RocCurveDisplay

ROC Curve visualization.

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

Constructors

constructor()

Signature

new RocCurveDisplay(opts?: object): RocCurveDisplay;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.estimator_name?`	`string`	Name of estimator. If `undefined`, the estimator name is not shown.
`opts.fpr?`	`ArrayLike`	False positive rate.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The class considered as the positive class when computing the roc auc metrics. By default, `estimators.classes\_\[1\]` is considered as the positive class.
`opts.roc_auc?`	`number`	Area under ROC curve. If `undefined`, the roc_auc score is not shown.
`opts.tpr?`	`ArrayLike`	True positive rate.

Returns

RocCurveDisplay

Defined in: generated/metrics/RocCurveDisplay.ts:25 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

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

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/metrics/RocCurveDisplay.ts:114 (opens in a new tab)

from_estimator()

Create a ROC Curve display from an estimator.

Signature

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

Parameters

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.chance_level_kw?`	`any`	Keyword arguments to be passed to matplotlib’s `plot` for rendering the chance level line.
`opts.drop_intermediate?`	`boolean`	Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves. `Default Value` `true`
`opts.estimator?`	`any`	Fitted classifier or a fitted `Pipeline` in which the last estimator is a classifier.
`opts.kwargs?`	`any`	Keyword arguments to be passed to matplotlib’s `plot`.
`opts.name?`	`string`	Name of ROC Curve for labeling. If `undefined`, use the name of the estimator.
`opts.plot_chance_level?`	`boolean`	Whether to plot the chance level. `Default Value` `false`
`opts.pos_label?`	`string` \| `number` \| `boolean`	The class considered as the positive class when computing the roc auc metrics. By default, `estimators.classes\_\[1\]` is considered as the positive class.
`opts.response_method?`	`"decision_function"` \| `"auto’} default=’auto"`	Specifies whether to use predict_proba or decision_function as the 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.

Returns

Promise<any>

Defined in: generated/metrics/RocCurveDisplay.ts:131 (opens in a new tab)

from_predictions()

Plot ROC curve given the true and predicted values.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.ax?`	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.chance_level_kw?`	`any`	Keyword arguments to be passed to matplotlib’s `plot` for rendering the chance level line.
`opts.drop_intermediate?`	`boolean`	Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves. `Default Value` `true`
`opts.kwargs?`	`any`	Additional keywords arguments passed to matplotlib `plot` function.
`opts.name?`	`string`	Name of ROC curve for labeling. If `undefined`, name will be set to `"Classifier"`.
`opts.plot_chance_level?`	`boolean`	Whether to plot the chance level. `Default Value` `false`
`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.

Returns

Promise<any>

Defined in: generated/metrics/RocCurveDisplay.ts:245 (opens in a new tab)

init()

Initializes the underlying Python resources.

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

Signature

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

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/metrics/RocCurveDisplay.ts:68 (opens in a new tab)

plot()

Plot visualization.

Extra keyword arguments will be passed to matplotlib’s plot.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.ax?`	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.chance_level_kw?`	`any`	Keyword arguments to be passed to matplotlib’s `plot` for rendering the chance level line.
`opts.kwargs?`	`any`	Keyword arguments to be passed to matplotlib’s `plot`.
`opts.name?`	`string`	Name of ROC Curve for labeling. If `undefined`, use `estimator\_name` if not `undefined`, otherwise no labeling is shown.
`opts.plot_chance_level?`	`boolean`	Whether to plot the chance level. `Default Value` `false`