Class: Aws::SageMaker::Types::TabularJobConfig
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::TabularJobConfig
- Defined in:
- gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb
Overview
The collection of settings used by an AutoML job V2 for the tabular problem type.
Constant Summary collapse
- SENSITIVE =
[]
Instance Attribute Summary collapse
-
#candidate_generation_config ⇒ Types::CandidateGenerationConfig
The configuration information of how model candidates are generated.
-
#completion_criteria ⇒ Types::AutoMLJobCompletionCriteria
How long a job is allowed to run, or how many candidates a job is allowed to generate.
-
#feature_specification_s3_uri ⇒ String
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2.
-
#generate_candidate_definitions_only ⇒ Boolean
Generates possible candidates without training the models.
-
#mode ⇒ String
The method that Autopilot uses to train the data.
-
#problem_type ⇒ String
The type of supervised learning problem available for the model candidates of the AutoML job V2.
-
#sample_weight_attribute_name ⇒ String
If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
-
#target_attribute_name ⇒ String
The name of the target variable in supervised learning, usually represented by 'y'.
Instance Attribute Details
#candidate_generation_config ⇒ Types::CandidateGenerationConfig
The configuration information of how model candidates are generated.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#completion_criteria ⇒ Types::AutoMLJobCompletionCriteria
How long a job is allowed to run, or how many candidates a job is allowed to generate.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#feature_specification_s3_uri ⇒ String
A URL to the Amazon S3 data source containing selected features from
the input data source to run an Autopilot job V2. You can input
FeatureAttributeNames
(optional) in JSON format as shown below:
\{ "FeatureAttributeNames":["col1", "col2", ...] \}
.
You can also specify the data type of the feature (optional) in the format shown below:
\{ "FeatureDataTypes":\{"col1":"numeric", "col2":"categorical" ...
\} \}
In ensembling mode, Autopilot only supports the following data
types: numeric
, categorical
, text
, and datetime
. In HPO
mode, Autopilot can support numeric
, categorical
, text
,
datetime
, and sequence
.
If only FeatureDataTypes
is provided, the column keys (col1
,
col2
,..) should be a subset of the column names in the input data.
If both FeatureDataTypes
and FeatureAttributeNames
are provided,
then the column keys should be a subset of the column names provided
in FeatureAttributeNames
.
The key name FeatureAttributeNames
is fixed. The values listed in
["col1", "col2", ...]
are case sensitive and should be a list of
strings containing unique values that are a subset of the column
names in the input data. The list of columns provided must not
include the target column.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#generate_candidate_definitions_only ⇒ Boolean
Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#mode ⇒ String
The method that Autopilot uses to train the data. You can either
specify the mode manually or let Autopilot choose for you based on
the dataset size by selecting AUTO
. In AUTO
mode, Autopilot
chooses ENSEMBLING
for datasets smaller than 100 MB, and
HYPERPARAMETER_TUNING
for larger ones.
The ENSEMBLING
mode uses a multi-stack ensemble model to predict
classification and regression tasks directly from your dataset. This
machine learning mode combines several base models to produce an
optimal predictive model. It then uses a stacking ensemble method to
combine predictions from contributing members. A multi-stack
ensemble model can provide better performance over a single model by
combining the predictive capabilities of multiple models. See
Autopilot algorithm support for a list of algorithms supported
by ENSEMBLING
mode.
The HYPERPARAMETER_TUNING
(HPO) mode uses the best hyperparameters
to train the best version of a model. HPO automatically selects an
algorithm for the type of problem you want to solve. Then HPO finds
the best hyperparameters according to your objective metric. See
Autopilot algorithm support for a list of algorithms supported
by HYPERPARAMETER_TUNING
mode.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#problem_type ⇒ String
The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.
ProblemType
and provide the AutoMLJobObjective metric, or
none at all.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#sample_weight_attribute_name ⇒ String
If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#target_attribute_name ⇒ String
The name of the target variable in supervised learning, usually represented by 'y'.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 42123 class TabularJobConfig < Struct.new( :candidate_generation_config, :completion_criteria, :feature_specification_s3_uri, :mode, :generate_candidate_definitions_only, :problem_type, :target_attribute_name, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |