SimpleModelComparison#

class SimpleModelComparison[source]#

Compare given model score to simple model score (according to given model type).

For classification models, the simple model is a dummy classifier the selects the predictions based on a strategy.

Parameters

strategystr, default=’prior’

Strategy to use to generate the predictions of the simple model.

‘most_frequent’ : The most frequent label in the training set is predicted. The probability vector is 1 for the most frequent label and 0 for the other predictions.
‘prior’ : The probability vector always contains the empirical class prior distribution (i.e. the class distribution observed in the training set).
‘stratified’ : The predictions are generated by sampling one-hot vectors from a multinomial distribution parametrized by the empirical class prior probabilities.
‘uniform’ : Generates predictions uniformly at random from the list of unique classes observed in y, i.e. each class has equal probability. The predicted class is chosen randomly.

alternative_metricsDict[str, Metric], default: None

A dictionary of metrics, where the key is the metric name and the value is an ignite.Metric object whose score should be used. If None are given, use the default metrics.

n_to_showint, default: 20

Number of classes to show in the report. If None, show all classes.

show_onlystr, default: ‘largest’

Specify which classes to show in the report. Can be one of the following: - ‘largest’: Show the largest classes. - ‘smallest’: Show the smallest classes. - ‘random’: Show random classes. - ‘best’: Show the classes with the highest score. - ‘worst’: Show the classes with the lowest score.

metric_to_show_bystr, default: None

Specify the metric to sort the results by. Relevant only when show_only is ‘best’ or ‘worst’. If None, sorting by the first metric in the default metrics list.

class_list_to_show: List[int], default: None

Specify the list of classes to show in the report. If specified, n_to_show, show_only and metric_to_show_by are ignored.

__init__(scorers: Optional[Union[Dict[str, Union[Metric, Callable, str]], List[Any]]] = None, strategy: str = 'most_frequent', alternative_metrics=None, n_to_show: int = 20, show_only: str = 'largest', metric_to_show_by: Optional[str] = None, class_list_to_show: Optional[List[int]] = None, **kwargs)[source]#

__new__(*args, **kwargs)#

Methods

`SimpleModelComparison.add_condition`(name, ...)	Add new condition function to the check.
`SimpleModelComparison.add_condition_gain_greater_than`([...])	Add condition - require gain between the model and the simple model to be greater than threshold.
`SimpleModelComparison.clean_conditions`()	Remove all conditions from this check instance.
`SimpleModelComparison.compute`(context)	Compute the metrics for the check.
`SimpleModelComparison.conditions_decision`(result)	Run conditions on given result.
`SimpleModelComparison.config`([...])	Return check configuration (conditions' configuration not yet supported).
`SimpleModelComparison.from_config`(conf[, ...])	Return check object from a CheckConfig object.
`SimpleModelComparison.from_json`(conf[, ...])	Deserialize check instance from JSON string.
`SimpleModelComparison.initialize_run`(context)	Initialize the metrics for the check, and validate task type is relevant.
`SimpleModelComparison.metadata`([with_doc_link])	Return check metadata.
`SimpleModelComparison.name`()	Name of class in split camel case.
`SimpleModelComparison.params`([show_defaults])	Return parameters to show when printing the check.
`SimpleModelComparison.remove_condition`(index)	Remove given condition by index.
`SimpleModelComparison.run`(train_dataset, ...)	Run check.
`SimpleModelComparison.to_json`([indent, ...])	Serialize check instance to JSON string.
`SimpleModelComparison.update`(context, batch, ...)	Update the metrics for the check.

Examples#

RobustnessReport.update

SimpleModelComparison.add_condition