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

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

RandomTreesEmbedding

Defined in: generated/ensemble/RandomTreesEmbedding.ts:27 (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:488 (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:515 (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:569 (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:542 (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:596 (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:623 (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)

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)

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_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:414 (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:451 (opens in a new tab)

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