Class: LearningCurveDisplay
Learning Curve visualization.
It is recommended to use from_estimator to create a LearningCurveDisplay 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 learning curve visualization.
Constructors
new LearningCurveDisplay()
new LearningCurveDisplay(
opts?):LearningCurveDisplay
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.score_name? | string | The name of the score used in learning_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. |
opts.train_sizes? | ArrayLike | Numbers of training examples that has been used to generate the learning curve. |
Returns LearningCurveDisplay
Defined in generated/model_selection/LearningCurveDisplay.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/LearningCurveDisplay.ts:23 |
_isInitialized | boolean | false | generated/model_selection/LearningCurveDisplay.ts:22 |
_py | PythonBridge | undefined | generated/model_selection/LearningCurveDisplay.ts:21 |
id | string | undefined | generated/model_selection/LearningCurveDisplay.ts:18 |
opts | any | undefined | generated/model_selection/LearningCurveDisplay.ts:19 |
Accessors
ax_
Get Signature
get ax_():
Promise<any>
Axes with the learning curve.
Returns Promise<any>
Defined in generated/model_selection/LearningCurveDisplay.ts:360
errorbar_
Get Signature
get errorbar_():
Promise<any>
When the std_display_style is "errorbar", this is a list of matplotlib.container.ErrorbarContainer objects. If another style is used, errorbar_ is undefined.
Returns Promise<any>
Defined in generated/model_selection/LearningCurveDisplay.ts:414
figure_
Get Signature
get figure_():
Promise<any>
Figure containing the learning curve.
Returns Promise<any>
Defined in generated/model_selection/LearningCurveDisplay.ts:387
fill_between_
Get Signature
get fill_between_():
Promise<any>
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.
Returns Promise<any>
Defined in generated/model_selection/LearningCurveDisplay.ts:468
lines_
Get Signature
get lines_():
Promise<any>
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.
Returns Promise<any>
Defined in generated/model_selection/LearningCurveDisplay.ts:441
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/model_selection/LearningCurveDisplay.ts:50
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/model_selection/LearningCurveDisplay.ts:106
from_estimator()
from_estimator(
opts):Promise<any>
Create a learning curve display from an estimator.
Read more in the User Guide for general information about the visualization API and detailed documentation regarding the learning curve visualization.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
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.exploit_incremental_learning? | boolean | If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes. |
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 context. \-1 means using all processors. See Glossary for more details. |
opts.negate_score? | boolean | Whether or not to negate the scores obtained through learning_curve. This is particularly useful when using the error denoted by neg_\* in scikit-learn. |
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’. |
opts.random_state? | number | Used when shuffle is true. Pass an int for reproducible output across multiple function calls. See Glossary. |
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". |
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.shuffle? | boolean | Whether to shuffle training data before taking prefixes of it based ontrain_sizes. |
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. |
opts.train_sizes? | ArrayLike | Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. |
opts.verbose? | number | Controls the verbosity: the higher, the more messages. |
opts.X? | ArrayLike[] | Training data, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | Target relative to X for classification or regression; undefined for unsupervised learning. |
Returns Promise<any>
Defined in generated/model_selection/LearningCurveDisplay.ts:125
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/model_selection/LearningCurveDisplay.ts:63
plot()
plot(
opts):Promise<any>
Plot visualization.
Parameters
| Parameter | 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 learning_curve. This is particularly useful when using the error denoted by neg_\* in scikit-learn. |
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". |
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. |
Returns Promise<any>
Defined in generated/model_selection/LearningCurveDisplay.ts:285