Class: PLSRegression
PLS regression.
PLSRegression is also known as PLS2 or PLS1, depending on the number of targets.
For a comparison between other cross decomposition algorithms, see Compare cross decomposition methods.
Read more in the User Guide.
Constructors
new PLSRegression()
new PLSRegression(
opts?):PLSRegression
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy? | boolean | Whether to copy X and Y in fit before applying centering, and potentially scaling. If false, these operations will be done inplace, modifying both arrays. |
opts.max_iter? | number | The maximum number of iterations of the power method when algorithm='nipals'. Ignored otherwise. |
opts.n_components? | number | Number of components to keep. Should be in \[1, n_features\]. |
opts.scale? | boolean | Whether to scale X and Y. |
opts.tol? | number | The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of u_i \- u_{i-1} is less than tol, where u corresponds to the left singular vector. |
Returns PLSRegression
Defined in generated/cross_decomposition/PLSRegression.ts:27
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/cross_decomposition/PLSRegression.ts:25 |
_isInitialized | boolean | false | generated/cross_decomposition/PLSRegression.ts:24 |
_py | PythonBridge | undefined | generated/cross_decomposition/PLSRegression.ts:23 |
id | string | undefined | generated/cross_decomposition/PLSRegression.ts:20 |
opts | any | undefined | generated/cross_decomposition/PLSRegression.ts:21 |
Accessors
coef_
Get Signature
get coef_():
Promise<ArrayLike[]>
The coefficients of the linear model such that Y is approximated as Y \= X @ coef_.T + intercept_.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:811
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/cross_decomposition/PLSRegression.ts:907
intercept_
Get Signature
get intercept_():
Promise<ArrayLike>
The intercepts of the linear model such that Y is approximated as Y \= X @ coef_.T + intercept_.
Returns Promise<ArrayLike>
Defined in generated/cross_decomposition/PLSRegression.ts:834
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/cross_decomposition/PLSRegression.ts:882
n_iter_
Get Signature
get n_iter_():
Promise<any[]>
Number of iterations of the power method, for each component.
Returns Promise<any[]>
Defined in generated/cross_decomposition/PLSRegression.ts:859
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/cross_decomposition/PLSRegression.ts:67
x_loadings_
Get Signature
get x_loadings_():
Promise<ArrayLike[]>
The loadings of X.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:661
x_rotations_
Get Signature
get x_rotations_():
Promise<ArrayLike[]>
The projection matrix used to transform X.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:761
x_scores_
Get Signature
get x_scores_():
Promise<ArrayLike[]>
The transformed training samples.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:711
x_weights_
Get Signature
get x_weights_():
Promise<ArrayLike[]>
The left singular vectors of the cross-covariance matrices of each iteration.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:611
y_loadings_
Get Signature
get y_loadings_():
Promise<ArrayLike[]>
The loadings of Y.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:686
y_rotations_
Get Signature
get y_rotations_():
Promise<ArrayLike[]>
The projection matrix used to transform Y.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:786
y_scores_
Get Signature
get y_scores_():
Promise<ArrayLike[]>
The transformed training targets.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:736
y_weights_
Get Signature
get y_weights_():
Promise<ArrayLike[]>
The right singular vectors of the cross-covariance matrices of each iteration.
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:636
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/cross_decomposition/PLSRegression.ts:119
fit()
fit(
opts):Promise<any>
Fit model to data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training vectors, where n_samples is the number of samples and n_features is the number of predictors. |
opts.y? | ArrayLike | Target vectors, where n_samples is the number of samples and n_targets is the number of response variables. |
opts.Y? | ArrayLike | Target vectors, where n_samples is the number of samples and n_targets is the number of response variables. |
Returns Promise<any>
Defined in generated/cross_decomposition/PLSRegression.ts:136
fit_transform()
fit_transform(
opts):Promise<ArrayLike[]>
Learn and apply the dimension reduction on the train data.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training vectors, where n_samples is the number of samples and n_features is the number of predictors. |
opts.y? | ArrayLike[] | Target vectors, where n_samples is the number of samples and n_targets is the number of response variables. |
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:178
get_feature_names_out()
get_feature_names_out(
opts):Promise<any>
Get output feature names for transformation.
The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class_name0", "class_name1", "class_name2"\].
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Only used to validate feature names with the names seen in fit. |
Returns Promise<any>
Defined in generated/cross_decomposition/PLSRegression.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/cross_decomposition/PLSRegression.ts:253
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/cross_decomposition/PLSRegression.ts:80
inverse_transform()
inverse_transform(
opts):Promise<ArrayLike[]>
Transform data back to its original space.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | New data, where n_samples is the number of samples and n_components is the number of pls components. |
opts.y? | ArrayLike | New target, where n_samples is the number of samples and n_components is the number of pls components. |
opts.Y? | ArrayLike[] | New target, where n_samples is the number of samples and n_components is the number of pls components. |
Returns Promise<ArrayLike[]>
Defined in generated/cross_decomposition/PLSRegression.ts:287
predict()
predict(
opts):Promise<ArrayLike>
Predict targets of given samples.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.copy? | boolean | Whether to copy X and Y, or perform in-place normalization. |
opts.X? | ArrayLike[] | Samples. |
Returns Promise<ArrayLike>
Defined in generated/cross_decomposition/PLSRegression.ts:331
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/cross_decomposition/PLSRegression.ts:372
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/cross_decomposition/PLSRegression.ts:416
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.copy? | string | boolean | Metadata routing for copy parameter in predict. |
Returns Promise<any>
Defined in generated/cross_decomposition/PLSRegression.ts:452
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/cross_decomposition/PLSRegression.ts:490
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.copy? | string | boolean | Metadata routing for copy parameter in transform. |
Returns Promise<any>
Defined in generated/cross_decomposition/PLSRegression.ts:528
transform()
transform(
opts):Promise<any>
Apply the dimension reduction.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.copy? | boolean | Whether to copy X and Y, or perform in-place normalization. |
opts.X? | ArrayLike[] | Samples to transform. |
opts.y? | ArrayLike[] | Target vectors. |
opts.Y? | ArrayLike[] | Target vectors. |
Returns Promise<any>
Defined in generated/cross_decomposition/PLSRegression.ts:562