Class: PassiveAggressiveRegressor
Passive Aggressive Regressor.
Read more in the User Guide.
Constructors
new PassiveAggressiveRegressor()
new PassiveAggressiveRegressor(
opts?):PassiveAggressiveRegressor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.average? | number | boolean | When set to true, computes the averaged SGD weights and stores the result in the coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples. |
opts.C? | number | Maximum step size (regularization). Defaults to 1.0. |
opts.early_stopping? | boolean | Whether to use early stopping to terminate training when validation. score is not improving. If set to true, it will automatically set aside a fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs. |
opts.epsilon? | number | If the difference between the current prediction and the correct label is below this threshold, the model is not updated. |
opts.fit_intercept? | boolean | Whether the intercept should be estimated or not. If false, the data is assumed to be already centered. Defaults to true. |
opts.loss? | string | The loss function to be used: epsilon_insensitive: equivalent to PA-I in the reference paper. squared_epsilon_insensitive: equivalent to PA-II in the reference paper. |
opts.max_iter? | number | The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method. |
opts.n_iter_no_change? | number | Number of iterations with no improvement to wait before early stopping. |
opts.random_state? | number | Used to shuffle the training data, when shuffle is set to true. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.shuffle? | boolean | Whether or not the training data should be shuffled after each epoch. |
opts.tol? | number | The stopping criterion. If it is not undefined, the iterations will stop when (loss > previous_loss - tol). |
opts.validation_fraction? | number | The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is true. |
opts.verbose? | number | The verbosity level. |
opts.warm_start? | boolean | When set to true, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. Repeatedly calling fit or partial_fit when warm_start is true can result in a different solution than when calling fit a single time because of the way the data is shuffled. |
Returns PassiveAggressiveRegressor
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/PassiveAggressiveRegressor.ts:21 |
_isInitialized | boolean | false | generated/linear_model/PassiveAggressiveRegressor.ts:20 |
_py | PythonBridge | undefined | generated/linear_model/PassiveAggressiveRegressor.ts:19 |
id | string | undefined | generated/linear_model/PassiveAggressiveRegressor.ts:16 |
opts | any | undefined | generated/linear_model/PassiveAggressiveRegressor.ts:17 |
Accessors
coef_
Get Signature
get coef_():
Promise<any>
Weights assigned to the features.
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:604
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/linear_model/PassiveAggressiveRegressor.ts:685
intercept_
Get Signature
get intercept_():
Promise<any>
Constants in decision function.
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:631
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:658
n_iter_
Get Signature
get n_iter_():
Promise<number>
The actual number of iterations to reach the stopping criterion.
Returns Promise<number>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:712
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:126
t_
Get Signature
get t_():
Promise<number>
Number of weight updates performed during training. Same as (n_iter_ \* n_samples + 1).
Returns Promise<number>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:739
Methods
densify()
densify(
opts):Promise<any>
Convert coefficient matrix to dense array format.
Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:201
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/linear_model/PassiveAggressiveRegressor.ts:182
fit()
fit(
opts):Promise<any>
Fit linear model with Passive Aggressive algorithm.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.coef_init? | any | The initial coefficients to warm-start the optimization. |
opts.intercept_init? | any | The initial intercept to warm-start the optimization. |
opts.X? | ArrayLike | Training data. |
opts.y? | any | Target values. |
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:231
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/linear_model/PassiveAggressiveRegressor.ts:284
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/linear_model/PassiveAggressiveRegressor.ts:139
partial_fit()
partial_fit(
opts):Promise<any>
Fit linear model with Passive Aggressive algorithm.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Subset of training data. |
opts.y? | any | Subset of target values. |
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:320
predict()
predict(
opts):Promise<any>
Predict using the linear model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Input data. |
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:361
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/linear_model/PassiveAggressiveRegressor.ts:399
set_fit_request()
set_fit_request(
opts):Promise<any>
Request metadata passed to the fit 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.coef_init? | string | boolean | Metadata routing for coef_init parameter in fit. |
opts.intercept_init? | string | boolean | Metadata routing for intercept_init parameter in fit. |
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:449
set_partial_fit_request()
set_partial_fit_request(
opts):Promise<any>
Request metadata passed to the partial_fit 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 partial_fit. |
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:494
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/linear_model/PassiveAggressiveRegressor.ts:534
sparsify()
sparsify(
opts):Promise<any>
Convert coefficient matrix to sparse format.
Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.
The intercept_ member is not converted.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/linear_model/PassiveAggressiveRegressor.ts:574