CfnMLTransformPropsMixin

class aws_cdk.mixins_preview.aws_glue.mixins.CfnMLTransformPropsMixin(props, *, strategy=None)

Bases: Mixin

The AWS::Glue::MLTransform is an AWS Glue resource type that manages machine learning transforms.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-glue-mltransform.html
CloudformationResource:: AWS::Glue::MLTransform
Mixin:: true
ExampleMetadata:: fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview import mixins
from aws_cdk.mixins_preview.aws_glue import mixins as glue_mixins

# tags: Any

cfn_mLTransform_props_mixin = glue_mixins.CfnMLTransformPropsMixin(glue_mixins.CfnMLTransformMixinProps(
    description="description",
    glue_version="glueVersion",
    input_record_tables=glue_mixins.CfnMLTransformPropsMixin.InputRecordTablesProperty(
        glue_tables=[glue_mixins.CfnMLTransformPropsMixin.GlueTablesProperty(
            catalog_id="catalogId",
            connection_name="connectionName",
            database_name="databaseName",
            table_name="tableName"
        )]
    ),
    max_capacity=123,
    max_retries=123,
    name="name",
    number_of_workers=123,
    role="role",
    tags=tags,
    timeout=123,
    transform_encryption=glue_mixins.CfnMLTransformPropsMixin.TransformEncryptionProperty(
        ml_user_data_encryption=glue_mixins.CfnMLTransformPropsMixin.MLUserDataEncryptionProperty(
            kms_key_id="kmsKeyId",
            ml_user_data_encryption_mode="mlUserDataEncryptionMode"
        ),
        task_run_security_configuration_name="taskRunSecurityConfigurationName"
    ),
    transform_parameters=glue_mixins.CfnMLTransformPropsMixin.TransformParametersProperty(
        find_matches_parameters=glue_mixins.CfnMLTransformPropsMixin.FindMatchesParametersProperty(
            accuracy_cost_tradeoff=123,
            enforce_provided_labels=False,
            precision_recall_tradeoff=123,
            primary_key_column_name="primaryKeyColumnName"
        ),
        transform_type="transformType"
    ),
    worker_type="workerType"
),
    strategy=mixins.PropertyMergeStrategy.OVERRIDE
)

Create a mixin to apply properties to AWS::Glue::MLTransform.

Parameters:

props (Union[CfnMLTransformMixinProps, Dict[str, Any]]) – L1 properties to apply.
strategy (Optional[PropertyMergeStrategy]) – (experimental) Strategy for merging nested properties. Default: - PropertyMergeStrategy.MERGE

Methods

apply_to(construct)

Apply the mixin properties to the construct.

Parameters:: construct (IConstruct)
Return type:: IConstruct

supports(construct)

Check if this mixin supports the given construct.

Parameters:: construct (IConstruct)
Return type:: bool

Attributes

CFN_PROPERTY_KEYS = ['description', 'glueVersion', 'inputRecordTables', 'maxCapacity', 'maxRetries', 'name', 'numberOfWorkers', 'role', 'tags', 'timeout', 'transformEncryption', 'transformParameters', 'workerType']

Static Methods

classmethod is_mixin(x)

(experimental) Checks if x is a Mixin.

Parameters:: x (Any) – Any object.
Return type:: bool
Returns:: true if x is an object created from a class which extends Mixin.
Stability:: experimental

FindMatchesParametersProperty

class CfnMLTransformPropsMixin.FindMatchesParametersProperty(*, accuracy_cost_tradeoff=None, enforce_provided_labels=None, precision_recall_tradeoff=None, primary_key_column_name=None)

Bases: object

The parameters to configure the find matches transform.

Parameters:

accuracy_cost_tradeoff (Union[int, float, None]) – The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy. Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall. Cost measures how many compute resources, and thus money, are consumed to run the transform.
enforce_provided_labels (Union[bool, IResolvable, None]) – The value to switch on or off to force the output to match the provided labels from users. If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model. Note that setting this value to true may increase the conflation execution time.
precision_recall_tradeoff (Union[int, float, None]) – The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision. The precision metric indicates how often your model is correct when it predicts a match. The recall metric indicates that for an actual match, how often your model predicts the match.
primary_key_column_name (Optional[str]) – The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-findmatchesparameters.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_glue import mixins as glue_mixins

find_matches_parameters_property = glue_mixins.CfnMLTransformPropsMixin.FindMatchesParametersProperty(
    accuracy_cost_tradeoff=123,
    enforce_provided_labels=False,
    precision_recall_tradeoff=123,
    primary_key_column_name="primaryKeyColumnName"
)

Attributes

accuracy_cost_tradeoff

The value that is selected when tuning your transform for a balance between accuracy and cost.

A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.

Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.

Cost measures how many compute resources, and thus money, are consumed to run the transform.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-findmatchesparameters.html#cfn-glue-mltransform-findmatchesparameters-accuracycosttradeoff

enforce_provided_labels

The value to switch on or off to force the output to match the provided labels from users.

If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.

Note that setting this value to true may increase the conflation execution time.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-findmatchesparameters.html#cfn-glue-mltransform-findmatchesparameters-enforceprovidedlabels

precision_recall_tradeoff

The value selected when tuning your transform for a balance between precision and recall.

A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.

The precision metric indicates how often your model is correct when it predicts a match.

The recall metric indicates that for an actual match, how often your model predicts the match.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-findmatchesparameters.html#cfn-glue-mltransform-findmatchesparameters-precisionrecalltradeoff

primary_key_column_name

The name of a column that uniquely identifies rows in the source table.

Used to help identify matching records.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-findmatchesparameters.html#cfn-glue-mltransform-findmatchesparameters-primarykeycolumnname

GlueTablesProperty

class CfnMLTransformPropsMixin.GlueTablesProperty(*, catalog_id=None, connection_name=None, database_name=None, table_name=None)

Bases: object

The database and table in the AWS Glue Data Catalog that is used for input or output data.

Parameters:

catalog_id (Optional[str]) – A unique identifier for the AWS Glue Data Catalog .
connection_name (Optional[str]) – The name of the connection to the AWS Glue Data Catalog .
database_name (Optional[str]) – A database name in the AWS Glue Data Catalog .
table_name (Optional[str]) – A table name in the AWS Glue Data Catalog .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-gluetables.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_glue import mixins as glue_mixins

glue_tables_property = glue_mixins.CfnMLTransformPropsMixin.GlueTablesProperty(
    catalog_id="catalogId",
    connection_name="connectionName",
    database_name="databaseName",
    table_name="tableName"
)

Attributes

catalog_id

A unique identifier for the AWS Glue Data Catalog .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-gluetables.html#cfn-glue-mltransform-gluetables-catalogid

connection_name

The name of the connection to the AWS Glue Data Catalog .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-gluetables.html#cfn-glue-mltransform-gluetables-connectionname

database_name

A database name in the AWS Glue Data Catalog .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-gluetables.html#cfn-glue-mltransform-gluetables-databasename

table_name

A table name in the AWS Glue Data Catalog .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-gluetables.html#cfn-glue-mltransform-gluetables-tablename

InputRecordTablesProperty

class CfnMLTransformPropsMixin.InputRecordTablesProperty(*, glue_tables=None)

Bases: object

A list of AWS Glue table definitions used by the transform.

Parameters:: glue_tables (Union[IResolvable, Sequence[Union[IResolvable, GlueTablesProperty, Dict[str, Any]]], None]) – The database and table in the AWS Glue Data Catalog that is used for input or output data.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables.html
ExampleMetadata:: fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_glue import mixins as glue_mixins

input_record_tables_property = glue_mixins.CfnMLTransformPropsMixin.InputRecordTablesProperty(
    glue_tables=[glue_mixins.CfnMLTransformPropsMixin.GlueTablesProperty(
        catalog_id="catalogId",
        connection_name="connectionName",
        database_name="databaseName",
        table_name="tableName"
    )]
)

Attributes

glue_tables

The database and table in the AWS Glue Data Catalog that is used for input or output data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-inputrecordtables.html#cfn-glue-mltransform-inputrecordtables-gluetables

MLUserDataEncryptionProperty

class CfnMLTransformPropsMixin.MLUserDataEncryptionProperty(*, kms_key_id=None, ml_user_data_encryption_mode=None)

Bases: object

The encryption-at-rest settings of the transform that apply to accessing user data.

Parameters:

kms_key_id (Optional[str]) – The ID for the customer-provided KMS key.
ml_user_data_encryption_mode (Optional[str]) – The encryption mode applied to user data. Valid values are:. - DISABLED: encryption is disabled. - SSEKMS: use of server-side encryption with AWS Key Management Service (SSE-KMS) for user data stored in Amazon S3.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-mluserdataencryption.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_glue import mixins as glue_mixins

m_lUser_data_encryption_property = glue_mixins.CfnMLTransformPropsMixin.MLUserDataEncryptionProperty(
    kms_key_id="kmsKeyId",
    ml_user_data_encryption_mode="mlUserDataEncryptionMode"
)

Attributes

kms_key_id

The ID for the customer-provided KMS key.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-mluserdataencryption.html#cfn-glue-mltransform-mluserdataencryption-kmskeyid

ml_user_data_encryption_mode

.

DISABLED: encryption is disabled.
SSEKMS: use of server-side encryption with AWS Key Management Service (SSE-KMS) for user data stored in Amazon S3.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-mluserdataencryption.html#cfn-glue-mltransform-mluserdataencryption-mluserdataencryptionmode
Type:: The encryption mode applied to user data. Valid values are

TransformEncryptionProperty

class CfnMLTransformPropsMixin.TransformEncryptionProperty(*, ml_user_data_encryption=None, task_run_security_configuration_name=None)

Bases: object

The encryption-at-rest settings of the transform that apply to accessing user data.

Machine learning transforms can access user data encrypted in Amazon S3 using KMS.

Additionally, imported labels and trained transforms can now be encrypted using a customer provided KMS key.

Parameters:

ml_user_data_encryption (Union[IResolvable, MLUserDataEncryptionProperty, Dict[str, Any], None]) – The encryption-at-rest settings of the transform that apply to accessing user data.
task_run_security_configuration_name (Optional[str]) – The name of the security configuration.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_glue import mixins as glue_mixins

transform_encryption_property = glue_mixins.CfnMLTransformPropsMixin.TransformEncryptionProperty(
    ml_user_data_encryption=glue_mixins.CfnMLTransformPropsMixin.MLUserDataEncryptionProperty(
        kms_key_id="kmsKeyId",
        ml_user_data_encryption_mode="mlUserDataEncryptionMode"
    ),
    task_run_security_configuration_name="taskRunSecurityConfigurationName"
)

Attributes

ml_user_data_encryption

The encryption-at-rest settings of the transform that apply to accessing user data.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption.html#cfn-glue-mltransform-transformencryption-mluserdataencryption

task_run_security_configuration_name

The name of the security configuration.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformencryption.html#cfn-glue-mltransform-transformencryption-taskrunsecurityconfigurationname

TransformParametersProperty

class CfnMLTransformPropsMixin.TransformParametersProperty(*, find_matches_parameters=None, transform_type=None)

Bases: object

The algorithm-specific parameters that are associated with the machine learning transform.

Parameters:

find_matches_parameters (Union[IResolvable, FindMatchesParametersProperty, Dict[str, Any], None]) – The parameters for the find matches algorithm.
transform_type (Optional[str]) – The type of machine learning transform. FIND_MATCHES is the only option. For information about the types of machine learning transforms, see Working with machine learning transforms .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.mixins_preview.aws_glue import mixins as glue_mixins

transform_parameters_property = glue_mixins.CfnMLTransformPropsMixin.TransformParametersProperty(
    find_matches_parameters=glue_mixins.CfnMLTransformPropsMixin.FindMatchesParametersProperty(
        accuracy_cost_tradeoff=123,
        enforce_provided_labels=False,
        precision_recall_tradeoff=123,
        primary_key_column_name="primaryKeyColumnName"
    ),
    transform_type="transformType"
)

Attributes

find_matches_parameters

The parameters for the find matches algorithm.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters.html#cfn-glue-mltransform-transformparameters-findmatchesparameters

transform_type

The type of machine learning transform. FIND_MATCHES is the only option.

For information about the types of machine learning transforms, see Working with machine learning transforms .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-glue-mltransform-transformparameters.html#cfn-glue-mltransform-transformparameters-transformtype