'eps_insensitive_squared_loss', 'eps_insensitive_absolute_loss',
'quantile_loss', 'huber_loss' or
'softmax_loss' or 'auto'.
wd(float): L2 regularization parameter i.e. the weight decay
parameter. Use 0 for no L2 regularization.
l1(float): L1 regularization parameter. Use 0 for no L1
regularization.
momentum(float): Momentum parameter of sgd optimizer.
learning_rate(float): The SGD learning rate
beta_1(float): Exponential decay rate for first moment estimates.
Only applies for adam optimizer.
beta_2(float): Exponential decay rate for second moment estimates.
Only applies for adam optimizer.
bias_lr_mult(float): Allows different learning rate for the bias
term. The actual learning rate for the bias is learning rate times bias_lr_mult.
bias_wd_mult(float): Allows different regularization for the bias
term. The actual L2 regularization weight for the bias is wd times bias_wd_mult.
By default there is no regularization on the bias term.
use_lr_scheduler(bool): If true, we use a scheduler for the
learning rate.
lr_scheduler_step(int): The number of steps between decreases of
the learning rate. Only applies to learning rate scheduler.
lr_scheduler_factor(float): Every lr_scheduler_step the learning
rate will decrease by this quantity. Only applies for learning
rate scheduler.
lr_scheduler_minimum_lr(float): The learning rate will never
decrease to a value lower than this. Only applies for learning rate scheduler.
normalize_data(bool): Normalizes the features before training to
have standard deviation of 1.0.
normalize_label(bool): Normalizes the regression label to have a
standard deviation of 1.0. If set for classification, it will be
ignored.
unbias_data(bool): If true, features are modified to have mean 0.0.
unbias_label(bool): If true, labels are modified to have mean 0.0.
num_point_for_scaler(int): The number of data points to use for
calculating the normalizing and unbiasing terms.
margin(float): The margin for hinge_loss.
quantile(float): Quantile for quantile loss. For quantile q, the
model will attempt to produce predictions such that true_label < prediction with
probability q.
loss_insensitivity(float): Parameter for epsilon insensitive loss
type. During training and metric evaluation, any error smaller than this is
considered to be zero.
huber_delta(float): Parameter for Huber loss. During training and
metric evaluation, compute L2 loss for errors smaller than delta and L1 loss for
errors larger than delta.
early_stopping_patience(int): The number of epochs to wait before ending training
if no improvement is made. The improvement is training loss if validation data is
not provided, or else it is the validation loss or the binary classification model
selection criteria like accuracy, f1-score etc. To disable early stopping,
set early_stopping_patience to a value larger than epochs.
early_stopping_tolerance(float): Relative tolerance to measure an
improvement in loss. If the ratio of the improvement in loss divided by the
previous best loss is smaller than this value, early stopping will
consider the improvement to be zero.
num_classes(int): The number of classes for the response variable.
Required when predictor_type is multiclass_classifier and ignored otherwise. The
classes are assumed to be labeled 0, ..., num_classes - 1.
accuracy_top_k(int): The value of k when computing the Top K
Accuracy metric for multiclass classification. An example is scored as correct
if the model assigns one of the top k scores to the true
label.
f_beta(float): The value of beta to use when calculating F score
metrics for binary or multiclass classification. Also used if
binary_classifier_model_selection_criteria is f_beta.
balance_multiclass_weights(bool): Whether to use class weights
which give each class equal importance in the loss function. Only used when
predictor_type is multiclass_classifier.
...: base class keyword argument values.
Method create_model()
Return a :class:'~sagemaker.amazon.LinearLearnerModel' referencing
the latest s3 model data produced by this Estimator.
Usage
LinearLearner$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 LinearLearnerModel constructor.
Method .prepare_for_training()
Set hyperparameters needed for training. This method will also
validate “source_dir“.
Usage
LinearLearner$.prepare_for_training(
records,
mini_batch_size = NULL,
job_name = NULL
)
Arguments
records(RecordSet) – The records to train this Estimator on.
mini_batch_size(int or None) – The size of each mini-batch to use
when training. If None, a default value will be used.
job_name(str): Name of the training job to be created. If not
specified, one is generated, using the base name given to the
constructor if applicable.
Method clone()
The objects of this class are cloneable with this method.
Usage
LinearLearner$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.
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| LinearLearner | R Documentation |
A supervised learning algorithms used for solving classification or regression problems.
Description
For input, you give the model labeled examples (x, y). x is a high-dimensional vector and y is a numeric label. For binary classification problems, the label must be either 0 or 1. For multiclass classification problems, the labels must be from 0 to num_classes - 1. For regression problems, y is a real number. The algorithm learns a linear function, or, for classification problems, a linear threshold function, and maps a vector x to an approximation of the label y
Super classes
sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::AmazonAlgorithmEstimatorBase -> LinearLearner
Public fields
repo_namesagemaker repo name for framework
repo_versionversion of framework
DEFAULT_MINI_BATCH_SIZEThe size of each mini-batch to use when training.
.modulemimic python module
Active bindings
predictor_typeThe type of predictor to learn. Either "binary_classifier" or "multiclass_classifier" or "regressor".
binary_classifier_model_selection_criteriaOne of 'accuracy', 'f1', 'f_beta', 'precision_at_target_recall', 'recall_at_target_precision', 'cross_entropy_loss', 'loss_function'
target_recallOnly applicable if binary_classifier_model_selection_criteria is precision_at_target_recall
target_precisionOnly applicable if binary_classifier_model_selection_criteria is recall_at_target_precision.
positive_example_weight_multThe importance weight of positive examples is multiplied by this constant.
epochsThe maximum number of passes to make over the training data.
use_biasWhether to include a bias field
num_modelsNumber of models to train in parallel
num_calibration_samplesNumber of observations to use from validation dataset for doing model calibration
init_methodFunction to use to set the initial model weights.
init_scaleFor "uniform" init, the range of values.
init_sigmaFor "normal" init, the standard-deviation.
init_biasInitial weight for bias term
optimizerOne of 'sgd', 'adam', 'rmsprop' or 'auto'
lossOne of 'logistic', 'squared_loss', 'absolute_loss', 'hinge_loss', 'eps_insensitive_squared_loss', 'eps_insensitive_absolute_loss', 'quantile_loss', 'huber_loss' or 'softmax_loss' or 'auto'.
wdL2 regularization parameter
l1L1 regularization parameter.
momentumMomentum parameter of sgd optimizer.
learning_rateThe SGD learning rate
beta_1Exponential decay rate for first moment estimates.
beta_2Exponential decay rate for second moment estimates.
bias_lr_multAllows different learning rate for the bias term.
bias_wd_multAllows different regularization for the bias term.
use_lr_schedulerIf true, we use a scheduler for the learning rate.
lr_scheduler_stepThe number of steps between decreases of the learning rate
lr_scheduler_factorEvery lr_scheduler_step the learning rate will decrease by this quantity.
lr_scheduler_minimum_lrEvery lr_scheduler_step the learning rate will decrease by this quantity.
normalize_dataNormalizes the features before training to have standard deviation of 1.0.
normalize_labelNormalizes the regression label to have a standard deviation of 1.0.
unbias_dataIf true, features are modified to have mean 0.0.
unbias_labelIf true, labels are modified to have mean 0.0.
num_point_for_scalerThe number of data points to use for calculating the normalizing and unbiasing terms.
marginThe margin for hinge_loss.
quantileQuantile for quantile loss.
loss_insensitivityParameter for epsilon insensitive loss type.
huber_deltaParameter for Huber loss.
early_stopping_patienceThe number of epochs to wait before ending training if no improvement is made.
early_stopping_toleranceRelative tolerance to measure an improvement in loss.
num_classesThe number of classes for the response variable.
accuracy_top_kThe value of k when computing the Top K
f_betaThe value of beta to use when calculating F score metrics for binary or multiclass classification.
balance_multiclass_weightsWhether to use class weights which give each class equal importance in the loss function.
Methods
Public methods
Inherited methods
-
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()
An :class:'Estimator' for binary classification and regression. Amazon SageMaker Linear Learner provides a solution for both classification and regression problems, allowing for exploring different training objectives simultaneously and choosing the best solution from a validation set. It allows the user to explore a large number of models and choose the best, which optimizes either continuous objectives such as mean square error, cross entropy loss, absolute error, etc., or discrete objectives suited for classification such as F1 measure, precision@recall, accuracy. The implementation provides a significant speedup over naive hyperparameter optimization techniques and an added convenience, when compared with solutions providing a solution only to continuous objectives. This Estimator may be fit via calls to :meth:'~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit_ndarray' or :meth:'~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit'. The former allows a LinearLearner model to be fit on a 2-dimensional numpy array. The latter requires Amazon :class:'~sagemaker.amazon.record_pb2.Record' protobuf serialized data to be stored in S3. 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.linear_learner.LinearLearnerPredictor' object that can be used to make class or regression predictions, using the trained model. LinearLearner Estimators can be configured by setting hyperparameters. The available hyperparameters for LinearLearner are documented below. For further information on the AWS LinearLearner algorithm, please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html
Usage
LinearLearner$new( role, instance_count, instance_type, predictor_type, binary_classifier_model_selection_criteria = NULL, target_recall = NULL, target_precision = NULL, positive_example_weight_mult = NULL, epochs = NULL, use_bias = NULL, num_models = NULL, num_calibration_samples = NULL, init_method = NULL, init_scale = NULL, init_sigma = NULL, init_bias = NULL, optimizer = NULL, loss = NULL, wd = NULL, l1 = NULL, momentum = NULL, learning_rate = NULL, beta_1 = NULL, beta_2 = NULL, bias_lr_mult = NULL, bias_wd_mult = NULL, use_lr_scheduler = NULL, lr_scheduler_step = NULL, lr_scheduler_factor = NULL, lr_scheduler_minimum_lr = NULL, normalize_data = NULL, normalize_label = NULL, unbias_data = NULL, unbias_label = NULL, num_point_for_scaler = NULL, margin = NULL, quantile = NULL, loss_insensitivity = NULL, huber_delta = NULL, early_stopping_patience = NULL, early_stopping_tolerance = NULL, num_classes = NULL, accuracy_top_k = NULL, f_beta = NULL, balance_multiclass_weights = 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'.
predictor_type(str): The type of predictor to learn. Either "binary_classifier" or "multiclass_classifier" or "regressor".
binary_classifier_model_selection_criteria(str): One of 'accuracy', 'f1', 'f_beta', 'precision_at_target_recall', 'recall_at_target_precision', 'cross_entropy_loss', 'loss_function'
target_recall(float): Target recall. Only applicable if binary_classifier_model_selection_criteria is precision_at_target_recall.
target_precision(float): Target precision. Only applicable if binary_classifier_model_selection_criteria is recall_at_target_precision.
positive_example_weight_mult(float): The importance weight of positive examples is multiplied by this constant. Useful for skewed datasets. Only applies for classification tasks.
epochs(int): The maximum number of passes to make over the training data.
use_bias(bool): Whether to include a bias field
num_models(int): Number of models to train in parallel. If not set, the number of parallel models to train will be decided by the algorithm itself. One model will be trained according to the given training parameter (regularization, optimizer, loss) and the rest by close by parameters.
num_calibration_samples(int): Number of observations to use from validation dataset for doing model calibration (finding the best threshold).
init_method(str): Function to use to set the initial model weights. One of "uniform" or "normal"
init_scale(float): For "uniform" init, the range of values.
init_sigma(float): For "normal" init, the standard-deviation.
init_bias(float): Initial weight for bias term
optimizer(str): One of 'sgd', 'adam', 'rmsprop' or 'auto'
loss(str): One of 'logistic', 'squared_loss', 'absolute_loss', 'hinge_loss', 'eps_insensitive_squared_loss', 'eps_insensitive_absolute_loss', 'quantile_loss', 'huber_loss' or 'softmax_loss' or 'auto'.
wd(float): L2 regularization parameter i.e. the weight decay parameter. Use 0 for no L2 regularization.
l1(float): L1 regularization parameter. Use 0 for no L1 regularization.
momentum(float): Momentum parameter of sgd optimizer.
learning_rate(float): The SGD learning rate
beta_1(float): Exponential decay rate for first moment estimates. Only applies for adam optimizer.
beta_2(float): Exponential decay rate for second moment estimates. Only applies for adam optimizer.
bias_lr_mult(float): Allows different learning rate for the bias term. The actual learning rate for the bias is learning rate times bias_lr_mult.
bias_wd_mult(float): Allows different regularization for the bias term. The actual L2 regularization weight for the bias is wd times bias_wd_mult. By default there is no regularization on the bias term.
use_lr_scheduler(bool): If true, we use a scheduler for the learning rate.
lr_scheduler_step(int): The number of steps between decreases of the learning rate. Only applies to learning rate scheduler.
lr_scheduler_factor(float): Every lr_scheduler_step the learning rate will decrease by this quantity. Only applies for learning rate scheduler.
lr_scheduler_minimum_lr(float): The learning rate will never decrease to a value lower than this. Only applies for learning rate scheduler.
normalize_data(bool): Normalizes the features before training to have standard deviation of 1.0.
normalize_label(bool): Normalizes the regression label to have a standard deviation of 1.0. If set for classification, it will be ignored.
unbias_data(bool): If true, features are modified to have mean 0.0.
unbias_label(bool): If true, labels are modified to have mean 0.0.
num_point_for_scaler(int): The number of data points to use for calculating the normalizing and unbiasing terms.
margin(float): The margin for hinge_loss.
quantile(float): Quantile for quantile loss. For quantile q, the model will attempt to produce predictions such that true_label < prediction with probability q.
loss_insensitivity(float): Parameter for epsilon insensitive loss type. During training and metric evaluation, any error smaller than this is considered to be zero.
huber_delta(float): Parameter for Huber loss. During training and metric evaluation, compute L2 loss for errors smaller than delta and L1 loss for errors larger than delta.
early_stopping_patience(int): The number of epochs to wait before ending training if no improvement is made. The improvement is training loss if validation data is not provided, or else it is the validation loss or the binary classification model selection criteria like accuracy, f1-score etc. To disable early stopping, set early_stopping_patience to a value larger than epochs.
early_stopping_tolerance(float): Relative tolerance to measure an improvement in loss. If the ratio of the improvement in loss divided by the previous best loss is smaller than this value, early stopping will consider the improvement to be zero.
num_classes(int): The number of classes for the response variable. Required when predictor_type is multiclass_classifier and ignored otherwise. The classes are assumed to be labeled 0, ..., num_classes - 1.
accuracy_top_k(int): The value of k when computing the Top K Accuracy metric for multiclass classification. An example is scored as correct if the model assigns one of the top k scores to the true label.
f_beta(float): The value of beta to use when calculating F score metrics for binary or multiclass classification. Also used if binary_classifier_model_selection_criteria is f_beta.
balance_multiclass_weights(bool): Whether to use class weights which give each class equal importance in the loss function. Only used when predictor_type is multiclass_classifier.
...: base class keyword argument values.
Method create_model()
Return a :class:'~sagemaker.amazon.LinearLearnerModel' referencing the latest s3 model data produced by this Estimator.
Usage
LinearLearner$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 LinearLearnerModel constructor.
Method .prepare_for_training()
Set hyperparameters needed for training. This method will also validate “source_dir“.
Usage
LinearLearner$.prepare_for_training( records, mini_batch_size = NULL, job_name = NULL )
Arguments
records(RecordSet) – The records to train this Estimator on.
mini_batch_size(int or None) – The size of each mini-batch to use when training. If None, a default value will be used.
job_name(str): Name of the training job to be created. If not specified, one is generated, using the base name given to the constructor if applicable.
Method clone()
The objects of this class are cloneable with this method.
Usage
LinearLearner$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
R Package Documentation
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