CfnDataset

class aws_cdk.aws_personalize.CfnDataset(scope, id, *, dataset_group_arn, dataset_type, name, schema_arn, dataset_import_job=None)

Bases: CfnResource

Creates an empty dataset and adds it to the specified dataset group.

Use CreateDatasetImportJob to import your training data to a dataset.

There are 5 types of datasets:

Item interactions
Items
Users
Action interactions (you can’t use CloudFormation to create an Action interactions dataset)
Actions (you can’t use CloudFormation to create an Actions dataset)

Each dataset type has an associated schema with required field types. Only the Item interactions dataset is required in order to train a model (also referred to as creating a solution).

A dataset can be in one of the following states:

CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS

To get the status of the dataset, call DescribeDataset .

Related APIs - CreateDatasetGroup

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-personalize-dataset.html
CloudformationResource:: AWS::Personalize::Dataset
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 import aws_personalize as personalize

# data_source: Any

cfn_dataset = personalize.CfnDataset(self, "MyCfnDataset",
    dataset_group_arn="datasetGroupArn",
    dataset_type="datasetType",
    name="name",
    schema_arn="schemaArn",

    # the properties below are optional
    dataset_import_job=personalize.CfnDataset.DatasetImportJobProperty(
        dataset_arn="datasetArn",
        dataset_import_job_arn="datasetImportJobArn",
        data_source=data_source,
        job_name="jobName",
        role_arn="roleArn"
    )
)

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
dataset_group_arn (str) – The Amazon Resource Name (ARN) of the dataset group.
dataset_type (str) – One of the following values:. - Interactions - Items - Users .. epigraph:: You can’t use CloudFormation to create an Action Interactions or Actions dataset.
name (str) – The name of the dataset.
schema_arn (str) – The ARN of the associated schema.
dataset_import_job (Union[IResolvable, DatasetImportJobProperty, Dict[str, Any], None]) – Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset. If you specify a dataset import job as part of a dataset, all dataset import job fields are required.

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

Parameters:: path (str) – The path of the value to delete.
Return type:: None

add_dependency(target)

Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

Parameters:

target (CfnResource) –

Return type:

None

add_depends_on(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

Parameters:

target (CfnResource) –

Deprecated:

use addDependency

Stability:

deprecated

Return type:

None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

key (str) –
value (Any) –

See:

Return type:

None

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

To add a property override, either use addPropertyOverride or prefix path with “Properties.” (i.e. Properties.TopicName).

If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.

To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.

For example:

cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"])
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")

would add the overrides Example:

"Properties": {
  "GlobalSecondaryIndexes": [
    {
      "Projection": {
        "NonKeyAttributes": [ "myattribute" ]
        ...
      }
      ...
    },
    {
      "ProjectionType": "INCLUDE"
      ...
    },
  ]
  ...
}

The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.

Parameters:

path (str) –
- The path of the property, you can use dot notation to override values in complex types. Any intermediate keys will be created as needed.
value (Any) –
- The value. Could be primitive or complex.

Return type:

None

add_property_deletion_override(property_path)

Adds an override that deletes the value of a property from the resource definition.

Parameters:: property_path (str) – The path to the property.
Return type:: None

add_property_override(property_path, value)

Adds an override to a resource property.

Syntactic sugar for addOverride("Properties.<...>", value).

Parameters:

property_path (str) – The path of the property.
value (Any) – The value.

Return type:

None

apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)

Sets the deletion policy of the resource based on the removal policy specified.

The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT). A list of resources that support this policy can be found in the following link:

Parameters:

policy (Optional[RemovalPolicy]) –
apply_to_update_replace_policy (Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: true
default (Optional[RemovalPolicy]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resource, please consult that specific resource’s documentation.

See:

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-deletionpolicy.html#aws-attribute-deletionpolicy-options

Return type:

None

get_att(attribute_name, type_hint=None)

Returns a token for an runtime attribute of this resource.

Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.

Parameters:

attribute_name (str) – The name of the attribute.
type_hint (Optional[ResolutionTypeHint]) –

Return type:

Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

Parameters:

key (str) –

See:

Return type:

Any

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:: inspector (TreeInspector) – tree inspector to collect and process attributes.
Return type:: None

obtain_dependencies()

Retrieves an array of resources this resource depends on.

This assembles dependencies on resources across stacks (including nested stacks) automatically.

Return type:: List[Union[Stack, CfnResource]]

obtain_resource_dependencies()

Get a shallow copy of dependencies between this resource and other resources in the same stack.

Return type:: List[CfnResource]

override_logical_id(new_logical_id)

Overrides the auto-generated logical ID with a specific ID.

Parameters:: new_logical_id (str) – The new logical ID to use for this stack element.
Return type:: None

remove_dependency(target)

Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:

target (CfnResource) –

Return type:

None

replace_dependency(target, new_target)

Replaces one dependency with another.

Parameters:

target (CfnResource) – The dependency to replace.
new_target (CfnResource) – The new dependency to add.

Return type:

None

to_string()

Returns a string representation of this construct.

Return type:: str
Returns:: a string representation of this resource

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::Personalize::Dataset'

attr_dataset_arn

The Amazon Resource Name (ARN) of the dataset.

CloudformationAttribute:: DatasetArn

cfn_options: Options for this resource, such as condition, update policy etc.

cfn_resource_type: AWS resource type.

creation_stack

return:

the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.

dataset_group_arn: The Amazon Resource Name (ARN) of the dataset group.

dataset_import_job: Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset.

dataset_type

.

Type:: One of the following values

logical_id

The logical ID for this CloudFormation stack element.

The logical ID of the element is calculated from the path of the resource node in the construct tree.

To override this value, use overrideLogicalId(newLogicalId).

Returns:

the logical ID as a stringified token. This value will only get resolved during synthesis.

name: The name of the dataset.

node: The tree node.

ref

Return a string that will be resolved to a CloudFormation { Ref } for this element.

If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).

schema_arn: The ARN of the associated schema.

stack

The stack in which this element is defined.

CfnElements must be defined within a stack scope (directly or indirectly).

Static Methods

classmethod is_cfn_element(x)

Returns true if a construct is a stack element (i.e. part of the synthesized cloudformation template).

Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.

Parameters:

x (Any) –

Return type:

bool

Returns:

The construct as a stack element or undefined if it is not a stack element.

classmethod is_cfn_resource(x)

Check whether the given object is a CfnResource.

Parameters:

x (Any) –

Return type:

bool

classmethod is_construct(x)

Checks if x is a construct.

Use this method instead of instanceof to properly detect Construct instances, even when the construct library is symlinked.

Explanation: in JavaScript, multiple copies of the constructs library on disk are seen as independent, completely different libraries. As a consequence, the class Construct in each copy of the constructs library is seen as a different class, and an instance of one class will not test as instanceof the other class. npm install will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the constructs library can be accidentally installed, and instanceof will behave unpredictably. It is safest to avoid using instanceof, and using this type-testing method instead.

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

DataSourceProperty

class CfnDataset.DataSourceProperty(*, data_location=None)

Bases: object

Describes the data source that contains the data to upload to a dataset, or the list of records to delete from Amazon Personalize.

Parameters:: data_location (Optional[str]) – For dataset import jobs, the path to the Amazon S3 bucket where the data that you want to upload to your dataset is stored. For data deletion jobs, the path to the Amazon S3 bucket that stores the list of records to delete. For example: s3://bucket-name/folder-name/fileName.csv If your CSV files are in a folder in your Amazon S3 bucket and you want your import job or data deletion job to consider multiple files, you can specify the path to the folder. With a data deletion job, Amazon Personalize uses all files in the folder and any sub folder. Use the following syntax with a / after the folder name: s3://bucket-name/folder-name/
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-dataset-datasource.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 import aws_personalize as personalize

data_source_property = personalize.CfnDataset.DataSourceProperty(
    data_location="dataLocation"
)

Attributes

data_location

For dataset import jobs, the path to the Amazon S3 bucket where the data that you want to upload to your dataset is stored.

For data deletion jobs, the path to the Amazon S3 bucket that stores the list of records to delete.

For example:

s3://bucket-name/folder-name/fileName.csv

If your CSV files are in a folder in your Amazon S3 bucket and you want your import job or data deletion job to consider multiple files, you can specify the path to the folder. With a data deletion job, Amazon Personalize uses all files in the folder and any sub folder. Use the following syntax with a / after the folder name:

s3://bucket-name/folder-name/

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-dataset-datasource.html#cfn-personalize-dataset-datasource-datalocation

DatasetImportJobProperty

class CfnDataset.DatasetImportJobProperty(*, dataset_arn=None, dataset_import_job_arn=None, data_source=None, job_name=None, role_arn=None)

Bases: object

Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset.

A dataset import job can be in one of the following states:

CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

If you specify a dataset import job as part of a dataset, all dataset import job fields are required.

Parameters:

dataset_arn (Optional[str]) – The Amazon Resource Name (ARN) of the dataset that receives the imported data.
dataset_import_job_arn (Optional[str]) – The ARN of the dataset import job.
data_source (Any) – The Amazon S3 bucket that contains the training data to import.
job_name (Optional[str]) – The name of the import job.
role_arn (Optional[str]) – The ARN of the IAM role that has permissions to read from the Amazon S3 data source.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-dataset-datasetimportjob.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 import aws_personalize as personalize

# data_source: Any

dataset_import_job_property = personalize.CfnDataset.DatasetImportJobProperty(
    dataset_arn="datasetArn",
    dataset_import_job_arn="datasetImportJobArn",
    data_source=data_source,
    job_name="jobName",
    role_arn="roleArn"
)