class LinearLearner(AmazonAlgorithmEstimatorBase):
repo = 'linear-learner:1'
DEFAULT_MINI_BATCH_SIZE = 1000
binary_classifier_model_selection_criteria = hp(
'binary_classifier_model_selection_criteria',
isin('accuracy', 'f1', 'precision_at_target_recall',
'recall_at_target_precision', 'cross_entropy_loss'),
data_type=str)
target_recall = hp('target_recall', (gt(0), lt(1)), "A float in (0,1)",
float)
target_precision = hp('target_precision', (gt(0), lt(1)),
"A float in (0,1)", float)
positive_example_weight_mult = hp('positive_example_weight_mult', gt(0),
"A float greater than 0", float)
epochs = hp('epochs', gt(0), "An integer greater-than 0", int)
predictor_type = hp('predictor_type', isin('binary_classifier',
'regressor'),
'One of "binary_classifier" or "regressor"', str)
use_bias = hp('use_bias', (), "Either True or False", bool)
num_models = hp('num_models', gt(0), "An integer greater-than 0", int)
num_calibration_samples = hp('num_calibration_samples', gt(0),
"An integer greater-than 0", int)
init_method = hp('init_method', isin('uniform', 'normal'),
'One of "uniform" or "normal"', str)
init_scale = hp('init_scale', (gt(-1), lt(1)), 'A float in (-1, 1)', float)
init_sigma = hp('init_sigma', (gt(0), lt(1)), 'A float in (0, 1)', float)
init_bias = hp('init_bias', (), 'A number', float)
optimizer = hp('optimizer', isin('sgd', 'adam', 'auto'),
'One of "sgd", "adam" or "auto', str)
loss = hp('loss', isin('logistic', 'squared_loss', 'absolute_loss',
'auto'),
'"logistic", "squared_loss", "absolute_loss" or"auto"', str)
wd = hp('wd', (gt(0), lt(1)), 'A float in (0,1)', float)
l1 = hp('l1', (gt(0), lt(1)), 'A float in (0,1)', float)
momentum = hp('momentum', (gt(0), lt(1)), 'A float in (0,1)', float)
learning_rate = hp('learning_rate', (gt(0), lt(1)), 'A float in (0,1)',
float)
beta_1 = hp('beta_1', (gt(0), lt(1)), 'A float in (0,1)', float)
beta_2 = hp('beta_1', (gt(0), lt(1)), 'A float in (0,1)', float)
bias_lr_mult = hp('bias_lr_mult', gt(0), 'A float greater-than 0', float)
bias_wd_mult = hp('bias_wd_mult', gt(0), 'A float greater-than 0', float)
use_lr_scheduler = hp('use_lr_scheduler', (), 'A boolean', bool)
lr_scheduler_step = hp('lr_scheduler_step', gt(0),
'An integer greater-than 0', int)
lr_scheduler_factor = hp('lr_scheduler_factor', (gt(0), lt(1)),
'A float in (0,1)', float)
lr_scheduler_minimum_lr = hp('lr_scheduler_minimum_lr', gt(0),
'A float greater-than 0', float)
normalize_data = hp('normalize_data', (), 'A boolean', bool)
normalize_label = hp('normalize_label', (), 'A boolean', bool)
unbias_data = hp('unbias_data', (), 'A boolean', bool)
unbias_label = hp('unbias_label', (), 'A boolean', bool)
num_point_for_scalar = hp('num_point_for_scalar', gt(0),
'An integer greater-than 0', int)
def __init__(self,
role,
train_instance_count,
train_instance_type,
predictor_type='binary_classifier',
binary_classifier_model_selection_criteria=None,
target_recall=None,
target_precision=None,
positive_example_weight_mult=None,
epochs=None,
use_bias=None,
num_models=None,
num_calibration_samples=None,
init_method=None,
init_scale=None,
init_sigma=None,
init_bias=None,
optimizer=None,
loss=None,
wd=None,
l1=None,
momentum=None,
learning_rate=None,
beta_1=None,
beta_2=None,
bias_lr_mult=None,
bias_wd_mult=None,
use_lr_scheduler=None,
lr_scheduler_step=None,
lr_scheduler_factor=None,
lr_scheduler_minimum_lr=None,
normalize_data=None,
normalize_label=None,
unbias_data=None,
unbias_label=None,
num_point_for_scalar=None,
**kwargs):
"""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
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.
train_instance_count (int): Number of Amazon EC2 instances to use for training.
train_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 "regressor".
binary_classifier_model_selection_criteria (str): One of 'accuracy', 'f1', 'precision_at_target_recall',
'recall_at_target_precision', 'cross_entropy_loss'
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' or 'auto'
loss (str): One of 'logistic', 'squared_loss', 'absolute_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.
lr_scheduler_minimum_lr (float): 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.
ubias_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.
**kwargs: base class keyword argument values.
"""
super(LinearLearner, self).__init__(role, train_instance_count,
train_instance_type, **kwargs)
self.predictor_type = predictor_type
self.binary_classifier_model_selection_criteria = binary_classifier_model_selection_criteria
self.target_recall = target_recall
self.target_precision = target_precision
self.positive_example_weight_mult = positive_example_weight_mult
self.epochs = epochs
self.use_bias = use_bias
self.num_models = num_models
self.num_calibration_samples = num_calibration_samples
self.init_method = init_method
self.init_scale = init_scale
self.init_sigma = init_sigma
self.init_bias = init_bias
self.optimizer = optimizer
self.loss = loss
self.wd = wd
self.l1 = l1
self.momentum = momentum
self.learning_rate = learning_rate
self.beta_1 = beta_1
self.beta_2 = beta_2
self.bias_lr_mult = bias_lr_mult
self.bias_wd_mult = bias_wd_mult
self.use_lr_scheduler = use_lr_scheduler
self.lr_scheduler_step = lr_scheduler_step
self.lr_scheduler_factor = lr_scheduler_factor
self.lr_scheduler_minimum_lr = lr_scheduler_minimum_lr
self.normalize_data = normalize_data
self.normalize_label = normalize_label
self.unbias_data = unbias_data
self.ubias_label = unbias_label
self.num_point_for_scaler = num_point_for_scalar
def create_model(self):
"""Return a :class:`~sagemaker.amazon.kmeans.LinearLearnerModel` referencing the latest
s3 model data produced by this Estimator."""
return LinearLearnerModel(self, self.model_data, self.role,
self.sagemaker_session)
def fit(self, records, mini_batch_size=None, **kwargs):
# mini_batch_size can't be greater than number of records or training job fails
default_mini_batch_size = min(
self.DEFAULT_MINI_BATCH_SIZE,
max(1, int(records.num_records / self.train_instance_count)))
use_mini_batch_size = mini_batch_size or default_mini_batch_size
super(LinearLearner, self).fit(records, use_mini_batch_size, **kwargs)
class LinearLearner(AmazonAlgorithmEstimatorBase):
"""A supervised learning algorithms used for solving classification or regression problems.
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.
"""
repo_name = "linear-learner"
repo_version = 1
DEFAULT_MINI_BATCH_SIZE = 1000
binary_classifier_model_selection_criteria = hp(
"binary_classifier_model_selection_criteria",
isin(
"accuracy",
"f1",
"f_beta",
"precision_at_target_recall",
"recall_at_target_precision",
"cross_entropy_loss",
"loss_function",
),
data_type=str,
)
target_recall = hp("target_recall", (gt(0), lt(1)), "A float in (0,1)",
float)
target_precision = hp("target_precision", (gt(0), lt(1)),
"A float in (0,1)", float)
positive_example_weight_mult = hp(
"positive_example_weight_mult", (),
"A float greater than 0 or 'auto' or 'balanced'", str)
epochs = hp("epochs", gt(0), "An integer greater-than 0", int)
predictor_type = hp(
"predictor_type",
isin("binary_classifier", "regressor", "multiclass_classifier"),
'One of "binary_classifier" or "multiclass_classifier" or "regressor"',
str,
)
use_bias = hp("use_bias", (), "Either True or False", bool)
num_models = hp("num_models", gt(0), "An integer greater-than 0", int)
num_calibration_samples = hp("num_calibration_samples", gt(0),
"An integer greater-than 0", int)
init_method = hp("init_method", isin("uniform", "normal"),
'One of "uniform" or "normal"', str)
init_scale = hp("init_scale", gt(0), "A float greater-than 0", float)
init_sigma = hp("init_sigma", gt(0), "A float greater-than 0", float)
init_bias = hp("init_bias", (), "A number", float)
optimizer = hp(
"optimizer",
isin("sgd", "adam", "rmsprop", "auto"),
'One of "sgd", "adam", "rmsprop" or "auto',
str,
)
loss = hp(
"loss",
isin(
"logistic",
"squared_loss",
"absolute_loss",
"hinge_loss",
"eps_insensitive_squared_loss",
"eps_insensitive_absolute_loss",
"quantile_loss",
"huber_loss",
"softmax_loss",
"auto",
),
'"logistic", "squared_loss", "absolute_loss", "hinge_loss", "eps_insensitive_squared_loss",'
' "eps_insensitive_absolute_loss", "quantile_loss", "huber_loss", "softmax_loss" or "auto"',
str,
)
wd = hp("wd", ge(0), "A float greater-than or equal to 0", float)
l1 = hp("l1", ge(0), "A float greater-than or equal to 0", float)
momentum = hp("momentum", (ge(0), lt(1)), "A float in [0,1)", float)
learning_rate = hp("learning_rate", gt(0), "A float greater-than 0", float)
beta_1 = hp("beta_1", (ge(0), lt(1)), "A float in [0,1)", float)
beta_2 = hp("beta_2", (ge(0), lt(1)), "A float in [0,1)", float)
bias_lr_mult = hp("bias_lr_mult", gt(0), "A float greater-than 0", float)
bias_wd_mult = hp("bias_wd_mult", ge(0),
"A float greater-than or equal to 0", float)
use_lr_scheduler = hp("use_lr_scheduler", (), "A boolean", bool)
lr_scheduler_step = hp("lr_scheduler_step", gt(0),
"An integer greater-than 0", int)
lr_scheduler_factor = hp("lr_scheduler_factor", (gt(0), lt(1)),
"A float in (0,1)", float)
lr_scheduler_minimum_lr = hp("lr_scheduler_minimum_lr", gt(0),
"A float greater-than 0", float)
normalize_data = hp("normalize_data", (), "A boolean", bool)
normalize_label = hp("normalize_label", (), "A boolean", bool)
unbias_data = hp("unbias_data", (), "A boolean", bool)
unbias_label = hp("unbias_label", (), "A boolean", bool)
num_point_for_scaler = hp("num_point_for_scaler", gt(0),
"An integer greater-than 0", int)
margin = hp("margin", ge(0), "A float greater-than or equal to 0", float)
quantile = hp("quantile", (gt(0), lt(1)), "A float in (0,1)", float)
loss_insensitivity = hp("loss_insensitivity", gt(0),
"A float greater-than 0", float)
huber_delta = hp("huber_delta", ge(0),
"A float greater-than or equal to 0", float)
early_stopping_patience = hp("early_stopping_patience", gt(0),
"An integer greater-than 0", int)
early_stopping_tolerance = hp("early_stopping_tolerance", gt(0),
"A float greater-than 0", float)
num_classes = hp("num_classes", (gt(0), le(1000000)),
"An integer in [1,1000000]", int)
accuracy_top_k = hp("accuracy_top_k", (gt(0), le(1000000)),
"An integer in [1,1000000]", int)
f_beta = hp("f_beta", gt(0), "A float greater-than 0", float)
balance_multiclass_weights = hp("balance_multiclass_weights", (),
"A boolean", bool)
def __init__(self,
role,
instance_count=None,
instance_type=None,
predictor_type=None,
binary_classifier_model_selection_criteria=None,
target_recall=None,
target_precision=None,
positive_example_weight_mult=None,
epochs=None,
use_bias=None,
num_models=None,
num_calibration_samples=None,
init_method=None,
init_scale=None,
init_sigma=None,
init_bias=None,
optimizer=None,
loss=None,
wd=None,
l1=None,
momentum=None,
learning_rate=None,
beta_1=None,
beta_2=None,
bias_lr_mult=None,
bias_wd_mult=None,
use_lr_scheduler=None,
lr_scheduler_step=None,
lr_scheduler_factor=None,
lr_scheduler_minimum_lr=None,
normalize_data=None,
normalize_label=None,
unbias_data=None,
unbias_label=None,
num_point_for_scaler=None,
margin=None,
quantile=None,
loss_insensitivity=None,
huber_delta=None,
early_stopping_patience=None,
early_stopping_tolerance=None,
num_classes=None,
accuracy_top_k=None,
f_beta=None,
balance_multiclass_weights=None,
**kwargs):
"""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
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): 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.
**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(LinearLearner, self).__init__(role, instance_count,
instance_type, **kwargs)
self.predictor_type = predictor_type
self.binary_classifier_model_selection_criteria = binary_classifier_model_selection_criteria
self.target_recall = target_recall
self.target_precision = target_precision
self.positive_example_weight_mult = positive_example_weight_mult
self.epochs = epochs
self.use_bias = use_bias
self.num_models = num_models
self.num_calibration_samples = num_calibration_samples
self.init_method = init_method
self.init_scale = init_scale
self.init_sigma = init_sigma
self.init_bias = init_bias
self.optimizer = optimizer
self.loss = loss
self.wd = wd
self.l1 = l1
self.momentum = momentum
self.learning_rate = learning_rate
self.beta_1 = beta_1
self.beta_2 = beta_2
self.bias_lr_mult = bias_lr_mult
self.bias_wd_mult = bias_wd_mult
self.use_lr_scheduler = use_lr_scheduler
self.lr_scheduler_step = lr_scheduler_step
self.lr_scheduler_factor = lr_scheduler_factor
self.lr_scheduler_minimum_lr = lr_scheduler_minimum_lr
self.normalize_data = normalize_data
self.normalize_label = normalize_label
self.unbias_data = unbias_data
self.unbias_label = unbias_label
self.num_point_for_scaler = num_point_for_scaler
self.margin = margin
self.quantile = quantile
self.loss_insensitivity = loss_insensitivity
self.huber_delta = huber_delta
self.early_stopping_patience = early_stopping_patience
self.early_stopping_tolerance = early_stopping_tolerance
self.num_classes = num_classes
self.accuracy_top_k = accuracy_top_k
self.f_beta = f_beta
self.balance_multiclass_weights = balance_multiclass_weights
if self.predictor_type == "multiclass_classifier" and (
num_classes is None or int(num_classes) < 3):
raise ValueError(
"For predictor_type 'multiclass_classifier', 'num_classes' should be set to a "
"value greater than 2.")
def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
"""Return a :class:`~sagemaker.amazon.LinearLearnerModel`.
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 LinearLearnerModel constructor.
"""
return LinearLearnerModel(
self.model_data,
self.role,
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"""
num_records = None
if isinstance(records, list):
for record in records:
if record.channel == "train":
num_records = record.num_records
break
if num_records is None:
raise ValueError("Must provide train channel.")
else:
num_records = records.num_records
# mini_batch_size can't be greater than number of records or training job fails
default_mini_batch_size = min(
self.DEFAULT_MINI_BATCH_SIZE,
max(1, int(num_records / self.instance_count)))
mini_batch_size = mini_batch_size or default_mini_batch_size
super(LinearLearner,
self)._prepare_for_training(records,
mini_batch_size=mini_batch_size,
job_name=job_name)
