MiniBatchDictionaryLearning
Mini-batch 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 MiniBatchDictionaryLearning(opts?: object): MiniBatchDictionaryLearning;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Sparsity controlling parameter. Default Value 1 |
opts.batch_size? | number | Number of samples in each mini-batch. Default Value 256 |
opts.callback? | any | A callable that gets invoked at the end of each iteration. |
opts.dict_init? | ArrayLike[] | Initial value of the dictionary for warm restart scenarios. |
opts.fit_algorithm? | "cd" | "lars" | The algorithm used: Default Value 'lars' |
opts.max_iter? | number | Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. If max\_iter is not undefined, n\_iter is ignored. |
opts.max_no_improvement? | number | Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed cost function. Used only if max\_iter is not undefined. To disable convergence detection based on cost function, set max\_no\_improvement to undefined. Default Value 10 |
opts.n_components? | number | Number of dictionary elements to extract. |
opts.n_iter? | number | Total number of iterations over data batches to perform. Default Value 1000 |
opts.n_jobs? | number | Number 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? | boolean | Whether to enforce positivity when finding the code. Default Value false |
opts.positive_dict? | boolean | Whether to enforce positivity when finding the dictionary. Default Value false |
opts.random_state? | number | Used 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.shuffle? | boolean | Whether to shuffle the samples before forming batches. Default Value true |
opts.split_sign? | boolean | Whether 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? | number | Control early stopping based on the norm of the differences in the dictionary between 2 steps. Used only if max\_iter is not undefined. To disable early stopping based on changes in the dictionary, set tol to 0.0. Default Value 0.001 |
opts.transform_algorithm? | "threshold" | "lars" | "lasso_lars" | "lasso_cd" | "omp" | Algorithm used to transform the data: Default Value 'omp' |
opts.transform_alpha? | number | If 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? | number | Maximum number of iterations to perform if algorithm='lasso\_cd' or 'lasso\_lars'. Default Value 1000 |
opts.transform_n_nonzero_coefs? | number | Number 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? | number | boolean | To control the verbosity of the procedure. Default Value false |
Returns
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:25 (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/MiniBatchDictionaryLearning.ts:255 (opens in a new tab)
fit()
Fit the model from data in X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:272 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:316 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Only used to validate feature names with the names seen in fit. |
Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:370 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:410 (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
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:183 (opens in a new tab)
partial_fit()
Update the model using the data in X as a mini-batch.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:448 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:493 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
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/MiniBatchDictionaryLearning.ts:533 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:21 (opens in a new tab)
id
string
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:18 (opens in a new tab)
opts
any
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:19 (opens in a new tab)
Accessors
components_
Components extracted from the data.
Signature
components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:571 (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/MiniBatchDictionaryLearning.ts:625 (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/MiniBatchDictionaryLearning.ts:598 (opens in a new tab)
n_iter_
Number of iterations over the full dataset.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:652 (opens in a new tab)
n_steps_
Number of mini-batches processed.
Signature
n_steps_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:679 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:170 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:174 (opens in a new tab)