Class: 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.

Constructors

new RandomTreesEmbedding()

new RandomTreesEmbedding(opts?): RandomTreesEmbedding

Parameters

Parameter	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.
`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:
`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.
`opts.min_samples_split`?	`number`	The minimum number of samples required to split an internal node:
`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.
`opts.n_estimators`?	`number`	Number of trees in the forest.
`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` 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.
`opts.verbose`?	`number`	Controls the verbosity when fitting and predicting.
`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 trees for details.

Returns RandomTreesEmbedding

Defined in generated/ensemble/RandomTreesEmbedding.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/ensemble/RandomTreesEmbedding.ts:25
`_isInitialized`	`boolean`	`false`	generated/ensemble/RandomTreesEmbedding.ts:24
`_py`	`PythonBridge`	`undefined`	generated/ensemble/RandomTreesEmbedding.ts:23
`id`	`string`	`undefined`	generated/ensemble/RandomTreesEmbedding.ts:20
`opts`	`any`	`undefined`	generated/ensemble/RandomTreesEmbedding.ts:21

Accessors

estimator_

Get Signature

get estimator_(): Promise<any>

The child estimator template used to create the collection of fitted sub-estimators.

Returns Promise<any>

Defined in generated/ensemble/RandomTreesEmbedding.ts:533

estimators_

Get Signature

get estimators_(): Promise<any>

The collection of fitted sub-estimators.

Returns Promise<any>

Defined in generated/ensemble/RandomTreesEmbedding.ts:560

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/ensemble/RandomTreesEmbedding.ts:614

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/ensemble/RandomTreesEmbedding.ts:587

n_outputs_

Get Signature

get n_outputs_(): Promise<number>

The number of outputs when fit is performed.

Returns Promise<number>

Defined in generated/ensemble/RandomTreesEmbedding.ts:641

one_hot_encoder_

Get Signature

get one_hot_encoder_(): Promise<any>

One-hot encoder used to create the sparse embedding.

Returns Promise<any>

Defined in generated/ensemble/RandomTreesEmbedding.ts:668

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/ensemble/RandomTreesEmbedding.ts:112

Methods

apply()

apply(opts): Promise<ArrayLike[]>

Apply trees in the forest to X, return leaf indices.

Parameters

Parameter	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:185

decision_path()

decision_path(opts): Promise<any[]>

Return the decision path in the forest.

Parameters

Parameter	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:219

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/ensemble/RandomTreesEmbedding.ts:168

fit()

fit(opts): Promise<any>

Fit estimator.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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.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.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any>

Defined in generated/ensemble/RandomTreesEmbedding.ts:255

fit_transform()

fit_transform(opts): Promise<any[]>

Fit estimator and transform dataset.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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.X`?	`ArrayLike`	Input data used to build forests. Use `dtype=np.float32` for maximum efficiency.
`opts.y`?	`any`	Not used, present for API consistency by convention.

Returns Promise<any[]>

Defined in generated/ensemble/RandomTreesEmbedding.ts:299

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Get output feature names for transformation.

Parameters

Parameter	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:345

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/ensemble/RandomTreesEmbedding.ts:383

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/ensemble/RandomTreesEmbedding.ts:125

set_fit_request()

set_fit_request(opts): Promise<any>

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:

Parameters

Parameter	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:423

set_output()

set_output(opts): Promise<any>

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.transform`?	`"default"` \| `"pandas"` \| `"polars"`	Configure output of `transform` and `fit_transform`.

Returns Promise<any>

Defined in generated/ensemble/RandomTreesEmbedding.ts:461

transform()

transform(opts): Promise<any[]>

Transform dataset.

Parameters

Parameter	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:497

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