MiniBatchKMeans
Mini-Batch K-Means clustering.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new MiniBatchKMeans(opts?: object): MiniBatchKMeans;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.batch_size? | number | Size of the mini batches. For faster computations, you can set the batch\_size greater than 256 * number of cores to enable parallelism on all cores. Default Value 1024 |
opts.compute_labels? | boolean | Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit. Default Value true |
opts.init? | ArrayLike[] | "k-means++" | "random" | Method for initialization: ‘k-means++’ : selects initial cluster centroids using sampling based on an empirical probability distribution of the points’ contribution to the overall inertia. This technique speeds up convergence. The algorithm implemented is “greedy k-means++”. It differs from the vanilla k-means++ by making several trials at each sampling step and choosing the best centroid among them. ‘random’: choose n\_clusters observations (rows) at random from data for the initial centroids. If an array is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization. Default Value 'k-means++' |
opts.init_size? | number | Number of samples to randomly sample for speeding up the initialization (sometimes at the expense of accuracy): the only algorithm is initialized by running a batch KMeans on a random subset of the data. This needs to be larger than n_clusters. If undefined, the heuristic is init\_size \= 3 \* batch\_size if 3 \* batch\_size < n\_clusters, else init\_size \= 3 \* n\_clusters. |
opts.max_iter? | number | Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. Default Value 100 |
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 inertia. To disable convergence detection based on inertia, set max_no_improvement to undefined. Default Value 10 |
opts.n_clusters? | number | The number of clusters to form as well as the number of centroids to generate. Default Value 8 |
opts.n_init? | number | "auto" | Number of random initializations that are tried. In contrast to KMeans, the algorithm is only run once, using the best of the n\_init initializations as measured by inertia. Several runs are recommended for sparse high-dimensional problems (see Clustering sparse data with k-means). When n\_init='auto', the number of runs depends on the value of init: 3 if using init='random', 1 if using init='k-means++'. Default Value 3 |
opts.random_state? | number | Determines random number generation for centroid initialization and random reassignment. Use an int to make the randomness deterministic. See Glossary. |
opts.reassignment_ratio? | number | Control the fraction of the maximum number of counts for a center to be reassigned. A higher value means that low count centers are more easily reassigned, which means that the model will take longer to converge, but should converge in a better clustering. However, too high a value may cause convergence issues, especially with a small batch size. Default Value 0.01 |
opts.tol? | number | Control early stopping based on the relative center changes as measured by a smoothed, variance-normalized of the mean center squared position changes. This early stopping heuristics is closer to the one used for the batch variant of the algorithms but induces a slight computational and memory overhead over the inertia heuristic. To disable convergence detection based on normalized center change, set tol to 0.0 (default). Default Value 0 |
opts.verbose? | number | Verbosity mode. Default Value 0 |
Returns
Defined in: generated/cluster/MiniBatchKMeans.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/cluster/MiniBatchKMeans.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/cluster/MiniBatchKMeans.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/cluster/MiniBatchKMeans.ts:19 (opens in a new tab)
id
string
Defined in: generated/cluster/MiniBatchKMeans.ts:16 (opens in a new tab)
opts
any
Defined in: generated/cluster/MiniBatchKMeans.ts:17 (opens in a new tab)
Accessors
cluster_centers_
Coordinates of cluster centers.
Signature
cluster_centers_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/cluster/MiniBatchKMeans.ts:597 (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/cluster/MiniBatchKMeans.ts:747 (opens in a new tab)
inertia_
The value of the inertia criterion associated with the chosen partition if compute_labels is set to true. If compute_labels is set to false, it’s an approximation of the inertia based on an exponentially weighted average of the batch inertiae. The inertia is defined as the sum of square distances of samples to their cluster center, weighted by the sample weights if provided.
Signature
inertia_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/MiniBatchKMeans.ts:647 (opens in a new tab)
labels_
Labels of each point (if compute_labels is set to true).
Signature
labels_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/cluster/MiniBatchKMeans.ts:622 (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/cluster/MiniBatchKMeans.ts:722 (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/cluster/MiniBatchKMeans.ts:672 (opens in a new tab)
n_steps_
Number of minibatches processed.
Signature
n_steps_(): Promise<number>;Returns
Promise<number>
Defined in: generated/cluster/MiniBatchKMeans.ts:697 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/cluster/MiniBatchKMeans.ts:124 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/cluster/MiniBatchKMeans.ts:128 (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/cluster/MiniBatchKMeans.ts:191 (opens in a new tab)
fit()
Compute the centroids on X by chunking it into mini-batches.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format. |
opts.sample_weight? | ArrayLike | The weights for each observation in X. If undefined, all observations are assigned equal weight. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/cluster/MiniBatchKMeans.ts:208 (opens in a new tab)
fit_predict()
Compute cluster centers and predict cluster index for each sample.
Convenience method; equivalent to calling fit(X) followed by predict(X).
Signature
fit_predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data to transform. |
opts.sample_weight? | ArrayLike | The weights for each observation in X. If undefined, all observations are assigned equal weight. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<ArrayLike>
Defined in: generated/cluster/MiniBatchKMeans.ts:257 (opens in a new tab)
fit_transform()
Compute clustering and transform X to cluster-distance space.
Equivalent to fit(X).transform(X), but more efficiently implemented.
Signature
fit_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data to transform. |
opts.sample_weight? | ArrayLike | The weights for each observation in X. If undefined, all observations are assigned equal weight. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<ArrayLike[]>
Defined in: generated/cluster/MiniBatchKMeans.ts:306 (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/cluster/MiniBatchKMeans.ts:355 (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/cluster/MiniBatchKMeans.ts:137 (opens in a new tab)
partial_fit()
Update k means estimate on a single mini-batch X.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format. |
opts.sample_weight? | ArrayLike | The weights for each observation in X. If undefined, all observations are assigned equal weight. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/cluster/MiniBatchKMeans.ts:391 (opens in a new tab)
predict()
Predict the closest cluster each sample in X belongs to.
In the vector quantization literature, cluster\_centers\_ is called the code book and each value returned by predict is the index of the closest code in the code book.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data to predict. |
opts.sample_weight? | ArrayLike | The weights for each observation in X. If undefined, all observations are assigned equal weight. |
Returns
Promise<ArrayLike>
Defined in: generated/cluster/MiniBatchKMeans.ts:440 (opens in a new tab)
score()
Opposite of the value of X on the K-means objective.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data. |
opts.sample_weight? | ArrayLike | The weights for each observation in X. If undefined, all observations are assigned equal weight. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<number>
Defined in: generated/cluster/MiniBatchKMeans.ts:480 (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/cluster/MiniBatchKMeans.ts:529 (opens in a new tab)
transform()
Transform X to a cluster-distance space.
In the new space, each dimension is the distance to the cluster centers. Note that even if X is sparse, the array returned by transform will typically be dense.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data to transform. |
Returns
Promise<ArrayLike[]>
Defined in: generated/cluster/MiniBatchKMeans.ts:564 (opens in a new tab)