PrecisionRecallDisplay

Precision Recall visualization.

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

Constructors

constructor()

Signature

new PrecisionRecallDisplay(opts?: object): PrecisionRecallDisplay;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.average_precision?`	`number`	Average precision. If `undefined`, the average precision is not shown.
`opts.estimator_name?`	`string`	Name of estimator. If `undefined`, then the estimator name is not shown.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The class considered as the positive class. If `undefined`, the class will not be shown in the legend.
`opts.precision?`	`ArrayLike`	Precision values.
`opts.prevalence_pos_label?`	`number`	The prevalence of the positive label. It is used for plotting the chance level line. If `undefined`, the chance level line will not be plotted even if `plot\_chance\_level` is set to `true` when plotting.
`opts.recall?`	`ArrayLike`	Recall values.

Returns

PrecisionRecallDisplay

Defined in: generated/metrics/PrecisionRecallDisplay.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/PrecisionRecallDisplay.ts:129 (opens in a new tab)

from_estimator()

Plot precision-recall curve given an estimator and some data.

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 precision-recall curve. This is useful in order to create lighter precision-recall curves. `Default Value` `false`
`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 for labeling curve. If `undefined`, no name is used.
`opts.plot_chance_level?`	`boolean`	Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during `from\_estimator` or `from\_predictions` call. `Default Value` `false`
`opts.pos_label?`	`string` \| `number` \| `boolean`	The class considered as the positive class when computing the precision and recall metrics. By default, `estimators.classes\_\[1\]` is considered as the positive class.
`opts.response_method?`	`"auto"` \| `"predict_proba"` \| `"decision_function"`	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. `Default Value` `'auto'`
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/metrics/PrecisionRecallDisplay.ts:146 (opens in a new tab)

from_predictions()

Plot precision-recall curve given binary class predictions.

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 precision-recall curve. This is useful in order to create lighter precision-recall curves. `Default Value` `false`
`opts.kwargs?`	`any`	Keyword arguments to be passed to matplotlib’s `plot`.
`opts.name?`	`string`	Name for labeling curve. If `undefined`, name will be set to `"Classifier"`.
`opts.plot_chance_level?`	`boolean`	Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during `from\_estimator` or `from\_predictions` call. `Default Value` `false`
`opts.pos_label?`	`string` \| `number` \| `boolean`	The class considered as the positive class when computing the precision and recall metrics.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y_pred?`	`ArrayLike`	Estimated probabilities or output of decision function.
`opts.y_true?`	`ArrayLike`	True binary labels.

Returns

Promise<any>

Defined in: generated/metrics/PrecisionRecallDisplay.ts:261 (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/PrecisionRecallDisplay.ts:73 (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 precision recall 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. The chance level is the prevalence of the positive label computed from the data passed during `from\_estimator` or `from\_predictions` call. `Default Value` `false`