opengate_data.ai_transformers.ai_transformers

AITransformersBuilder

AITransformersBuilder Objects

class AITransformersBuilder()

Class transformer builder

with_organization_name

def with_organization_name(organization_name: str) -> "AITransformersBuilder"

Specify the organization name.

Arguments:

  • organization_name str - The name of the organization.

Returns:

  • AITransformersBuilder - Returns self for chaining.

Example:

builder.with_organization_name('organization_name')

with_identifier

def with_identifier(identifier: str) -> "AITransformersBuilder"

Specify the identifier for the pipeline.

Arguments:

  • identifier str - The identifier for the pipeline.

Returns:

  • AITransformersBuilder - Returns self for chaining.

Example:

builder.with_identifier('identifier')

with_env

def with_env(data_env: str) -> "AITransformersBuilder"

Specify the environment variable.

Arguments:

  • data_env str - The environment variable.

Returns:

  • AITransformersBuilder - Returns self for chaining.

Example:

builder.with_env('TRANSFORMER_ID')

with_config_file

def with_config_file(config_file: str, section: str,
                     config_key: str) -> "AITransformersBuilder"

This method allows specifying a configuration file, a section within that file, and a key to retrieve a specific value from the section.

Arguments:

  • config_file str - The path to the.ini configuration file.
  • section str - The section name within the.ini file where the desired configuration is located.
  • config_key str - The key within the specified section whose value will be retrieved.

Raises:

  • TypeError - If the provided config_file is not a string.
  • TypeError - If the provided section is not a string.
  • TypeError - If the provided config_key is not a string.

Returns:

AITransformersBuilder

Example:

[id]
model_id = afe07216-14ec-4134-97ae-c483b11d965a

config_file_path = os.path.join(os.path.dirname(__file__), 'config_test.ini')
builder.with_config_file(config_file_path, 'id', 'model_id')

add_file

def add_file(file_path: str, filetype: str = None)

Adds a file to the transformer resource.

This method allows specifying one or more files to be included in the transformer resource being created. The content type for each file can be specified if needed.

Arguments:

  • file_path str - Full path to the file to add.
  • filetype str, optional - Content type of the file. Defaults to None, meaning the content type will be automatically inferred.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

ai_transformer_create = client.new_ai_transformers_builder().with_organization_name('organization')
.add_file('exittransformer.py', 'text/python')
.add_file('pkl_encoder.pkl')

with_find_by_name

def with_find_by_name(find_name: str) -> "AITransformersBuilder"

Specify the name to find.

Arguments:

  • find_name str - The name of the transformer.

Returns:

  • AITransformersBuilder - Returns self for chaining.

Example:

builder.with_find_by_name('transformer_name')

with_evaluate

def with_evaluate(data_evaluate: dict) -> "AITransformersBuilder"

Evaluate with transformer

Arguments:

  • data_evaluate dict - Evaluate

Raises:

  • TypeError - If to evaluate is not a dict.

Returns:

  • AITransformersBuilder - Returns itself to allow for method chaining.

Example:

evaluate_data = {
- `"data"` - {
- `"PPLast12H"` - 0,
- `"PPLast24H"` - 0,
- `"PPLast72H"` - 1,
- `"currentTemp"` - -2,
- `"changeTemp"` - -2
},
- `"date"` - "2022-06-13T13:59:34.779+02:00"
}
builder.with_evaluate(evaluate_data)

with_output_file_path

def with_output_file_path(output_file_path: str) -> "AITransformersBuilder"

Sets the output file path for the transformer.

This method allows you to specify the path where the output file will be saved. It is particularly useful for operations that involve downloading or saving files.

Arguments:

  • output_file_path str - The path where the output file will be saved.

Returns:

  • AITransformersBuilder - The instance of the AIModelsBuilder class.

Example:

builder.with_output_file_path("rute/prueba.onnx")

with_file_name

def with_file_name(file_name: str) -> "AITransformersBuilder"

Specifies the name of the file to be processed.

This method allows you to specify the name of the file that will be used in operations such as download or evaluation. It is particularly useful when working with specific files that require unique identifiers or names for processing.

Arguments:

  • file_name str - The name of the file to be processed.

Returns:

  • AITransformersBuilder - Returns self for chaining.

Example:

builder.with_file_name('pkl_encoder.pkl')

create

def create() -> "AITransformersBuilder"

Prepares the creation of the transformer resource.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

builder.with_organization_name('organization').add_file(
'exittransformer.py', 'text/python'
).add_file('pkl_encoder.pkl').create().build().execute()

find_all

def find_all() -> "AITransformersBuilder"

Searches for all available transformer resources.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

builder.with_organization_name('my_organization').find_all()

find_one

def find_one() -> "AITransformersBuilder"

Searches for a single transformer resource by its identifier.

This method prepares the request to find a specific transformer based on its identifier. The identifier is obtained automatically if not explicitly defined or can be obtained from a configuration file or environment variables.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

builder.with_organization_name('my_organization').with_identifier('identifier').find_one()

update

def update() -> "AITransformersBuilder"

Updates an existing transformer resource.

This method prepares the URL and HTTP method necessary to send a PUT request to the API to update an existing transformer. It is necessary to configure the relevant attributes of the AITransformersBuilder instance, including the identifier of the transformer to update, before calling this method.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

builder.with_organization_name('my_organization').with_identifier('identifier').update()

delete

def delete() -> "AITransformersBuilder"

Deletes an existing transformer resource.

This method prepares the URL and HTTP method necessary to send a DELETE request to the API to delete an existing transformer. It is necessary to configure the identifier attribute of the AITransformersBuilder instance before calling this method.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

builder.with_organization_name('my_organization').with_identifier('identifier').delete()

download

def download() -> "AITransformersBuilder"

Download the model file.

This method sets up the AIModelsBuilder instance to download the file of a specific model associated with the specified organization and identifier. It configures the URL endpoint for the download operation and sets the operation type to ‘download’.

Returns:

  • AITransformersBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").with_identifier("model_identifier").with_output_file_path("model.onnx").download().build().execute()
builder.with_organization_name("MyOrganization").with_find_by_name("model_name.onnx").with_output_file_path("model.onnx").download().build().execute()
config_file_path = os.path.join(os.path.dirname(__file__), 'config_test.ini')
builder.with_organization_name("MyOrganization").with_config_file(config_file_path, 'id', 'model').with_output_file_path("model.onnx").download().build().execute()

evaluate

def evaluate() -> "AITransformersBuilder"

Prepares the evaluation of the transformer with provided data.

This method sets up the URL and method for evaluating the transformer using the provided data. The evaluation data should be set using the with_evaluate method before calling this method.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

builder.with_organization_name('my_organization').with_identifier('identifier').with_evaluate(evaluate_data).evaluate()

save

def save() -> "AITransformersBuilder"

Saves the transformer configuration.

This method prepares the URL and method for saving the transformer configuration. It checks if the identifier is set from the environment or configuration file and then either updates or creates the transformer accordingly.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

builder.with_organization_name('my_organization').with_env('TRANSFORMER_ID').save()

set_config_file_identifier

def set_config_file_identifier() -> "AITransformersBuilder"

Sets the transformer identifier in the configuration file.

This method sets the transformer identifier in the specified configuration file. It reads the configuration file, updates the identifier, and writes the changes back to the file.

Returns:

  • AITransformersBuilder - Returns the current instance to allow method chaining.

Example:

config_file_path = os.path.join(os.path.dirname(__file__), 'config_test.ini')
builder.with_config_file(config_file_path, 'id', 'transformer_id').set_config_file_identifier()

set_env_identifier

def set_env_identifier() -> "AITransformersBuilder"

Selects the operation that writes the transformer identifier into the .env file.

It stores the identifier held by the builder under the variable named with with_env(), so a later run can pick the transformer up from the environment. The variable must already exist in the .env file.

Returns:

  • AITransformersBuilder - Returns itself to allow for method chaining.

Raises:

  • ValueError - If the variable is not in the .env file.

Example:

builder.with_identifier("transformer_identifier").with_env(
"TRANSFORMER_ID"
).set_env_identifier().build().execute()

build

def build() -> "AITransformersBuilder"

Finalizes the construction of the IoT collection configuration.

This method prepares the builder to execute the collection by ensuring all necessary configurations are set and validates the overall integrity of the build. It should be called before executing the collection to ensure that the configuration is complete and valid.

The build process involves checking that mandatory fields such as the device identifier are set. It also ensures that method calls that are incompatible with each other (like build and build_execute) are not both used.

Returns:

  • AITransformersBuilder - Returns itself to allow for method chaining, enabling further actions like execute.

Raises:

  • ValueError - If required configurations are missing or if incompatible methods are used together.

Example:

builder.build()

build_execute

def build_execute()

Executes the data sets search immediately after building the configuration.

This method is a shortcut that combines building and executing in a single step.

Returns:

  • dict - A dictionary containing the execution response which includes the status code and potentially other metadata about the execution.

Raises:

  • ValueError - If build has already been called on this builder instance.

Example:

builder.build_execute()

execute

def execute() -> requests.Response

Sends the selected operation to the platform.

Which request goes out depends on the operation chosen in the chain: create, find, update, delete, download, evaluate, save, or resolving the identifier from a configuration file or from the environment.

Returns:

requests.Response | dict | str: Whatever the selected operation produces — the transformer payload, the contents of a downloaded file, or a dict carrying status_code and either data or error.

Raises:

  • ValueError - If no operation was selected in the chain.

Example:

builder.with_organization_name("organization").find_all().build().execute()

opengate_data.ai_transformers