RandomTreesEmbedding
An ensemble of totally random trees.
An unsupervised transformation of a dataset to a high-dimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a one-hot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest.
The dimensionality of the resulting representation is n\_out <= n\_estimators \* max\_leaf\_nodes. If max\_leaf\_nodes \== None, the number of leaf nodes is at most n\_estimators \* 2 \*\* max\_depth.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new RandomTreesEmbedding(opts?: object): RandomTreesEmbedding;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.max_depth? | number | The maximum depth of each tree. If undefined, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. Default Value 5 |
opts.max_leaf_nodes? | number | Grow trees with max\_leaf\_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If undefined then unlimited number of leaf nodes. |
opts.min_impurity_decrease? | number | A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following: Default Value 0 |
opts.min_samples_leaf? | number | The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min\_samples\_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. Default Value 1 |
opts.min_samples_split? | number | The minimum number of samples required to split an internal node: Default Value 2 |
opts.min_weight_fraction_leaf? | number | The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. Default Value 0 |
opts.n_estimators? | number | Number of trees in the forest. Default Value 100 |
opts.n_jobs? | number | The number of jobs to run in parallel. fit, transform, decision\_path and apply are all parallelized over the trees. 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.random_state? | number | Controls the generation of the random y used to fit the trees and the draw of the splits for each feature at the trees’ nodes. See Glossary for details. |
opts.sparse_output? | boolean | Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators. Default Value true |
opts.verbose? | number | Controls the verbosity when fitting and predicting. Default Value 0 |
opts.warm_start? | boolean | When set to true, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See Glossary and Fitting additional weak-learners for details. Default Value false |
Returns
Defined in: generated/ensemble/RandomTreesEmbedding.ts:27 (opens in a new tab)
Methods
apply()
Apply trees in the forest to X, return leaf indices.
Signature
apply(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr\_matrix. |
Returns
Promise<ArrayLike[]>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:202 (opens in a new tab)
decision_path()
Return the decision path in the forest.
Signature
decision_path(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr\_matrix. |
Returns
Promise<any[]>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:237 (opens in a new tab)
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/ensemble/RandomTreesEmbedding.ts:185 (opens in a new tab)
fit()
Fit estimator.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input samples. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csc\_matrix for maximum efficiency. |
opts.sample_weight? | ArrayLike | Sample weights. If undefined, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:274 (opens in a new tab)
fit_transform()
Fit estimator and transform dataset.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Input data used to build forests. Use dtype=np.float32 for maximum efficiency. |
opts.sample_weight? | ArrayLike | Sample weights. If undefined, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<any[]>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:323 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
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/ensemble/RandomTreesEmbedding.ts:374 (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/ensemble/RandomTreesEmbedding.ts:414 (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/ensemble/RandomTreesEmbedding.ts:125 (opens in a new tab)
set_fit_request()
Request metadata passed to the fit 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_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:456 (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/ensemble/RandomTreesEmbedding.ts:496 (opens in a new tab)
transform()
Transform dataset.
Signature
transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Input data to be transformed. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csr\_matrix for maximum efficiency. |
Returns
Promise<any[]>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:533 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/ensemble/RandomTreesEmbedding.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/ensemble/RandomTreesEmbedding.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/ensemble/RandomTreesEmbedding.ts:23 (opens in a new tab)
id
string
Defined in: generated/ensemble/RandomTreesEmbedding.ts:20 (opens in a new tab)
opts
any
Defined in: generated/ensemble/RandomTreesEmbedding.ts:21 (opens in a new tab)
Accessors
estimator_
The child estimator template used to create the collection of fitted sub-estimators.
Signature
estimator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:570 (opens in a new tab)
estimators_
The collection of fitted sub-estimators.
Signature
estimators_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/RandomTreesEmbedding.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/ensemble/RandomTreesEmbedding.ts:651 (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/ensemble/RandomTreesEmbedding.ts:624 (opens in a new tab)
n_outputs_
The number of outputs when fit is performed.
Signature
n_outputs_(): Promise<number>;Returns
Promise<number>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:678 (opens in a new tab)
one_hot_encoder_
One-hot encoder used to create the sparse embedding.
Signature
one_hot_encoder_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/RandomTreesEmbedding.ts:705 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/ensemble/RandomTreesEmbedding.ts:112 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/ensemble/RandomTreesEmbedding.ts:116 (opens in a new tab)