CfnDocumentClassifier

class aws_cdk.aws_comprehend.CfnDocumentClassifier(scope, id, *, data_access_role_arn, document_classifier_name, input_data_config, language_code, mode=None, model_kms_key_id=None, model_policy=None, output_data_config=None, tags=None, version_name=None, volume_kms_key_id=None, vpc_config=None)

Bases: CfnResource

This resource creates and trains a document classifier to categorize documents.

You provide a set of training documents that are labeled with the categories that you want to identify. After the classifier is trained you can use it to categorize a set of labeled documents into the categories. For more information, see Document Classification in the Comprehend Developer Guide.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-comprehend-documentclassifier.html
CloudformationResource:: AWS::Comprehend::DocumentClassifier
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_comprehend as comprehend

cfn_document_classifier = comprehend.CfnDocumentClassifier(self, "MyCfnDocumentClassifier",
    data_access_role_arn="dataAccessRoleArn",
    document_classifier_name="documentClassifierName",
    input_data_config=comprehend.CfnDocumentClassifier.DocumentClassifierInputDataConfigProperty(
        augmented_manifests=[comprehend.CfnDocumentClassifier.AugmentedManifestsListItemProperty(
            attribute_names=["attributeNames"],
            s3_uri="s3Uri",

            # the properties below are optional
            split="split"
        )],
        data_format="dataFormat",
        document_reader_config=comprehend.CfnDocumentClassifier.DocumentReaderConfigProperty(
            document_read_action="documentReadAction",

            # the properties below are optional
            document_read_mode="documentReadMode",
            feature_types=["featureTypes"]
        ),
        documents=comprehend.CfnDocumentClassifier.DocumentClassifierDocumentsProperty(
            s3_uri="s3Uri",

            # the properties below are optional
            test_s3_uri="testS3Uri"
        ),
        document_type="documentType",
        label_delimiter="labelDelimiter",
        s3_uri="s3Uri",
        test_s3_uri="testS3Uri"
    ),
    language_code="languageCode",

    # the properties below are optional
    mode="mode",
    model_kms_key_id="modelKmsKeyId",
    model_policy="modelPolicy",
    output_data_config=comprehend.CfnDocumentClassifier.DocumentClassifierOutputDataConfigProperty(
        kms_key_id="kmsKeyId",
        s3_uri="s3Uri"
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )],
    version_name="versionName",
    volume_kms_key_id="volumeKmsKeyId",
    vpc_config=comprehend.CfnDocumentClassifier.VpcConfigProperty(
        security_group_ids=["securityGroupIds"],
        subnets=["subnets"]
    )
)

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
data_access_role_arn (str) – The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data.
document_classifier_name (str) – The name of the document classifier.
input_data_config (Union[IResolvable, DocumentClassifierInputDataConfigProperty, Dict[str, Any]]) – Specifies the format and location of the input data for the job.
language_code (str) – The language of the input documents. You can specify any of the languages supported by Amazon Comprehend. All documents must be in the same language.
mode (Optional[str]) – Indicates the mode in which the classifier will be trained. The classifier can be trained in multi-class (single-label) mode or multi-label mode. Multi-class mode identifies a single class label for each document and multi-label mode identifies one or more class labels for each document. Multiple labels for an individual document are separated by a delimiter. The default delimiter between labels is a pipe (|).
model_kms_key_id (Optional[str]) – ID for the AWS KMS key that Amazon Comprehend uses to encrypt trained custom models. The ModelKmsKeyId can be either of the following formats: - KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" - Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
model_policy (Optional[str]) – The resource-based policy to attach to your custom document classifier model. You can use this policy to allow another AWS account to import your custom model. Provide your policy as a JSON body that you enter as a UTF-8 encoded string without line breaks. To provide valid JSON, enclose the attribute names and values in double quotes. If the JSON body is also enclosed in double quotes, then you must escape the double quotes that are inside the policy: "{\"attribute\": \"value\", \"attribute\": [\"value\"]}" To avoid escaping quotes, you can use single quotes to enclose the policy and double quotes to enclose the JSON names and values: '{"attribute": "value", "attribute": ["value"]}'
output_data_config (Union[IResolvable, DocumentClassifierOutputDataConfigProperty, Dict[str, Any], None]) – Provides output results configuration parameters for custom classifier jobs.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – Tags to associate with the document classifier. A tag is a key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with “Sales” as the key might be added to a resource to indicate its use by the sales department.
version_name (Optional[str]) – The version name given to the newly created classifier. Version names can have a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The version name must be unique among all models with the same classifier name in the AWS account / AWS Region .
volume_kms_key_id (Optional[str]) – ID for the AWS Key Management Service (KMS) key that Amazon Comprehend uses to encrypt data on the storage volume attached to the ML compute instance(s) that process the analysis job. The VolumeKmsKeyId can be either of the following formats: - KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" - Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
vpc_config (Union[IResolvable, VpcConfigProperty, Dict[str, Any], None]) – Configuration parameters for a private Virtual Private Cloud (VPC) containing the resources you are using for your custom classifier. For more information, see Amazon VPC .

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::Comprehend::DocumentClassifier'

attr_arn

The Amazon Resource Name (ARN) of the document classifier.

CloudformationAttribute:: Arn

cdk_tag_manager: Tag Manager which manages the tags for this resource.

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_access_role_arn: The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data.

document_classifier_name: The name of the document classifier.

input_data_config: Specifies the format and location of the input data for the job.

language_code: The language of the input documents.

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.

mode: Indicates the mode in which the classifier will be trained.

model_kms_key_id: ID for the AWS KMS key that Amazon Comprehend uses to encrypt trained custom models.

model_policy: The resource-based policy to attach to your custom document classifier model.

node: The tree node.

output_data_config: Provides output results configuration parameters for custom classifier jobs.

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 }).

stack

The stack in which this element is defined.

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

tags: Tags to associate with the document classifier.

version_name: The version name given to the newly created classifier.

volume_kms_key_id: ID for the AWS Key Management Service (KMS) key that Amazon Comprehend uses to encrypt data on the storage volume attached to the ML compute instance(s) that process the analysis job.

vpc_config: Configuration parameters for a private Virtual Private Cloud (VPC) containing the resources you are using for your custom classifier.

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.

AugmentedManifestsListItemProperty

class CfnDocumentClassifier.AugmentedManifestsListItemProperty(*, attribute_names, s3_uri, split=None)

Bases: object

An augmented manifest file that provides training data for your custom model.

An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.

Parameters:

attribute_names (Sequence[str]) – The JSON attribute that contains the annotations for your training documents. The number of attribute names that you specify depends on whether your augmented manifest file is the output of a single labeling job or a chained labeling job. If your file is the output of a single labeling job, specify the LabelAttributeName key that was used when the job was created in Ground Truth. If your file is the output of a chained labeling job, specify the LabelAttributeName key for one or more jobs in the chain. Each LabelAttributeName key provides the annotations from an individual job.
s3_uri (str) – The Amazon S3 location of the augmented manifest file.
split (Optional[str]) – The purpose of the data you’ve provided in the augmented manifest. You can either train or test this data. If you don’t specify, the default is train. TRAIN - all of the documents in the manifest will be used for training. If no test documents are provided, Amazon Comprehend will automatically reserve a portion of the training documents for testing. TEST - all of the documents in the manifest will be used for testing.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.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_comprehend as comprehend

augmented_manifests_list_item_property = comprehend.CfnDocumentClassifier.AugmentedManifestsListItemProperty(
    attribute_names=["attributeNames"],
    s3_uri="s3Uri",

    # the properties below are optional
    split="split"
)

Attributes

attribute_names

The JSON attribute that contains the annotations for your training documents.

The number of attribute names that you specify depends on whether your augmented manifest file is the output of a single labeling job or a chained labeling job.

If your file is the output of a single labeling job, specify the LabelAttributeName key that was used when the job was created in Ground Truth.

If your file is the output of a chained labeling job, specify the LabelAttributeName key for one or more jobs in the chain. Each LabelAttributeName key provides the annotations from an individual job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.html#cfn-comprehend-documentclassifier-augmentedmanifestslistitem-attributenames

s3_uri

The Amazon S3 location of the augmented manifest file.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.html#cfn-comprehend-documentclassifier-augmentedmanifestslistitem-s3uri

split

The purpose of the data you’ve provided in the augmented manifest.

You can either train or test this data. If you don’t specify, the default is train.

TRAIN - all of the documents in the manifest will be used for training. If no test documents are provided, Amazon Comprehend will automatically reserve a portion of the training documents for testing.

TEST - all of the documents in the manifest will be used for testing.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-augmentedmanifestslistitem.html#cfn-comprehend-documentclassifier-augmentedmanifestslistitem-split

DocumentClassifierDocumentsProperty

class CfnDocumentClassifier.DocumentClassifierDocumentsProperty(*, s3_uri, test_s3_uri=None)

Bases: object

The location of the training documents.

This parameter is required in a request to create a semi-structured document classification model.

Parameters:

s3_uri (str) – The S3 URI location of the training documents specified in the S3Uri CSV file.
test_s3_uri (Optional[str]) – The S3 URI location of the test documents included in the TestS3Uri CSV file. This field is not required if you do not specify a test CSV file.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierdocuments.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_comprehend as comprehend

document_classifier_documents_property = comprehend.CfnDocumentClassifier.DocumentClassifierDocumentsProperty(
    s3_uri="s3Uri",

    # the properties below are optional
    test_s3_uri="testS3Uri"
)

Attributes

s3_uri

The S3 URI location of the training documents specified in the S3Uri CSV file.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierdocuments.html#cfn-comprehend-documentclassifier-documentclassifierdocuments-s3uri

test_s3_uri

The S3 URI location of the test documents included in the TestS3Uri CSV file.

This field is not required if you do not specify a test CSV file.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierdocuments.html#cfn-comprehend-documentclassifier-documentclassifierdocuments-tests3uri

DocumentClassifierInputDataConfigProperty

class CfnDocumentClassifier.DocumentClassifierInputDataConfigProperty(*, augmented_manifests=None, data_format=None, document_reader_config=None, documents=None, document_type=None, label_delimiter=None, s3_uri=None, test_s3_uri=None)

Bases: object

The input properties for training a document classifier.

For more information on how the input file is formatted, see Preparing training data in the Comprehend Developer Guide.

Parameters:

augmented_manifests (Union[IResolvable, Sequence[Union[IResolvable, AugmentedManifestsListItemProperty, Dict[str, Any]]], None]) – A list of augmented manifest files that provide training data for your custom model. An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth. This parameter is required if you set DataFormat to AUGMENTED_MANIFEST .
data_format (Optional[str]) – The format of your training data:. - COMPREHEND_CSV : A two-column CSV file, where labels are provided in the first column, and documents are provided in the second. If you use this value, you must provide the S3Uri parameter in your request. - AUGMENTED_MANIFEST : A labeled dataset that is produced by Amazon SageMaker Ground Truth. This file is in JSON lines format. Each line is a complete JSON object that contains a training document and its associated labels. If you use this value, you must provide the AugmentedManifests parameter in your request. If you don’t specify a value, Amazon Comprehend uses COMPREHEND_CSV as the default.
document_reader_config (Union[IResolvable, DocumentReaderConfigProperty, Dict[str, Any], None]) –
documents (Union[IResolvable, DocumentClassifierDocumentsProperty, Dict[str, Any], None]) – The S3 location of the training documents. This parameter is required in a request to create a native document model.
document_type (Optional[str]) – The type of input documents for training the model. Provide plain-text documents to create a plain-text model, and provide semi-structured documents to create a native document model.
label_delimiter (Optional[str]) – Indicates the delimiter used to separate each label for training a multi-label classifier. The default delimiter between labels is a pipe (|). You can use a different character as a delimiter (if it’s an allowed character) by specifying it under Delimiter for labels. If the training documents use a delimiter other than the default or the delimiter you specify, the labels on that line will be combined to make a single unique label, such as LABELLABELLABEL.
s3_uri (Optional[str]) – The Amazon S3 URI for the input data. The S3 bucket must be in the same Region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of input files. For example, if you use the URI S3://bucketName/prefix , if the prefix is a single file, Amazon Comprehend uses that file as input. If more than one file begins with the prefix, Amazon Comprehend uses all of them as input. This parameter is required if you set DataFormat to COMPREHEND_CSV .
test_s3_uri (Optional[str]) – This specifies the Amazon S3 location that contains the test annotations for the document classifier. The URI must be in the same AWS Region as the API endpoint that you are calling.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.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_comprehend as comprehend

document_classifier_input_data_config_property = comprehend.CfnDocumentClassifier.DocumentClassifierInputDataConfigProperty(
    augmented_manifests=[comprehend.CfnDocumentClassifier.AugmentedManifestsListItemProperty(
        attribute_names=["attributeNames"],
        s3_uri="s3Uri",

        # the properties below are optional
        split="split"
    )],
    data_format="dataFormat",
    document_reader_config=comprehend.CfnDocumentClassifier.DocumentReaderConfigProperty(
        document_read_action="documentReadAction",

        # the properties below are optional
        document_read_mode="documentReadMode",
        feature_types=["featureTypes"]
    ),
    documents=comprehend.CfnDocumentClassifier.DocumentClassifierDocumentsProperty(
        s3_uri="s3Uri",

        # the properties below are optional
        test_s3_uri="testS3Uri"
    ),
    document_type="documentType",
    label_delimiter="labelDelimiter",
    s3_uri="s3Uri",
    test_s3_uri="testS3Uri"
)

Attributes

augmented_manifests

A list of augmented manifest files that provide training data for your custom model.

An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.

This parameter is required if you set DataFormat to AUGMENTED_MANIFEST .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-augmentedmanifests

data_format

.

COMPREHEND_CSV : A two-column CSV file, where labels are provided in the first column, and documents are provided in the second. If you use this value, you must provide the S3Uri parameter in your request.
AUGMENTED_MANIFEST : A labeled dataset that is produced by Amazon SageMaker Ground Truth. This file is in JSON lines format. Each line is a complete JSON object that contains a training document and its associated labels.

If you use this value, you must provide the AugmentedManifests parameter in your request.

If you don’t specify a value, Amazon Comprehend uses COMPREHEND_CSV as the default.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-dataformat
Type:: The format of your training data

document_reader_config

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-documentreaderconfig

Type:: see

document_type

The type of input documents for training the model.

Provide plain-text documents to create a plain-text model, and provide semi-structured documents to create a native document model.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-documenttype

documents

The S3 location of the training documents.

This parameter is required in a request to create a native document model.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-documents

label_delimiter

Indicates the delimiter used to separate each label for training a multi-label classifier.

The default delimiter between labels is a pipe (|). You can use a different character as a delimiter (if it’s an allowed character) by specifying it under Delimiter for labels. If the training documents use a delimiter other than the default or the delimiter you specify, the labels on that line will be combined to make a single unique label, such as LABELLABELLABEL.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-labeldelimiter

s3_uri

The Amazon S3 URI for the input data.

The S3 bucket must be in the same Region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of input files.

For example, if you use the URI S3://bucketName/prefix , if the prefix is a single file, Amazon Comprehend uses that file as input. If more than one file begins with the prefix, Amazon Comprehend uses all of them as input.

This parameter is required if you set DataFormat to COMPREHEND_CSV .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-s3uri

test_s3_uri

This specifies the Amazon S3 location that contains the test annotations for the document classifier.

The URI must be in the same AWS Region as the API endpoint that you are calling.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifierinputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifierinputdataconfig-tests3uri

DocumentClassifierOutputDataConfigProperty

class CfnDocumentClassifier.DocumentClassifierOutputDataConfigProperty(*, kms_key_id=None, s3_uri=None)

Bases: object

Provide the location for output data from a custom classifier job.

This field is mandatory if you are training a native document model.

Parameters:

kms_key_id (Optional[str]) – ID for the AWS Key Management Service (KMS) key that Amazon Comprehend uses to encrypt the output results from an analysis job. The KmsKeyId can be one of the following formats: - KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" - Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" - KMS Key Alias: "alias/ExampleAlias" - ARN of a KMS Key Alias: "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
s3_uri (Optional[str]) – When you use the OutputDataConfig object while creating a custom classifier, you specify the Amazon S3 location where you want to write the confusion matrix and other output files. The URI must be in the same Region as the API endpoint that you are calling. The location is used as the prefix for the actual location of this output file. When the custom classifier job is finished, the service creates the output file in a directory specific to the job. The S3Uri field contains the location of the output file, called output.tar.gz . It is a compressed archive that contains the confusion matrix.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifieroutputdataconfig.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_comprehend as comprehend

document_classifier_output_data_config_property = comprehend.CfnDocumentClassifier.DocumentClassifierOutputDataConfigProperty(
    kms_key_id="kmsKeyId",
    s3_uri="s3Uri"
)

Attributes

kms_key_id

ID for the AWS Key Management Service (KMS) key that Amazon Comprehend uses to encrypt the output results from an analysis job.

The KmsKeyId can be one of the following formats:

KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS Key Alias: "alias/ExampleAlias"
ARN of a KMS Key Alias: "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifieroutputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifieroutputdataconfig-kmskeyid

s3_uri

When you use the OutputDataConfig object while creating a custom classifier, you specify the Amazon S3 location where you want to write the confusion matrix and other output files.

The URI must be in the same Region as the API endpoint that you are calling. The location is used as the prefix for the actual location of this output file.

When the custom classifier job is finished, the service creates the output file in a directory specific to the job. The S3Uri field contains the location of the output file, called output.tar.gz . It is a compressed archive that contains the confusion matrix.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentclassifieroutputdataconfig.html#cfn-comprehend-documentclassifier-documentclassifieroutputdataconfig-s3uri

DocumentReaderConfigProperty

class CfnDocumentClassifier.DocumentReaderConfigProperty(*, document_read_action, document_read_mode=None, feature_types=None)

Bases: object

Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.

By default, Amazon Comprehend performs the following actions to extract text from files, based on the input file type:

Word files - Amazon Comprehend parser extracts the text.
Digital PDF files - Amazon Comprehend parser extracts the text.
Image files and scanned PDF files - Amazon Comprehend uses the Amazon Textract DetectDocumentText API to extract the text.

DocumentReaderConfig does not apply to plain text files or Word files.

For image files and PDF documents, you can override these default actions using the fields listed below. For more information, see Setting text extraction options in the Comprehend Developer Guide.

Parameters:

document_read_action (str) – This field defines the Amazon Textract API operation that Amazon Comprehend uses to extract text from PDF files and image files. Enter one of the following values: - TEXTRACT_DETECT_DOCUMENT_TEXT - The Amazon Comprehend service uses the DetectDocumentText API operation. - TEXTRACT_ANALYZE_DOCUMENT - The Amazon Comprehend service uses the AnalyzeDocument API operation.
document_read_mode (Optional[str]) – Determines the text extraction actions for PDF files. Enter one of the following values:. - SERVICE_DEFAULT - use the Amazon Comprehend service defaults for PDF files. - FORCE_DOCUMENT_READ_ACTION - Amazon Comprehend uses the Textract API specified by DocumentReadAction for all PDF files, including digital PDF files.
feature_types (Optional[Sequence[str]]) – Specifies the type of Amazon Textract features to apply. If you chose TEXTRACT_ANALYZE_DOCUMENT as the read action, you must specify one or both of the following values: - TABLES - Returns additional information about any tables that are detected in the input document. - FORMS - Returns additional information about any forms that are detected in the input document.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.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_comprehend as comprehend

document_reader_config_property = comprehend.CfnDocumentClassifier.DocumentReaderConfigProperty(
    document_read_action="documentReadAction",

    # the properties below are optional
    document_read_mode="documentReadMode",
    feature_types=["featureTypes"]
)

Attributes

document_read_action

This field defines the Amazon Textract API operation that Amazon Comprehend uses to extract text from PDF files and image files.

Enter one of the following values:

TEXTRACT_DETECT_DOCUMENT_TEXT - The Amazon Comprehend service uses the DetectDocumentText API operation.
TEXTRACT_ANALYZE_DOCUMENT - The Amazon Comprehend service uses the AnalyzeDocument API operation.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.html#cfn-comprehend-documentclassifier-documentreaderconfig-documentreadaction

document_read_mode

.

SERVICE_DEFAULT - use the Amazon Comprehend service defaults for PDF files.
FORCE_DOCUMENT_READ_ACTION - Amazon Comprehend uses the Textract API specified by DocumentReadAction for all PDF files, including digital PDF files.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.html#cfn-comprehend-documentclassifier-documentreaderconfig-documentreadmode
Type:: Determines the text extraction actions for PDF files. Enter one of the following values

feature_types

Specifies the type of Amazon Textract features to apply.

If you chose TEXTRACT_ANALYZE_DOCUMENT as the read action, you must specify one or both of the following values:

TABLES - Returns additional information about any tables that are detected in the input document.
FORMS - Returns additional information about any forms that are detected in the input document.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-documentreaderconfig.html#cfn-comprehend-documentclassifier-documentreaderconfig-featuretypes

VpcConfigProperty

class CfnDocumentClassifier.VpcConfigProperty(*, security_group_ids, subnets)

Bases: object

Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for the job.

For more information, see Amazon VPC .

Parameters:

security_group_ids (Sequence[str]) – The ID number for a security group on an instance of your private VPC. Security groups on your VPC function serve as a virtual firewall to control inbound and outbound traffic and provides security for the resources that you’ll be accessing on the VPC. This ID number is preceded by “sg-”, for instance: “sg-03b388029b0a285ea”. For more information, see Security Groups for your VPC .
subnets (Sequence[str]) – The ID for each subnet being used in your private VPC. This subnet is a subset of the a range of IPv4 addresses used by the VPC and is specific to a given availability zone in the VPC’s Region. This ID number is preceded by “subnet-”, for instance: “subnet-04ccf456919e69055”. For more information, see VPCs and Subnets .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-vpcconfig.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_comprehend as comprehend

vpc_config_property = comprehend.CfnDocumentClassifier.VpcConfigProperty(
    security_group_ids=["securityGroupIds"],
    subnets=["subnets"]
)

Attributes

security_group_ids

The ID number for a security group on an instance of your private VPC.

Security groups on your VPC function serve as a virtual firewall to control inbound and outbound traffic and provides security for the resources that you’ll be accessing on the VPC. This ID number is preceded by “sg-”, for instance: “sg-03b388029b0a285ea”. For more information, see Security Groups for your VPC .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-vpcconfig.html#cfn-comprehend-documentclassifier-vpcconfig-securitygroupids

subnets

The ID for each subnet being used in your private VPC.

This subnet is a subset of the a range of IPv4 addresses used by the VPC and is specific to a given availability zone in the VPC’s Region. This ID number is preceded by “subnet-”, for instance: “subnet-04ccf456919e69055”. For more information, see VPCs and Subnets .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-comprehend-documentclassifier-vpcconfig.html#cfn-comprehend-documentclassifier-vpcconfig-subnets