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Classes
DictionaryLearning

DictionaryLearning

Dictionary learning.

Finds a dictionary (a set of atoms) that performs well at sparsely encoding the fitted data.

Solves the optimization problem:

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new DictionaryLearning(opts?: object): DictionaryLearning;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberSparsity controlling parameter. Default Value 1
opts.code_init?ArrayLike[]Initial value for the code, for warm restart. Only used if code\_init and dict\_init are not undefined.
opts.dict_init?ArrayLike[]Initial values for the dictionary, for warm restart. Only used if code\_init and dict\_init are not undefined.
opts.fit_algorithm?"cd" | "lars"'lars': uses the least angle regression method to solve the lasso problem (lars\_path); Default Value 'lars'
opts.max_iter?numberMaximum number of iterations to perform. Default Value 1000
opts.n_components?numberNumber of dictionary elements to extract. If undefined, then n\_components is set to n\_features.
opts.n_jobs?numberNumber of parallel jobs to run. 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.positive_code?booleanWhether to enforce positivity when finding the code. Default Value false
opts.positive_dict?booleanWhether to enforce positivity when finding the dictionary. Default Value false
opts.random_state?numberUsed for initializing the dictionary when dict\_init is not specified, randomly shuffling the data when shuffle is set to true, and updating the dictionary. Pass an int for reproducible results across multiple function calls. See Glossary.
opts.split_sign?booleanWhether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers. Default Value false
opts.tol?numberTolerance for numerical error. Default Value 1e-8
opts.transform_algorithm?"threshold" | "lars" | "lasso_lars" | "lasso_cd" | "omp"Algorithm used to transform the data: Default Value 'omp'
opts.transform_alpha?numberIf algorithm='lasso\_lars' or algorithm='lasso\_cd', alpha is the penalty applied to the L1 norm. If algorithm='threshold', alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If undefined, defaults to alpha.
opts.transform_max_iter?numberMaximum number of iterations to perform if algorithm='lasso\_cd' or 'lasso\_lars'. Default Value 1000
opts.transform_n_nonzero_coefs?numberNumber of nonzero coefficients to target in each column of the solution. This is only used by algorithm='lars' and algorithm='omp'. If undefined, then transform\_n\_nonzero\_coefs=int(n\_features / 10).
opts.verbose?booleanTo control the verbosity of the procedure. Default Value false

Returns

DictionaryLearning

Defined in: generated/decomposition/DictionaryLearning.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/decomposition/DictionaryLearning.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/decomposition/DictionaryLearning.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/decomposition/DictionaryLearning.ts:21 (opens in a new tab)

id

string

Defined in: generated/decomposition/DictionaryLearning.ts:18 (opens in a new tab)

opts

any

Defined in: generated/decomposition/DictionaryLearning.ts:19 (opens in a new tab)

Accessors

components_

dictionary atoms extracted from the data

Signature

components_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/DictionaryLearning.ts:445 (opens in a new tab)

error_

vector of errors at each iteration

Signature

error_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/decomposition/DictionaryLearning.ts:472 (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/decomposition/DictionaryLearning.ts:526 (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/decomposition/DictionaryLearning.ts:499 (opens in a new tab)

n_iter_

Number of iterations run.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/decomposition/DictionaryLearning.ts:553 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/decomposition/DictionaryLearning.ts:140 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/decomposition/DictionaryLearning.ts:144 (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/decomposition/DictionaryLearning.ts:221 (opens in a new tab)

fit()

Fit the model from data in X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training vector, where n\_samples is the number of samples and n\_features is the number of features.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/decomposition/DictionaryLearning.ts:238 (opens in a new tab)

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/decomposition/DictionaryLearning.ts:280 (opens in a new tab)

get_feature_names_out()

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: \["class\_name0", "class\_name1", "class\_name2"\].

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.input_features?anyOnly used to validate feature names with the names seen in fit.

Returns

Promise<any>

Defined in: generated/decomposition/DictionaryLearning.ts:333 (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/decomposition/DictionaryLearning.ts:153 (opens in a new tab)

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/decomposition/DictionaryLearning.ts:373 (opens in a new tab)

transform()

Encode the data as a sparse combination of the dictionary atoms.

Coding method is determined by the object parameter transform\_algorithm.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Test data to be transformed, must have the same number of features as the data used to train the model.

Returns

Promise<ArrayLike[]>

Defined in: generated/decomposition/DictionaryLearning.ts:410 (opens in a new tab)