CfnDataset
- class aws_cdk.aws_forecast.CfnDataset(scope, id, *, dataset_name, dataset_type, domain, schema, data_frequency=None, encryption_config=None, tags=None)
Bases:
CfnResourceA CloudFormation
AWS::Forecast::Dataset.Creates an Amazon Forecast dataset. The information about the dataset that you provide helps Forecast understand how to consume the data for model training. This includes the following:
``DataFrequency`` - How frequently your historical time-series data is collected.
``Domain`` and ``DatasetType`` - Each dataset has an associated dataset domain and a type within the domain. Amazon Forecast provides a list of predefined domains and types within each domain. For each unique dataset domain and type within the domain, Amazon Forecast requires your data to include a minimum set of predefined fields.
``Schema`` - A schema specifies the fields in the dataset, including the field name and data type.
After creating a dataset, you import your training data into it and add the dataset to a dataset group. You use the dataset group to create a predictor. For more information, see Importing datasets .
To get a list of all your datasets, use the ListDatasets operation.
For example Forecast datasets, see the Amazon Forecast Sample GitHub repository . .. epigraph:
The ``Status`` of a dataset must be ``ACTIVE`` before you can import training data. Use the `DescribeDataset <https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDataset.html>`_ operation to get the status.
- CloudformationResource:
AWS::Forecast::Dataset
- Link:
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-forecast-dataset.html
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_forecast as forecast # encryption_config: Any # schema: Any cfn_dataset = forecast.CfnDataset(self, "MyCfnDataset", dataset_name="datasetName", dataset_type="datasetType", domain="domain", schema=schema, # the properties below are optional data_frequency="dataFrequency", encryption_config=encryption_config, tags=[forecast.CfnDataset.TagsItemsProperty( key="key", value="value" )] )
Create a new
AWS::Forecast::Dataset.- Parameters:
scope (
Construct) –scope in which this resource is defined.
id (
str) –scoped id of the resource.
dataset_name (
str) – The name of the dataset.dataset_type (
str) – The dataset type.domain (
str) – The domain associated with the dataset.schema (
Any) – The schema for the dataset. The schema attributes and their order must match the fields in your data. The datasetDomainandDatasetTypethat you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see Dataset Domains and Dataset Types .data_frequency (
Optional[str]) – The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets. Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, “1D” indicates every day and “15min” indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following: - Minute - 1-59 - Hour - 1-23 - Day - 1-6 - Week - 1-4 - Month - 1-11 - Year - 1 Thus, if you want every other week forecasts, specify “2W”. Or, if you want quarterly forecasts, you specify “3M”.encryption_config (
Optional[Any]) – A Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.tags (
Optional[Sequence[Union[TagsItemsProperty,Dict[str,Any]]]]) – An array of key-value pairs to apply to this resource. For more information, see Tag .
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_depends_on(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_metadata(key, value)
Add a value to the CloudFormation Resource Metadata.
- Parameters:
key (
str)value (
Any)
- See:
- Return type:
None
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
addPropertyOverrideor prefixpathwith “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
valueargument toaddOverridewill 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 intermdediate 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).- Parameters:
policy (
Optional[RemovalPolicy])apply_to_update_replace_policy (
Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: truedefault (
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 resoure, please consult that specific resource’s documentation.
- Return type:
None
- get_att(attribute_name)
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.- Return type:
- get_metadata(key)
Retrieve a value value from the CloudFormation Resource Metadata.
- Parameters:
key (
str)- See:
- Return type:
Any
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
- 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
- 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::Forecast::Dataset'
- attr_arn
The Amazon Resource Name (ARN) of the dataset.
- CloudformationAttribute:
Arn
- 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.
- data_frequency
The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, “1D” indicates every day and “15min” indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
Thus, if you want every other week forecasts, specify “2W”. Or, if you want quarterly forecasts, you specify “3M”.
- dataset_name
The name of the dataset.
- dataset_type
The dataset type.
- domain
The domain associated with the dataset.
- encryption_config
A Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
- 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.
- node
The construct tree node associated with this construct.
- 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
The schema for the dataset.
The schema attributes and their order must match the fields in your data. The dataset
DomainandDatasetTypethat you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see Dataset Domains and Dataset Types .
- 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
trueif a construct is a stack element (i.e. part of the synthesized cloudformation template).Uses duck-typing instead of
instanceofto 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(construct)
Check whether the given construct is a CfnResource.
- Parameters:
construct (
IConstruct)- Return type:
bool
- classmethod is_construct(x)
Return whether the given object is a Construct.
- Parameters:
x (
Any)- Return type:
bool
AttributesItemsProperty
- class CfnDataset.AttributesItemsProperty(*, attribute_name=None, attribute_type=None)
Bases:
object- Parameters:
attribute_name (
Optional[str]) –CfnDataset.AttributesItemsProperty.AttributeName.attribute_type (
Optional[str]) –CfnDataset.AttributesItemsProperty.AttributeType.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_forecast as forecast attributes_items_property = forecast.CfnDataset.AttributesItemsProperty( attribute_name="attributeName", attribute_type="attributeType" )
Attributes
- attribute_name
CfnDataset.AttributesItemsProperty.AttributeName.
- attribute_type
CfnDataset.AttributesItemsProperty.AttributeType.
EncryptionConfigProperty
- class CfnDataset.EncryptionConfigProperty(*, kms_key_arn=None, role_arn=None)
Bases:
objectAn AWS Key Management Service (KMS) key and an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
You can specify this optional object in the
CreateDatasetandCreatePredictorrequests.- Parameters:
kms_key_arn (
Optional[str]) – The Amazon Resource Name (ARN) of the KMS key.role_arn (
Optional[str]) – The ARN of the IAM role that Amazon Forecast can assume to access the AWS KMS key. Passing a role across AWS accounts is not allowed. If you pass a role that isn’t in your account, you get anInvalidInputExceptionerror.
- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_forecast as forecast encryption_config_property = forecast.CfnDataset.EncryptionConfigProperty( kms_key_arn="kmsKeyArn", role_arn="roleArn" )
Attributes
- kms_key_arn
The Amazon Resource Name (ARN) of the KMS key.
- role_arn
The ARN of the IAM role that Amazon Forecast can assume to access the AWS KMS key.
Passing a role across AWS accounts is not allowed. If you pass a role that isn’t in your account, you get an
InvalidInputExceptionerror.
SchemaProperty
- class CfnDataset.SchemaProperty(*, attributes=None)
Bases:
objectDefines the fields of a dataset.
- Parameters:
attributes (
Union[IResolvable,Sequence[Union[IResolvable,AttributesItemsProperty,Dict[str,Any]]],None]) – An array of attributes specifying the name and type of each field in a dataset.- Link:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_forecast as forecast schema_property = forecast.CfnDataset.SchemaProperty( attributes=[forecast.CfnDataset.AttributesItemsProperty( attribute_name="attributeName", attribute_type="attributeType" )] )
Attributes
- attributes
An array of attributes specifying the name and type of each field in a dataset.