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Classes
RocCurveDisplay

RocCurveDisplay

ROC Curve visualization.

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

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new RocCurveDisplay(opts?: object): RocCurveDisplay;

Parameters

NameTypeDescription
opts?object-
opts.estimator_name?stringName of estimator. If undefined, the estimator name is not shown.
opts.fpr?ArrayLikeFalse positive rate.
opts.pos_label?string | numberThe 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?numberArea under ROC curve. If undefined, the roc_auc score is not shown.
opts.tpr?ArrayLikeTrue positive rate.

Returns

RocCurveDisplay

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

Properties

_isDisposed

boolean = false

Defined in: generated/metrics/RocCurveDisplay.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/metrics/RocCurveDisplay.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/metrics/RocCurveDisplay.ts:21 (opens in a new tab)

id

string

Defined in: generated/metrics/RocCurveDisplay.ts:18 (opens in a new tab)

opts

any

Defined in: generated/metrics/RocCurveDisplay.ts:19 (opens in a new tab)

Accessors

ax_

Axes with ROC Curve.

Signature

ax_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/RocCurveDisplay.ts:388 (opens in a new tab)

figure_

Figure containing the curve.

Signature

figure_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/RocCurveDisplay.ts:411 (opens in a new tab)

line_

ROC Curve.

Signature

line_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/RocCurveDisplay.ts:365 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/metrics/RocCurveDisplay.ts:55 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/metrics/RocCurveDisplay.ts:59 (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

NameTypeDescription
optsobject-
opts.X?ArrayLikeInput values.
opts.ax?anyAxes object to plot on. If undefined, a new figure and axes is created.
opts.drop_intermediate?booleanWhether 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?anyFitted classifier or a fitted Pipeline in which the last estimator is a classifier.
opts.kwargs?anyKeyword arguments to be passed to matplotlib’s plot.
opts.name?stringName of ROC Curve for labeling. If undefined, use the name of the estimator.
opts.pos_label?string | numberThe 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?ArrayLikeSample weights.
opts.y?ArrayLikeTarget 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.

Read more in the User Guide.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.ax?anyAxes object to plot on. If undefined, a new figure and axes is created.
opts.drop_intermediate?booleanWhether 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?anyAdditional keywords arguments passed to matplotlib plot function.
opts.name?stringName of ROC curve for labeling. If undefined, name will be set to "Classifier".
opts.pos_label?string | numberThe 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?ArrayLikeSample weights.
opts.y_pred?ArrayLikeTarget 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?ArrayLikeTrue labels.

Returns

Promise<any>

Defined in: generated/metrics/RocCurveDisplay.ts:231 (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

NameType
pyPythonBridge

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

NameTypeDescription
optsobject-
opts.ax?anyAxes object to plot on. If undefined, a new figure and axes is created.
opts.kwargs?anyKeyword arguments to be passed to matplotlib’s plot.
opts.name?stringName of ROC Curve for labeling. If undefined, use estimator\_name if not undefined, otherwise no labeling is shown.

Returns

Promise<any>

Defined in: generated/metrics/RocCurveDisplay.ts:320 (opens in a new tab)