ConfusionMatrixDisplay

Confusion Matrix visualization.

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

Constructors

constructor()

Signature

new ConfusionMatrixDisplay(opts?: object): ConfusionMatrixDisplay;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.confusion_matrix?`	`ArrayLike`[]	Confusion matrix.
`opts.display_labels?`	`ArrayLike`	Display labels for plot. If `undefined`, display labels are set from 0 to `n\_classes \- 1`.

Returns

ConfusionMatrixDisplay

Defined in: generated/metrics/ConfusionMatrixDisplay.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/ConfusionMatrixDisplay.ts:102 (opens in a new tab)

from_estimator()

Plot Confusion Matrix 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.cmap?`	`string`	Colormap recognized by matplotlib. `Default Value` `'viridis'`
`opts.colorbar?`	`boolean`	Whether or not to add a colorbar to the plot. `Default Value` `true`
`opts.display_labels?`	`ArrayLike`	Target names used for plotting. By default, `labels` will be used if it is defined, otherwise the unique labels of `y\_true` and `y\_pred` will be used.
`opts.estimator?`	`any`	Fitted classifier or a fitted `Pipeline` in which the last estimator is a classifier.
`opts.im_kw?`	`any`	Dict with keywords passed to `matplotlib.pyplot.imshow` call.
`opts.include_values?`	`boolean`	Includes values in confusion matrix. `Default Value` `true`
`opts.labels?`	`ArrayLike`	List of labels to index the confusion matrix. This may be used to reorder or select a subset of labels. If `undefined` is given, those that appear at least once in `y\_true` or `y\_pred` are used in sorted order.
`opts.normalize?`	`"all"` \| `"true"` \| `"pred"`	Either to normalize the counts display in the matrix:
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.text_kw?`	`any`	Dict with keywords passed to `matplotlib.pyplot.text` call.
`opts.values_format?`	`string`	Format specification for values in confusion matrix. If `undefined`, the format specification is ‘d’ or ‘.2g’ whichever is shorter.
`opts.xticks_rotation?`	`number` \| `"vertical"` \| `"horizontal"`	Rotation of xtick labels. `Default Value` `'horizontal'`
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/metrics/ConfusionMatrixDisplay.ts:121 (opens in a new tab)

from_predictions()

Plot Confusion Matrix given true and predicted labels.

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.cmap?`	`string`	Colormap recognized by matplotlib. `Default Value` `'viridis'`
`opts.colorbar?`	`boolean`	Whether or not to add a colorbar to the plot. `Default Value` `true`
`opts.display_labels?`	`ArrayLike`	Target names used for plotting. By default, `labels` will be used if it is defined, otherwise the unique labels of `y\_true` and `y\_pred` will be used.
`opts.im_kw?`	`any`	Dict with keywords passed to `matplotlib.pyplot.imshow` call.
`opts.include_values?`	`boolean`	Includes values in confusion matrix. `Default Value` `true`
`opts.labels?`	`ArrayLike`	List of labels to index the confusion matrix. This may be used to reorder or select a subset of labels. If `undefined` is given, those that appear at least once in `y\_true` or `y\_pred` are used in sorted order.
`opts.normalize?`	`"all"` \| `"true"` \| `"pred"`	Either to normalize the counts display in the matrix:
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.text_kw?`	`any`	Dict with keywords passed to `matplotlib.pyplot.text` call.
`opts.values_format?`	`string`	Format specification for values in confusion matrix. If `undefined`, the format specification is ‘d’ or ‘.2g’ whichever is shorter.
`opts.xticks_rotation?`	`number` \| `"vertical"` \| `"horizontal"`	Rotation of xtick labels. `Default Value` `'horizontal'`
`opts.y_pred?`	`ArrayLike`	The predicted labels given by the method `predict` of an classifier.
`opts.y_true?`	`ArrayLike`	True labels.

Returns

Promise<any>

Defined in: generated/metrics/ConfusionMatrixDisplay.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/ConfusionMatrixDisplay.ts:53 (opens in a new tab)

plot()

Plot visualization.

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.cmap?`	`string`	Colormap recognized by matplotlib. `Default Value` `'viridis'`
`opts.colorbar?`	`boolean`	Whether or not to add a colorbar to the plot. `Default Value` `true`
`opts.im_kw?`	`any`	Dict with keywords passed to `matplotlib.pyplot.imshow` call.
`opts.include_values?`	`boolean`	Includes values in confusion matrix. `Default Value` `true`
`opts.text_kw?`	`any`	Dict with keywords passed to `matplotlib.pyplot.text` call.
`opts.values_format?`	`string`	Format specification for values in confusion matrix. If `undefined`, the format specification is ‘d’ or ‘.2g’ whichever is shorter.
`opts.xticks_rotation?`	`number` \| `"vertical"` \| `"horizontal"`	Rotation of xtick labels. `Default Value` `'horizontal'`