ValidationCurveDisplay

Validation Curve visualization.

It is recommended to use from\_estimator to create a ValidationCurveDisplay instance. All parameters are stored as attributes.

Read more in the User Guide for general information about the visualization API and detailed documentation regarding the validation curve visualization.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new ValidationCurveDisplay(opts?: object): ValidationCurveDisplay;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.param_name?`	`string`	Name of the parameter that has been varied.
`opts.param_range?`	`ArrayLike`	The values of the parameter that have been evaluated.
`opts.score_name?`	`string`	The name of the score used in `validation\_curve`. It will override the name inferred from the `scoring` parameter. If `score` is `undefined`, we use `"Score"` if `negate\_score` is `false` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `\_` by spaces and capitalize the first letter. We remove `neg\_` and replace it by `"Negative"` if `negate\_score` is `false` or just remove it otherwise.
`opts.test_scores?`	`ArrayLike`[]	Scores on test set.
`opts.train_scores?`	`ArrayLike`[]	Scores on training sets.

Returns

ValidationCurveDisplay

Defined in: generated/model_selection/ValidationCurveDisplay.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/model_selection/ValidationCurveDisplay.ts:122 (opens in a new tab)

from_estimator()

Create a validation curve display from an estimator.

Read more in the User Guide for general information about the visualization API and detailed documentation regarding the validation curve visualization.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training data, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.ax?`	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.cv?`	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.error_score?`	`"raise"`	Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised.
`opts.errorbar_kw?`	`any`	Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score.
`opts.estimator?`	`any`	An object of that type which is cloned for each validation.
`opts.fill_between_kw?`	`any`	Additional keyword arguments passed to the `plt.fill\_between` used to draw the score standard deviation.
`opts.fit_params?`	`any`	Parameters to pass to the fit method of the estimator.
`opts.groups?`	`ArrayLike`	Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., `GroupKFold`).
`opts.line_kw?`	`any`	Additional keyword arguments passed to the `plt.plot` used to draw the mean score.
`opts.n_jobs?`	`number`	Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the different training and test sets. `undefined` means 1 unless in a `joblib.parallel\_backend` (opens in a new tab) context. `\-1` means using all processors. See Glossary for more details.
`opts.negate_score?`	`boolean`	Whether or not to negate the scores obtained through `validation\_curve`. This is particularly useful when using the error denoted by `neg\_\*` in `scikit-learn`. `Default Value` `false`
`opts.param_name?`	`string`	Name of the parameter that will be varied.
`opts.param_range?`	`ArrayLike`	The values of the parameter that will be evaluated.
`opts.pre_dispatch?`	`string` \| `number`	Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The str can be an expression like ‘2*n_jobs’. `Default Value` `'all'`
`opts.score_name?`	`string`	The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `undefined`, we use `"Score"` if `negate\_score` is `false` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `\_` by spaces and capitalize the first letter. We remove `neg\_` and replace it by `"Negative"` if `negate\_score` is `false` or just remove it otherwise.
`opts.score_type?`	`"both"` \| `"test"` \| `"train"`	The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`. `Default Value` `'both'`
`opts.scoring?`	`string`	A string (see The scoring parameter: defining model evaluation rules) or a scorer callable object / function with signature `scorer(estimator, X, y)` (see Defining your scoring strategy from metric functions).
`opts.std_display_style?`	`"errorbar"` \| `"fill_between"`	The style used to display the score standard deviation around the mean score. If `undefined`, no representation of the standard deviation is displayed. `Default Value` `'fill_between'`
`opts.verbose?`	`number`	Controls the verbosity: the higher, the more messages. `Default Value` `0`
`opts.y?`	`ArrayLike`	Target relative to X for classification or regression; `undefined` for unsupervised learning.

Returns

Promise<any>

Defined in: generated/model_selection/ValidationCurveDisplay.ts:141 (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/model_selection/ValidationCurveDisplay.ts:68 (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.errorbar_kw?`	`any`	Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score.
`opts.fill_between_kw?`	`any`	Additional keyword arguments passed to the `plt.fill\_between` used to draw the score standard deviation.
`opts.line_kw?`	`any`	Additional keyword arguments passed to the `plt.plot` used to draw the mean score.
`opts.negate_score?`	`boolean`	Whether or not to negate the scores obtained through `validation\_curve`. This is particularly useful when using the error denoted by `neg\_\*` in `scikit-learn`. `Default Value` `false`
`opts.score_name?`	`string`	The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `undefined`, we use `"Score"` if `negate\_score` is `false` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `\_` by spaces and capitalize the first letter. We remove `neg\_` and replace it by `"Negative"` if `negate\_score` is `false` or just remove it otherwise.
`opts.score_type?`	`"both"` \| `"test"` \| `"train"`	The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`. `Default Value` `'both'`
`opts.std_display_style?`	`"errorbar"` \| `"fill_between"`	The style used to display the score standard deviation around the mean score. If `undefined`, no standard deviation representation is displayed. `Default Value` `'fill_between'`

Returns

Promise<any>

Defined in: generated/model_selection/ValidationCurveDisplay.ts:313 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/model_selection/ValidationCurveDisplay.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/model_selection/ValidationCurveDisplay.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/model_selection/ValidationCurveDisplay.ts:21 (opens in a new tab)

id

string

Defined in: generated/model_selection/ValidationCurveDisplay.ts:18 (opens in a new tab)

opts

any

Defined in: generated/model_selection/ValidationCurveDisplay.ts:19 (opens in a new tab)

Accessors

ax_

Axes with the validation curve.

Signature

ax_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/model_selection/ValidationCurveDisplay.ts:395 (opens in a new tab)

errorbar_

When the std\_display\_style is "errorbar", this is a list of matplotlib.container.ErrorbarContainer objects. If another style is used, errorbar\_ is undefined.

Signature

errorbar_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/model_selection/ValidationCurveDisplay.ts:449 (opens in a new tab)

figure_

Figure containing the validation curve.

Signature

figure_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/model_selection/ValidationCurveDisplay.ts:422 (opens in a new tab)

fill_between_

When the std\_display\_style is "fill\_between", this is a list of matplotlib.collections.PolyCollection objects. If another style is used, fill\_between\_ is undefined.

Signature

fill_between_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/model_selection/ValidationCurveDisplay.ts:503 (opens in a new tab)

lines_

When the std\_display\_style is "fill\_between", this is a list of matplotlib.lines.Line2D objects corresponding to the mean train and test scores. If another style is used, line\_ is undefined.