cuvis_ai.preprocessor.sklearn_wrapped.NMF

class cuvis_ai.preprocessor.sklearn_wrapped.NMF(n_components='auto', *, init=None, solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha_W=0.0, alpha_H='same', l1_ratio=0.0, verbose=0, shuffle=False)[source]

Bases: Node, Preprocessor, SklearnWrapped

__init__(n_components='auto', *, init=None, solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha_W=0.0, alpha_H='same', l1_ratio=0.0, verbose=0, shuffle=False)[source]

Methods

`__init__`([n_components, init, solver, ...])
`check_input_dim`(X)	Check that the parameters for the input data data match user expectations
`check_output_dim`(X)	Check that the parameters for the output data data match user expectations
`fit`(X[, warm_start])	Fit the preprocessor to the data.
`forward`(X)	Transform the input data.
`get_fit_requested_meta`()
`get_forward_requested_meta`()
`load`(params, data_dir)	Load from serialized format into an object
`serialize`(data_dir)	Convert the class into a serialized representation
`set_fit_meta_request`(**kwargs)
`set_forward_meta_request`(**kwargs)

Attributes

`input_dim`	Returns the needed shape for the input data.
`output_dim`	Returns the shape for the output data.

check_input_dim(X)

Check that the parameters for the input data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

check_output_dim(X)

Check that the parameters for the output data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

fit(X: ndarray, warm_start=False)

Fit the preprocessor to the data.

Parameters:: X (array-like) – Input data.
Return type:: self

forward(X: ndarray)

Transform the input data.

Parameters: X (array-like): Input data.

Returns: Transformed data.

get_fit_requested_meta()

get_forward_requested_meta()

property input_dim

Returns the needed shape for the input data. If a dimension is not important, it will return -1 in the specific position.

Returns: (tuple) needed shape for data

load(params: dict, data_dir: Path) → None: Load from serialized format into an object

property output_dim

Returns the shape for the output data. If a dimension is dependent on the input, it will return -1 in the specific position.

Returns: (tuple) expected output shape for data

serialize(data_dir: Path) → dict: Convert the class into a serialized representation

set_fit_meta_request(**kwargs)

set_forward_meta_request(**kwargs)