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train | SamApiProvider._merge_apis | Quite often, an API is defined both in Implicit and Explicit API definitions. In such cases, Implicit API
definition wins because that conveys clear intent that the API is backed by a function. This method will
merge two such list of Apis with the right order of precedence. If a Path+Method combination ... | samcli/commands/local/lib/sam_api_provider.py | def _merge_apis(collector):
"""
Quite often, an API is defined both in Implicit and Explicit API definitions. In such cases, Implicit API
definition wins because that conveys clear intent that the API is backed by a function. This method will
merge two such list of Apis with the right or... | def _merge_apis(collector):
"""
Quite often, an API is defined both in Implicit and Explicit API definitions. In such cases, Implicit API
definition wins because that conveys clear intent that the API is backed by a function. This method will
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train | SamApiProvider._normalize_apis | Normalize the APIs to use standard method name
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apis : list of samcli.commands.local.lib.provider.Api
List of APIs to replace normalize
Returns
-------
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List of normalized APIs | samcli/commands/local/lib/sam_api_provider.py | def _normalize_apis(apis):
"""
Normalize the APIs to use standard method name
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----------
apis : list of samcli.commands.local.lib.provider.Api
List of APIs to replace normalize
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-------
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Normalize the APIs to use standard method name
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train | SamApiProvider._extract_apis_from_function | Fetches a list of APIs configured for this SAM Function resource.
Parameters
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logical_id : str
Logical ID of the resource
function_resource : dict
Contents of the function resource including its properties
collector : ApiCollector
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"""
Fetches a list of APIs configured for this SAM Function resource.
Parameters
----------
logical_id : str
Logical ID of the resource
function_resource : dict
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Fetches a list of APIs configured for this SAM Function resource.
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train | SamApiProvider._extract_apis_from_events | Given an AWS::Serverless::Function Event Dictionary, extract out all 'Api' events and store within the
collector
Parameters
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function_logical_id : str
LogicalId of the AWS::Serverless::Function
serverless_function_events : dict
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Given an AWS::Serverless::Function Event Dictionary, extract out all 'Api' events and store within the
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train | SamApiProvider._convert_event_api | Converts a AWS::Serverless::Function's Event Property to an Api configuration usable by the provider.
:param str lambda_logical_id: Logical Id of the AWS::Serverless::Function
:param dict event_properties: Dictionary of the Event's Property
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"""
Converts a AWS::Serverless::Function's Event Property to an Api configuration usable by the provider.
:param str lambda_logical_id: Logical Id of the AWS::Serverless::Function
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train | SamApiProvider._normalize_http_methods | Normalizes Http Methods. Api Gateway allows a Http Methods of ANY. This is a special verb to denote all
supported Http Methods on Api Gateway.
:param str http_method: Http method
:yield str: Either the input http_method or one of the _ANY_HTTP_METHODS (normalized Http Methods) | samcli/commands/local/lib/sam_api_provider.py | def _normalize_http_methods(http_method):
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Normalizes Http Methods. Api Gateway allows a Http Methods of ANY. This is a special verb to denote all
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:param str http_method: Http method
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train | ApiCollector.add_apis | Stores the given APIs tagged under the given logicalId
Parameters
----------
logical_id : str
LogicalId of the AWS::Serverless::Api resource
apis : list of samcli.commands.local.lib.provider.Api
List of APIs available in this resource | samcli/commands/local/lib/sam_api_provider.py | def add_apis(self, logical_id, apis):
"""
Stores the given APIs tagged under the given logicalId
Parameters
----------
logical_id : str
LogicalId of the AWS::Serverless::Api resource
apis : list of samcli.commands.local.lib.provider.Api
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Stores the given APIs tagged under the given logicalId
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logical_id : str
LogicalId of the AWS::Serverless::Api resource
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train | ApiCollector.add_binary_media_types | Stores the binary media type configuration for the API with given logical ID
Parameters
----------
logical_id : str
LogicalId of the AWS::Serverless::Api resource
binary_media_types : list of str
List of binary media types supported by this resource | samcli/commands/local/lib/sam_api_provider.py | def add_binary_media_types(self, logical_id, binary_media_types):
"""
Stores the binary media type configuration for the API with given logical ID
Parameters
----------
logical_id : str
LogicalId of the AWS::Serverless::Api resource
binary_media_types : list... | def add_binary_media_types(self, logical_id, binary_media_types):
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Stores the binary media type configuration for the API with given logical ID
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----------
logical_id : str
LogicalId of the AWS::Serverless::Api resource
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train | ApiCollector._get_apis_with_config | Returns the list of APIs in this resource along with other extra configuration such as binary media types,
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----------
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train | ApiCollector._get_properties | Returns the properties of resource with given logical ID. If a resource is not found, then it returns an
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logical_id : str
Logical ID of the resource
Returns
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Parameters
----------
logical_id : str
Logical ID of the resource
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-------
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logical_id : str
Logical ID of the resource
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train | _unzip_file | Helper method to unzip a file to a temporary directory
:param string filepath: Absolute path to this file
:return string: Path to the temporary directory where it was unzipped | samcli/local/lambdafn/runtime.py | def _unzip_file(filepath):
"""
Helper method to unzip a file to a temporary directory
:param string filepath: Absolute path to this file
:return string: Path to the temporary directory where it was unzipped
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temp_dir = tempfile.mkdtemp()
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os.chmod(temp_dir... | def _unzip_file(filepath):
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Helper method to unzip a file to a temporary directory
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train | LambdaRuntime.invoke | Invoke the given Lambda function locally.
##### NOTE: THIS IS A LONG BLOCKING CALL #####
This method will block until either the Lambda function completes or timed out, which could be seconds.
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stderr=None):
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Invoke the given Lambda function locally.
##### NOTE: THIS IS A LONG BLOCKING CALL #####
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train | LambdaRuntime._configure_interrupt | When a Lambda function is executing, we setup certain interrupt handlers to stop the execution.
Usually, we setup a function timeout interrupt to kill the container after timeout expires. If debugging though,
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When a Lambda function is executing, we setup certain interrupt handlers to stop the execution.
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train | LambdaRuntime._get_code_dir | Method to get a path to a directory where the Lambda function code is available. This directory will
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This method handles a few different cases for ``code_path``:
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"""
Method to get a path to a directory where the Lambda function code is available. This directory will
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This method handles a few different cases for ``code_path``:
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"""
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train | LambdaImage.build | Build the image if one is not already on the system that matches the runtime and layers
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runtime str
Name of the Lambda runtime
layers list(samcli.commands.local.lib.provider.Layer)
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"""
Build the image if one is not already on the system that matches the runtime and layers
Parameters
----------
runtime str
Name of the Lambda runtime
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Build the image if one is not already on the system that matches the runtime and layers
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runtime str
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train | LambdaImage._generate_docker_image_version | Generate the Docker TAG that will be used to create the image
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layers list(samcli.commands.local.lib.provider.Layer)
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runtime str
Runtime of the image to create
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-------
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"""
Generate the Docker TAG that will be used to create the image
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----------
layers list(samcli.commands.local.lib.provider.Layer)
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Generate the Docker TAG that will be used to create the image
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train | LambdaImage._build_image | Builds the image
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Docker tag (REPOSITORY:TAG) to use when building the image
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Docker tag (REPOSITORY:TAG) to use when building the image
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Docker tag (REPOSITORY:TAG) to use when building the image
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train | LambdaImage._generate_dockerfile | Generate the Dockerfile contents
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train | LocalApiService.start | Creates and starts the local API Gateway service. This method will block until the service is stopped
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Creates and starts the local API Gateway service. This method will block until the service is stopped
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Creates and starts the local API Gateway service. This method will block until the service is stopped
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Returns a list of routes to configure the Local API Service based on the APIs configured in the template.
Parameters
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api_provider : samcli.commands.local.lib.sam_api_provider.SamApiProvider
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Returns a list of routes to configure the Local API Service based on the APIs configured in the template.
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api_provider : samcli.commands.local.lib.sam_api_provider.SamApiProvider
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train | LocalApiService._print_routes | Helper method to print the APIs that will be mounted. This method is purely for printing purposes.
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Grouping routes by Function Name + Path is the bulk of the logic.
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train | LocalApiService._make_static_dir_path | This method returns the path to the directory where static files are to be served from. If static_dir is a
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train | LocalLambdaInvokeService.create | Creates a Flask Application that can be started. | samcli/local/lambda_service/local_lambda_invoke_service.py | def create(self):
"""
Creates a Flask Application that can be started.
"""
self._app = Flask(__name__)
path = '/2015-03-31/functions/<function_name>/invocations'
self._app.add_url_rule(path,
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"""
Creates a Flask Application that can be started.
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train | LocalLambdaInvokeService.validate_request | Validates the incoming request
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2. Query Parameters are sent to the endpoint
3. The Request Content-Type is not application/json
4. 'X-Amz-Log-Type' header is not 'None'
5. 'X-Amz-I... | samcli/local/lambda_service/local_lambda_invoke_service.py | def validate_request():
"""
Validates the incoming request
The following are invalid
1. The Request data is not json serializable
2. Query Parameters are sent to the endpoint
3. The Request Content-Type is not application/json
4. 'X-Amz-Log-Type' ... | def validate_request():
"""
Validates the incoming request
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train | LocalLambdaInvokeService._construct_error_handling | Updates the Flask app with Error Handlers for different Error Codes | samcli/local/lambda_service/local_lambda_invoke_service.py | def _construct_error_handling(self):
"""
Updates the Flask app with Error Handlers for different Error Codes
"""
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self._app.register_error_handler(404, LambdaErrorResponses.generic_path_not_foun... | def _construct_error_handling(self):
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train | LocalLambdaInvokeService._invoke_request_handler | Request Handler for the Local Lambda Invoke path. This method is responsible for understanding the incoming
request and invoking the Local Lambda Function
Parameters
----------
function_name str
Name of the function to invoke
Returns
-------
A Flask ... | samcli/local/lambda_service/local_lambda_invoke_service.py | def _invoke_request_handler(self, function_name):
"""
Request Handler for the Local Lambda Invoke path. This method is responsible for understanding the incoming
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Parameters
----------
function_name str
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train | unzip | Unzip the given file into the given directory while preserving file permissions in the process.
Parameters
----------
zip_file_path : str
Path to the zip file
output_dir : str
Path to the directory where the it should be unzipped to
permission : octal int
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"""
Unzip the given file into the given directory while preserving file permissions in the process.
Parameters
----------
zip_file_path : str
Path to the zip file
output_dir : str
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Unzip the given file into the given directory while preserving file permissions in the process.
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train | _set_permissions | Sets permissions on the extracted file by reading the ``external_attr`` property of given file info.
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zip_file_info : zipfile.ZipInfo
Object containing information about a file within a zip archive
extracted_path : str
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Sets permissions on the extracted file by reading the ``external_attr`` property of given file info.
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zip_file_info : zipfile.ZipInfo
Object containing information about a file within a zip archive
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Sets permissions on the extracted file by reading the ``external_attr`` property of given file info.
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train | unzip_from_uri | Download the LayerVersion Zip to the Layer Pkg Cache
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unzip_output_dir str
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uri str
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Creates a Flask Application that can be started.
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train | LocalApigwService._construct_error_handling | Updates the Flask app with Error Handlers for different Error Codes | samcli/local/apigw/local_apigw_service.py | def _construct_error_handling(self):
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train | LocalApigwService._request_handler | We handle all requests to the host:port. The general flow of handling a request is as follows
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train | LocalApigwService._get_current_route | Get the route (Route) based on the current request
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train | LocalApigwService._parse_lambda_output | Parses the output from the Lambda Container
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train | LocalApigwService._should_base64_decode_body | Whether or not the body should be decoded from Base64 to Binary
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binary_types list(basestring)
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binary_types list(basestring)
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Whether or not the body should be decoded from Base64 to Binary
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train | LocalApigwService._construct_event | Helper method that constructs the Event to be passed to Lambda
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Helper method that constructs the Event to be passed to Lambda
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train | LocalApigwService._query_string_params | Constructs an APIGW equivalent query string dictionary
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"""
Constructs an APIGW equivalent query string dictionary
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Lambda function to generate the configuration for
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train | LocalLambdaRunner.get_aws_creds | Returns AWS credentials obtained from the shell environment or given profile
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train | ContextIdentity.to_dict | Constructs an dictionary representation of the Identity Object to be used in serializing to JSON
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Constructs an dictionary representation of the Identity Object to be used in serializing to JSON
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train | RequestContext.to_dict | Constructs an dictionary representation of the RequestContext Object to be used in serializing to JSON
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Constructs an dictionary representation of the RequestContext Object to be used in serializing to JSON
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identity_dict = {}
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Constructs an dictionary representation of the RequestContext Object to be used in serializing to JSON
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train | ApiGatewayLambdaEvent.to_dict | Constructs an dictionary representation of the ApiGatewayLambdaEvent Object to be used in serializing to JSON
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train | do_cli | Implementation of the ``cli`` method | samcli/commands/logs/command.py | def do_cli(function_name, stack_name, filter_pattern, tailing, start_time, end_time):
"""
Implementation of the ``cli`` method
"""
LOG.debug("'logs' command is called")
with LogsCommandContext(function_name,
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train | ContainerManager.is_docker_reachable | Checks if Docker daemon is running. This is required for us to invoke the function locally
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Checks if Docker daemon is running. This is required for us to invoke the function locally
Returns
-------
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True, if Docker is available, False otherwise
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try:
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Checks if Docker daemon is running. This is required for us to invoke the function locally
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True, if Docker is available, False otherwise
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try:
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Create and run a Docker container based on the given configuration.
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train | ContainerManager.pull_image | Ask Docker to pull the container image with given name.
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Name of the image
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Optional stream writer to output to. Defaults to stderr
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"""
Ask Docker to pull the container image with given name.
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----------
image_name str
Name of the image
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Ask Docker to pull the container image with given name.
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Name of the image
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train | ContainerManager.has_image | Is the container image with given name available?
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:return bool: True, if image is available. False, otherwise | samcli/local/docker/manager.py | def has_image(self, image_name):
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Is the container image with given name available?
:param string image_name: Name of the image
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try:
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"""
Is the container image with given name available?
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try:
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train | do_cli | Publish the application based on command line inputs. | samcli/commands/publish/command.py | def do_cli(ctx, template, semantic_version):
"""Publish the application based on command line inputs."""
try:
template_data = get_template_data(template)
except ValueError as ex:
click.secho("Publish Failed", fg='red')
raise UserException(str(ex))
# Override SemanticVersion in t... | def do_cli(ctx, template, semantic_version):
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try:
template_data = get_template_data(template)
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click.secho("Publish Failed", fg='red')
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train | _gen_success_message | Generate detailed success message for published applications.
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----------
publish_output : dict
Output from serverlessrepo publish_application
Returns
-------
str
Detailed success message | samcli/commands/publish/command.py | def _gen_success_message(publish_output):
"""
Generate detailed success message for published applications.
Parameters
----------
publish_output : dict
Output from serverlessrepo publish_application
Returns
-------
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Detailed success message
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Generate detailed success message for published applications.
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publish_output : dict
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train | _print_console_link | Print link for the application in AWS Serverless Application Repository console.
Parameters
----------
region : str
AWS region name
application_id : str
The Amazon Resource Name (ARN) of the application | samcli/commands/publish/command.py | def _print_console_link(region, application_id):
"""
Print link for the application in AWS Serverless Application Repository console.
Parameters
----------
region : str
AWS region name
application_id : str
The Amazon Resource Name (ARN) of the application
"""
if not reg... | def _print_console_link(region, application_id):
"""
Print link for the application in AWS Serverless Application Repository console.
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----------
region : str
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application_id : str
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train | ServiceErrorResponses.lambda_failure_response | Helper function to create a Lambda Failure Response
:return: A Flask Response | samcli/local/apigw/service_error_responses.py | def lambda_failure_response(*args):
"""
Helper function to create a Lambda Failure Response
:return: A Flask Response
"""
response_data = jsonify(ServiceErrorResponses._LAMBDA_FAILURE)
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Helper function to create a Lambda Failure Response
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train | ServiceErrorResponses.lambda_not_found_response | Constructs a Flask Response for when a Lambda function is not found for an endpoint
:return: a Flask Response | samcli/local/apigw/service_error_responses.py | def lambda_not_found_response(*args):
"""
Constructs a Flask Response for when a Lambda function is not found for an endpoint
:return: a Flask Response
"""
response_data = jsonify(ServiceErrorResponses._NO_LAMBDA_INTEGRATION)
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Constructs a Flask Response for when a Lambda function is not found for an endpoint
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train | ServiceErrorResponses.route_not_found | Constructs a Flask Response for when a API Route (path+method) is not found. This is usually
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:return: a Flask Response | samcli/local/apigw/service_error_responses.py | def route_not_found(*args):
"""
Constructs a Flask Response for when a API Route (path+method) is not found. This is usually
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"""
respon... | def route_not_found(*args):
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Constructs a Flask Response for when a API Route (path+method) is not found. This is usually
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train | progressbar | Creates a progressbar
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----------
length int
Length of the ProgressBar
label str
Label to give to the progressbar
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click.progressbar
Progressbar | samcli/lib/utils/progressbar.py | def progressbar(length, label):
"""
Creates a progressbar
Parameters
----------
length int
Length of the ProgressBar
label str
Label to give to the progressbar
Returns
-------
click.progressbar
Progressbar
"""
return click.progressbar(length=length,... | def progressbar(length, label):
"""
Creates a progressbar
Parameters
----------
length int
Length of the ProgressBar
label str
Label to give to the progressbar
Returns
-------
click.progressbar
Progressbar
"""
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Removes wrapping double quotes and any '\ ' characters. They are usually added to preserve spaces when passing
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Examples
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>>> _unquote("hel\ lo")
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----------
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r"""
Removes wrapping double quotes and any '\ ' characters. They are usually added to preserve spaces when passing
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Examples
--------
>>> _unquote('val\ ue')
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>>> _unquote("hel\ lo")
hello
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r"""
Removes wrapping double quotes and any '\ ' characters. They are usually added to preserve spaces when passing
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Examples
--------
>>> _unquote('val\ ue')
value
>>> _unquote("hel\ lo")
hello
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Raises
------
RuntimeError
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"""
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Note: This is a **blocking call**
Raises
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:param str body: Response body as a string
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:param int status_code: status_code for response
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"""
Constructs a Flask Response from the body, headers, and status_code.
:param str body: Response body as a string
:param dict headers: headers for the response
:param int status_code: status_code for response
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Constructs a Flask Response from the body, headers, and status_code.
:param str body: Response body as a string
:param dict headers: headers for the response
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train | LambdaOutputParser.get_lambda_output | This method will extract read the given stream and return the response from Lambda function separated out
from any log statements it might have outputted. Logs end up in the stdout stream if the Lambda function
wrote directly to stdout using System.out.println or equivalents.
Parameters
... | samcli/local/services/base_local_service.py | def get_lambda_output(stdout_stream):
"""
This method will extract read the given stream and return the response from Lambda function separated out
from any log statements it might have outputted. Logs end up in the stdout stream if the Lambda function
wrote directly to stdout using Syst... | def get_lambda_output(stdout_stream):
"""
This method will extract read the given stream and return the response from Lambda function separated out
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train | LambdaOutputParser.is_lambda_error_response | Check to see if the output from the container is in the form of an Error/Exception from the Lambda invoke
Parameters
----------
lambda_response str
The response the container returned
Returns
-------
bool
True if the output matches the Error/Exce... | samcli/local/services/base_local_service.py | def is_lambda_error_response(lambda_response):
"""
Check to see if the output from the container is in the form of an Error/Exception from the Lambda invoke
Parameters
----------
lambda_response str
The response the container returned
Returns
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"""
Check to see if the output from the container is in the form of an Error/Exception from the Lambda invoke
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----------
lambda_response str
The response the container returned
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train | ApplicationBuilder.build | Build the entire application
Returns
-------
dict
Returns the path to where each resource was built as a map of resource's LogicalId to the path string | samcli/lib/build/app_builder.py | def build(self):
"""
Build the entire application
Returns
-------
dict
Returns the path to where each resource was built as a map of resource's LogicalId to the path string
"""
result = {}
for lambda_function in self._functions_to_build:
... | def build(self):
"""
Build the entire application
Returns
-------
dict
Returns the path to where each resource was built as a map of resource's LogicalId to the path string
"""
result = {}
for lambda_function in self._functions_to_build:
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train | ApplicationBuilder.update_template | Given the path to built artifacts, update the template to point appropriate resource CodeUris to the artifacts
folder
Parameters
----------
template_dict
original_template_path : str
Path where the template file will be written to
built_artifacts : dict
... | samcli/lib/build/app_builder.py | def update_template(self, template_dict, original_template_path, built_artifacts):
"""
Given the path to built artifacts, update the template to point appropriate resource CodeUris to the artifacts
folder
Parameters
----------
template_dict
original_template_path... | def update_template(self, template_dict, original_template_path, built_artifacts):
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Given the path to built artifacts, update the template to point appropriate resource CodeUris to the artifacts
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Parameters
----------
template_dict
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train | ApplicationBuilder._build_function | Given the function information, this method will build the Lambda function. Depending on the configuration
it will either build the function in process or by spinning up a Docker container.
Parameters
----------
function_name : str
Name or LogicalId of the function
... | samcli/lib/build/app_builder.py | def _build_function(self, function_name, codeuri, runtime):
"""
Given the function information, this method will build the Lambda function. Depending on the configuration
it will either build the function in process or by spinning up a Docker container.
Parameters
----------
... | def _build_function(self, function_name, codeuri, runtime):
"""
Given the function information, this method will build the Lambda function. Depending on the configuration
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----------
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train | LambdaBuildContainer._get_container_dirs | Provides paths to directories within the container that is required by the builder
Parameters
----------
source_dir : str
Path to the function source code
manifest_dir : str
Path to the directory containing manifest
Returns
-------
dict
... | samcli/local/docker/lambda_build_container.py | def _get_container_dirs(source_dir, manifest_dir):
"""
Provides paths to directories within the container that is required by the builder
Parameters
----------
source_dir : str
Path to the function source code
manifest_dir : str
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Provides paths to directories within the container that is required by the builder
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source_dir : str
Path to the function source code
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train | LambdaBuildContainer._convert_to_container_dirs | Use this method to convert a list of host paths to a list of equivalent paths within the container
where the given host path is mounted. This is necessary when SAM CLI needs to pass path information to
the Lambda Builder running within the container.
If a host path is not mounted within the con... | samcli/local/docker/lambda_build_container.py | def _convert_to_container_dirs(host_paths_to_convert, host_to_container_path_mapping):
"""
Use this method to convert a list of host paths to a list of equivalent paths within the container
where the given host path is mounted. This is necessary when SAM CLI needs to pass path information to
... | def _convert_to_container_dirs(host_paths_to_convert, host_to_container_path_mapping):
"""
Use this method to convert a list of host paths to a list of equivalent paths within the container
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train | SamTranslatorWrapper.__translate | This method is unused and a Work In Progress | samcli/lib/samlib/wrapper.py | def __translate(self, parameter_values):
"""
This method is unused and a Work In Progress
"""
template_copy = self.template
sam_parser = Parser()
sam_translator = Translator(managed_policy_map=self.__managed_policy_map(),
sam_parser=s... | def __translate(self, parameter_values):
"""
This method is unused and a Work In Progress
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sam_translator = Translator(managed_policy_map=self.__managed_policy_map(),
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train | SamTranslatorWrapper.__managed_policy_map | This method is unused and a Work In Progress | samcli/lib/samlib/wrapper.py | def __managed_policy_map(self):
"""
This method is unused and a Work In Progress
"""
try:
iam_client = boto3.client('iam')
return ManagedPolicyLoader(iam_client).load()
except Exception as ex:
if self._offline_fallback:
# If of... | def __managed_policy_map(self):
"""
This method is unused and a Work In Progress
"""
try:
iam_client = boto3.client('iam')
return ManagedPolicyLoader(iam_client).load()
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train | _SamParserReimplemented._validate | Validates the template and parameter values and raises exceptions if there's an issue
:param dict sam_template: SAM template | samcli/lib/samlib/wrapper.py | def _validate(self, sam_template):
""" Validates the template and parameter values and raises exceptions if there's an issue
:param dict sam_template: SAM template
"""
if "Resources" not in sam_template or not isinstance(sam_template["Resources"], dict) \
or not sam_tem... | def _validate(self, sam_template):
""" Validates the template and parameter values and raises exceptions if there's an issue
:param dict sam_template: SAM template
"""
if "Resources" not in sam_template or not isinstance(sam_template["Resources"], dict) \
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train | ServiceCommand.get_command | gets the subcommands under the service name
Parameters
----------
ctx : Context
the context object passed into the method
cmd_name : str
the service name
Returns
-------
EventTypeSubCommand:
returns subcommand if successful, No... | samcli/commands/local/generate_event/event_generation.py | def get_command(self, ctx, cmd_name):
"""
gets the subcommands under the service name
Parameters
----------
ctx : Context
the context object passed into the method
cmd_name : str
the service name
Returns
-------
EventTypeSu... | def get_command(self, ctx, cmd_name):
"""
gets the subcommands under the service name
Parameters
----------
ctx : Context
the context object passed into the method
cmd_name : str
the service name
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-------
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train | LimeBase.feature_selection | Selects features for the model. see explain_instance_with_data to
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train | id_generator | Helper function to generate random div ids. This is useful for embedding
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"""Helper function to generate random div ids. This is useful for embedding
HTML into ipython notebooks."""
chars = list(string.ascii_uppercase + string.digits)
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chars = list(string.ascii_uppercase + string.digits)
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train | Explanation.available_labels | Returns the list of classification labels for which we have any explanations. | lime/explanation.py | def available_labels(self):
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train | Explanation.show_in_notebook | Shows html explanation in ipython notebook.
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fn : arbitrary function
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result : dict, dictionary containing variables
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fn : arbitrary function
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train | TextDomainMapper.map_exp_ids | Maps ids to words or word-position strings.
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positions: if True, also return word positions
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examples: ('bad', 1) or ('bad_3-6-12',... | lime/lime_text.py | def map_exp_ids(self, exp, positions=False):
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Args:
exp: list of tuples [(id, weight), (id,weight)]
positions: if True, also return word positions
Returns:
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train | TextDomainMapper.visualize_instance_html | Adds text with highlighted words to visualization.
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label: label id (integer)
div_name: name of div object to be used for rendering(in js)
exp_object_name: name of js explanation object
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text=True, opacity=True):
"""Adds text with highlighted words to visualization.
Args:
exp: list of tuples [(id, weight), (id,weight)]
label: label id (integer)
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exp: list of tuples [(id, weight), (id,weight)]
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train | IndexedString.string_position | Returns a np array with indices to id_ (int) occurrences | lime/lime_text.py | def string_position(self, id_):
"""Returns a np array with indices to id_ (int) occurrences"""
if self.bow:
return self.string_start[self.positions[id_]]
else:
return self.string_start[[self.positions[id_]]] | def string_position(self, id_):
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train | IndexedString.inverse_removing | Returns a string after removing the appropriate words.
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Args:
words_to_remove: list of ids (ints) to remove
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original raw string with appropriate words removed. | lime/lime_text.py | def inverse_removing(self, words_to_remove):
"""Returns a string after removing the appropriate words.
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Args:
words_to_remove: list of ids (ints) to remove
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"""Returns a string after removing the appropriate words.
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Args:
words_to_remove: list of ids (ints) to remove
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train | IndexedString._segment_with_tokens | Segment a string around the tokens created by a passed-in tokenizer | lime/lime_text.py | def _segment_with_tokens(text, tokens):
"""Segment a string around the tokens created by a passed-in tokenizer"""
list_form = []
text_ptr = 0
for token in tokens:
inter_token_string = []
while not text[text_ptr:].startswith(token):
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train | IndexedString.__get_idxs | Returns indexes to appropriate words. | lime/lime_text.py | def __get_idxs(self, words):
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if self.bow:
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else:
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train | LimeTextExplainer.explain_instance | Generates explanations for a prediction.
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labels=(1,),
top_labels=None,
num_features=10,
num_samples=5000,
distance_metric='cosine... | def explain_instance(self,
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num_samples=5000,
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train | LimeTextExplainer.__data_labels_distances | Generates a neighborhood around a prediction.
Generates neighborhood data by randomly removing words from
the instance, and predicting with the classifier. Uses cosine distance
to compute distances between original and perturbed instances.
Args:
indexed_string: document (Ind... | lime/lime_text.py | def __data_labels_distances(self,
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Generates neighborhoo... | def __data_labels_distances(self,
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train | TableDomainMapper.map_exp_ids | Maps ids to feature names.
Args:
exp: list of tuples [(id, weight), (id,weight)]
Returns:
list of tuples (feature_name, weight) | lime/lime_tabular.py | def map_exp_ids(self, exp):
"""Maps ids to feature names.
Args:
exp: list of tuples [(id, weight), (id,weight)]
Returns:
list of tuples (feature_name, weight)
"""
names = self.exp_feature_names
if self.discretized_feature_names is not None:
... | def map_exp_ids(self, exp):
"""Maps ids to feature names.
Args:
exp: list of tuples [(id, weight), (id,weight)]
Returns:
list of tuples (feature_name, weight)
"""
names = self.exp_feature_names
if self.discretized_feature_names is not None:
... | [
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train | TableDomainMapper.visualize_instance_html | Shows the current example in a table format.
Args:
exp: list of tuples [(id, weight), (id,weight)]
label: label id (integer)
div_name: name of div object to be used for rendering(in js)
exp_object_name: name of js explanation object
show_table: i... | lime/lime_tabular.py | def visualize_instance_html(self,
exp,
label,
div_name,
exp_object_name,
show_table=True,
show_all=False):
"""Shows the ... | def visualize_instance_html(self,
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train | LimeTabularExplainer.validate_training_data_stats | Method to validate the structure of training data stats | lime/lime_tabular.py | def validate_training_data_stats(training_data_stats):
"""
Method to validate the structure of training data stats
"""
stat_keys = list(training_data_stats.keys())
valid_stat_keys = ["means", "mins", "maxs", "stds", "feature_values", "feature_frequencies"]
missing_key... | def validate_training_data_stats(training_data_stats):
"""
Method to validate the structure of training data stats
"""
stat_keys = list(training_data_stats.keys())
valid_stat_keys = ["means", "mins", "maxs", "stds", "feature_values", "feature_frequencies"]
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train | LimeTabularExplainer.explain_instance | Generates explanations for a prediction.
First, we generate neighborhood data by randomly perturbing features
from the instance (see __data_inverse). We then learn locally weighted
linear models on this neighborhood data to explain each of the classes
in an interpretable way (see lime_b... | lime/lime_tabular.py | def explain_instance(self,
data_row,
predict_fn,
labels=(1,),
top_labels=None,
num_features=10,
num_samples=5000,
distance_metric='euclidean',
... | def explain_instance(self,
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labels=(1,),
top_labels=None,
num_features=10,
num_samples=5000,
distance_metric='euclidean',
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train | LimeTabularExplainer.__data_inverse | Generates a neighborhood around a prediction.
For numerical features, perturb them by sampling from a Normal(0,1) and
doing the inverse operation of mean-centering and scaling, according to
the means and stds in the training data. For categorical features,
perturb by sampling according ... | lime/lime_tabular.py | def __data_inverse(self,
data_row,
num_samples):
"""Generates a neighborhood around a prediction.
For numerical features, perturb them by sampling from a Normal(0,1) and
doing the inverse operation of mean-centering and scaling, according to
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For numerical features, perturb them by sampling from a Normal(0,1) and
doing the inverse operation of mean-centering and scaling, according to
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train | RecurrentTabularExplainer._make_predict_proba | The predict_proba method will expect 3d arrays, but we are reshaping
them to 2D so that LIME works correctly. This wraps the function
you give in explain_instance to first reshape the data to have
the shape the the keras-style network expects. | lime/lime_tabular.py | def _make_predict_proba(self, func):
"""
The predict_proba method will expect 3d arrays, but we are reshaping
them to 2D so that LIME works correctly. This wraps the function
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the shape the the keras-style network expe... | def _make_predict_proba(self, func):
"""
The predict_proba method will expect 3d arrays, but we are reshaping
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