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Augmented Intent Single Task Adaptive Components

Project description

1   What is Project Hadron

Project Hadron is a change of approach in terms of improving productivity of the data scientists. This approach deconstructs the machine learning discovery vertical into a set of capabilities, ideas and knowledge. It presents a completely novel approach to the traditional process automation and model wrapping that is broadly offered as a solution to solve the considerable challenges that currently restrict the effectiveness of machine learning in the enterprise business.

2   What is AI-STAC

‘Exploratory data analysis can never be the whole story, but nothing else can serve as the foundation stone.’ — John Tukey

Augmented Intent - Single Task Accelerator components (AI-STAC) is a unique approach to data recovery, discovery, mitigation, drift, synthesis and modeling that innovates the approach to data science and its transition to production. Its origins came from an incubator project that shadowed a team of Ph.D. data scientists in connection with the development and delivery of machine learning initiatives to define measurable benefit propositions for customer success. From this, a number of observable ‘capabilities’ were identified as unique and separate concerns. The challenges of the data scientist, and in turn the production teams, were to effectively leverage that separation of concern, distribute and loosely couple the specialist capability needs, to the appropriate skills set.

In addition, the need to remove the opaque nature of the machine learning end-to-end required better transparency and traceability, to better inform to the broadest of interested parties and be able to adapt without leaving code ‘sludge’ of redundant ideas. AI-STAC is a disruptive innovation, changing the way we approach the challenges of Machine Learning and Augmented Intent, introducing the ideas of ‘Single Task Adaptive Component’ around the core concept of ‘Parameterised Intent’

3   Background

Born out of the frustration of time constraints and the inability to show business value within a business expectation, this project aims to provide a set of tools to quickly produce visual and observational results. It also aims to improve the communication outputs needed by ML delivery to talk to Pre-Sales, Stakeholders, Business SME’s, Data SME’s product coders and tooling engineers while still remaining within familiar code paragigms.

The package looks to build a set of outputs as part of standard data wrangling and ML exploration that, by their nature, are familiar tools to the various reliant people and processes. For example Data dictionaries for SME’s, Visual representations for clients and stakeholders and configuration contracts for architects, tool builders and data ingestion.

4   Installation

package install

The best way to install this package is directly from the Python Package Index repository using pip

$ pip install aistac-foundation

if you want to upgrade your current version then using pip

$ pip install --upgrade aistac-foundation

5   Package Overview

5.1   AbstractComponent

The AbstractComponent class is a foundation class for the component build. It provides an encapsulated view of the Property Management and Parameterised Intent

Abstract AI Single Task Application Component (AI-STAC) component class provides all the basic building blocks of a components build including property management, augmented knowledge notes and parameterised intent pipeline.

For convenience there are three Factory Initialisation methods available``from_env(…)``, from_memory(...) and from_uri(...) the first two being abstract methods. The thrid factory method initialises the concrete PropertyManager and IntentModel classes and use the parent _init_properties(...) methods to set the properties connector. When creating the concrete class the from_uri(...) should be implemented. The following method can be used as a template replacing ExamplePropertyManager and ExampleIntentModel with your oen concrete implementations

def from_uri(cls, task_name: str, uri_pm_path: str, username: str, uri_pm_repo: str=None,
             pm_file_type: str=None, pm_module: str=None, pm_handler: str=None, pm_kwargs: dict=None,
             default_save=None, reset_templates: bool=None, template_path: str=None, template_module: str=None,
             template_source_handler: str=None, template_persist_handler: str=None, align_connectors: bool=None,
             default_save_intent: bool=None, default_intent_level: bool=None, order_next_available: bool=None,
             default_replace_intent: bool=None, has_contract: bool=None):
    pm_file_type = pm_file_type if isinstance(pm_file_type, str) else 'json'
    pm_module = pm_module if isinstance(pm_module, str) else cls.DEFAULT_MODULE
    pm_handler = pm_handler if isinstance(pm_handler, str) else cls.DEFAULT_PERSIST_HANDLER
    _pm = ExamplePropertyManager(task_name=task_name, username=username)
    _intent_model = ExampleIntentModel(property_manager=_pm, default_save_intent=default_save_intent,
    super()._init_properties(property_manager=_pm, uri_pm_path=uri_pm_path, default_save=default_save,
                             uri_pm_repo=uri_pm_repo, pm_file_type=pm_file_type, pm_module=pm_module,
                             pm_handler=pm_handler, pm_kwargs=pm_kwargs, has_contract=has_contract)
    return cls(property_manager=_pm, intent_model=_intent_model, default_save=default_save,
               reset_templates=reset_templates, template_path=template_path, template_module=template_module,
               template_source_handler=template_source_handler, template_persist_handler=template_persist_handler,

5.2   AbstractPropertyManager

The AbstractPropertiesManager facilitates the management of all the contract properties including that of the connector handlers, parameterised intent and Augmented Knowledge

Abstract AI Single Task Application Component (AI-STAC) class that creates a super class for all properties managers

The Class initialisation is abstracted and is the only abstracted method. A concrete implementation of the overloaded __init__ manages the root_key and knowledge_key for this construct. The root_key adds a key property reference to the root of the properties and can be referenced directly with <name>_key. Likewise the knowledge_key adds a catalog key to the restricted catalog keys.

More complex root_key constructs, where a grouping of keys might be desirable, passing a dictionary of name value pairs as part of the list allows a root base to group related next level keys. For example

root_key = [{base: [primary, secondary}]

would add base.primary_key and base.secondary_key to the list of keys.

Here is a default example of an initialisation method:

def __init__(self, task_name: str):
    # set additional keys
    root_keys = []
    knowledge_keys = []
    super().__init__(task_name=task_name, root_keys=root_keys, knowledge_keys=knowledge_keys)

The property manager is not responsible for persisting the properties but provides the methods to load and persist its in memory structure. To initialise the load and persist a ConnectorContract must be set up.

The following is a code snippet of setting a ConnectorContract and loading its content

if self.get_connector_handler(self.CONNECTOR_PM_CONTRACT).exists():

When using the property manager it will not automatically persist its properties and must be explicitely managed in the component class. This removes the persist decision making away from the property manager. To persist the properties use the method call persist_properties()

5.3   AbstractIntentModel

The AbstractIntentModel facilitates the Parameterised Intent, giving the base methods to record and replay intent.

Abstract AI Single Task Application Component (AI-STAC) Class for Parameterised Intent containing parameterised intent registration methods _intent_builder(...) and _set_intend_signature(...).

it is creating a construct initialisation to allow for the control and definition of an intent_param_exclude list, default_save_intent boolean and a default_intent_level value.

As an example of an initialisation method

def __init__(self, property_manager: AbstractPropertyManager, default_save_intent: bool=None,
             default_intent_level: bool=None, order_next_available: bool=None, default_replace_intent: bool=None):
    # set all the defaults
    default_save_intent = default_save_intent if isinstance(default_save_intent, bool) else True
    default_replace_intent = default_replace_intent if isinstance(default_replace_intent, bool) else True
    default_intent_level = default_intent_level if isinstance(default_intent_level, (str, int, float)) else 0
    default_intent_order = -1 if isinstance(order_next_available, bool) and order_next_available else 0
    intent_param_exclude = ['data', 'inplace']
    intent_type_additions = []
    super().__init__(property_manager=property_manager, default_save_intent=default_save_intent,
                     intent_param_exclude=intent_param_exclude, default_intent_level=default_intent_level,
                     default_intent_order=default_intent_order, default_replace_intent=default_replace_intent,

in order to define the run pattern for the component task run_intent_pipeline(...) is an abstracted method that defines the run pipeline of the intent.

As an example of a run_pipeline that iteratively updates a canonical with each intent

def run_intent_pipeline(self, canonical, intent_levels: [int, str, list]=None, **kwargs):
    # test if there is any intent to run
    if self._pm.has_intent():
        # get the list of levels to run
        if isinstance(intent_levels, (int, str, list)):
            intent_levels = Commons.list_formatter(intent_levels)
            intent_levels = sorted(self._pm.get_intent().keys())
        for level in intent_levels:
            level_key = self._pm.join(self._pm.KEY.intent_key, level)
            for order in sorted(self._pm.get(level_key, {})):
                for method, params in self._pm.get(self._pm.join(level_key, order), {}).items():
                    if method in self.__dir__():
                        # add method kwargs to the params
                        if isinstance(kwargs, dict):
                        # add excluded parameters to the params
                        params.update({'inplace': False, 'save_intent': False})
                        canonical = eval(f"self.{method}(canonical, **{params})", globals(), locals())
    return canonical

The code signature for an intent method would have the following construct

def <method>(self, <params>..., save_intent: bool=None, intent_level: [int, str]=None, intent_order: int=None,
             replace_intent: bool=None, remove_duplicates: bool=None):
    # resolve intent persist options
    self._set_intend_signature(self._intent_builder(method=inspect.currentframe().f_code.co_name, params=locals()),
                               intent_level=intent_level, intent_order=intent_order, replace_intent=replace_intent,
                               remove_duplicates=remove_duplicates, save_intent=save_intent)
    # intend code block on the canonical

6   Reference

6.1   Python version

Python 3.6 or less is not supported. Although Python 3.7 is supported, it is recommended to install aistac-foundation against the latest Python 3.8.x or greater whenever possible.

6.4   Licence

BSD-3-Clause: LICENSE.

6.5   Authors

Gigas64 (@gigas64) created aistac-foundation.

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