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PassiveAggressiveClassifier

PassiveAggressiveClassifier

Passive Aggressive Classifier.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new PassiveAggressiveClassifier(opts?: object): PassiveAggressiveClassifier;

Parameters

NameTypeDescription
opts?object-
opts.C?numberMaximum step size (regularization). Defaults to 1.0. Default Value 1
opts.average?number | booleanWhen 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. Default Value false
opts.class_weight?anyPreset for the class_weight fit parameter. Weights associated with classes. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n\_samples / (n\_classes \* np.bincount(y)).
opts.early_stopping?booleanWhether to use early stopping to terminate training when validation. score is not improving. If set to true, it will automatically set aside a stratified 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. Default Value false
opts.fit_intercept?booleanWhether the intercept should be estimated or not. If false, the data is assumed to be already centered. Default Value true
opts.loss?stringThe loss function to be used: hinge: equivalent to PA-I in the reference paper. squared_hinge: equivalent to PA-II in the reference paper. Default Value 'hinge'
opts.max_iter?numberThe maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the PassiveAggressive.partial\_fit method. Default Value 1000
opts.n_iter_no_change?numberNumber of iterations with no improvement to wait before early stopping. Default Value 5
opts.n_jobs?numberThe number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details.
opts.random_state?numberUsed 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?booleanWhether or not the training data should be shuffled after each epoch. Default Value true
opts.tol?numberThe stopping criterion. If it is not undefined, the iterations will stop when (loss > previous_loss - tol). Default Value 0.001
opts.validation_fraction?numberThe 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. Default Value 0.1
opts.verbose?numberThe verbosity level. Default Value 0
opts.warm_start?booleanWhen 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. Default Value false

Returns

PassiveAggressiveClassifier

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:23 (opens in a new tab)

Methods

decision_function()

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

Signature

decision_function(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe data matrix for which we want to get the confidence scores.

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:227 (opens in a new tab)

densify()

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.

Signature

densify(opts: object): Promise<any>;

Parameters

NameType
optsobject

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:267 (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/linear_model/PassiveAggressiveClassifier.ts:208 (opens in a new tab)

fit()

Fit linear model with Passive Aggressive algorithm.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining data.
opts.coef_init?ArrayLike[]The initial coefficients to warm-start the optimization.
opts.intercept_init?ArrayLikeThe initial intercept to warm-start the optimization.
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:297 (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

NameTypeDescription
optsobject-
opts.routing?anyA MetadataRequest encapsulating routing information.

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:359 (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/linear_model/PassiveAggressiveClassifier.ts:146 (opens in a new tab)

partial_fit()

Fit linear model with Passive Aggressive algorithm.

Signature

partial_fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeSubset of the training data.
opts.classes?ArrayLikeClasses across all calls to partial_fit. Can be obtained by via np.unique(y\_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
opts.y?ArrayLikeSubset of the target values.

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:397 (opens in a new tab)

predict()

Predict class labels for samples in X.

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe data matrix for which we want to get the predictions.

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:449 (opens in a new tab)

score()

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Signature

score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Test samples.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue labels for X.

Returns

Promise<number>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:489 (opens in a new tab)

set_fit_request()

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:

Signature

set_fit_request(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.coef_init?string | booleanMetadata routing for coef\_init parameter in fit.
opts.intercept_init?string | booleanMetadata routing for intercept\_init parameter in fit.

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:544 (opens in a new tab)

set_partial_fit_request()

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:

Signature

set_partial_fit_request(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.classes?string | booleanMetadata routing for classes parameter in partial\_fit.

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:591 (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

NameTypeDescription
optsobject-
opts.sample_weight?string | booleanMetadata routing for sample\_weight parameter in score.

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:633 (opens in a new tab)

sparsify()

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.

Signature

sparsify(opts: object): Promise<any>;

Parameters

NameType
optsobject

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:675 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:21 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:20 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:19 (opens in a new tab)

id

string

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:16 (opens in a new tab)

opts

any

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:17 (opens in a new tab)

Accessors

classes_

The unique classes labels.

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:840 (opens in a new tab)

coef_

Weights assigned to the features.

Signature

coef_(): Promise<ArrayLike[][]>;

Returns

Promise<ArrayLike[][]>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:705 (opens in a new tab)

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/linear_model/PassiveAggressiveClassifier.ts:786 (opens in a new tab)

intercept_

Constants in decision function.

Signature

intercept_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:732 (opens in a new tab)

loss_function_

Loss function used by the algorithm.

Signature

loss_function_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:894 (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/linear_model/PassiveAggressiveClassifier.ts:759 (opens in a new tab)

n_iter_

The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:813 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:133 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:137 (opens in a new tab)

t_

Number of weight updates performed during training. Same as (n\_iter\_ \* n\_samples + 1).

Signature

t_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/PassiveAggressiveClassifier.ts:867 (opens in a new tab)