Output

analora.output

Contain outputs.

analora.output.BaseLazyOutput

Bases: BaseOutput

Define a base class that partially implements the lazy computation logic.

analora.output.BaseOutput

Bases: ABC

Define the base class to implement an output.

Example usage:

>>> from analora.output import Output
>>> from analora.content import ContentGenerator
>>> from analora.evaluator import Evaluator
>>> output = Output(content=ContentGenerator("meow"), evaluator=Evaluator())
>>> output
Output(
  (content): ContentGenerator()
  (evaluator): Evaluator(count=0)
)
>>> output.get_content_generator()
ContentGenerator()
>>> output.get_evaluator()
Evaluator(count=0)

analora.output.BaseOutput.compute abstractmethod

compute() -> BaseOutput

Compute the results and return a new ouptut.

Returns:

Type	Description
BaseOutput	A new ouptut with the computed results.

Example usage:

>>> from analora.output import Output
>>> from analora.content import ContentGenerator
>>> from analora.evaluator import Evaluator
>>> output = Output(
...     content=ContentGenerator("meow"), evaluator=Evaluator({"accuracy": 0.42})
... )
>>> out = output.compute()
>>> out
Output(
  (content): ContentGenerator()
  (evaluator): Evaluator(count=1)
)

analora.output.BaseOutput.equal abstractmethod

equal(other: Any, equal_nan: bool = False) -> bool

Indicate if two outputs are equal or not.

Parameters:

Name	Type	Description	Default
other	Any	The other output to compare.	required
equal_nan	bool	Whether to compare NaN's as equal. If True, NaN's in both objects will be considered equal.	False

Returns:

Type	Description
bool	True if the two outputs are equal, otherwise False.

Example usage:

>>> from analora.output import Output
>>> from analora.content import ContentGenerator
>>> from analora.evaluator import Evaluator
>>> output1 = Output(content=ContentGenerator("meow"), evaluator=Evaluator())
>>> output2 = Output(content=ContentGenerator("meow"), evaluator=Evaluator())
>>> output3 = Output(
...     content=ContentGenerator("hello"), evaluator=Evaluator({"accuracy": 0.42})
... )
>>> output1.equal(output2)
True
>>> output1.equal(output3)
False

analora.output.BaseOutput.get_content_generator abstractmethod

get_content_generator(
    lazy: bool = True,
) -> BaseContentGenerator

Get the HTML content generator associated to the output.

Parameters:

Name	Type	Description	Default
lazy	bool	If True, it forces the computation of the content, otherwise it returns a content generator object that contains the logic to generate the content.	True

Returns:

Type	Description
BaseContentGenerator	The HTML content generator.

Example usage:

>>> from analora.output import Output
>>> from analora.content import ContentGenerator
>>> from analora.evaluator import Evaluator
>>> output = Output(content=ContentGenerator("meow"), evaluator=Evaluator())
>>> output.get_content_generator()
ContentGenerator()

analora.output.BaseOutput.get_evaluator abstractmethod

get_evaluator(lazy: bool = True) -> BaseEvaluator

Get the evaluator associated to the output.

Parameters:

Name	Type	Description	Default
lazy	bool	If True, it forces the computation of the metrics, otherwise it returns an evaluator object that contains the logic to evaluate the metrics.	True

Returns:

Type	Description
BaseEvaluator	The evaluator.

Example usage:

```pycon

from analora.output import Output from analora.content import ContentGenerator from analora.evaluator import Evaluator output = Output(content=ContentGenerator("meow"), evaluator=Evaluator()) output.get_evaluator() Evaluator(count=0)

analora.output.EmptyOutput

Bases: Output

Implement the accuracy output.

Example usage:

>>> from analora.output import EmptyOutput
>>> output = EmptyOutput()
>>> output
EmptyOutput()
>>> output.get_content_generator()
ContentGenerator()
>>> output.get_evaluator()
Evaluator(count=0)

analora.output.Output

Bases: BaseLazyOutput

Implement a simple output.

Parameters:

Name	Type	Description	Default
content	BaseContentGenerator	The HTML content generator.	required
evaluator	BaseEvaluator	The evaluator.	required

Example usage:

>>> from analora.output import Output
>>> from analora.content import ContentGenerator
>>> from analora.evaluator import Evaluator
>>> output = Output(content=ContentGenerator("meow"), evaluator=Evaluator())
>>> output
Output(
  (content): ContentGenerator()
  (evaluator): Evaluator(count=0)
)
>>> output.get_content_generator()
ContentGenerator()
>>> output.get_evaluator()
Evaluator(count=0)