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Classes
IsotonicRegression

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

NameTypeDescription
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?numberUpper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf.
opts.y_min?numberLower 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 (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

NameType
pythonBridgePythonBridge

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

NameTypeDescription
optsobject-
opts.X?number | ArrayLikeTraining data.
opts.sample_weight?ArrayLikeWeights. If set to undefined, all weights will be set to 1 (equal weights).
opts.y?ArrayLikeTraining 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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget 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

NameTypeDescription
optsobject-
opts.input_features?anyIgnored.

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

NameType
pyPythonBridge

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

NameTypeDescription
optsobject-
opts.T?number | ArrayLikeData 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

NameTypeDescription
optsobject-
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?ArrayLikeSample weights.
opts.y?ArrayLikeTrue 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

NameTypeDescription
optsobject-
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

NameTypeDescription
optsobject-
opts.T?number | ArrayLikeData to transform.

Returns

Promise<ArrayLike>

Defined in: generated/isotonic/IsotonicRegression.ts:391 (opens in a new tab)