Class: PrecisionRecallDisplay
Precision Recall visualization.
It is recommend to use from_estimator or from_predictions to create a PrecisionRecallDisplay. All parameters are stored as attributes.
Read more in the User Guide.
Constructors
new PrecisionRecallDisplay()
new PrecisionRecallDisplay(
opts?):PrecisionRecallDisplay
Parameters
| Parameter | 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
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/metrics/PrecisionRecallDisplay.ts:23 |
_isInitialized | boolean | false | generated/metrics/PrecisionRecallDisplay.ts:22 |
_py | PythonBridge | undefined | generated/metrics/PrecisionRecallDisplay.ts:21 |
id | string | undefined | generated/metrics/PrecisionRecallDisplay.ts:18 |
opts | any | undefined | generated/metrics/PrecisionRecallDisplay.ts:19 |
Accessors
ax_
Get Signature
get ax_():
Promise<any>
Axes with precision recall curve.
Returns Promise<any>
Defined in generated/metrics/PrecisionRecallDisplay.ts:427
chance_level_
Get Signature
get chance_level_():
Promise<any>
The chance level line. It is undefined if the chance level is not plotted.
Returns Promise<any>
Defined in generated/metrics/PrecisionRecallDisplay.ts:400
figure_
Get Signature
get figure_():
Promise<any>
Figure containing the curve.
Returns Promise<any>
Defined in generated/metrics/PrecisionRecallDisplay.ts:454
line_
Get Signature
get line_():
Promise<any>
Precision recall curve.
Returns Promise<any>
Defined in generated/metrics/PrecisionRecallDisplay.ts:373
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/metrics/PrecisionRecallDisplay.ts:60
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/metrics/PrecisionRecallDisplay.ts:116
from_estimator()
from_estimator(
opts):Promise<any>
Plot precision-recall curve given an estimator and some data.
Parameters
| Parameter | 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. |
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. |
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. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike | Input values. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/metrics/PrecisionRecallDisplay.ts:133
from_predictions()
from_predictions(
opts):Promise<any>
Plot precision-recall curve given binary class predictions.
Parameters
| Parameter | 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. |
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. |
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:230
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/metrics/PrecisionRecallDisplay.ts:73
plot()
plot(
opts):Promise<any>
Plot visualization.
Extra keyword arguments will be passed to matplotlib’s plot.
Parameters
| Parameter | 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. |
Returns Promise<any>