IsotonicRegression
Isotonic regression model.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new IsotonicRegression(opts?: object): IsotonicRegression;Parameters
| Name | 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. Default Value true |
opts.out_of_bounds? | "nan" | "clip" | "raise" | Handles how X values outside of the training domain are handled during prediction. Default Value 'nan' |
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
Defined in: generated/isotonic/IsotonicRegression.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/isotonic/IsotonicRegression.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/isotonic/IsotonicRegression.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/isotonic/IsotonicRegression.ts:19 (opens in a new tab)
id
string
Defined in: generated/isotonic/IsotonicRegression.ts:16 (opens in a new tab)
opts
any
Defined in: generated/isotonic/IsotonicRegression.ts:17 (opens in a new tab)
Accessors
X_max_
Maximum value of input array X\_ for right bound.
Signature
X_max_(): Promise<number>;Returns
Promise<number>
Defined in: generated/isotonic/IsotonicRegression.ts:453 (opens in a new tab)
X_min_
Minimum value of input array X\_ for left bound.
Signature
X_min_(): Promise<number>;Returns
Promise<number>
Defined in: generated/isotonic/IsotonicRegression.ts:426 (opens in a new tab)
X_thresholds_
Unique ascending X values used to interpolate the y = f(X) monotonic function.
Signature
X_thresholds_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/isotonic/IsotonicRegression.ts:480 (opens in a new tab)
f_
The stepwise interpolating function that covers the input domain X.
Signature
f_(): Promise<any>;Returns
Promise<any>
Defined in: generated/isotonic/IsotonicRegression.ts:534 (opens in a new tab)
increasing_
Inferred value for increasing.
Signature
increasing_(): Promise<boolean>;Returns
Promise<boolean>
Defined in: generated/isotonic/IsotonicRegression.ts:559 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/isotonic/IsotonicRegression.ts:52 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/isotonic/IsotonicRegression.ts:56 (opens in a new tab)
y_thresholds_
De-duplicated y values suitable to interpolate the y = f(X) monotonic function.
Signature
y_thresholds_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/isotonic/IsotonicRegression.ts:507 (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/isotonic/IsotonicRegression.ts:111 (opens in a new tab)
fit()
Fit the model using X, y as training data.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | number | ArrayLike | Training data. |
opts.sample_weight? | ArrayLike | Weights. If set to undefined, all weights will be set to 1 (equal weights). |
opts.y? | ArrayLike | Training target. |
Returns
Promise<any>
Defined in: generated/isotonic/IsotonicRegression.ts:128 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/isotonic/IsotonicRegression.ts:179 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Ignored. |
Returns
Promise<any>
Defined in: generated/isotonic/IsotonicRegression.ts:230 (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/isotonic/IsotonicRegression.ts:65 (opens in a new tab)
predict()
Predict new data by linear interpolation.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.T? | number | ArrayLike | Data to transform. |
Returns
Promise<ArrayLike>
Defined in: generated/isotonic/IsotonicRegression.ts:268 (opens in a new tab)
score()
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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/isotonic/IsotonicRegression.ts:305 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/isotonic/IsotonicRegression.ts:356 (opens in a new tab)
transform()
Transform new data by linear interpolation.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.T? | number | ArrayLike | Data to transform. |
Returns
Promise<ArrayLike>
Defined in: generated/isotonic/IsotonicRegression.ts:391 (opens in a new tab)