lale.lib.autogen.random_trees_embedding module

class lale.lib.autogen.random_trees_embedding.RandomTreesEmbedding(*, n_estimators=10, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, min_impurity_decrease=0.0, sparse_output=True, n_jobs=1, random_state=None, verbose=0, warm_start=False)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_estimators (integer, >=10 for optimizer, <=100 for optimizer, uniform distribution, default 10) – Number of trees in the forest

  • max_depth (integer, >=3 for optimizer, <=5 for optimizer, uniform distribution, default 5) – The maximum depth of each tree

  • min_samples_split (union type, default 2) –

    The minimum number of samples required to split an internal node: - If int, then consider min_samples_split as the minimum number

    • integer, not for optimizer

    • or float, >=0.01 for optimizer, <=0.5 for optimizer, uniform distribution

  • min_samples_leaf (union type, default 1) –

    The minimum number of samples required to be at a leaf node

    • integer, not for optimizer

    • or float, >=0.01 for optimizer, <=0.5 for optimizer, uniform distribution

  • min_weight_fraction_leaf (float, not for optimizer, default 0.0) – The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node

  • max_leaf_nodes (union type, not for optimizer, default None) –

    Grow trees with max_leaf_nodes in best-first fashion

    • integer

    • or None

  • min_impurity_decrease (float, not for optimizer, default 0.0) – A node will be split if this split induces a decrease of the impurity greater than or equal to this value

  • sparse_output (boolean, default True) – Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators.

  • n_jobs (union type, not for optimizer, default 1) –

    The number of jobs to run in parallel for both fit and predict

    • integer

    • or None

  • random_state (union type, not for optimizer, default None) –

    If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

    • integer

    • or numpy.random.RandomState

    • or None

  • verbose (integer, not for optimizer, default 0) – Controls the verbosity when fitting and predicting.

  • warm_start (boolean, not for optimizer, default False) – 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

Notes

constraint-1 : any type

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (union type) –

    The input samples

    • array of items : Any

    • or array of items : array of items : float

  • sample_weight (union type, optional) –

    Sample weights

    • array of items : float

    • or None

transform(X, y=None)

Transform the data.

Note: The transform method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (union type) –

Input data to be transformed

  • array of items : Any

  • or array of items : array of items : float

Returns

result – Transformed dataset.

Return type

array of items : array of items : float