Pipeline
Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be ‘transforms’, that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using memory argument.
The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '\_\_', as in the example below. A step’s estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or undefined.
For an example use case of Pipeline combined with GridSearchCV, refer to Selecting dimensionality reduction with Pipeline and GridSearchCV. The example Pipelining: chaining a PCA and a logistic regression shows how to grid search on a pipeline using '\_\_' as a separator in the parameter names.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new Pipeline(opts?: object): Pipeline;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.memory? | string | Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute named\_steps or steps to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. |
opts.steps? | any | List of (name, transform) tuples (implementing fit/transform) that are chained in sequential order. The last transform must be an estimator. |
opts.verbose? | boolean | If true, the time elapsed while fitting each step will be printed as it is completed. Default Value false |
Returns
Defined in: generated/pipeline/Pipeline.ts:29 (opens in a new tab)
Methods
decision_function()
Transform the data, and apply decision\_function with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls decision\_function method. Only valid if the final estimator implements decision\_function.
Signature
decision_function(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to predict on. Must fulfill input requirements of first step of the pipeline. |
Returns
Promise<ArrayLike[]>
Defined in: generated/pipeline/Pipeline.ts:124 (opens in a new tab)
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/pipeline/Pipeline.ts:105 (opens in a new tab)
fit()
Fit the model.
Fit all the transformers one after the other and transform the data. Finally, fit the transformed data using the final estimator.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Training data. Must fulfill input requirements of first step of the pipeline. |
opts.fit_params? | any | Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s\_\_p. |
opts.y? | any | Training targets. Must fulfill label requirements for all steps of the pipeline. |
Returns
Promise<any>
Defined in: generated/pipeline/Pipeline.ts:159 (opens in a new tab)
fit_predict()
Transform the data, and apply fit\_predict with the final estimator.
Call fit\_transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls fit\_predict method. Only valid if the final estimator implements fit\_predict.
Signature
fit_predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Training data. Must fulfill input requirements of first step of the pipeline. |
opts.fit_params? | any | Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s\_\_p. |
opts.y? | any | Training targets. Must fulfill label requirements for all steps of the pipeline. |
Returns
Promise<ArrayLike>
Defined in: generated/pipeline/Pipeline.ts:206 (opens in a new tab)
fit_transform()
Fit the model and transform with the final estimator.
Fits all the transformers one after the other and transform the data. Then uses fit\_transform on transformed data with the final estimator.
Signature
fit_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Training data. Must fulfill input requirements of first step of the pipeline. |
opts.fit_params? | any | Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s\_\_p. |
opts.y? | any | Training targets. Must fulfill label requirements for all steps of the pipeline. |
Returns
Promise<ArrayLike[]>
Defined in: generated/pipeline/Pipeline.ts:253 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Transform input features using the pipeline.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. |
Returns
Promise<any>
Defined in: generated/pipeline/Pipeline.ts:300 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/pipeline/Pipeline.ts:337 (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/pipeline/Pipeline.ts:64 (opens in a new tab)
inverse_transform()
Apply inverse\_transform for each step in a reverse order.
All estimators in the pipeline must support inverse\_transform.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.Xt? | ArrayLike[] | Data samples, where n\_samples is the number of samples and n\_features is the number of features. Must fulfill input requirements of last step of pipeline’s inverse\_transform method. |
Returns
Promise<ArrayLike[]>
Defined in: generated/pipeline/Pipeline.ts:372 (opens in a new tab)
predict()
Transform the data, and apply predict with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict method. Only valid if the final estimator implements predict.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to predict on. Must fulfill input requirements of first step of the pipeline. |
opts.predict_params? | any | Parameters to the predict called at the end of all transformations in the pipeline. Note that while this may be used to return uncertainties from some models with return_std or return_cov, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/pipeline/Pipeline.ts:407 (opens in a new tab)
predict_log_proba()
Transform the data, and apply predict\_log\_proba with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict\_log\_proba method. Only valid if the final estimator implements predict\_log\_proba.
Signature
predict_log_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to predict on. Must fulfill input requirements of first step of the pipeline. |
opts.predict_log_proba_params? | any | Parameters to the predict\_log\_proba called at the end of all transformations in the pipeline. |
Returns
Promise<ArrayLike[]>
Defined in: generated/pipeline/Pipeline.ts:447 (opens in a new tab)
predict_proba()
Transform the data, and apply predict\_proba with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict\_proba method. Only valid if the final estimator implements predict\_proba.
Signature
predict_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to predict on. Must fulfill input requirements of first step of the pipeline. |
opts.predict_proba_params? | any | Parameters to the predict\_proba called at the end of all transformations in the pipeline. |
Returns
Promise<ArrayLike[]>
Defined in: generated/pipeline/Pipeline.ts:489 (opens in a new tab)
score()
Transform the data, and apply score with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls score method. Only valid if the final estimator implements score.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to predict on. Must fulfill input requirements of first step of the pipeline. |
opts.sample_weight? | ArrayLike | If not undefined, this argument is passed as sample\_weight keyword argument to the score method of the final estimator. |
opts.y? | any | Targets used for scoring. Must fulfill label requirements for all steps of the pipeline. |
Returns
Promise<number>
Defined in: generated/pipeline/Pipeline.ts:529 (opens in a new tab)
score_samples()
Transform the data, and apply score\_samples with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls score\_samples method. Only valid if the final estimator implements score\_samples.
Signature
score_samples(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to predict on. Must fulfill input requirements of first step of the pipeline. |
Returns
Promise<ArrayLike>
Defined in: generated/pipeline/Pipeline.ts:576 (opens in a new tab)
set_output()
Set the output container when "transform" and "fit\_transform" are called.
Calling set\_output will set the output of all estimators in steps.
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/pipeline/Pipeline.ts:611 (opens in a new tab)
set_score_request()
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:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in score. |
Returns
Promise<any>
Defined in: generated/pipeline/Pipeline.ts:648 (opens in a new tab)
transform()
Transform the data, and apply transform with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls transform method. Only valid if the final estimator implements transform.
This also works where final estimator is undefined in which case all prior transformations are applied.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to transform. Must fulfill input requirements of first step of the pipeline. |
Returns
Promise<ArrayLike[]>
Defined in: generated/pipeline/Pipeline.ts:685 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/pipeline/Pipeline.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/pipeline/Pipeline.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/pipeline/Pipeline.ts:25 (opens in a new tab)
id
string
Defined in: generated/pipeline/Pipeline.ts:22 (opens in a new tab)
opts
any
Defined in: generated/pipeline/Pipeline.ts:23 (opens in a new tab)
Accessors
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/pipeline/Pipeline.ts:51 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/pipeline/Pipeline.ts:55 (opens in a new tab)