DetCurveDisplay

DET curve visualization.

It is recommend to use from\_estimator or from\_predictions to create a visualizer. All parameters are stored as attributes.

Constructors

new DetCurveDisplay(opts?: object): DetCurveDisplay;

Name	Type	Description
`opts?`	`object`	-
`opts.estimator_name?`	`string`	Name of estimator. If `undefined`, the estimator name is not shown.
`opts.fnr?`	`ArrayLike`	False negative rate.
`opts.fpr?`	`ArrayLike`	False positive rate.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The label of the positive class.

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

dispose(): Promise<void>;

Promise<void>

Plot DET curve given an estimator and data.

from_estimator(opts: object): Promise<any>;

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Input values.
`opts.ax?`	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.estimator?`	`any`	Fitted classifier or a fitted `Pipeline` in which the last estimator is a classifier.
`opts.kwargs?`	`any`	Additional keywords arguments passed to matplotlib `plot` function.
`opts.name?`	`string`	Name of DET curve for labeling. If `undefined`, use the name of the estimator.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The label of the positive class. When `pos\_label=None`, if `y\_true` is in {-1, 1} or {0, 1}, `pos\_label` is set to 1, otherwise an error will be raised.
`opts.response_method?`	`"decision_function"` \| `"auto’} default=’auto"`	Specifies whether to use predict_proba or decision_function as the predicted target response. If set to ‘auto’, predict_proba is tried first and if it does not exist decision_function is tried next.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	Target values.

Promise<any>

Plot the DET curve given the true and predicted labels.

from_predictions(opts: object): Promise<any>;

Name	Type	Description
`opts`	`object`	-
`opts.ax?`	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.kwargs?`	`any`	Additional keywords arguments passed to matplotlib `plot` function.
`opts.name?`	`string`	Name of DET curve for labeling. If `undefined`, name will be set to `"Classifier"`.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The label of the positive class. When `pos\_label=None`, if `y\_true` is in {-1, 1} or {0, 1}, `pos\_label` is set to 1, otherwise an error will be raised.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y_pred?`	`ArrayLike`	Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by `decision\_function` on some classifiers).
`opts.y_true?`	`ArrayLike`	True labels.

Promise<any>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

init(py: PythonBridge): Promise<void>;

Name	Type
`py`	`PythonBridge`

Promise<void>

Plot visualization.

plot(opts: object): Promise<any>;

Name	Type	Description
`opts`	`object`	-
`opts.ax?`	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.kwargs?`	`any`	Additional keywords arguments passed to matplotlib `plot` function.
`opts.name?`	`string`	Name of DET curve for labeling. If `undefined`, use `estimator\_name` if it is not `undefined`, otherwise no labeling is shown.