aisquared.config.feedback package

Submodules

aisquared.config.feedback.BinaryFeedback module

class aisquared.config.feedback.BinaryFeedback.BinaryFeedback(label_map: list)

Bases: BaseObject

Feedback for binary classification

Example usage:

>>> import aisquared
>>> my_obj = aisquared.config.feedback.BinaryFeedback(['class1', 'class2'])
>>> my_obj.to_dict()
{'className': 'BinaryFeedback', 'params': {'labelMap': ['class1', 'class2']}}

property label_map

to_dict() → dict

Return the object as a dictionary

aisquared.config.feedback.ModelFeedback module

class aisquared.config.feedback.ModelFeedback.ModelFeedback

Bases: BaseObject

Feedback object for questions and answers for an individual model.

Example usage:

>>> import aisquared
>>> my_obj = aisquared.config.feedback.ModelFeedback()
>>> my_obj.add_question(
    'How is the model performing?',
    choices = ['very poorly', 'poorly', 'neutral', 'well', 'very well']
)
>>> my_obj.add_question(
    'Any additional feedback?',
    'text'
)
>>> my_obj.to_dict()
{'className': 'ModelFeedback',
'params': {'questions': [{'question': 'How is the model performing?',
'answerType': 'singleChoice',
'choices': ['very poorly', 'poorly', 'neutral', 'well', 'very well']},
{'question': 'Any additional feedback?', 'answerType': 'text'}]}}

add_question(question: str, answer_type: str = 'singleChoice', choices: list = [])

Add a question to be asked.

Parameters:

• question (str) – The question to be asked.

• answer_type (str (default 'singleChoice')) – One of either ‘singleChoice’, ‘multiChoice’, or ‘text’

• choices (list (default [])) – The choices to be provided, if answer_type is ‘singleChoice’ or ‘multiChoice’

to_dict() → dict

Return the object as a dictionary

aisquared.config.feedback.MulticlassFeedback module

class aisquared.config.feedback.MulticlassFeedback.MulticlassFeedback(label_map: list)

Bases: BaseObject

Feedback for multiclass classification

Example Usage:

>>> import aisquared
>>> my_obj = aisquared.config.feedback.MulticlassFeedback(['class1', 'class2', 'class3'])
>>> my_obj.to_dict()
{'className': 'MulticlassFeedback',
'params': {'labelMap': ['class1', 'class2', 'class3']}}

property label_map

to_dict() → dict

Return the object as a dictionary

aisquared.config.feedback.QualitativeFeedback module

class aisquared.config.feedback.QualitativeFeedback.QualitativeFeedback

Bases: BaseObject

Feedback object for questions and answers for individual predictions.

Example usage:

>>> import aisquared
>>> my_obj = aisquared.config.feedback.QualitativeFeedback()
>>> my_obj.add_question('Any additional feedback?', 'text')
>>> my_obj.to_dict()
{'className': 'QualitativeFeedback',
'params': {'questions': [{'question': 'Any additional feedback?',
'answerType': 'text'}]}}

add_question(question: str, answer_type: str = 'singleChoice', choices: list = [])

Add a question to be asked.

Parameters:

• question (str) – The question to be asked.

• answer_type (str (default 'singleChoice')) – One of either ‘singleChoice’, ‘multiChoice’, or ‘text’

• choices (list (default [])) – The choices to be provided, if answer_type is ‘singleChoice’ or ‘multiChoice’

to_dict() → dict

Return the object as a dictionary

aisquared.config.feedback.RegressionFeedback module

class aisquared.config.feedback.RegressionFeedback.RegressionFeedback

Bases: BaseObject

Feedback for regression

Example usage:

>>> import aisquared
>>> my_obj = aisquared.config.feedback.RegressionFeedback()
>>> my_obj.to_dict()
{'className': 'RegressionFeedback', 'params': {}}

to_dict() → dict

Return the object as a dictionary

aisquared.config.feedback.SimpleFeedback module

class aisquared.config.feedback.SimpleFeedback.SimpleFeedback

Bases: BaseObject

Simple thumbs-up/thumbs-down feedback for predictions

Example usage:

>>> import aisquared
>>> my_obj = aisquared.config.feedback.SimpleFeedback()
>>> my_obj.to_dict()
{'className': 'SimpleFeedback', 'params': {}}

to_dict() → dict

Return the object as a dictionary

Module contents

The aisquared.config.feedback subpackage contains objects for configuring feedback in aisquared models.