Class: IsotonicRegression
Isotonic regression model.
Read more in the User Guide.
Constructors
new IsotonicRegression()
new IsotonicRegression(
opts?):IsotonicRegression
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.increasing? | boolean | "auto" | Determines whether the predictions should be constrained to increase or decrease with X. ‘auto’ will decide based on the Spearman correlation estimate’s sign. |
opts.out_of_bounds? | "nan" | "clip" | "raise" | Handles how X values outside of the training domain are handled during prediction. |
opts.y_max? | number | Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf. |
opts.y_min? | number | Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf. |
Returns IsotonicRegression
Defined in generated/isotonic/IsotonicRegression.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/isotonic/IsotonicRegression.ts:21 |
_isInitialized | boolean | false | generated/isotonic/IsotonicRegression.ts:20 |
_py | PythonBridge | undefined | generated/isotonic/IsotonicRegression.ts:19 |
id | string | undefined | generated/isotonic/IsotonicRegression.ts:16 |
opts | any | undefined | generated/isotonic/IsotonicRegression.ts:17 |
Accessors
f_
Get Signature
get f_():
Promise<any>
The stepwise interpolating function that covers the input domain X.
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:709
increasing_
Get Signature
get increasing_():
Promise<boolean>
Inferred value for increasing.
Returns Promise<boolean>
Defined in generated/isotonic/IsotonicRegression.ts:734
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/isotonic/IsotonicRegression.ts:52
X_max_
Get Signature
get X_max_():
Promise<number>
Maximum value of input array X_ for right bound.
Returns Promise<number>
Defined in generated/isotonic/IsotonicRegression.ts:628
X_min_
Get Signature
get X_min_():
Promise<number>
Minimum value of input array X_ for left bound.
Returns Promise<number>
Defined in generated/isotonic/IsotonicRegression.ts:601
X_thresholds_
Get Signature
get X_thresholds_():
Promise<ArrayLike>
Unique ascending X values used to interpolate the y = f(X) monotonic function.
Returns Promise<ArrayLike>
Defined in generated/isotonic/IsotonicRegression.ts:655
y_thresholds_
Get Signature
get y_thresholds_():
Promise<ArrayLike>
De-duplicated y values suitable to interpolate the y = f(X) monotonic function.
Returns Promise<ArrayLike>
Defined in generated/isotonic/IsotonicRegression.ts:682
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/isotonic/IsotonicRegression.ts:108
fit()
fit(
opts):Promise<any>
Fit the model using X, y as training data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Weights. If set to undefined, all weights will be set to 1 (equal weights). |
opts.X? | number | ArrayLike | Training data. |
opts.y? | ArrayLike | Training target. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:125
fit_transform()
fit_transform(
opts):Promise<any[]>
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Additional fit parameters. |
opts.X? | ArrayLike[] | Input samples. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any[]>
Defined in generated/isotonic/IsotonicRegression.ts:171
get_feature_names_out()
get_feature_names_out(
opts):Promise<any>
Get output feature names for transformation.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Ignored. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:217
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:255
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/isotonic/IsotonicRegression.ts:65
predict()
predict(
opts):Promise<ArrayLike>
Predict new data by linear interpolation.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.T? | number | ArrayLike | Data to transform. |
Returns Promise<ArrayLike>
Defined in generated/isotonic/IsotonicRegression.ts:291
score()
score(
opts):Promise<number>
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true \- y_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y_true \- y_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator. |
opts.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/isotonic/IsotonicRegression.ts:327
set_fit_request()
set_fit_request(
opts):Promise<any>
Request metadata passed to the fit method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:375
set_output()
set_output(
opts):Promise<any>
Set output container.
See Introducing the set_output API for an example on how to use the API.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | "polars" | Configure output of transform and fit_transform. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:413
set_predict_request()
set_predict_request(
opts):Promise<any>
Request metadata passed to the predict method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.T? | string | boolean | Metadata routing for T parameter in predict. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:451
set_score_request()
set_score_request(
opts):Promise<any>
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:491
set_transform_request()
set_transform_request(
opts):Promise<any>
Request metadata passed to the transform method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.T? | string | boolean | Metadata routing for T parameter in transform. |
Returns Promise<any>
Defined in generated/isotonic/IsotonicRegression.ts:531
transform()
transform(
opts):Promise<ArrayLike>
Transform new data by linear interpolation.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.T? | number | ArrayLike | Data to transform. |
Returns Promise<ArrayLike>
Defined in generated/isotonic/IsotonicRegression.ts:567