BernoulliNB

Naive Bayes classifier for multivariate Bernoulli models.

Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.

Constructors

constructor()

Signature

new BernoulliNB(opts?: object): BernoulliNB;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.alpha?`	`number` \| `ArrayLike`	Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=`true`, for no smoothing). `Default Value` `1`
`opts.binarize?`	`number`	Threshold for binarizing (mapping to booleans) of sample features. If `undefined`, input is presumed to already consist of binary vectors. `Default Value` `0`
`opts.class_prior?`	`ArrayLike`	Prior probabilities of the classes. If specified, the priors are not adjusted according to the data.
`opts.fit_prior?`	`boolean`	Whether to learn class prior probabilities or not. If false, a uniform prior will be used. `Default Value` `true`
`opts.force_alpha?`	`boolean`	If `false` and alpha is less than 1e-10, it will set alpha to 1e-10. If `true`, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0. `Default Value` `false`

Returns

BernoulliNB

Defined in: generated/naive_bayes/BernoulliNB.ts:25

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/naive_bayes/BernoulliNB.ts:122

fit()

Fit Naive Bayes classifier according to X, y.

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 features.
`opts.sample_weight?`	`ArrayLike`	Weights applied to individual samples (1. for unweighted).
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/naive_bayes/BernoulliNB.ts:139

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/naive_bayes/BernoulliNB.ts:188

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/naive_bayes/BernoulliNB.ts:76

partial_fit()

Incremental fit on a batch of samples.

This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.

This is especially useful when the whole dataset is too big to fit in memory at once.

This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.

Signature

partial_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 features.
`opts.classes?`	`ArrayLike`	List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls.
`opts.sample_weight?`	`ArrayLike`	Weights applied to individual samples (1. for unweighted).
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/naive_bayes/BernoulliNB.ts:229

predict()

Perform classification on an array of test vectors X.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The input samples.

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/BernoulliNB.ts:287

predict_joint_log_proba()

Return joint log probability estimates for the test vector X.

For each row x of X and class y, the joint log probability is given by log P(x, y) \= log P(y) + log P(x|y), where log P(y) is the class prior probability and log P(x|y) is the class-conditional probability.

Signature

predict_joint_log_proba(opts: object): Promise<ArrayLike[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/BernoulliNB.ts:322

predict_log_proba()

Return log-probability estimates for the test vector X.

Signature

predict_log_proba(opts: object): Promise<ArrayLike[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/BernoulliNB.ts:358

predict_proba()

Return probability estimates for the test vector X.

Signature

predict_proba(opts: object): Promise<ArrayLike[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/BernoulliNB.ts:391

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

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test samples.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	True labels for `X`.

Returns

Promise<number>

Defined in: generated/naive_bayes/BernoulliNB.ts:426

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

Name	Type	Description
`opts`	`object`	-
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `fit`.

Returns

Promise<any>

Defined in: generated/naive_bayes/BernoulliNB.ts:477

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

Name	Type	Description
`opts`	`object`	-
`opts.classes?`	`string` \| `boolean`	Metadata routing for `classes` parameter in `partial\_fit`.
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `partial\_fit`.

Returns

Promise<any>

Defined in: generated/naive_bayes/BernoulliNB.ts:514

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/naive_bayes/BernoulliNB.ts:558

Properties

_isDisposed

boolean = false

Defined in: generated/naive_bayes/BernoulliNB.ts:23

_isInitialized

boolean = false

Defined in: generated/naive_bayes/BernoulliNB.ts:22

_py

PythonBridge

Defined in: generated/naive_bayes/BernoulliNB.ts:21

id

string

Defined in: generated/naive_bayes/BernoulliNB.ts:18

opts

any

Defined in: generated/naive_bayes/BernoulliNB.ts:19

Accessors

class_count_

Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.

Signature

class_count_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/BernoulliNB.ts:591

class_log_prior_

Log probability of each class (smoothed).

Signature

class_log_prior_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/BernoulliNB.ts:616

classes_

Class labels known to the classifier

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/BernoulliNB.ts:641

feature_count_

Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.

Signature

feature_count_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/BernoulliNB.ts:664

feature_log_prob_

Empirical log probability of features given a class, P(x_i|y).

Signature

feature_log_prob_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/BernoulliNB.ts:689

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/naive_bayes/BernoulliNB.ts:739

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/naive_bayes/BernoulliNB.ts:714

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/naive_bayes/BernoulliNB.ts:63

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/naive_bayes/BernoulliNB.ts:67

Files

BernoulliNB.md

Latest commit

History

BernoulliNB.md

File metadata and controls

BernoulliNB

Constructors

constructor()

Signature

Parameters

Returns

Methods

dispose()

Signature

Returns

fit()

Signature

Parameters

Returns

get_metadata_routing()

Signature

Parameters

Returns

init()

Signature

Parameters

Returns

partial_fit()

Signature

Parameters

Returns

predict()

Signature

Parameters

Returns

predict_joint_log_proba()

Signature

Parameters

Returns

predict_log_proba()

Signature

Parameters

Returns

predict_proba()

Signature

Parameters

Returns

score()

Signature

Parameters

Returns

set_fit_request()

Signature

Parameters

Returns

set_partial_fit_request()

Signature

Parameters

Returns

set_score_request()

Signature

Parameters

Returns

Properties

_isDisposed

_isInitialized

_py

id

opts

Accessors

class_count_

Signature

Returns

class_log_prior_

Signature

Returns

classes_

Signature

Returns