Documentation
Classes
CategoricalNB

CategoricalNB

Naive Bayes classifier for categorical features.

The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new CategoricalNB(opts?: object): CategoricalNB;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberAdditive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=true, for no smoothing). Default Value 1
opts.class_prior?ArrayLikePrior probabilities of the classes. If specified, the priors are not adjusted according to the data.
opts.fit_prior?booleanWhether to learn class prior probabilities or not. If false, a uniform prior will be used. Default Value true
opts.force_alpha?booleanIf 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
opts.min_categories?number | ArrayLikeMinimum number of categories per feature.

Returns

CategoricalNB

Defined in: generated/naive_bayes/CategoricalNB.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/naive_bayes/CategoricalNB.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/naive_bayes/CategoricalNB.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/naive_bayes/CategoricalNB.ts:21 (opens in a new tab)

id

string

Defined in: generated/naive_bayes/CategoricalNB.ts:18 (opens in a new tab)

opts

any

Defined in: generated/naive_bayes/CategoricalNB.ts:19 (opens in a new tab)

Accessors

category_count_

Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the number of samples encountered for each class and category of the specific feature.

Signature

category_count_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/naive_bayes/CategoricalNB.ts:436 (opens in a new tab)

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/CategoricalNB.ts:461 (opens in a new tab)

class_log_prior_

Smoothed empirical log probability for each class.

Signature

class_log_prior_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/CategoricalNB.ts:486 (opens in a new tab)

classes_

Class labels known to the classifier

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/CategoricalNB.ts:511 (opens in a new tab)

feature_log_prob_

Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the empirical log probability of categories given the respective feature and class, P(x\_i|y).

Signature

feature_log_prob_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/naive_bayes/CategoricalNB.ts:536 (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/naive_bayes/CategoricalNB.ts:586 (opens in a new tab)

n_categories_

Number of categories for each feature. This value is inferred from the data or set by the minimum number of categories.

Signature

n_categories_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/CategoricalNB.ts:611 (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/naive_bayes/CategoricalNB.ts:561 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/naive_bayes/CategoricalNB.ts:61 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/naive_bayes/CategoricalNB.ts:65 (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/naive_bayes/CategoricalNB.ts:120 (opens in a new tab)

fit()

Fit Naive Bayes classifier according to X, y.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining vectors, where n\_samples is the number of samples and n\_features is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, …, n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder.
opts.sample_weight?ArrayLikeWeights applied to individual samples (1. for unweighted).
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/naive_bayes/CategoricalNB.ts:137 (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/naive_bayes/CategoricalNB.ts:74 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining vectors, where n\_samples is the number of samples and n\_features is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, …, n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder.
opts.classes?ArrayLikeList 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?ArrayLikeWeights applied to individual samples (1. for unweighted).
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/naive_bayes/CategoricalNB.ts:190 (opens in a new tab)

predict()

Perform classification on an array of test vectors X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/CategoricalNB.ts:248 (opens in a new tab)

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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/CategoricalNB.ts:283 (opens in a new tab)

predict_log_proba()

Return log-probability estimates for the test vector X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/CategoricalNB.ts:319 (opens in a new tab)

predict_proba()

Return probability estimates for the test vector X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/CategoricalNB.ts:354 (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/naive_bayes/CategoricalNB.ts:389 (opens in a new tab)