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 3 |
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)
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:538 (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:619 (opens in a new tab)
inner_stats_
Internal sufficient statistics that are kept by the algorithm. Keeping them is useful in online settings, to avoid losing the history of the evolution, but they shouldn’t have any use for the end user. A (n\_components, n\_components) is the dictionary covariance matrix. B (n\_features, n\_components) is the data approximation matrix.
Signature
inner_stats_(): Promise<any>;Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:565 (opens in a new tab)
iter_offset_
The number of iteration on data batches that has been performed before.
Signature
iter_offset_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:673 (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:592 (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:646 (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:727 (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)
random_state_
RandomState instance that is generated either from a seed, the random number generattor or by np.random.
Signature
random_state_(): Promise<any>;Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:700 (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)
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.iter_offset? | number | The number of iteration on data batches that has been performed before this call to partial\_fit. This is optional: if no number is passed, the memory of the object is used. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchDictionaryLearning.ts:408 (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:460 (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:500 (opens in a new tab)