LabelSpreading
LabelSpreading model for semi-supervised learning.
This model is similar to the basic Label Propagation algorithm, but uses affinity matrix based on the normalized graph Laplacian and soft clamping across the labels.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LabelSpreading(opts?: object): LabelSpreading;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Clamping factor. A value in (0, 1) that specifies the relative amount that an instance should adopt the information from its neighbors as opposed to its initial label. alpha=0 means keeping the initial label information; alpha=1 means replacing all initial information. Default Value 0.2 |
opts.gamma? | number | Parameter for rbf kernel. Default Value 20 |
opts.kernel? | "rbf" | "knn" | String identifier for kernel function to use or the kernel function itself. Only ‘rbf’ and ‘knn’ strings are valid inputs. The function passed should take two inputs, each of shape (n_samples, n_features), and return a (n_samples, n_samples) shaped weight matrix. Default Value 'rbf' |
opts.max_iter? | number | Maximum number of iterations allowed. Default Value 30 |
opts.n_jobs? | number | The 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.n_neighbors? | number | Parameter for knn kernel which is a strictly positive integer. Default Value 7 |
opts.tol? | number | Convergence tolerance: threshold to consider the system at steady state. Default Value 0.001 |
Returns
Defined in: generated/semi_supervised/LabelSpreading.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/semi_supervised/LabelSpreading.ts:136 (opens in a new tab)
fit()
Fit a semi-supervised label propagation model to X.
The input samples (labeled and unlabeled) are provided by matrix X, and target labels are provided by matrix y. We conventionally apply the label -1 to unlabeled samples in matrix y in a semi-supervised classification.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | ArrayLike | Target class values with unlabeled points marked as -1. All unlabeled samples will be transductively assigned labels internally, which are stored in transduction\_. |
Returns
Promise<any>
Defined in: generated/semi_supervised/LabelSpreading.ts:155 (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/semi_supervised/LabelSpreading.ts:197 (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/semi_supervised/LabelSpreading.ts:90 (opens in a new tab)
predict()
Perform inductive inference across the model.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns
Promise<ArrayLike>
Defined in: generated/semi_supervised/LabelSpreading.ts:232 (opens in a new tab)
predict_proba()
Predict probability for each possible outcome.
Compute the probability estimates for each single sample in X and each possible outcome seen during training (categorical distribution).
Signature
predict_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix. |
Returns
Promise<ArrayLike[]>
Defined in: generated/semi_supervised/LabelSpreading.ts:267 (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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/semi_supervised/LabelSpreading.ts:302 (opens in a new tab)
set_score_request()
Request metadata passed to the score method.
Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in score. |
Returns
Promise<any>
Defined in: generated/semi_supervised/LabelSpreading.ts:353 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/semi_supervised/LabelSpreading.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/semi_supervised/LabelSpreading.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/semi_supervised/LabelSpreading.ts:21 (opens in a new tab)
id
string
Defined in: generated/semi_supervised/LabelSpreading.ts:18 (opens in a new tab)
opts
any
Defined in: generated/semi_supervised/LabelSpreading.ts:19 (opens in a new tab)
Accessors
X_
Input array.
Signature
X_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/semi_supervised/LabelSpreading.ts:389 (opens in a new tab)
classes_
The distinct labels used in classifying instances.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/semi_supervised/LabelSpreading.ts:412 (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/semi_supervised/LabelSpreading.ts:512 (opens in a new tab)
label_distributions_
Categorical distribution for each item.
Signature
label_distributions_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/semi_supervised/LabelSpreading.ts:437 (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/semi_supervised/LabelSpreading.ts:487 (opens in a new tab)
n_iter_
Number of iterations run.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/semi_supervised/LabelSpreading.ts:537 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/semi_supervised/LabelSpreading.ts:77 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/semi_supervised/LabelSpreading.ts:81 (opens in a new tab)
transduction_
Label assigned to each item during fit.
Signature
transduction_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/semi_supervised/LabelSpreading.ts:462 (opens in a new tab)