PLSCanonical
Partial Least Squares transformer and regressor.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new PLSCanonical(opts?: object): PLSCanonical;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.algorithm? | "nipals" | "svd" | The algorithm used to estimate the first singular vectors of the cross-covariance matrix. ‘nipals’ uses the power method while ‘svd’ will compute the whole SVD. Default Value 'nipals' |
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. Default Value true |
opts.max_iter? | number | The maximum number of iterations of the power method when algorithm='nipals'. Ignored otherwise. Default Value 500 |
opts.n_components? | number | Number of components to keep. Should be in \[1, min(n\_samples, n\_features, n\_targets)\]. Default Value 2 |
opts.scale? | boolean | Whether to scale X and Y. Default Value true |
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. Default Value 0.000001 |
Returns
Defined in: generated/cross_decomposition/PLSCanonical.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/cross_decomposition/PLSCanonical.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/cross_decomposition/PLSCanonical.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/cross_decomposition/PLSCanonical.ts:19 (opens in a new tab)
id
string
Defined in: generated/cross_decomposition/PLSCanonical.ts:16 (opens in a new tab)
opts
any
Defined in: generated/cross_decomposition/PLSCanonical.ts:17 (opens in a new tab)
Accessors
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/cross_decomposition/PLSCanonical.ts:702 (opens in a new tab)
intercept_
The intercepts of the linear model such that Y is approximated as Y \= X @ coef\_ + intercept\_.
Signature
intercept_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/cross_decomposition/PLSCanonical.ts:629 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cross_decomposition/PLSCanonical.ts:677 (opens in a new tab)
n_iter_
Number of iterations of the power method, for each component. Empty if algorithm='svd'.
Signature
n_iter_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:654 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/cross_decomposition/PLSCanonical.ts:70 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/cross_decomposition/PLSCanonical.ts:74 (opens in a new tab)
x_loadings_
The loadings of X.
Signature
x_loadings_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:529 (opens in a new tab)
x_rotations_
The projection matrix used to transform X.
Signature
x_rotations_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:579 (opens in a new tab)
x_weights_
The left singular vectors of the cross-covariance matrices of each iteration.
Signature
x_weights_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:479 (opens in a new tab)
y_loadings_
The loadings of Y.
Signature
y_loadings_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:554 (opens in a new tab)
y_rotations_
The projection matrix used to transform Y.
Signature
y_rotations_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:604 (opens in a new tab)
y_weights_
The right singular vectors of the cross-covariance matrices of each iteration.
Signature
y_weights_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:504 (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/cross_decomposition/PLSCanonical.ts:127 (opens in a new tab)
fit()
Fit model to data.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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<any>
Defined in: generated/cross_decomposition/PLSCanonical.ts:144 (opens in a new tab)
fit_transform()
Learn and apply the dimension reduction on the train data.
Signature
fit_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | 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/PLSCanonical.ts:184 (opens in a new tab)
get_feature_names_out()
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: \["class\_name0", "class\_name1", "class\_name2"\].
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | 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/PLSCanonical.ts:226 (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/cross_decomposition/PLSCanonical.ts:83 (opens in a new tab)
inverse_transform()
Transform data back to its original space.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | 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. |
Returns
Promise<ArrayLike[]>
Defined in: generated/cross_decomposition/PLSCanonical.ts:262 (opens in a new tab)
predict()
Predict targets of given samples.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Samples. |
opts.copy? | boolean | Whether to copy X and Y, or perform in-place normalization. Default Value true |
Returns
Promise<ArrayLike>
Defined in: generated/cross_decomposition/PLSCanonical.ts:304 (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/cross_decomposition/PLSCanonical.ts:348 (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/cross_decomposition/PLSCanonical.ts:397 (opens in a new tab)
transform()
Apply the dimension reduction.
Signature
transform(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Samples to transform. |
opts.Y? | ArrayLike[] | Target vectors. |
opts.copy? | boolean | Whether to copy X and Y, or perform in-place normalization. Default Value true |
Returns
Promise<any>
Defined in: generated/cross_decomposition/PLSCanonical.ts:430 (opens in a new tab)