Record

Information on the software to evaluate is stored in a Record instance.

exception dffml.record.NoSuchFeature

class dffml.record.Record(key: str, *, data: Optional[Dict[str, Any]] = None, extra: Optional[Dict[str, Any]] = None)

Manages feature independent information and actions for a record.

evaluated(results: Dict[str, Any], overwrite=False)

Updates features with the result dict

Parameters:

• results (dict) – The results that will be added to the features.

• overwrite (boolean) – If ‘True’, the function overwrites the current features with the results provided. If ‘Fasle’, the function updates the current features with the results provided.

Examples

>>> from dffml import *
>>>
>>> example = Record("example", data=dict(features=dict(dead="beef")))
>>> print(example.features())
{'dead': 'beef'}
>>> results = {"new": "feature"}
>>> example.evaluated({"feed": "face"})
>>> print(example.features())
{'dead': 'beef', 'feed': 'face'}
>>> example.evaluated(results, overwrite=True)
>>> print(example.features())
{'new': 'feature'}

feature(name: str) → Any

Returns a feature of the record.

Parameters:

name (str) – The name of the feature that will be returned.

Returns:

feature.

Return type:

any

Examples

>>> from dffml import *
>>>
>>> example = Record("example", data=dict(features=dict(dead="beef")))
>>> print(example.feature("dead"))
beef

features(subset: List[str] = []) → Dict[str, Any]

Returns all features for the record or the subset specified.

Parameters:

subset (list[str]) – The subset of features that will be returned.

Returns:

features.

Return type:

dict

Examples

>>> from dffml import *
>>>
>>> example = Record("example", data=dict(features=dict(dead="beef")))
>>>
>>> print(example.features(["dead"]))
{'dead': 'beef'}

predicted(target: str, value: Any, confidence: float)

Set the prediction for this record.

Parameters:

• target (str) – The target you want to store the prediction at.

• value (Any) – The prediction.

Examples

>>> from dffml import *
>>>
>>> example = Record("example", data=dict(features=dict(dead="beef")))
>>> example.predicted("target_name", "feed", 1.00)
>>> print(example.prediction("target_name"))
{'confidence': 1.0, 'value': 'feed'}

prediction(target: str) → RecordPrediction

Get the prediction for this record.

Parameters:

target (str) – The name of the feature that will be returned.

Returns:

The prediction of the target specified.

Return type:

RecordPrediction

Examples

>>> from dffml import *
>>>
>>> example = Record("example", data=dict(features=dict(dead="beef")))
>>> example.predicted("target_name", "feed", 1.00)
>>> print(example.prediction("target_name"))
{'confidence': 1.0, 'value': 'feed'}

predictions(subset: List[str] = []) → Dict[str, Any]

Get the predictions for the subset of record.

Parameters:

subset (list[str]) – The list of subset of the record that predictions are returned for.

Returns:

The prediction of the specified subset.

Return type:

dict

Examples

>>> from dffml import *
>>>
>>> example = Record("example", data=dict(features=dict(dead="beef")))
>>> example.predicted("target_name1", "feed", 1.00)
>>> example.predicted("target_name2", "deed", 0.97)
>>> print(example.predictions(["target_name1", "target_name2"]))
{'target_name1': {'confidence': 1.0, 'value': 'feed'}, 'target_name2': {'confidence': 0.97, 'value': 'deed'}}

class dffml.record.RecordPrediction(*, confidence: float = 0.0, value: Optional[Any] = None)