abacusai.pipeline_step

Classes

`CodeSource`	Code source for python-based custom feature groups and models
`PythonFunction`	Customer created python function
`AbstractApiClass`
`PipelineStep`	A step in a pipeline.

Module Contents

class abacusai.pipeline_step.CodeSource(client, sourceType=None, sourceCode=None, applicationConnectorId=None, applicationConnectorInfo=None, packageRequirements=None, status=None, error=None, publishingMsg=None, moduleDependencies=None)

Bases: abacusai.return_class.AbstractApiClass

Code source for python-based custom feature groups and models

Parameters:

client (ApiClient) – An authenticated API Client instance
sourceType (str) – The type of the source, one of TEXT, PYTHON, FILE_UPLOAD, or APPLICATION_CONNECTOR
sourceCode (str) – If the type of the source is TEXT, the raw text of the function
applicationConnectorId (str) – The Application Connector to fetch the code from
applicationConnectorInfo (str) – Args passed to the application connector to fetch the code
packageRequirements (list) – The pip package dependencies required to run the code
status (str) – The status of the code and validations
error (str) – If the status is failed, an error message describing what went wrong
publishingMsg (dict) – Warnings in the source code
moduleDependencies (list) – The list of internal modules dependencies required to run the code

__repr__(): Return repr(self).

to_dict()

Get a dict representation of the parameters in this class

Returns:: The dict value representation of the class parameters
Return type:: dict

import_as_cell(): Adds the source code as an unexecuted cell in the notebook.

class abacusai.pipeline_step.PythonFunction(client, notebookId=None, name=None, createdAt=None, functionVariableMappings=None, outputVariableMappings=None, functionName=None, pythonFunctionId=None, functionType=None, packageRequirements=None, codeSource={})

Bases: abacusai.return_class.AbstractApiClass

Customer created python function

Parameters:

client (ApiClient) – An authenticated API Client instance
notebookId (str) – The unique identifier of the notebook used to spin up the notebook upon creation.
name (str) – The name to identify the algorithm, only uppercase letters, numbers, and underscores allowed (i.e. it must be a valid Python identifier)
createdAt (str) – The ISO-8601 string representing when the Python function was created.
functionVariableMappings (dict) – A description of the function variables.
outputVariableMappings (dict) – A description of the variables returned by the function
functionName (str) – The name of the Python function to be used.
pythonFunctionId (str) – The unique identifier of the Python function.
functionType (str) – The type of the Python function.
packageRequirements (list) – The pip package dependencies required to run the code
codeSource (CodeSource) – Information about the source code of the Python function.

__repr__(): Return repr(self).

to_dict()

Get a dict representation of the parameters in this class

Returns:: The dict value representation of the class parameters
Return type:: dict

add_graph_to_dashboard(graph_dashboard_id, function_variable_mappings=None, name=None)

Add a python plot function to a dashboard

Parameters:

graph_dashboard_id (str) – Unique string identifier for the graph dashboard to update.
function_variable_mappings (List) – List of arguments to be supplied to the function as parameters, in the format [{‘name’: ‘function_argument’, ‘variable_type’: ‘FEATURE_GROUP’, ‘value’: ‘name_of_feature_group’}].
name (str) – Name of the added python plot

Returns:

An object describing the graph dashboard.

Return type:

GraphDashboard

validate_locally(kwargs=None)

Validates a Python function by running it with the given input values in an local environment. Taking Input Feature Group as either name(string) or Pandas DataFrame in kwargs.

Parameters:

kwargs (dict) – A dictionary mapping function arguments to values to pass to the function. Feature group names will automatically be converted into pandas dataframes.

Returns:

The result of executing the python function

Return type:

any

Raises:

TypeError – If an Input Feature Group argument has an invalid type or argument is missing.
Exception – If an error occurs while validating the Python function.

class abacusai.pipeline_step.AbstractApiClass(client, id)

__eq__(other): Return self==value.

_get_attribute_as_dict(attribute)

class abacusai.pipeline_step.PipelineStep(client, pipelineStepId=None, pipelineId=None, stepName=None, pipelineName=None, createdAt=None, updatedAt=None, pythonFunctionId=None, stepDependencies=None, cpuSize=None, memory=None, timeout=None, pythonFunction={}, codeSource={})

Bases: abacusai.return_class.AbstractApiClass

A step in a pipeline.

Parameters:

client (ApiClient) – An authenticated API Client instance
pipelineStepId (str) – The reference to this step.
pipelineId (str) – The reference to the pipeline this step belongs to.
stepName (str) – The name of the step.
pipelineName (str) – The name of the pipeline this step is a part of.
createdAt (str) – The date and time which this step was created.
updatedAt (str) – The date and time when this step was last updated.
pythonFunctionId (str) – The python function_id.
stepDependencies (list[str]) – List of steps this step depends on.
cpuSize (str) – CPU size specified for the step function.
memory (int) – Memory in GB specified for the step function.
timeout (int) – Timeout for the step in minutes, default is 300 minutes.
pythonFunction (PythonFunction) – Information about the python function for the step.
codeSource (CodeSource) – Information about the source code of the step function.

__repr__(): Return repr(self).

to_dict()

Get a dict representation of the parameters in this class

Returns:: The dict value representation of the class parameters
Return type:: dict

delete()

Deletes a step from a pipeline.

Parameters:: pipeline_step_id (str) – The ID of the pipeline step.

update(function_name=None, source_code=None, step_input_mappings=None, output_variable_mappings=None, step_dependencies=None, package_requirements=None, cpu_size=None, memory=None, timeout=None)

Creates a step in a given pipeline.

Parameters:

function_name (str) – The name of the Python function.
source_code (str) – Contents of a valid Python source code file. The source code should contain the transform feature group functions. A list of allowed imports and system libraries for each language is specified in the user functions documentation section.
step_input_mappings (List) – List of Python function arguments.
output_variable_mappings (List) – List of Python function outputs.
step_dependencies (list) – List of step names this step depends on.
package_requirements (list) – List of package requirement strings. For example: [‘numpy==1.2.3’, ‘pandas>=1.4.0’].
cpu_size (str) – Size of the CPU for the step function.
memory (int) – Memory (in GB) for the step function.
timeout (int) – Timeout for the pipeline step, default is 300 minutes.

Returns:

Object describing the pipeline.

Return type:

PipelineStep

rename(step_name)

Renames a step in a given pipeline.

Parameters:: step_name (str) – The name of the step.
Returns:: Object describing the pipeline.
Return type:: PipelineStep

refresh()

Calls describe and refreshes the current object’s fields

Returns:: The current object
Return type:: PipelineStep

describe()

Deletes a step from a pipeline.

Parameters:: pipeline_step_id (str) – The ID of the pipeline step.
Returns:: An object describing the pipeline step.
Return type:: PipelineStep